Safe and Clean Vehicles for Healthier and More Productive Societies Safe and Clean Vehicles for Healthier A and More Productive Societies © 2025 International Bank for Reconstruction and Development / The World Bank Group 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank Group with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank Group, its Board of Executive Directors, or the governments they represent. The World Bank Group does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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Please cite the work as follows: World Bank. (2025). Safe and Clean Vehicles for Healthier and More Productive Societies. May 2025. Washington DC: World Bank Group. Edited by: Chitra Arcot, MA, PMP. Note: All dollars ($) refer to US dollars unless otherwise specified. Table of Contents Acknowledgements.........................................................................................................................iv Foreword.........................................................................................................................................v Abbreviations..................................................................................................................................vi Executive Summary..........................................................................................................................1 Introduction.....................................................................................................................................6 Chapter 1. Health Impacts of Motorized Road Transport ...............................................................14 1.1 Health impact of key risks from motorized road transport ............................................................. 14 1.2 The economic cost of motorized road transport.............................................................................18 Chapter 2. Vehicle Safety and Emission Standards ........................................................................22 2.1 Vehicle standards impacting health outcomes.................................................................................22 2.2 Modeling vehicle standards for health outcomes ........................................................................... 26 Chapter 3. Impact of Vehicle Standards on Safety and Air Quality .................................................30 3.1 Assessing the need for vehicle standards adoption at the country level ........................................ 31 3.2 Evaluating the health impact of vehicle policies at the country level ............................................. 35 3.3 Quantifying the potential economic benefits of improved vehicle standard adoption...................48 Chapter 4. Policy Implications for Decision Makers........................................................................53 Appendix A: Countries-At-A-Glance...............................................................................................61 Argentina................................................................................................................................................ 61 Brazil....................................................................................................................................................... 63 Arab Republic of Egypt........................................................................................................................... 65 Ghana...................................................................................................................................................... 67 India........................................................................................................................................................ 69 Kazakhstan.............................................................................................................................................. 71 Lao PDR................................................................................................................................................... 73 Mexico.................................................................................................................................................... 75 Appendix B: Global Calibration Parameters...................................................................................77 Appendix C: Country-Specific Datasets ..........................................................................................79 Appendix D: Technical Estimations for Air Quality Impact..............................................................82 References.....................................................................................................................................91 Safe and Clean Vehicles for Healthier i and More Productive Societies List of Figures Figure I.1 Number of four wheelers and motorcycles in use by region, 2000-2020 (millions)........................................... 7 Figure 1.1 Road traffic deaths in emerging and developing economies by income, in shares of totals........................... 15 Figure 1.2 Proportion of total NO2 emissions generated by road transport...................................................................... 17 Figure 1.3 Annual cost of road traffic crashes including fatalities and serious injuries as 2021....................................... 18 Figure 1.4 Annual cost of PM2.5 attributable to road transport as of 2021........................................................................ 20 Figure 1.5 Annual cost of NO2 from road transport as of 2021...........................................................................................21 Figure 2.1. Model for estimating simultaneous vehicle safety and air quality impacts.....................................................26 Figure 2.2 Sample forecast motorization rates for Egypt and Mexico...............................................................................27 Figure 3.1 Motorized road transport’s contribution to population weighted ambient PM2.5............................................32 Figure 3.2 Selected health cost associated to road transport by country as of 2021....................................................... 33 Figure 3.3 Prevented FSIs by safety technology mandates per vehicle type (Scenario 1)................................................ 39 Figure 3.4 Prevented FSI by safety technology mandates per vehicle type (Scenario 2)................................................... 41 Figure 3.6 Primary and secondary PM2.5 emissions............................................................................................................ 49 Figure D6.1 Relative risk of mortality from annual NO2 exposure..................................................................................... 90 List of Tables Table 1.1 Lives lost due to road traffic crashes and air pollution from motorized road transport, 2019......................... 18 Table 2.1. Standards for clean vehicles............................................................................................................................... 28 Table 2.2. Potential powertrains for each mode................................................................................................................ 29 Table 3.1 General country statistics, 2023.......................................................................................................................... 31 Table 3.2 Selected statistics on road crashes, 2021............................................................................................................ 31 Table 3.3 Deaths and IQ losses from PM2.5 attributable to motorized road transport, 2021.............................................32 Table 3.4 Ambient NO2 and mortality in eight select countries......................................................................................... 33 Table 3.5 Health impacts and costs associated to road transport by country as of 2021................................................. 34 Table 3.6 State of analyzed motorization policies as of 2024............................................................................................ 35 Table 3.7 Motorization management scenarios................................................................................................................. 36 Table 3.8 Impact of motorization Scenario 1, cumulative impact over 2025–2050 compared to BAU............................ 38 Table 3.9 Pollutant percentage reduction Scenario 1, cumulative impact over 2025–2050 compared to BAU............... 39 Table 3.10 Impact of motorization Scenario 2, cumulative impact over 2025–2050 compared to BAU...........................41 Table 3.11 Pollutant percentage reduction Scenario 2, cumulative impact over 2025—2050 compared to BAU.............42 Table 3.12 Impact of motorization Scenario 3, cumulative impact over 2025–2050 compared to BAU.......................... 43 Table 3.13 Pollutant percentage reduction Scenario 3, Cumulative impact over 2025–2050 compared to BAU............ 44 Table 3.14 Impact of motorization Scenario 4, cumulative impact over 2025–2050 compared to BAU.......................... 45 Safe and Clean Vehicles for Healthier ii and More Productive Societies Table 3.15 Impact of motorization Scenario 5, cumulative impact over 2025—2050 compared to BAU......................... 46 Table 3.16 Cross-country comparision of safety and emissions outcomes........................................................................ 47 Table 3.17 PV of the safety benefit estimated for Scenario 3 over the period 2025–2050............................................... 48 Table 3.18 Envelope of potential economic benefits due to improved vehicle standards, cumulative benefit over 2025-2050................................................................................................................................................................... 50 Table 4.1 Gains of adoption of alternative standards: policy option 1.............................................................................. 55 Table 4.2 Gains of adoption of alternative standards: policy option 2.............................................................................. 56 Table 4.3 Gains of adoption of alternative standards: policy option 3...............................................................................57 Table 4.4 Gains of adoption of alternative emission standards: policy option 4-1............................................................ 58 Table 4.5 Gains of adoption of alternative emission standards: policy option 4-2............................................................59 Table 4.6 Gains of cumulative adoption of standards: policy option 5.............................................................................. 60 Table D2.1 Parameters for estimation of the cost of IQ losses.......................................................................................... 84 Table D5.1 Disability weights associated with PM2.5 air pollution...................................................................................... 88 Table D6.1 Hazard ratios for mortality outcomes from annual NO2 exposure.................................................................. 89 Table D6.2 Mortality hazard ratios per 10 µg/m3 of annual NO2 exposure....................................................................... 89 List of Boxes Box 1.1 Cognitive impacts of ambient PM2.5....................................................................................................................... 16 Box 1.2. Estimating the economic cost of car crashes and air pollution (or the economic benefits of preventing mortality and morbidity) .................................................................................................................................................. 19 Box 2.1 Global evolution of vehicle safety features........................................................................................................... 23 Box 2.2 Adoption of emission standards in emerging and developing economies........................................................... 25 Safe and Clean Vehicles for Healthier iii and More Productive Societies Acknowledgements This report was prepared by a World Bank team comprising (in alphabetical order by last name): Dipan Bose (Senior Transport Specialist), Cecilia Briceño-Garmendia (Lead Economist and Global Lead for Climate and Transport), Leslie Mills (Transport Specialist), Kazuyuki Neki (Transport Analyst), and Ernesto Sanchez-Triana (Lead Environmental Specialist). Contributions to the report were also received from World Bank consultants Bjorn Larsen, Markus Amann and Santiago Enriquez. The study was peer-reviewed by Nobuhiko Daito (Senior Transport Specialist, World Bank) and A S Harinath (Senior Environmental Engineer, World Bank). Administrative support was provided by Faustina Chande and report design was undertaken by Benjamin Holzman and Duina Reyes. The team would like to express its appreciation to Integrated Transport Planning and TRL Limited, for developing the air quality and road safety models respectively for this report. Overall leadership and management of the project was provided by Nicolas Peltier (Global Director for Transport, World Bank), Said Dahdah (Program Manager, Global Road Safety Facility, World Bank), and Binyam Reja (Global Practice Manager, Transport, World Bank). About GRSF This report was produced by the Global Road Safety Facility (GRSF)—a multidonor trust fund managed by the World Bank that supports efforts in low and middle-income countries to halve their road traffic fatalities and serious injuries. GRSF is supported by Bloomberg Philanthropies Initiative for Global Road Safety; TotalEnergies Foundation; and the United Kingdom of Great Britain and Northern Ireland acting through the Department of Health and Social Care. Website: www.globalroadsafetyfacility.org Email: grsf@worldbank.org Safe and Clean Vehicles for Healthier iv and More Productive Societies Foreword The expansion of motorized road transport has been a powerful driver of economic and social development, offering people greater access to jobs, markets, education, and essential services. Yet its rapid growth, especially in emerging and developing economies, has come with mounting costs for public health, safety, and the environment. As vehicles fill roads across the globe, so too do the risks: increased traffic crashes, worsening air pollution, and their severe impacts on human capital—that is, the aggregate health, skills, and productivity of people in a society. This study is the first of its kind to examine the twin crises of vehicle safety and emissions in an integrated and holistic manner. It brings together a robust, cross-sectoral evidence base to demonstrate that the management of motorized transport is not merely a public health, transport or environmental concern—it is closely linked to the level of human capital in emerging and developing economies and therefore has far-reaching downstream effects. The scale of the problem is staggering. Road crashes claim roughly 1.2 million lives each year, while pollution from road transport—including PM2.5 and NO₂ emissions—caused an estimated 550,000 premature deaths in 2021. These losses are not evenly distributed. Over 90% of road traffic fatalities and a vast majority of pollution-related illnesses occur in emerging and developing economies. These injuries, illnesses, and deaths are more than health outcomes— they represent lost productivity, missed educational opportunities, and barriers to inclusive growth. What makes this pioneering study so critical—and timely—is its focus on how countries can better manage the quality and quantity of their motorized vehicle fleets across the full life cycle: from vehicle entry; to in-use maintenance; to end-of-life scrappage. The report provides new modeling and evidence, as well as clear policy recommendations, for strengthening safety and emissions standards, managing used-vehicle imports, improving fuel quality, and retiring old, non-compliant vehicles. Importantly, the report highlights the underestimated threat of NO₂ emissions, especially in middle-income countries, and calls for expanded attention to this pollutant. This study moves beyond technological fixes. While strict standards for vehicle safety and emissions are the cornerstone of the solution, they are not enough. A comprehensive motorization management strategy—combining regulations, enforcement, consumer awareness, periodic vehicle inspections, and real-time data systems—is essential to achieving safer, cleaner, and more equitable motorized transport networks. We hope this report will serve as both a wake-up call and a roadmap. By addressing the safety and environmental performance of vehicles simultaneously, policymakers can protect lives, reduce economic losses, and build healthier, more productive societies. The World Bank Group is ready to support countries in making this transition—not only as a matter of transport policy, but as a pillar of growth and productivity. Nicolas Peltier-Thiberge Genevieve Connors Global Director for Transport Acting Global Director for Environment World Bank World Bank Safe and Clean Vehicles for Healthier v and More Productive Societies Abbreviations ABS Anti-lock braking system AEBS Advanced emergency braking systems AQG Air quality guideline ASEAN Association of South-East Asian Nations BAU Business-as-usual CNG Compressed Natural Gas CVD Cardiovascular disease COPD Chronic obstructive pulmonary disease DPF Diesel particulate filter EAP East Asia and Pacific ECA Europe and Central Asia ELV End-of-life vehicle ESC Electronic stability control EV Electric vehicle FRUPD Front- and Rear-Underrun Protective Devices FSI Fatalities and serious injuries GBD Global burden of disease GNI Gross national income GTR Global technical regulation HDV Heavy duty vehicle IHD Ischemic heart disease LAC Latin America and the Caribbean LDV Light duty vehicle LEZ Low emission zone LPG Liquefied Petroleum Gas MAPS Major air pollution sources MENA Middle East and North Africa MTW Motorized two wheelers NPV Net present value OECD Organisation for Economic Co-operation and Development SAR South Asia Region SCR Selective catalytic reduction SSA Sub-Saharan Africa ULEZ Ultra-low emission zone UNEP United Nations Environment Programme VSF Vehicle stability function VSL Value of statistical life WHO World Health Organization YLD Years lived with disability Safe and Clean Vehicles for Healthier vi and More Productive Societies Executive Summary Message 1: Motorization management is a critical health issue. Road crashes and air pollution from motorized road transport impose a substantial health burden in emerging and developing economies, causing hundreds of thousands of deaths and disabilities annually. Road traffic crashes lead to an estimated 1.19 million fatalities every year and countless more injuries, amounting to $2.9 trillion in costs (author’s calculation based on Wijnen, Dahdah and Pkhikidze, 2025). Pollutants emitted from vehicles also cause significant health effects. This report finds that emissions of particulate matter 2.5 microns or smaller in diameter (commonly referred to as PM2.5) from road transport caused an estimated 311,000 premature deaths globally in 2021, resulting in an economic welfare cost of $385 billion. Vehicle emissions of nitrogen dioxide (NO2)—another noxious air pollutant—caused an estimated 240,000 premature deaths, representing a welfare cost of $420 billion. Exposure to fine particles also impairs children’s cognitive development. In 2021, PM2.5 emissions from motorized vehicles caused the loss of 64 million intelligence quotient (IQ) points globally, resulting in productivity losses estimated at $157 billion. In addition to claiming lives, these impacts reduce workers’ productivity, impair children’s ability to learn, and increase the proportion of scarce resources spent on health expenses, negatively affecting the ability of emerging and developing economies to achieve their development goals. Message 2: Priority should be given to applying standards to the existing fleet— including retirement of non-roadworthy vehicles—rather than just focusing on stricter standards for brand-new vehicles entering the fleet. Only regulating new vehicles entering the fleet has a limited impact if no requirements are adopted related to used vehicles entering the fleet, maintenance of the existing fleet, and fleet retirement. The largest gains—particularly pertaining to emissions reduction—tend to be achieved when old (over twenty-year old), non-roadworthy vehicles are removed from the fleet. In practice, this involves mandating vehicle standards but also setting in place a) periodic vehicle inspection and maintenance procedures—especially for high-usage diesel vehicles; b) incentives for scrapping; c) relevant technologies like diesel particle filters for PM2.5 and selective catalytic reduction systems for nitrogen oxides. The most impactful approach for improving road safety is through simultaneously improving the standards of old vehicles in the existing fleet as well as used vehicle imports entering the fleet. For instance, in Kazakhstan, Ghana and Lao PDR, the safety benefits were 6, 15 and 35 times higher, respectively, if standards are mandated for imports and also for old vehicles in the existing fleet. For health benefits linked to air pollution, the most impactful policy is vehicle retirement—reductions of PM2.5 and NOX emissions can increase by an order of magnitude when retirement is added to vehicle standards. Message 3: Trade policies that aim to lower vehicle users’ costs by allowing lightly regulated used vehicle imports tend to backfire when considering the adverse health and productivity implications. Perhaps the key motivation to allow the importation of used vehicles is to increase the supply of affordable vehicles and, with that, increase mobility and access to opportunities for populations in need. However, this report demonstrates that, unless carefully crafted, a trade policy allowing used vehicle importation can backfire, leading to non-complaint vehicles and outdated safety and emissions standards, as well as undermining technology diffusion. Safe and Clean Vehicles for Healthier 1 and More Productive Societies In a broader context, it can also promote overconsumption of private vehicles, creating congestion for already over- stretched infrastructure, as well as lead to incentives for high-income countries to export rather than scrap their non-roadworthy vehicles. Countries that rely on used vehicle imports would benefit from enforcing stricter mandates at entry and revamping inspection procedures. By implementing these measures across the fleet mix of light duty vehicles (LDVs), heavy duty vehicles (HDVs) and motorized two wheelers (MTWs), countries can reduce crash-related fatalities by up to nine percent. Including imported used vehicles in mandates also results in significant emissions reductions: up to 20 percent for PM2.5 and up to 30 percent for NOx. In Ghana, emissions reductions almost doubled for mandates covering both new and imported used vehicles relative to enforcement only applied to new vehicles. Emerging and developing economies would benefit from the shift from age-based vehicle import restrictions to performance-based criteria that directly assess roadworthiness, safety features, and environmental compliance. Relying solely on vehicle age as a proxy for quality is an imprecise and often ineffective strategy (World Bank 2022). Implementation of harmonized standards would ideally be enforced by conducting inspections in both export and importing countries. New Zealand’s two-stage import certification process, a potential model for emerging and developing economies, ensures that vehicle imports meet national safety and emissions standards before entering the fleet. Message 4: Adoption of electric vehicles, while effective in achieving zero tailpipe emissions, only lead to modest health and air quality gains when no other reforms are implemented. In countries with mandates for “Euro 4” (the European vehicle emissions standard that sets legal limits on the amount of pollutants that vehicles can emit, the fourth version of which was introduced in phases around 2005) and lower, the largest emissions savings come from leapfrogging to Euro 6—the standard introduced around 2015. Vehicle electrification leads to emission reduction gains significantly greater than mandating an incremental standard improvement to Euro 5 (the standard introduced around 2010), but not as high as moving to Euro 6. This is the case of Egypt, Ghana, Kazakhstan and Mexico. In countries with fleets of older, used vehicles, especially those without diesel particulate filters (DPFs) or selective catalytic reduction (SCR) systems, the impact of electric vehicle (EV) adoption alone is modest since air pollution remains high from the existing fleet. Older vehicles, particularly those using diesel, emit very high levels of PM2.5, nitrogen oxides, sulfur oxide, and other pollutants. Without emission control technologies like DPFs or SCRs, their per-vehicle pollution is many times higher than newer vehicles. The key challenge is that EVs typically enter the fleet as an addition to—rather than as a substitute of—non- roadworthy and non-compliant vehicles. Enforcing emissions standards through inspection and maintenance can quickly identify non-roadworthy vehicles and reduce emissions through the engine tuning, replacing worn-out components, retrofitting, and increasing the use of DPFs and SCRs. Message 5: Vehicle electrification may contribute significantly to non-exhaust emissions and add to environmental challenges. When focusing on tailpipe emissions—and particularly on greenhouse gas emissions—the cleanest vehicle technology is electrification. But even vehicle electrification as a tailpipe zero-emission technology has limited effects in addressing some critical air quality challenges. First, while beneficial in terms of reducing emissions of PM2.5 and nitrogen oxides, EV uptake showed less impact in the short term compared to adoption of Euro 6 standards if not paired with the retirement of old vehicles and if vans and trucks were not included in the transition. Second, EVs are heavier than equivalent-sized internal combustion engine vehicles and therefore tend to emit more non-exhaust Safe and Clean Vehicles for Healthier 2 and More Productive Societies emissions. The heavier mass of EVs also makes these vehicles more dangerous for pedestrians and other non- motorized road users in traffic crashes. In the analysis, these two factors resulted in significant reduction of nitrogen oxide emissions from increased vehicle electrification, yet relatively minor reductions in PM2.5 emissions. Moreover, non-exhaust emissions from EVs include PM2.5 and toxic heavy metals such as cadmium and lead from tire, brake, and road wear. Increased power plant electricity production for EVs also adds to PM2.5, NOx (incl. NO2) and other emissions. Converting existing diesel-fueled vehicles to run on Compressed Natural Gas (CNG) or Liquefied Petroleum Gas (LPG), supported by investments in refueling infrastructure, could offer immediate air quality benefits, so decision makers could emphasize the roll-out of training programs for mechanics and technicians to ensure proper repair and maintenance. Other environmental impacts of vehicle electrification could be addressed as part of, or at least in tandem with, motorization policies; for instance, by adding regulations for batteries and tire reuse and disposal. This is particularly pertinent for electrification of two- and three-wheelers with lead acid batteries (LABs). Informal recycling and inadequate disposal of used lead acid batteries (ULABs) are among the most significant sources of lead pollution globally. Exposure to lead is estimated to cause 5.5 million cardiovascular deaths and a loss of 765 million IQ points annually (Larsen and Sánchez-Triana 2023). Message 6: The health and economic impacts from vehicle-related nitrogen dioxide emissions are particularly high in middle-income countries due to their motorization levels and rates. Globally, deaths from road transport-generated NO2—about 240,000 annually—are equivalent to 77 percent of the total premature deaths from road transport-generated PM2.5, estimated at 311,000 per year, which makes NO2 a health concern of roughly similar magnitude. But the level of ambient NO2 relative to ambient PM2.5 rises with country income level. Notably, deaths from road transport-generated NO2 are increasingly salient in higher income economies. Road transport-related premature deaths from NO2 are about 30-36 percent of road transport deaths from PM2.5 in low- and lower-middle income countries, 84 percent in upper-middle income countries and close to 158 percent in high-income countries. Regionally, premature deaths from transport NO2 are particularly problematic in East Asia, equivalent to 80-90 percent of PM2.5 related deaths. In Latin America and the Caribbean—in countries such as Argentina, Brazil and Mexico, for example—annual deaths from road transport-generated NO2 are substantially higher than from road transport- generated PM2.5, or equivalent to 136 percent. The global welfare cost of mortality from road transport-generated NO2 was equivalent to 0.44 percent of global GDP in 2021. Combating NO2 emissions is particularly important in countries with relatively moderate ambient PM2.5 levels but rapid motorization rates. Message 7: Incremental adoption of newer standards might not be cost effective. Countries should attempt to leapfrog as much as possible. Early adoption of safety technology and emissions standards is the key to achieving greater air quality and health benefits over the coming decades. For every new safety standard, the beneficial returns are proportionally higher when the new standard is introduced globally and immediately mandated in a specific country. The additional benefit of a standard then tends to decrease over time with the inflow of imported used vehicles and a slower adoption of the new standard to the existing fleet when there is no mandate to do so. Similar patterns have been observed for passive safety crashworthiness features—as fleet renewal occurs over time, the average quality of vehicle safety performance typically improves to a certain degree without the influence of mandatory adoption of safety technology. For example, and per the study, Ghana, which does not mandate electronic stability control (ESC) Safe and Clean Vehicles for Healthier 3 and More Productive Societies technology for LDVs, may see increased ESC benefits through early adoption up to about year 2038, after which time the benefits are expected to be proportionally smaller as the overall fleet renews over time. With respect to emissions, adopting Euro 6 offers significantly greater reductions in harmful emissions, particularly NO2—compared to Euro 5. This leapfrogging strategy presents a powerful opportunity to maximize public health benefits, including reductions in premature mortality, morbidity, and cognitive impairments linked to vehicle-related air pollution. The benefits stemming from adopting Euro 6 as the emissions mandate, rather than investing in enforcing Euro 5, are many times higher. PM2.5 reductions with Euro 6 are from 30 percent higher to 8 times higher than with Euro 5 adoption. Euro 6 gains for NO2 reduction comes in even higher ratios vis-à-vis Euro 5; for example, spanning 3 times more beneficial in Kazakhstan and Lao PDR to 16 and 18 times more beneficial in Egypt and Ghana respectively. If vehicle electrification is the path decided by a country, evidence supports that more aggressive strategies lead to greater health benefits. A scenario of 30 percent of new cars, buses, and minibuses, and 70 percent of new motorcycles, going electric by 2030 (“30X30”) results in higher emissions reductions than an electrification scenario of 50 percent of new cars, buses, minibuses, motorcycles, and vans going electric by 2050 and 50 percent of new trucks going electric by 2055 (50x50). This observation holds across all countries analyzed under the study. Emissions control in the power sector is, however, important for vehicle electrification to yield maximum emissions reduction benefits. Concluding Remarks: Reaping the health and productivity benefits of improved safety and emissions mandates requires comprehensive motorization management and multi-sectoral policies. Addressing road transport’s significant impacts on human capital calls for the adoption of comprehensive motorization management strategies. The modeling results show that policies focused on the entry of new and imported used vehicles to a country’s vehicle stock can result in significant safety and emissions benefits. However, the benefits are significantly enhanced in most countries when older vehicles that do not comply with safety and air quality standards are retired from the fleet. Motorization management is a structured, policy-driven approach designed to guide the growth and governance of a country’s motor vehicle stock across its full life cycle—from vehicle entry to active use to end-of-life scrappage. This approach is accompanied by the adoption and enforcement of standards as well as periodic vehicle inspections as part of the vehicle life cycle process. Motorization management seeks to improve safety, environmental, and fuel efficiency outcomes by applying targeted regulations and institutional measures at each stage of a vehicle’s life. In emerging and developing economies with older vehicles and high importation rates of used vehicles, it is particularly important to focus on the existing fleet, regulation of imports, and scrapping policies, even though policy makers might be inclined to limit their focus to ambitious targets for new technology adoption at vehicle entry. A comprehensive motorization management approach would also prioritize the uptake of safety technology for commercial-use vehicles, which are often overlooked. Further, to raise the public demand in countries which lack robust regulatory framework, consumer awareness programs can be extremely effective. Motorization management is also increasingly understood as a multi-sectoral agenda. Linkages with trade policies are evident from the analysis of this report. Coordinating with energy policies is critical given the role that vehicle fuel quality plays in emissions and vehicle well-functioning. Lowering the sulfur content in diesel from 2000 parts per million to 500 parts per million alone can reduce PM2.5 emissions by at least 20 percent. Designing fuel standards in tandem with vehicle emissions standards is essential to achieve the desired reductions in pollutant emissions. Safe and Clean Vehicles for Healthier 4 and More Productive Societies Many emerging and developing economies have already implemented diesel sulfur standards based on international frameworks, and these countries could be supported to limit sulfur content to 10 parts per million required for achieving Euro 5 and Euro 6 emission standards. Policies developed using an avoid, shift, improve (ASI) framework can lead to significant reductions of vehicle emissions and risks of traffic crashes, as well as incentivize consumers to avoid purchasing private vehicles altogether. Similarly, the introduction of bus rapid transit systems has shifted many former private vehicle users to public transport, thereby reducing emissions and contributing positively to the urban environment. Air quality can be improved through urban planning policies that establish low emission zones (LEZs) or ultra-low emission zones (ULEZs) in urban areas where the entry of older and diesel-driven vehicles is restricted or prohibited. This encourages vehicle owners to upgrade to cleaner alternatives to access these areas. References SMEP (Sustainable Manufacturing and Environmental Pollution Programme). 2023. Unified Policies, Healthier Journeys: Addressing the Used Lead-Acid-Battery challenge in Bangladesh. https://unctad.org/system/files/non- official-document/%5B_SMEP_%5D_ULABs_26-03-2024.pdf Larsen and Sánchez-Triana. 2023. Global health burden and cost of lead exposure in children and adults: a health impact and economic modelling analysis. Lancet Planet Health Oct;7(10):e831-e840. doi:10.1016/S2542- 5196(23)00166-3. Epub 2023 Sep 12. Wijnen, Dahdah, and Pkhikidze. 2025. “The Value of a Statistical Life in the Context of Road Safety: A New Value Transfer Approach.” Traffic Injury Prevention, doi:10.1080/15389588.2025.2476607. World Bank. 2022. Motorization Management for Development; World Bank. https://hdl.handle.net/10986/37589 World Bank. 2025. Accelerating Access to Clean Air on a Livable Planet. World Bank. http://documents.worldbank. org/curated/en/099032625132535486 Safe and Clean Vehicles for Healthier 5 and More Productive Societies Introduction Background Motorization in road transport links closely to economic and population growth, particularly in emerging and developing economies. Vehicle growth rates in emerging and developing economies could reach up to 10 percent annually in certain regions, potentially doubling the global vehicle fleet by 2050 (EIA 2021) currently estimated at between 1.6 and 2.2 billion vehicles. The increase in motorized road transport offers significant opportunities, including greater access to economic opportunities, services, and social activities. However, it also brings significant health-related challenges. Road crashes and air pollution from road motor vehicles result in millions of premature deaths, injuries, and illnesses. Addressing these issues is essential to harness the growth of motor road vehicles and ensure that their benefits significantly outweigh their costs. The adoption and use of motor vehicles worldwide have grown substantially over the last two decades. The World Road Statistics estimates that the global population of 960 million vehicles in use in the year 2000 increased steadily to 2.2 billion in 2020 at an average growth rate of 4.2 percent per year. In 2020, North America had a median motorization rate of 623 motor vehicles per 1,000 persons, compared with just over 40 in Sub-Saharan Africa. East Asia and the Pacific experienced the largest increase in vehicles during this period, from 254 million in 2000 to 730 million in 2020. Vehicle numbers increased at a rate of 9.8 percent per year in South Asia, the highest rate globally, leading to an increase of 271 million vehicles (6.5 times) within the same period (World Road Statistics 2022). This growth has regional disparities (figure I.1) but is spurred by high motorization rate in emerging and developing economies. Globally, passenger cars, vans, and pickups account for about 73 percent of all in-use vehicles, while motorcycles and other two-wheelers account for an additional 23 percent. Only about four percent of the worldwide vehicle stock are trucks (World Bank 2022). Safe and Clean Vehicles for Healthier 6 and More Productive Societies Figure I.1 Number of four wheelers and motorcycles in use by region, 2000-2020 (millions) 2,500 2,000 1,500 1,000 500 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa North America South Asia Sub-Saharan Africa Source: Author’s rendition using data from World Road Statistics. 450 0.6 450 420 0.7 Most 420 emerging and developing0.54 economies have limited domestic 0.59 manufacturing capabilities and rely on vehicle Share of GDP equivalent (percent) Share of GDP equivalent (percent) 400 400 0.6 imports to address demand for vehicle stock. 0.5 many emerging and developing economies, these imports are For 350 350 0.44 0.43 0.5 primarily used vehicles that have been previously 300 0.4 used in other geographic jurisdictions. 300 0.45 In fewer emerging and (US$ billion) (US$ billion) 257 0.44 0.4 or developing economies, imported used vehicles are banned 250 250and demand is met through locally manufactured 0.3 0.35 assembled vehicles or importation of new vehicles. Estimates 200 200 indicate that by 2022, approximately 70 percent 0.3 of 156 150 150 0.2 used vehicles 0.22 emerging and developing economies imported more new ones in 2018, and than123 58 percent imported 0.2 100 100 more than three times as many 0.09 used vehicles as new 0.1 ones (World Bank 2022). 0.1 0.1 50 50 0.01 0.1 16 16 4.1 0.1 0.01 7 3.3 0 New and imported used vehicles in developing0 countries are 0 of lower quality than those manufactured in 0 high- World LI LMI UMI HI World EAP ECA LAC MNA SA SSA income countries, members of the Organisation for Economic Co-operation and Development (OECD). In most emerging and developing economies, most vehicles are imported used vehicles that have been previously used in other geographic jurisdictions. A large share of the used vehicles traded does not meet globally accepted road 120 6 12 0.6 safety and air quality standards 0.55 and is a major contributor to crashes and air pollution. Imported used vehicles into 105 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 5.0 10.2 emerging and developing economies are also likely 100 5 to be equipped 10 with outdated safety and emissions technology 0.5 4.2 4.2 0.42 relative 80 to 84 the most stringent levels in high income 4 OECD countries 8 (UNEP 2020). 0.4 0.35 (US$ billion) (US$ billion) 3.3 0.31 0.32 Importance 60 3.1 3.3 safety of vehicle 3.2 standards 56 3 6 in emerging and developing 5.4 0.3 economies 40 2.4 2 4 2.6 0.16 2.3 0.2 20 15 1 2 0.08 0.14 0.1 10 1.5 6 0.7 Road 3 a leading cause traffic injuries are 1 of death and disability worldwide, disproportionately 0.06 affecting 0.02 emerging 0 0 0 0 and developing economies. According to the World Health Organization (2023), approximately 1.19 million people Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico died in 2021 from road crashes, with more than 90 percent of these fatalities occurring in emerging and developing economies, despite these countries owning only about 60 percent of the world’s motor vehicles. A major contributing factor is the lack of effective vehicle safety standards and regulatory enforcement in these regions (WHO 2023). Vehicle safety standards are crucial for reducing crash risks and minimizing injury severity. These standards include crashworthiness—ensuring vehicle design protects occupants—and crash avoidance technologies like antilock braking systems (ABS), electronic stability control (ESC), and seatbelt reminders. In high income countries, such standards Safe and Clean Vehicles for Healthier 7 and More Productive Societies are mandated by law and strictly enforced, accompanied by strong consumer awareness programs. However, in emerging and developing economies, many vehicles lack these vital safety features, creating a safety disparity. Despite global recognition of vehicle safety, progress in emerging and developing economies has been inconsistent. The UN Decade of Action for Road Safety 2021–2030 prioritizes safer vehicles, and initiatives like the Safer Cars for India campaign have driven some policy and awareness improvements. For example, India mandated driver airbags in all new vehicles in 2019, followed by requirements for ABS and front passenger airbags. Similar advances have occurred in Latin America, with countries like Brazil and Argentina mandating ESC in new models, following the Latin New Car Assessment Program (NCAP) recommendations. Nevertheless, these advancements are isolated and insufficient. Many emerging and developing economies still do not mandate minimum crashworthiness standards or advanced safety technologies. Outdated regulatory frameworks, weak enforcement, and financial constraints hinder progress. In some cases, political and commercial interests further delay stricter regulations. However, vehicle safety improvements have occurred due to global advancements in original equipment manufacturer (OEM) technology. Over the past two decades, OEMs have made significant strides in enhancing vehicle safety, driven by innovation, competitive pressures, and regulatory requirements in high income countries. These advances include stronger vehicle structures, crumple zones, advanced airbag systems, ESC, and collision avoidance technologies. As global production platforms become more harmonized and manufacturing costs decrease, many safety technologies have been incorporated into vehicle models sold across diverse markets, including emerging and developing economies. However, without formal regulatory frameworks, the adoption of such features remains inconsistent and market dependent, rather than a guaranteed baseline of protection for all vehicles. A complicating factor is the widespread importation of used vehicles into emerging and developing economies, many of which do not meet modern safety standards. The United Nations Environment Programme (UNEP) reported that between 2015 and 2018, more than 14 million used vehicles were exported from Europe, Japan, and the United States to emerging and developing economies, particularly in Africa and Southeast Asia. Many of these vehicles lack essential safety features and are not subject to adequate pre-export inspection (UNEP 2020). Without comprehensive national regulations or import controls, these vehicles exacerbate existing safety challenges. The absence of effective vehicle safety regulations is costly from a public health and economic perspective. Road traffic injuries can result in welfare costs equivalent to between three and five percent of countries’ annual GDP due to lost productivity, healthcare costs, and social impacts (WHO 2023). A United Nations Economic Commission for Europe (UNECE) case study analysis found that effective regulations significantly reduced road fatalities and injuries, particularly in emerging and developing economies (UNECE 2021). These findings underscore that vehicle safety is a technical issue and a strategic investment in sustainable development. A critical barrier to policy adoption in many emerging and developing economies is the limited availability of localized data quantifying the economic benefits of adopting vehicle safety technologies. While global studies have demonstrated the cost effectiveness of interventions such as ESC, airbags, and seatbelt reminders, a lack of disaggregated data remains at the national or regional level in many developing countries. This evidence gap makes it difficult for policy makers to conduct comprehensive cost-benefit analyses or justify safety regulations against competing development priorities. Moreover, the indirect societal and economic costs of road trauma—such as long- term disability, loss of family income, and strain on public health systems—are often underreported or excluded from national planning tools. Developing robust methodologies to estimate these impacts, supported by real-world data from health, insurance, and transport sectors, are essential to strengthen policy case for vehicle safety standards and investments in safer technologies. Safe and Clean Vehicles for Healthier 8 and More Productive Societies Air pollution emissions from road vehicles in emerging and developing economies The transport sector is a major source of air pollution, contributing significantly to particulate matter (PM2.5) and nitrogen oxides—including nitrogen dioxide and nitric oxide—in the atmosphere. These pollutants are harmful to human health and the environment. Air pollution caused an estimated 8.1 million premature deaths in 2021, 95 percent in emerging and developing economies (IHME 2024). While several air pollutants are harmful, fine particulate matter or PM2.5 is responsible for the majority of these impacts. PM2.5 can penetrate deep into the respiratory system and cause multiple diseases, including ischemic heart disease, stroke, lung cancer, chronic obstructive pulmonary disease, pneumonia, type 2 diabetes, and neonatal disorders (World Bank 2025). In 2021, The burden of morbidity from PM2.5 exposure was equivalent to 105 billion days lived with disease (IHME 2024). Scientific evidence has also found that exposure to PM2.5 during pregnancy and early childhood can lead to impaired cognitive development (Alter et al. 2024) PM2.5 in the atmosphere originates from various sources, notably fossil fuel combustion processes (e.g., vehicles, coal and oil-powered power plants and industry, and the burning of biomass). Trace constituents from PM2.5 and PM2.5 mass from fossil fuel combustion are among the greatest contributors to PM toxicity. Of the fossil fuel combustion particles, the health risks associated with PM2.5 from road transport traffic are particularly significant. Long- and short-term exposures to particles from both sources are most consistently associated with cardiovascular mortality, especially ischemic heart disease. Several epidemiological and toxicological studies indicate that sulfate or particulate sulfur is among the most, if not the most, important constituents of PM2.5 associated with adverse health effects such as additional hospital admissions and mortality. With respect to traffic-related sources, chemical species in the PM2.5 emissions from diesel-fueled vehicles are particularly associated with adverse human health impacts (The World Bank 2021; Thurston et al. 2021; Thurston et al. 2024). Nitrogen dioxide (NO2) is another harmful air pollutant. Exposure to NO2) causes premature deaths and illnesses such as asthma in children and adults, and lower respiratory infections in children. Nitrogen emissions also contribute to secondary particles that is particulate matter formed from gaseous pollutants through chemical reactions in the atmosphere. They form from nitrogen oxides and ammonia (Anenberg 2022; Chakraborty 2020; Cooper 2022; Gu 2023). Air pollution has broad social and economic consequences. The WHO estimates that 99 percent of the global population breathes harmful concentrations of air pollutants. Increased illnesses and hampered cognitive development increase health expenditures, affect human capital, and reduce workers’ productivity, resulting in lost earnings. These effects thwart economic growth in individual emerging and developing economies and globally (Dechezleprêtre et.al. 2020; Dong et al. 2021; Mujtaba and Shahzad 2020). Motorized road transport is a major source of ambient air pollution globally. Urban areas are particularly affected by transport-related air pollution due to high vehicle density and traffic congestion. Fossil fuel-powered vehicles release PM2.5, nitrogen oxides, and many other harmful pollutants through exhaust emissions (World Bank 2025). Additionally, vehicles emit non-exhaust emissions from tires, brakes, and road dust suspension (HEI 2022). Motorized road vehicles account for approximately 6.7 percent of population-weighted ambient PM2.5 globally, ranging from about three percent in low income countries to 6.9 percent in upper middle income countries (McDuffie et al. 2021a,b). NO2emissions from road transport are estimated to account for 26 percent of global NO2 emissions. Road transport contributes about 34 percent of total nitrogen dioxide emissions in upper middle income countries and low income countries, compared with 12 percent in low income countries (Duffie et al. 2020 a,b). Safe and Clean Vehicles for Healthier 9 and More Productive Societies Policy makers have used several tools to limit road motor vehicles' pollutant emissions. These include emission standards that set quantitative limits on the permissible amount of specific air pollutants that may be released, including PM2.5 and nitrogen oxides. Mandatory vehicle inspection and maintenance programs help monitor vehicles’ emissions and the condition of emission control systems, ensuring that vehicles meet existing emission standards. The design and implementation of air quality management policies in the transport sector contributed to reducing air pollution in high income OECD countries and many middle income countries such as China, Chile, Mexico, and Peru (World Bank 2025). Objective This report aims to strengthen the evidence base for tackling the health effects of road transport as a key element of the human capital agenda, particularly across emerging and developing economies. It presents the findings of analytical work conducted to assess the health and economic effects of increased road transport motorization. It also provides evidence-based recommendations to inform the design and implementation of policies that emerging and developing economies can adopt to enhance the safety and environmental performance of road vehicles. Approach The report presents findings of innovative research and modeling conducted to estimate the benefits of adopting alternative policies to improve safety and environmental performance of road transport. Drawing from economics, epidemiology, engineering, and other disciplines, the report estimates the number and societal costs of fatalities and injuries caused by crashes, and of increased mortality, morbidity and impairment of children’s cognitive development caused by road vehicles’ pollutant emissions. The approach includes state-of-the-art economic analyses to estimate the costs of increased mortality and serious injuries from crashes, increased mortality and morbidity from air pollution, and innovative modeling to estimate the impacts of PM2.5 pollution from road vehicles on children's cognitive development. The cost of health effects from road traffic crashes and vehicle air pollution is estimated by a welfare measure of mortality and serious injuries, productivity cost of morbidity, and lifetime income losses from IQ impairments. The welfare measure of mortality is country-specific values of statistical life (VSL) that reflect people’s willingness-to-pay (WTP) to reduce the risk of death. The welfare cost of serious injuries is estimated as a fraction, or 25 percent, of VSL. The productivity cost of morbidity is estimated as a fraction of daily wages per day lived with illness of varying degrees of disability. The cost of IQ impairments is estimated based on a loss of lifetime income or economic productivity per IQ point. The analytical work also developed a spreadsheet-based model designed to assess simultaneously the effects of key socioeconomic trends on the composition of a country’s vehicle fleet and the associated safety and air pollution implications. The model forecasted the effects of alternative policy scenarios in eight countries: Argentina, Brazil, Egypt, Ghana, India, Kazakhstan, Lao PDR and Mexico. These countries were selected for their geographic diversity, varying income levels and motorization characteristics, and the availability of quality data on motorization management. These criteria ensured that the model could be effectively used to generate insights relevant across diverse contexts. This report assesses road safety and air quality impacts from different vehicle classes—that is, MTWs, LDVs, and HDVs —and analyzes vehicle crash and air pollution effects for these eight countries Additionally, estimates of the effects of PM2.5 exposure on young children’s cognitive development and the health burden caused by nitrogen dioxide exposure are presented in this report for the first time, incorporating recent scientific evidence. Safe and Clean Vehicles for Healthier 10 and More Productive Societies Structure of the report The report has four chapters in addition to this overview. Chapter 1 discusses the global cost of road transport crashes and air pollution. It estimates the number of people that die and suffer non-fatal injuries every year due to road traffic crashes. It quantifies the premature mortality and morbidity caused by exposure to two harmful air pollutants emitted by road motorized vehicles: PM2.5 and NO2. Chapter 1 also estimates the monetary value of the health effects of road transport. The evidence presented in the chapter builds a strong case for the need to address the safety and environmental risks associated with road transport as part of the human capital agenda in emerging and developing economies. Chapter 2 provides an overview of the adoption of vehicle standards impacting safety and air pollution outcomes globally and in emerging and developing economies. It also highlights the need for an improved understanding of the contributions of internationally recognized standards to reduce fatalities and serious injuries and air pollution from road motor vehicles. It also introduces a simple and stylized model to estimate the impact of adopting alternative motorization management policies that simultaneously address vehicle crash safety and air pollution in eight selected emerging and developing economies. The model has been developed to assess the contribution of alternative standards to prevent and mitigate the significant number of preventable deaths, injuries, illnesses, and IQ losses, and the associated costs, caused by road motor transport. Chapter 3 assesses the safety and emissions-related impacts of motorized road transport in the eight select countries to set the stage to apply the proposed model at the country level. The assessment’s findings underscore the need to identify evidence-based policies to mitigate these risks, designed to address country specific conditions, such as the vehicle fleet’s composition by age and types of vehicles, existing policies influencing the fleet’s composition, and existing requirements for safety and emissions control technologies. The proof-of-concept of the model uses data from the eight selected emerging and developing economies to illustrate how the effects of alternative policies lead to different results, depending on factors such as fleet composition, the existing safety and emission standards, and policies related to the import of used vehicles. The findings presented in the chapter identify policies that result in improved outcomes across the eight countries, as well as the varying impacts of different technologies that are influenced by the characteristics of each country’s vehicle fleet. The findings highlight how policies must be developed based on a life-of-vehicle approach, including specific interventions when vehicles enter the fleet, while they are in use and when they exit the fleet. Chapter 4 provides policy recommendations to improve road vehicle safety and air quality performance in emerging and developing economies. Building on the analysis of the model’s results, it provides evidence-based recommendations to realize the social and economic benefits of adopting available technologies and standards for road vehicles, based on a life-of-vehicle approach. The recommendations highlight opportunities to build synergies by designing and implementing policies aimed at simultaneously reducing fatalities and serious injuries, and those designed to reduce air pollution. The recommendations prioritize the policy options that would result in the most significant benefits in the medium to long term. While the analysis focused on eight countries, the recommendations present policy options that other emerging and developing economies might assess to reduce the significant costs caused by motorized road transport, including negative health effects and the associated impacts on human capital, productivity, and well-being. Safe and Clean Vehicles for Healthier 11 and More Productive Societies Notes 1. For the purpose of this document, emerging and developing economies include, from the World Bank country income classifications of countries LI = low income, LMI=lower middle income, UMI=upper middle income. In other contexts, it is also referred as the Global South. References Alter, N.C., Whitman, E.M., Bellinger, D.C. et al. 2024. Quantifying the association between PM2.5 air pollution and IQ loss in children: a systematic review and meta-analysis. Environ Health 23, 101 (2024). https://doi.org/10.1186/ s12940-024-01122-x. Anenberg SC, Mohegh A, Goldberg DL, Kerr GH, Brauer M, Burkart K, Hystad P, Larkin A, Wozniak S, and Lamsal L. 2022. Long-term trends in urban NITROGEN DIOXIDE concentrations and associated paediatric asthma incidence: estimates from global datasets. Lancet Planet Health, 6(1): e49-e58. doi: 10.1016/S2542-5196(21)00255-2. Chakraborty, P., Jayachandran, S., Padalkar, P. et al. 2020. Exposure to Nitrogen Dioxide (NO2) from Vehicular Emission Could Increase the COVID-19 Pandemic Fatality in India: A Perspective. Bull Environ Contam Toxicol 105, 198–204 https://doi.org/10.1007/s00128-020-02937-3. Cooper MJ, Martin RV, Hammer MS, Levelt PF, Veefkind P, Lamsal LN, Krotkov NA, Brook JR, McLinden CA. 2022. Global fine-scale changes in ambient NO2 during COVID-19 lockdowns. Nature, 601(7893):380-387. doi: 10.1038/s41586-021- 04229-0. Dechezleprêtre, A., Rivers, N. and Stadler, B. 2020. The Economic Cost of Air Pollution: Evidence from Europe. OECD Economics Department Working Papers No. 1584. Available at: https://dx.doi.org/10.1787/56119490-en (Accessed: 17 April 2025). Dong, D., Xu, B., Shen, N. and He, Q. 2021. The Adverse Impact of Air Pollution on China’s Economic Growth. https:// doi.org/10.3390/su13169056 (Accessed: 17 April 2025). HEI Panel on the Health Effects of Long-Term Exposure to Traffic-Related Air Pollution. 2022. Systematic Review and Meta-analysis of Selected Health Effects of Long-Term Exposure to Traffic-Related Air Pollution. Special Report 23. Boston, MA: Health Effects Institute. McDuffie E, Martin R, Yin H, Brauer M. 2021a. Global Burden of Disease from Major Air Pollution Sources (GBD MAPS): A Global Approach. Health Effects Institute. Research Report 210. Boston. USA. McDuffie E, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. Source sector and fuel contributions to ambient PM2.5 and attributable mortality. EIA (United States Energy Information Administration). 2021. International Energy Outlook 2021. Gu, Y., Henze, D. K., Nawaz, M. O., Cao, H., and Wagner, U. J. 2023. Sources of PM2.5-associated health risks in Europe and corresponding emission-induced changes during 2005–2015. GeoHealth, 7, e2022GH000767. https://doi. org/10.1029/2022GH000767 IHME (Institute for Health Metrics and Evaluation). 2024. Global Burden of Disease 2021: Findings from the GBD 2021. Seattle, Washington: IHME. Mujtaba, G. and Shahzad, S.J.H. 2020. Air pollutants, economic growth, and public health: implications for sustainable development in OECD countries. Environmental Science and Pollution Research. Available at: https: //doi.org/10.1007/ s11356-020-11212-1 (Accessed: 19 April 2025). Safe and Clean Vehicles for Healthier 12 and More Productive Societies Thurston, G.D., Awe, Y. A., Ostro, B. D, Sanchez-Triana, E. 2021. Are All Air Pollution Particles Equal?: How Constituents and Sources of Fine Air Pollution Particles (PM 2.5) Affect Health (English). Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/810141630705865331 UNECE. 2021. UN Vehicle Regulations for Road Safety: Cost-Benefit Methodology and Socioeconomic Impact. Geneva: United Nations Economic Commission for Europe 2021. https://unece.org/sites/default/files/2021-09/CBA%20 publication%20E%20web_0.pdf. UNEP. 2020. Used Vehicles and the Environment. A Global Overview of Used Light Duty Vehicles: Flow, Scale and Regulation. United Nations Environment Programme. WHO. 2023. Global Status Report on Road Safety 2023. Geneva: World Health Organization, 2023. https://iris.who. int/handle/10665/375016 World Bank. 2021. The Global Health Cost of PM 2.5 Air Pollution: A Case for Action Beyond 2021 (English). International Development in Focus Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/ en/455211643691938459 World Bank. 2022. Motorization Management for Development; World Bank. https://hdl.handle.net/10986/37589 World Bank. 2025. Accelerating Access to Clean Air on a Livable Planet. World Bank. http://documents.worldbank. org/curated/en/099032625132535486 IRF (International Road Federation). World Road Statistics. 2022. Geneva: IRF. Safe and Clean Vehicles for Healthier 13 and More Productive Societies Chapter 1. Health Impacts of Motorized Road Transport Motorization in road transport is commonly correlated with increased economic and population growth and its rate of growth can only be expected to increase as emerging and developing economies advance. Vehicle growth rates in emerging and developing economies could reach up to 10 percent annually in certain regions, potentially doubling the global vehicle fleet (EIA 2021) from the prevailing 1.6–2.0 billion by 2050. The increase in motorized road transport offers significant opportunities, including greater access to economic opportunities, services, and social activities, However, the quality and quantity of a country’s motor vehicle stock fundamentally influence the unintended risks posed by road transport, including road crashes and air pollution. This chapter addresses questions about the depth of the human capital problem of poor vehicle standards and the opportunity cost of not assuming motorization management as a centerpiece of a health agenda. 1.1 Health impact of key risks from motorized road transport Deaths from road transport exceed those from HIV, tuberculosis, or malaria. Injuries and pollution from road vehicles contribute to six of the top 10 causes of death globally. Annually, approximately 1.19 million lives are lost in road traffic crashes (WHO 2023). Additionally, around 50 million more people suffer non-fatal injuries, with many incurring a disability because of their injury. Global air pollution—to which road transport is a key contributor — was responsible for 8.1 million premature deaths in 2021 (IHME 2024) equivalent to approximately 14 percent of all non- COVID19 deaths that year. An estimated 311,0000 premature deaths can be attributed to PM2.5 air pollution linked to road transport and 240,000 premature deaths to NO2 air pollution from road vehicles. Fatalities and serious injuries because of vehicle crashes The burden of road traffic deaths is also disproportionately high in countries with lower incomes, relative to the number of vehicles in circulation. Low income, lower middle income and upper middle income countries together account for 73 percent of the global vehicle population but suffer close to 92 percent of road traffic deaths (figure 1.1). Low-income countries have one percent of the world’s motor vehicles but comprise 13 percent of global road deaths. On the other hand, high income countries account for 28 percent of the global fleet and not even eight percent of global fatalities. Safe and Clean Vehicles for Healthier 14 and More Productive Societies Figure 1.1 Road traffic deaths in emerging and developing economies by income, in shares of totals Estimated road fatalities Powered vehicles <1% 13% 8% 28% 34% 44% 35% 38% High-income Upper middle-income Lower middle-income Low-income Source: WHO Global Status Report 2023. Air pollution from road transport Motorized transport is a principal source of harmful air pollutants such as primary and secondary fine particulate matter(PM2.5), nitrogen dioxide (NO2), and precursors of ground level ozone. The mechanism through which these pollutants impact health and enter the environment varies greatly. Fine particles smaller than 2.5 microns, best known as PM2.5, can penetrate deep into the respiratory system, causing diseases such as stroke, ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD). Exposure to PM2.5 also causes a large burden of morbidity, measured by the global burden of disease (GBD) as years lived with disability (YLDs). Days lived with type 2 diabetes, COPD, stroke, cataract, and IHD account for nearly 4 billion or 99 percent of the days lived with illness from exposure to road transport PM2.5. Recent research documents that, besides increases in morbidity, exposure to PM2.5 during prenatal development and early childhood impairs children's cognitive development, which can be measured by declines in their IQ. This impact is particularly concerning given the long-term consequences of reduced cognitive ability on individual productivity and societal well-being (box 1.1). Ambient PM2.5 originates from all types of combustion, including motor vehicles, household use of solid fuels for cooking and other purposes, solid waste burning, burning of agricultural residues, use of nitrogen-based fertilizers power plants, road and construction dust, forest fires, and some industrial processes. Motorized road transport is a significant source of ambient PM2.5 via tailpipe emissions, evaporative emissions, resuspension of road dust, particles from brake and tire wear. Globally, motorized road vehicles account for approximately seven percent of population-weighted ambient PM2.5 –or the equivalent of 2.75 µg/m3 of annual PM2.5. They are estimated to cause about 311,000 premature deaths per year and a loss of 64 million IQ points. HDVs are responsible for more than half of PM2.5 emissions from road transport. While correlated with traffic density and activity levels, ambient PM2.5 from road transport is influenced by structural factors such as fuel quality, fuel type, vehicle and engine types, and emission control technologies. Fuel quality standards play a critical role, as sulfur concentrations in fuels exceeding 10 parts per million degrade the performance of Euro 5/V and Euro 6/VI compliant technologies. This impairs the effectiveness of DPFs and SCRs, which control Safe and Clean Vehicles for Healthier 15 and More Productive Societies Box 1.1 Cognitive impacts of ambient PM2.5 A recent meta-analysis shows a linear relationship between ambient PM2.5 exposure and IQ decline, with higher exposure levels associated with greater IQ loss. IQ declines linearly by 0.27 points per µg/m3 increase in prenatal or early childhood exposure to ambient PM2.5 (Alter et al. 2024). Further analysis and inclusion of additional outcomes from studies of PM2.5 exposure and IQ may suggest that the decline in IQ is as large as 0.69 IQ points per µg/m3 increase in PM2.5 for PM2.5 exposures below 11 µg/m3 and 0.31 IQ points for exposures above 11 µg/ m3. This implies an IQ loss of 10 points at an exposure of 30 µg/m3 of PM2.5. To be conservative, one may assume there is no additional IQ loss above this exposure level. Applying these PM2.5 – IQ loss relationships to ambient PM2.5 globally suggests an average loss of 8.4 IQ points per child from ambient PM2.5 exposure. This amounts to a global loss of 1.1 billion IQ points among children in 2021. The estimate is nearly 50 percent greater than the estimated losses of 765 million IQ points from lead exposure in 2019 (Larsen and Sanchez-Triana 2023). As many as 96 percent of the IQ losses from PM2.5 exposure occurred in emerging and developing economies, including 62 percent of losses in South Asia and Sub-Saharan Africa. Average IQ losses ranged from 3.3 points in high income OECD countries to 9.2 points in lower middle income countries. Regionally, the average IQ loss per child ranges from 5.4 points in LAC to 10 points in South Asia region. These losses result from multiyear exposure to ambient PM2.5 For technical details on the analysis see Appendix D. References Alter, N.C., Whitman, E.M., Bellinger, D.C. et al. 2024. Quantifying the association between PM2.5 air pollution and IQ loss in children: a systematic review and meta-analysis. Environ Health 23, 101 (2024). https://doi. org/10.1186/s12940-024-01122-x Larsen, B., Sánchez-Triana, E. 2023. Global health burden and cost of lead exposure in children and adults: a healthimpact and economic modelling analysis. Lancet Planet Health Oct;7(10):e831-e840.doi: 10.1016/S2542- 5196(23)00166-3. Epub 2023 Sep 12. PM2.5 and nitrogen oxides emissions. Sulfur oxides and nitrogen oxide emissions from vehicles contribute to the formation of secondary PM2.5. When sulfur in fuel combusts, it forms sulfur dioxide and small amounts of sulfur trioxide. The incomplete combustion of fuels at high temperatures inside engines contributes to the reaction of nitrogen and oxygen from the air to form nitrogen oxides and nitrogen dioxide, collectively called nitrogen oxides. Long-term exposure to ambient NO2 has significant mortality effects. Additional health effects of nitrogen dioxide include asthma in children and adults, and lower respiratory infections in children. In 2019, the global population- weighted concentration of NO2 was 14.6 micrograms per cubic meter, which is 46 percent higher than the WHO annual air quality guideline (AQG) of 10 micrograms per cubic meter. In 2021, global premature deaths attributable to ambient NO2 exposure exceeding the WHO annual AQG are estimated at 943,000. Of these, 77 percent, or 726,000 deaths, are from cardiovascular disease (CVD), 14 percent from respiratory disease, and nine percent from lung cancer. In 2017, about 26 percent of global NO2 emissions come from road transport (Duffie et al. 2020 a,b).1 These emissions rise steeply from 12 percent of total NO2 emissions in low-income countries to about 34 percent in upper-middle income and high-income countries (figure 1.2). Per capita NO2 emissions from road transport rose from 0.4 kilograms Safe and Clean Vehicles for Healthier 16 and More Productive Societies in low income countries to 7.1 kilograms in high income countries. Regionally, in emerging and developing economies, NO2 emissions from road transport as a share of total NO2 emissions are by far highest in LAC and lowest in SSA and ECA (figure 1.2). However, per capita NO2 emissions from road transport are highest in ECA at 11.5 kilograms, followed by EAP, MNA and LAC from five to 6.8 kilograms. Figure 1.2 Proportion of total NO2 emissions generated by road transport Road transport NO2 (% of total NO2) Road transport NO2 (% of total NO2) By country income level By region 50% 50% 46 40% 40% 33 34 30% 26 30% 25 25 19 19 20% 20% 12 11 11 10% 10% 0% 0% World LI LMI UMI HI SA EAP SSA MNA ECA LAC Notes: LI=Low income; LMI=Lower middle income; UMI=upper middle income; HI=high income. SA=South Asia; EAP=East Asia and Pacific; SSA=Sub-Saharan Africa; MNA=Middle East and North Africa; ECA=Europe and Central Asia; LAC=Latin America and Caribbean. Source: Authors’ derivation with data from McDuffie et al. 2020b. Percent of ambient PM Ambient PM contribution (μg/m3) Annual premature deaths from road transport 2.5 NO2 emissions are estimated at 2.5240,000 in 2021.2 Seventy-seven percent of the deaths are in emerging 9.0 Argentina Argentinaand 60 percent and developing economies, 1.3 of these deaths are in EAP. As a percent of GDP, Brazil the cost of road transport NO 2 increased markedly Brazil with country 0.8 income level, ranging from 0.01 6.3 percent in low income countries to 0.54 percent in upper middle income countries, and then declined to 0.43 Egypt 5.8 Egypt 3.9 percent in high income countries. This pattern is observed because ambient NO2 relative to ambient PM2.5 from road Ghana 3.1 Ghana 1.5 transport rises with income. In LAC, such as in Argentina, Brazil and Mexico, annual deaths from road transport NO2 India India 5.7 road transport PM . Thus, combating 3.4 are substantially higher than from 2.5 NO2 emissions is particularly important in Kazakhstan countries with relatively moderate 5.1 Kazakhstan 1.1 ambient PM2.5 but rapid motorization of road transport. Lao PDR 5.7 Lao PDR 1.3 The health impacts and cost of nitrogen-based emissions from road transport go beyond the impacts of NO2. Nitrogen Mexico 12.3 Mexico 1.8 oxides emissions form into secondary PM2.5 in the atmosphere. A large study of 40 countries in Europe estimated that 0 1 2 3 4 5 nitrogen oxide0% 2% 4% emissions from6%road8% 10% 12% transport 14% contributed 10 percent of all PM2.5 deaths to as much as 66 percent of transport-related PM2.5 deaths (Gu et al. 2023). In summary, road crashes and pollution-related illnesses and mortality are silent pandemics (table 1.1). Road crashes seem to disproportionately affect low income countries and vulnerable populations. PM2.5 and NO2 from road transport disproportionately affect middle income countries, and NO2 especially a upper middle income countries. As a way of example, the death toll of ambient PM2.5 and NO2 from motorized road transport is, respectively, eight and eighteen times higher in middle income countries than in low income countries, being as well the third leading risk factor for death among children under five in year 2021 after malnutrition and low birthweight or short gestation (IHME 2024). Meanwhile, the average crash fatality rate in low income countries is 27.5 deaths per 100,000 population, more than three times higher than the 8.3 deaths per 100,000 population in middle income countries (table 1.1). Based on the age distribution of all-cause mortality, crashes are the leading cause of death for children and young people aged 5–29 years (WHO, 2023). Safe and Clean Vehicles for Healthier 17 and More Productive Societies Table 1.1 Lives lost due to road traffic crashes and air pollution from motorized road transport, 2019 (deaths/100,000 pop.) Low Income Countries Middle Income Countries Road Crashes (*) 27.5 8.3 Ambient PM2.5 (**) 0.55 4.60 Ambient NO2 (***) 0.17 3.10 Sources: (*) WHO (2023), (**) Authors estimates based on Global Burden of Disease (GBD) data(HEI 2024) and McDuffie et al. 2021a,b. (***) Authors estimates based on GBD data and McDuffie et al. 2020a,b. Comparing NO2 and PM2.5, the ratio of road transport deaths from NO2 to road transport deaths from PM2.5 rises from 0.3 to 0.36 in low income and lower middle income countries to 0.84 in upper middle income countries and 1.58 in high income countries. Regionally, in emerging and developing economies, the ratio is 0.39-0.42 in SA and MNA; 0.77-0.89 in EAP and ECA; and as high as 1.36 in LAC. This pattern is observed because ambient NO2 relative to ambient PM2.5 from road transport rises with income. In LAC, such as in Argentina, Brazil and Mexico, annual deaths from road transport nitrogen dioxide are substantially higher than from road transport PM2.5. Thus, combating NO2 emissions is particularly important in countries with relatively moderate ambient PM2.5 but rapid motorization of road transport. 1.2 The economic cost of motorized road transport This report estimates that the annual cost of deaths and serious injuries caused by road traffic crashes amounts to about $2.9 trillion while mortality and morbidity from PM2.5 from road transport is estimated at $385 billion per year. Productivity losses due to cognitive impairment from PM2.5 emissions from road transport is $157 billion. The cost from road transport is 7.7 percent of the cost of total ambient PM2.5. The global welfare cost of mortality from NO2 estimated at $420 from road transport isPopulation billion. Estimated 3 In fatalities road 2021, the global cost of Powered annual IQ lossesPaved vehicles totaled $2 trillion, which roadsa represents the present value of the associated lifetime income losses and is equivalent <1% to 2.1 percent <1% of global GDP (box 1.2). 2% 9% 13% 8% 16% 10% Aggregates mask significant income and regional disparities. Emerging and developing 28% economies altogether account 34% for $1.6 trillion or 55 percent of the aggregate economic cost for road crashes. By region, these costs range from 43% $98 billion in Sub-Saharan 32%to $1.16 trillion Africa 35% 44% in East 88% toll of road Asia and the Pacific (figure 1.3). The economic 38% transport fatalities and injuries translates to 3.0 percent of the global GDP, with upper middle income countries amounting to a staggering 4.9 percent of their GDP (figure 1.3). High-income Upper middle-income Lower middle-income Low-income Figure 1.3 Annual cost of road traffic crashes including fatalities and serious injuries as 2021 aExcludes expressways By country income level By region 3,500 6.0 3,500 6.0 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 2,934 3,000 2,934 5.1 3,000 4.9 5.0 5.0 4.3 2,500 2,500 3.9 3.8 4.0 3.8 4.0 (US$ billion) (US$ billion) 2,000 2,000 3.5 3.0 3.1 3.0 3.1 1,500 3.1 1,500 1,321 1,287 1,166 2.0 2.2 2.0 1,000 1,000 1.6 500 1.0 500 353 1.0 307 221 261 18 127 98 0 0.0 0 0.0 World LI LMI UMI HI World EAP ECA LAC MNA SA SSA Notes: LI=Low income; LMI=Lower middle income; UMI=upper middle income; HI=high income. SA=South Asia; EAP=East Asia and Pacific; 450 SSA=Sub-Saharan Africa; MNA=Middle East and North Africa; ECA=Europe 0.65 0.70 450 Asia; LAC=Latin America and Caribbean. and Central 0.70 0.64 DP equivalent (percent) DP equivalent (percent) 400 385 400 385 0.60 Source: Authors’ estimates, based on GBD 2021 data. 0.60 350 350 0.50 0.50 300 300 (US$ billion) (US$ billion) 0.40 0.40 0.40 0.39 0.38 0.40 250 250 Safe and Clean Vehicles for Healthier 18 200 188 176 0.30 200 0.30 Productive and More 0.30 Societies 0.29 0.30 158 150 150 0.26 0.20 Box 1.2. Estimating the economic cost of car crashes and air pollution (or the economic benefits of preventing mortality and morbidity) The economic cost of premature deaths, injuries, days of illness, and impaired cognitive development in children caused by road traffic crashes and air pollution is estimated using a value of statistical life (VSL) approach for mortality; cost of illness approach for morbidity; and a lost productivity approach for cognitive losses. The VSL is a welfare metric that reflects people’s willingness to pay for a reduction in risk of death. VSL is used to quantify the cost of mortality, or benefit of reducing the risk of death, primarily in cost–benefit analyses of public policies related to health, environment, and transport safety. This approach allows policy makers to evaluate the economic justification for safety and pollution control interventions by comparing the cost of the intervention with the value of the mortality risk reduction it achieves. The avoidable cost for each fatality for traffic crashes is assumed to be equivalent to the VSL estimated for each year of analysis from 2025 to 2050 based on the emerging and developing economies model described in Wijnen et al., 2025. VSL for each study country was calculated with 2020 prices. The cost of serious injury avoided is assumed to be a quarter of the VSL value for that year based on McMahon and Dahdah, 2008. The cumulative economic benefit of FSI saved over 2025 to 2050 is calculated in its present value (PV). Country specific VSLs are also applied to estimate the cost of mortality from air pollution (Sanchez-Triana et al, 2021; World Bank 2022). The cost of IQ impairments is estimated based on a loss of two percent of lifetime income per IQ point (Appendix D, Larsen and Sanchez-Triana 2023). References Larsen B., Sánchez-Triana, E. 2023. Global health burden and cost of lead exposure in children and adults: a health impact and economic modelling analysis. Lancet Planet Health Oct;7(10):e831-e840.doi: 10.1016/S2542- 5196(23)00166-3. Epub 2023 Sep 12. McMahon, K. and Dahdah, S. 2008. The true costs of road crashes. International Road Assessment Programme (iRAP) publication. Sánchez-Triana, E.,  et al. 2021. Estimating the Health Effects of Ambient PM2.5  Air Pollution in Developing Countries. In: Shugart, H., et al. Eds., Oxford Research Encyclopedia of Environmental Science, Oxford University Press. https://doi.org/10.1093/acrefore/9780199389414.013.559 Wijnen et al., 2025. Wijnen, W., Dahdah, S., and Pkhikidze, N. 2025. The Value of a Statistical Life in the context of Road Safety: A New Value Transfer Approach. Traffic Injury Prevention. https://doi.org/10.1080/15389588.2 025.2476607 World Bank 2022. Motorization Management for Development. World Bank. Safe and Clean Vehicles for Healthier 19 and More Productive Societies 2% Population 9% Estimated 13% 8% road fatalities Powered vehicles Paved roadsa 16% 10% 28% <1% <1% 34% 2% 43% 9% 32% 13% 44% 8% 35% 16% 10% 88% 28% 38% 34% The economic cost of IQ losses from road transport PM2.5 in comparison to GDP is greatest in low income and lower 43% 32% 44% 35% middle income countries and declines with income level. For emerging and developing countries by region, the cost 88% High-income Upper middle-income 38% Lower middle-income Low-income of IQ losses in comparison to GDP is highest in MNA and lowest in ECA. aExcludes expressways The cost of health impacts (mortality and morbidity) of road transport PM2.5 in comparison to GDP dominates in upper High-income Upper middle-income Lower middle-income Low-income 3,500 income countries and is by far the lowest 6.0 middle in low income countries. In emerging and developing countries 3,500 6.0 by equivalent (percent) equivalent (percent) aExcludes expressways region, 3,000 the cost 2,934 of PM 2.5 from road 4.9 transport is greatest in EAP 3,000and lowest 2,934 in SSA (figure 1.4). The high 5.1cost of IQ 5.0 5.0 losses 2,500 compared to the cost of mortality and morbidity in the low income countries and SSA is primarily due to the 2,500 4.3 3.8 4.0 3.9 3.8 4.0 3,500 3,500 6.0 vulnerable 6.0 population age structure with larger child populations to cognitive impairment. of GDP(percent) (US$ billion)(US$ billion) (US$ billion)(US$ billion) of GDP(percent) 2,000 2,000 5.1 3,000 2,934 3.5 4.9 3.03,000 2,934 3.1 3.0 5.0 3.1 5.0 1,500 1,321 1,287 1,500 3.1 4.3 2,500 2,500 1,166 as of 2021 3.9 2.0 equivalent Figure 2.0 1,000 1.4 Annual cost of PM2.5 attributable to 4.0 transport road 3.8 2.2 3.8 4.0 equivalent 1,000 1.6 2,000 2,000 500 3.5 1.0 3.0 500 353 1.0 3.0 3.1 Share 3.1 307 261 Health Impact 221 Share 1,500 1,321 1,287 1,500 3.1 127 98 18 1,166 Share of GDP 0 0.0 2.0 0 0.0 2.0 Share of GDP 2.2 1,000 1,000 World EAP ECA1.6 LAC MNA SA SSA World LI LMI UMI HI By country income level 1.0 By region 1.0 500 307 500 353 221 261 18 127 98 450 0 0.65 0.70 0.0 4500 0.70 0.0 0.64 equivalent (percent) equivalent (percent) 400 385 World LI LMI UMI HI 400 World 385 EAP ECA LAC MNA SA SSA 0.60 0.60 350 350 0.50 0.50 300 450 0.70 300 450 0.70 (US$ billion)(US$ billion) 0.65 (US$ billion)(US$ billion) 0.64 of GDP(percent) of GDP(percent) 0.40 0.40 0.40 0.39 0.38 0.40 250 400 385 250 400 385 0.60 0.60 200 188 176 200 350 0.30 350 0.30 0.30 0.29 0.30 158 0.50 150 0.50 150 equivalent equivalent 300 0.20 300 0.26 0.20 100 0.40 0.40 100 0.40 0.39 0.38 0.40 250 250 0.10 0.10 0.10 Share Share 50 200 0.1 188 176 50 200 0.02 21 0.29 0.30 0.30 158 16 12 9 12 0.30 1.9 0.30 0 equivalent (percent) Share of GDP equivalent (percent) Share of GDP 0 150 0 0 150 0.20 0.26 0.20 World LI LMI UMI HI World EAP ECA LAC MNA SA SSA 100 100 0.10 0.10 0.10 50 0.1 21 50 0.02 16 12 9 12 0 0 IQ Losses 1.9 0 180 0.35 1800 0.35 World LI LMI UMI HI World EAP ECA LAC MNA SA SSA 160 157 0.30 0.30 160 157 0.31 By country income level 0.3 By region 0.3 140 140 0.25 0.28 120 180 0.35 120 180 0.27 0.25 0.35 0.20 (US$ billion)(US$ billion) (US$ billion)(US$ billion) of GDP(percent) of GDP(percent) 100 160 157 0.30 0.30 0.2 100 160 157 0.18 0.31 0.2 0.3 0.3 80 0.16 76.4 80 0.17 140 0.15 140 0.16 0.15 0.25 0.28 60 120 57 0.13 60 120 0.15 0.27 0.25 equivalent equivalent 0.20 0.1 44 0.1 40 100 0.2 40 100 0.18 0.2 0.16 21 76.4 0.05 0.17 0.05 20 20 13 Share Share 80 0.15 80 0.16 8 6 0.15 1.3 4 5.3 0 57 0.13 0 0 60 0.15 0 Share of GDP Share of GDP 60 World LI LMI UMI HI 0.1 World 44 EAP ECA LAC MNA SA SSA 0.1 40 40 21 0.05 0.05 20 20 13 1.3 4 8 6 5.3 0 0 0 0 World LI LMI UMI HI World EAP ECA LAC MNA SA SSA Notes: Only low and middle income countries are included within the regions, whereas all countries are included in the “World” columns. LI=Low income; LMI=Lower middle income; UMI=upper middle income; HI=high income. SA=South Asia; EAP=East Asia and Pacific; SSA=Sub- Saharan Africa; MNA=Middle East and North Africa; ECA=Europe and Central Asia; LAC=Latin America and Caribbean. Source: Authors’ calculations 2025. The global welfare cost of mortality from road transport NO2 emissions was $420 billion in 2021. The cost in emerging and developing countries are estimated at $163 billion per year, somewhat less than in high income countries (figure 1.5).4 Regionally in emerging and developing countries, the dominant share of the cost rests in the upper middle Safe and Clean Vehicles for Healthier 20 and More Productive Societies 1,500 1,000 500 income countries of EAP. The cost of transport NO2 was equivalent in magnitude to 0.44 percent of global GDP in 0 a clear ascending pattern from 0.01 percent in low income countries to 0.54 percent in upper middle 2021, with 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 income countries, and then declining to 0.43 percent in high income countries. (figure 1.5). East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa Figure 1.5 Annual cost of North NO2 from road America transport South Asia as of 2021 Sub-Saharan Africa By country income level By region 450 0.6 450 420 0.7 420 0.54 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 400 400 0.59 0.6 0.5 350 350 0.44 0.43 0.5 300 0.4 300 0.45 (US$ billion) (US$ billion) 257 0.44 0.4 250 250 0.3 0.35 200 200 0.3 156 150 0.2 150 123 0.22 0.2 100 100 0.09 0.1 0.1 0.1 50 50 0.01 0.1 16 16 4.1 0.1 0.01 7 3.3 0 0 0 0 World LI LMI UMI HI World EAP ECA LAC MNA SA SSA Notes: Only low and middle income countries are included within the regions, whereas all countries are included in the “World” columns. LI=Low income; LMI=Lower middle income; UMI=upper middle income; HI=high income. SA=South Asia; EAP=East Asia and Pacific; SSA=Sub- Saharan Africa; MNA=Middle East and North Africa; ECA=Europe and Central Asia; LAC=Latin America and Caribbean. 120 6 12 0.6 0.55 Source: Authors’ calculations, 2025. 105 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 5.0 10.2 100 5 10 0.5 4.2 4.2 0.42 84 8 0.4 Notes 80 4 0.35 (US$ billion) (US$ billion) 3.3 0.31 0.32 60 3.3 56 3 6 5.4 0.3 1. Based on an anthropogenic emission3.2 3.1 inventory of atmospheric pollutants for more than 200 countries. 40 2.4 2 4 0.16 0.2 2. The estimate is based on the share of total nitrogen dioxide emissions from road transport and not ambient share 2.6 2.3 20 nitrogen of 15 dioxide. The estimate should therefore 1 2 1.5 a rough approximation be considered 0.08 only. 0.14 0.1 10 0.7 3 6 0 cost from road transport in each country is 3. The 1 0 calculated0 as the cost of ambient nitrogen dioxide 0.06 0.02 multiplied 0 by the share of nitrogen dioxide that originates from road transport, and should therefore be considered a rough Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico approximation of cost. 4. The cost from road transport in each country is calculated as the cost of ambient nitrogen dioxide multiplied by the share of nitrogen dioxide that originates from road transport. References EIA (United States Energy Information Administration). 2021. International Energy Outlook 2021. GBD. 2021. Risk Factors Collaborators. 2024. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet; 403(10440):2162-2203. doi: 10.1016/S0140-6736(24)00933-4. Gu, Y., Henze, D. K., Nawaz, M. O., Cao, H., and Wagner, U. J. 2023. Sources of PM2.5-associated health risks in Europe and corresponding emission-induced changes during 2005–2015. GeoHealth, 7. https://doi.org/10.1029/2022GH000767 HEI. 2024. State of Global Air 2024. Special Report. Available: www.stateofglobalair.org [accessed 12/15/2024]. Data source: Global Burden of Disease Study 2021. IHME, 2024. Boston, MA: Health Effects Institute. IHME. 2024. Risk factors driving the global burden of disease. https://www.healthdata.org/research-analysis/library/ risk-factors-driving-global-burden-disease WHO. 2023. Global Status Report on Road Safety 2023. Geneva: World Health Organization, 2023. https://iris.who. int/handle/10665/375016 Safe and Clean Vehicles for Healthier 21 and More Productive Societies Chapter 2. Vehicle Safety and Emission Standards In the absence of motorization management policies, emerging and developing economies—with the highest growth of motor vehicle fleets—tend to be disproportionately at risk of the health costs linked to crashes and air pollution for, at least two reasons: • Reliance on used vehicles and sub-standard vehicle technologies: In 2018, 70 percent of emerging and developing economies imported more used vehicles than new ones, with 58 percent importing more than three times as many used vehicles. Many of these used vehicles do not meet globally accepted road safety and air quality standards. (Ministry of Infrastructure and Water Management of the Netherlands 2020). • Weak systems and policies: Emerging and developing economies often lack comprehensive policies based on a life-of-vehicle approach, with guidelines and operational protocols designed for different phases of the vehicle lifecycle, including entry, active use, and exit. Fragmented policies, uncoordinated systems, and ad hoc protocols result in gaps in regulatory and operational oversight of vehicle stocks (World Bank 2022). This chapter addresses issues of vehicle standards, how internationally accepted principles for vehicle safety and emission standards influence, patterns and behaviors of the fleet and impact the potential health burden of traffic crashes and air pollution. 2.1 Vehicle standards impacting health outcomes Most emerging and developing economies have limited domestic vehicle manufacturing capabilities and rely on imports to address demand for vehicle stock. A significant portion of these imports are used vehicles, accounting for an estimated 30 percent of newly registered vehicles and totaling 360 million vehicles annually (World Bank 2022). These imported used vehicles often come with outdated safety and emissions technology relative to the most stringent standards in high income OECD countries (Ministry of Infrastructure and Water Management of the Netherlands 2019). The situation is exacerbated by the lack of regulations or inadequate regulations governing the quality of imported used vehicles in emerging and developing economies. Even where regulations exist, they are poorly enforced or lack the governance structures to ensure compliance. Consequently, a high likelihood prevails that imported used vehicles do not meet minimum roadworthiness or emissions standards. Safe and Clean Vehicles for Healthier 22 and More Productive Societies This observation is underpinned by a governmental study carried out in the Netherlands in 2019. The ministry tested 160 vehicles in the port of Amsterdam destined for export to Africa and found that more than 80 percent of the vehicles were below the Euro 4 standard. Most of these vehicles did not have valid roadworthy certificates and many had key emissions and safety equipment either removed or not working (Ministry of Infrastructure and Water Management of the Netherlands 2020). Most vehicles imported by emerging and developing economies are not required to meet internationally accepted vehicle safety standards, and often, local crashworthiness and safety standards are non-existent. Imported used vehicles face significant challenges in meeting crashworthiness and road fitness requirements (World Bank 2022). Safe vehicles encompass both vehicle crashworthiness and fitness features. Crashworthiness refers to the design aspects of the vehicle, such as seat belt anchorages, front and side airbags, adequate child-restraint systems, adequate front crumple zones, structural design to avoid rollovers, pedestrian contact height, and advanced crash avoidance features like ESC. Fitness refers to the maintenance of the vehicle, ensuring that key features such as brakes, lights, and turn signals are functional, tire treads are not substantially worn, sight lines are not obstructed, and no other hazardous conditions that could compromise passenger safety in the event of a crash. The legislative landscape for vehicle safety varied widely across emerging and developing economies in 2022. They related to with requirements and standards of five core vehicle safety equipment: seat belts and seat belt anchorages, front and side impact protection, pedestrian protection, ESC, and braking systems (WHO 2023). Thirty-five countries had legislation mandating all five core areas of safety equipment; ten countries had legislation for four core areas, nine countries had legislation for three core areas; eight countries had legislation for two core areas, and twenty-nine countries had legislation for only one of the five core areas. Seventy-nine countries reported no legislation on vehicle safety at all (box 2.1). Box 2.1 Global evolution of vehicle safety features Vehicle safety features have evolved significantly over the past several decades, focusing on avoiding crashes and reducing injuries in the event of a crash. In OECD countries, modern vehicles are equipped with sophisticated restraint systems, air bags, traction control, antilock brakes (ABS), electronic stability control (ESC), and crumple zones. These safety features are required and enforced in OECD countries by regulatory agencies. The National Highway Traffic Safety Administration (NHTSA) in the U.S. oversees a 5-star safety rating system based on results from front, side, and rollover crash tests. The implementation of these safety features has had a measurable impact on reducing the number and severity of vehicle crashes, thereby reducing traffic-related injury and deaths. The next generation of safety features in the form of Advanced Driver Assistance Systems (ADAS) has emerged in the past decade. ADAS include technologies such as brake assist, forward collision warnings, pedestrian detection, and lane-crossing warning. The Global New Car Assessment Programme (Global NCAP) has identified a core set of safety features based on OECD standards to serve as a benchmark for developing countries. These include seat belts, impact protection, pedestrian safety measures, and the use of child seats. The United Nation’s World Forum for Harmonization of Vehicle Regulations (WP.29) continuously defines and updates technical safety standards for motor vehicles that can be applied worldwide. Many world regions have adopted at least some of these safety standards, laid down in about 170 UN Regulations, as a prerequisite for new vehicles to be allowed into the fleet. These standards have contributed to substantial vehicle safety improvements beginning in 1958. The Global Plan by the Decade of Action for Road Safety 2021–2030 highlights eight WP.29 safety standards for light duty vehicles as being most important for worldwide safety: • Occupant Protection in Frontal Impact; UN Regulation No. 94 • Occupant Protection in Side Impact; UN Regulation No. 95 • Pedestrian Impact Protection; UN Regulation No. 127 Safe and Clean Vehicles for Healthier 23 and More Productive Societies Box 2.1 Global evolution of vehicle safety features (contd.) • Safety Belts; UN Regulation No. 16 • Safety Belt Anchorages; UN Regulation No. 14 • Child Restraint Systems; UN Regulations No. 44 and 129 • Electronic Stability Control; UN Regulation No. 140 • Advanced Emergency Braking System; UN Regulation No. 152 The first six of these are passive safety standards, that is the design standards for vehicles and child seats to ensure that vehicle occupants and vulnerable road users—such as pedestrians and cyclists—are adequately protected from injury in case of a collision. The last two standards in the above list are active safety systems like the advanced driver assistance systems (ADAS) that use sensors to detect imminent collisions and avoid them if possible or at least mitigate their severity. Despite being technologically mature and well-established in some world regions, the adoption of these safety technologies remains low in emerging and developing economies, even among new vehicles entering their fleets. The Global Plan by the Decade of Action for Road Safety 2021–2030 does not list specific priority safety standards for Heavy Duty Vehicles (HDVs), but collisions between Light Duty Vehicles (LDVs) and HDVs continue to lead to severe injury outcomes for LDVs occupants. Single vehicle collisions, particularly rollovers, present high injury risks for HDVs occupants. Passenger car occupants are at particular risk of severe injuries when colliding against HDVs, which generally have a higher ground clearance. This allows cars to underrun the heavy vehicle, resulting in catastrophic damage to the occupant survival cell. In comparison, occupants of trucks, buses, and coaches are generally at lower risk of injury in vehicle-to-vehicle collisions compared to car occupants. This is because their vehicles are heavier, thereby absorbing less of the crash energy, and their seating positions are higher, reducing the risk of intrusion. However, rollovers and collisions with fixed objects or other heavy vehicles have a high potential for serious consequences, particularly when occupants are not wearing seat belts. The Global Plan by the Decade of Action for Road Safety 2021–2030 prioritizes the implementation of active safety system to address collisions involving motorized two wheelers (MTWs). Motorcycle and moped riders are at high risk of losing control of their vehicle during heavy braking maneuvers or moderate braking on low-grip surfaces, which can cause the front or rear wheel to ‘lock-up.’ Such incidents often result in serious consequences. Sources: WHO 2023; UNECE 2021. References UNECE. 2021. UN Vehicle Regulations for Road Safety: Cost-Benefit Methodology and Socioeconomic Impact. Geneva: United Nations Economic Commission for Europe 2021. https://unece.org/sites/default/files/2021-09/ CBA%20publication%20E%20web_0.pdf. WHO. 2023. Global Status Report on Road Safety 2023. Geneva: World Health Organization, 2023. https://iris. who.int/handle/10665/375016 Safe and Clean Vehicles for Healthier 24 and More Productive Societies Used vehicles and new vehicles with obsolete technology are sources of comparatively high pollutant emissions. The UN Vehicle Agreements under WP.29 provide a framework that allows countries and vehicle manufacturers to ensure vehicles meet emission requirements. These requirements are achieved through emission standards that set quantitative limits on the permissible amount of specific air pollutants that vehicles can release. These limits are thresholds above which different types of vehicle emission control technologies might be needed. Emission performance standards dictate limits for conventional pollutants such as oxides of nitrogen, hydrocarbons, and fine particulate matter. The United Nations Environment Programme (UNEP) compiled emission standards in Emerging and developing economies in 2020, categorized by prevailing Euro standard level (box 2.2). This compilation revealed that many countries either have no emission standards or adhere to the first- generation Euro 3 standard or below. Box 2.2 Adoption of emission standards in emerging and developing economies Different regions and countries have varying standards for vehicle emission, but three main sets of standards dominate: United States, Japanese, and European. Various markets mostly use these as their base. For example, since 1993, the European Union has applied its Euro 1, 2, 3, 4, 5 and 6 standards to measure and control the environmental impact of new cars sold within its jurisdiction. These standards are defined in a series of European Union Directives and allow for staging the progressive introduction of increasingly stringent requirements. Emission standards, when supported by well-managed inspection and enforcement programs, can help drive the adoption of baseline vehicle technologies needed to produce cleaner vehicles. Source: UNEP. 2020. Used Vehicles and the Environment. A Global Overview of Used Light Duty Vehicles: Flow, Scale and Regulation. United Nations Environment Programme. Many emerging and developing economies lack the leverage and resources to manage the emissions performance of new vehicles manufactured in OECD countries. As a result, they often inherit performance standards set by other countries, which may be a generation behind prevailing technology. In addition, emerging and developing economies importing used vehicles inherit fleets that may no longer comply with original standards, may have environmental technology removed, or require significant oversight and repair to achieve intended levels. Different factors contribute to ineffective management of emissions performance in many emerging and developing economies. These inefficiencies are normally observed at vehicle entry or during the vehicle in-use life. For example, in some emerging and developing economies, black markets facilitate the removal and trade of catalytic convertors from vehicles, which are then sold as precious metals. In others, catalytic convertors and oxygen sensors are removed from vehicles because technicians are not trained in their functioning and maintenance, and believe they hamper vehicle performance (World Bank 2022). In addition to vehicle technology, fuel specifications play a crucial role in motor vehicle pollution outcomes. As such, it is important to design and implement vehicle emission standards in conjunction with fuel quality standards. For example, studies have shown that sulfur concentrations in fuels greater than 50 parts per million progressively degrade the effectiveness of Euro IV/4 technology, with concentrations greater than 500 parts per million rendering them ineffective. Even more restricted levels of sulfur, generally less than 10 parts per million, are required for more stringent Euro emissions control technology. While lead has largely been removed from gasoline supply streams worldwide, other anti-knocking additives, such as methylcyclopentadienyl manganese tricarbonyl (MMT), continue to be used in many regions. Manganese, like lead, can inhibit the functioning of emissions control equipment and is a neurotoxin. Similar to vehicle safety, internationally adopted standards exist for clean vehicles. The three main standards— United States, Japanese, and European—are widely used globally and have similar impacts on emissions control. The relevant vehicle age limit for a car to be considered roadworthy is another standard as older vehicles have higher emissions because of engine and vehicle degradation. Emission factors consider the type of vehicle and the powertrain per mode moving at low speed. Safe and Clean Vehicles for Healthier 25 and More Productive Societies 2.2 Modeling vehicle standards for health outcomes Global experience shows that motorized vehicles contribute significantly to improving a country’s safety and air quality outcomes when they meet internationally recognized safety and emissions standards. Literature highlights the positive effects of vehicle standards and technology on safety and air quality outcomes. For example, European Union countries registered a 55-percent reduction in car occupant fatalities between 2001 and 2012 because of the adoption of automotive safety technologies (UNECE 2021). Similarly, an analysis of vehicle-based crash rates in the Australian state of New South Wales estimates that occupant fatality risk for cars built in 2010 is 75 percent lower than for those built in 1995 (Anderson and Searson 2015). New passenger vehicles produced in the U.S. today are 98-99 percent cleaner for most tailpipe pollutants compared to those from the 1960s (EPA 2025). This chapter introduces a simple and stylized model that links the quality and quantity of vehicle fleet with health outcomes and allows for understanding the potential impact of alternative sets of policy levers when performance is compared against the business-as-usual scenario, which responds to projections that retain the existing status quo (figure 2.1). Figure 2.1. Model for estimating simultaneous vehicle safety and air quality impacts Socio-economic Trends In-use vehicle fleet Emission Standard Emission Safety Fatal and Composition per Factors by Technology Serious Injuries Vintage of Target Vintage Dispersion on (FSI) Baseline Vehicle Fleet Target Vehicle Projection Projected Annual Average Energy Powertrain Vehicle Consumption and Composition Mileage Emission Factors by Vintage by age of per powertrain and Vehicle Category Collision Impact Vehicle vintage Occupied Pairing Direction Safety Effectiveness Emission Engine Resulting FSIs Local Air Pollutant Emissions Source: Original figure for this publication. The model starts by using the socioeconomic trends to project the in-use vehicle fleet, including aspects pertaining to vehicle age profile and vehicle mileage. The projection of the level of motorization—or number of vehicles per population—follows the estimation of calibrated Gompertz (S) curves for each country. This traditional approach uses projected population and GDP growth rates to forecast motorization rates (figure 2.2 presents calibrated Gompertz curve for Egypt and Mexico). Safe and Clean Vehicles for Healthier 26 and More Productive Societies A A Ka Ka Forecast motorization rate Figure 2.2 Sample forecast motorization rates for Egypt and Mexico 600 500 Cars per 1,000 capita 400 Mexico Mexico, Forecast 300 Egypt 200 Egypt, Forecast 100 0 2010 2020 2030 2040 2050 2060 2070 Source: Original figure for this publication. The alternative set of policy levers are classified in three categories (shaded areas in figure 2.1): • In-use vehicles by mode considers changing EVcomposition of vehicle stocks owing to influx of new and imported uptake trajectories used vehicles, and the growing electric vehicle (EV) fleets, • 1.1 Technology dispersion on target vehicle fleet captures the impact of safety measurement adoption; and • 1 Emission standard composition allows for modeling various emission pollutant scenarios based on given air Proportion of vehicles of this vintage 0.9 standards. quality 0.8 30x30 Motorcycle the metrics of health performance are grouped in two categories: Finally,0.7 0.6 30x30: Car, bus • Road safety performance: the number of fatalities and serious injuries (FSIs) from road traffic crashes that could 0.5 be prevented by mandating the fitment of six vehicle safety technologies starting in 2030. 50x50: bus, technologies These Car, 0.4 motorcycle, are linked to respective global safety standards and vary by vehicle category —LDVs, HDVs andvan MTWs. 0.3 • Air quality: the reduction in particle pollutant concentrations expected from adopting applicable emissions 0.2 50x50: Truck standards. The air pollutants analyzed include exhaust or tailpipe emissions and non-exhaust emissions from 0.1 brake and tire wear. 0 2020 2030 2040 2050 2060 2070 The six vehicle safety technologies included covers the mandatory fitment of UN safety technologies (box 2.1): (i) Active safety measures for LDVs: • Electronic stability control (ESC), a system to prevent loss of control and rollover by targeted brake interventions to pull the vehicle on course (UN Regulation 140). • Advanced emergency braking (AEB) for: • vehicles — a system to prevent frontal impacts against other motor vehicles or wide objects, by warning the driver and automated emergency brake interventions (UN Regulation 152); • pedestrians and cyclists — as AEB above but technologically more advanced and designed to prevent impacts with pedestrians and cyclists (UN Regulation 152). (ii) Passive safety measures for HDVs to increase the geometric compatibility with cars: • Front- and rear underrun protective devices (FRUPD) — strong metal structures fitted to the front and rear of HDVs to engage the crash-absorbing structures of passenger cars in impacts (UN Regulations No. 58 and 93). • Vehicle stability function (VSF), a system similar to ESC, which prevents loss of control, rollover, and jack- knifing (UN Regulation No. 13). Safe and Clean Vehicles for Healthier 27 and More Productive Societies (iii) Well-established technology for MTWs: • Anti-lock brake system (ABS) — a system that detects locking wheels and automatically releases brake pressure helping the driver to maintain control (UN Regulation No. 78). The model considers the adoption of European standards, namely Euro 4, Euro 5 and Euro 6 for air quality (table 2.2). Emissions standards for new vehicles joining the fleet in future years are held at the prevailing standard adopted by each country for the business-as-usual scenario. For example, if a country follows Euro 4, then no Euro 5 or Euro 6 vehicles would join the fleet in future years. The Euro standards differ based on the type of vehicle and monitor the environmental impact of exhaust pollutants such as carbon monoxide, nitrogen oxides, hydrocarbons, and particulate matter (table 2.1). The emissions factors for both exhaust and non-exhaust emissions follow the tier 2 methodology presented in the European Environment Agency (2024). This methodology considers vehicle size, powertrain, Euro standard, and average speeds. The model uses emission factors for medium-sized vehicles in each Euro standard, assuming a low average speed of 40 kilometers per hour. The low average speed was chosen to better match driving conditions in a city environment, where emissions are a greater concern. A limitation of tier 2 is that this methodology has been developed for a European environment, where road roughness and vehicle maintenance conditions are likely to be better than in emerging and developing economies. For the two pollutants of main concern in this report, the difference in PM and NOx emissions between a compliant Euro 4 and Euro 6 diesel vehicle is substantial while the difference in emissions for petrol vehicles is minor. Moreover, old pre-Euro 1 and Euro 1 in-use heavy duty diesel vehicles may emit one hundred times more PM and 40 times more NOx per vehicle km than a corresponding compliant Euro 4 diesel vehicle, indicating the importance of addressing emissions from in-use diesel vehicles. Table 2.1. Standards for clean vehicles. Emission Sulfur content Description standard in diesel Euro 4 Euro 4 standard was introduced in 2006. 50 ppm • For petrol vehicles, the emission limits are set at 1.0 g/km for CO, 0.10 g/km for HC, and 0.08 g/km for NOx. • Diesel vehicles have tougher restrictions of 0.50 g/km for CO, 0.30 g/km for a combination of HC and NOx, 0.25 g/km for NOx alone, and 0.025 g/km for PM. Euro 5 Introduced in 2009, Euro 5 standard officially came into force in 2011. 10 ppm • For petrol vehicles, the emission limits were set at 1.0 g/km for CO, 0.10 g/km for HC, 0.06 g/km for NOx, and 0.005 g/km for PM. • Diesel vehicles faced stricter demands, with limits of 0.50 g/km for CO, 0.23 g/km for HC and NOx, 0.18 g/km for NOx alone, and 0.005 g/km for PM, along with a particulate number limit of 6.0 × 10^11 particles per km. Euro 6 • Euro 6 limits for petrol vehicles are set at 1.0 g/km for CO, 0.10 g/km for HC, and 0.06 g/ 10 ppm km for NOx. Direct injection petrol engines are also subject to a PM limit of 0.005 g/km and a particulate number limit of 6.0 × 10^11 particles per km. • Diesel vehicles have stricter requirements, with limits of 0.50 g/km for CO, 0.17 g/km for a combination of HC and NOx, 0.08 g/km for NOx alone, and 0.005 g/km for PM. A particulate number limit of 6.0 × 10^11 particles per km also applies to diesel engines. Net Zero • These are vehicles that do not produce direct exhaust or tailpipe emissions locally. They Not applicable Vehicles comprise battery-electric vehicles, hydrogen fuel cell vehicles and plug-in vehicles. Electric vehicles are considered for this study. Source: Michelin 2025. Safe and Clean Vehicles for Healthier 28 and More Productive Societies The emissions estimations account for both direct—tank-to-wheel (TTW)—and indirect —wheel-to-tank (WTT) — emissions. They include passenger modes such as car, motorcycle, bus, minibus, and freight modes such as van, medium truck, and heavy truck. Different powertrain and fuel options are included as appropriate for each mode (table 2.2). Table 2.2. Potential powertrains for each mode. Medium Car Bus Minibus Motorcycle Van Heavy Truck Truck Gasoline Gasoline Gasoline Gasoline Gasoline Diesel Diesel Diesel Diesel Diesel Diesel Diesel Plugin hybrid Plugin hybrid diesel diesel Plugin hybrid Electric vehicle Electric vehicle Electric Plugin hybrid gasoline gasoline Electric Electric Hydrogen fuel Hybrid electric Plugin hybrid cell electric diesel Plugin hybrid Hydrogen fuel Hydrogen diesel diesel cell fuel cell LPG Hydrogen fuel cell Electric Electric LPG CNG CNG LPG Hydrogen Hydrogen fuel CNG fuel cell Trolleybus CNG cell LPG LPG CNG CNG Source: Original table for this publication. The key metrics for air quality and clean vehicle performance are emissions at the national level, including exhaust emissions, nitrogen oxides, PM2.5 and sulfur, and non-exhaust emissions from brake wear and tire wear, for example, PM2.5). The model for these calculations,: (i) considers the trend for older vehicles to have a lower annual mileage than newer vehicles; (ii) assumes older vehicles have higher emissions owing to engine degradation through use, and (iii) considers the local sulfur content of fuel to calculate sulfur emissions. References Anderson, R. W. G., and Searson, D. J. 2015. Use of age–period–cohort models to estimate effects of vehicle age, year of crash and year of vehicle manufacture on driver injury and fatality rates in single vehicle crashes in New South Wales, 2003–2010. Accident Analysis & Prevention. EPA. 2025. “Accomplishments and Successes of Reducing Air Pollution from Transportation in the United States,” Transportation, Air Pollution, and Climate Change, last modified February 13, 2025. https://www.epa.gov/ transportation-air-pollution-and-climate-change/accomplishments-and-successes-reducing-air#:~:text=New%20 passenger%20vehicles%20are%2098,they%20were%20prior%20to%20regulation. European Environment Agency 2023. EMEP Air Pollutant Emission Inventory Guidebook 2023 Update 2024. Michelin. 2025. Euro Emission Standards. https://connectedfleet.michelin.com/blog/euro-emissions-standards. Ministry of Infrastructure and Water Management of the Netherlands. 2020. Used vehicles exported to Africa: A study on the quality of used export vehicles. Netherlands Human Environment and Transport Inspectorate, Ministry of Infrastructure and Water Management. World Bank. 2022. Motorization Management for Development. World Bank. WHO. 2023. Global Status Report on Road Safety 2023. Geneva: World Health Organization, 2023. https://iris.who. int/handle/10665/375016 Safe and Clean Vehicles for Healthier 29 and More Productive Societies Chapter 3. Impact of Vehicle Standards on Safety and Air Quality Motor vehicles are a salient common element from road transport that directly impact both vehicle crashes and transport emissions contributing to ambient pollution, among other things fatal and non-fatal injuries and air quality. The nature of a country’s motor vehicle stock and how it grows affect crucial safety and air quality outcomes. First, the quality of the motor vehicle stock impinges road safety outcomes—that is, the number of people killed or seriously injured in motor vehicle crashes. The characteristics of vehicles and their fitness or roadworthiness can influence fatality and serious injury (FSI) outcomes. Second, the quality of the motor vehicle fleet alters air quality, particularly in cities. Motor vehicles are a key source of harmful air pollution, including fine particulates (PM2.5), nitrogen oxides (NOx, NO2) sulfur oxides, various hydrocarbons, carbon monoxide and other pollutants. The amount of these pollutants they emit is directly related to how the vehicle was built and how well it is maintained. In addition to vehicle technology, fuel specification plays an important role in motor vehicle pollution outcomes. As such, it is important to design and implement vehicle emission standards in conjunction with fuel quality standards. The estimates in this chapter are based on results that only account for the reduction of primary PM2.5. In many contexts, secondary PM2.5 formed in the atmosphere from precursor gases (sulfur dioxide, nitrogen oxides, and volatile organic compounds) constitutes the majority of total PM2.5 levels. The model used in this study estimates the effect that the implementation of vehicle emission standards would have on emissions of NOx, encompassing both NO2 and NO. Consequently, the actual reductions in NO2 emissions and the associated health benefits may be less pronounced. This chapter addresses questions about how policy decisions pertaining to vehicles influence mortality and morbidity of road transport, the adoption of good vehicle safety and emission standards sparing deaths, serious injuries and disabilities, and whether it makes economic sense to invest in policies and institutions that improve motorization patterns. Safe and Clean Vehicles for Healthier 30 and More Productive Societies 3.1 Assessing the need for vehicle standards adoption at the country level This study selected eight emerging and developing economies to assess the key health impacts caused by road motor vehicles through the lens of road crashes and air pollution. The countries— Argentina, Brazil, Egypt, Ghana, India, Kazakhstan, Lao PDR, and Mexico—were selected for their diverse levels of income, geographic spread, population, motorization levels, existing motorization management policies, and availability of data (table 3.1). Table 3.1 General country statistics, 2023 Motorization rate Imported Income GDP per capita Population (registered Region vehicles category (nominal US$) (million) vehicles/1000 (units) population)* Argentina Latin America Upper middle 14,187 45.5 583 178,567 Brazil Latin America Upper middle 10,295 211.1 520 201,626 Egypt Middle East Lower middle 3,457 114.5 100 225,000 Ghana Sub-Saharan Africa Lower middle 2,260 33.8 101 73,417 India South Asia Lower middle 2,481 1,438.1 232 14,583 Kazakhstan Eastern Europe Upper middle 12,919 20.3 226 32,985 Lao PDR East Asia and Pacific Lower middle 2,067 7.7 274 (**) 9,000 Mexico Latin America Upper middle 13,790 129.7 419 519,167 Notes: (*) including 2/3 wheelers; (**) 2016 data. Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data). Used vehicles imports – World Road Statistics (Latest data available 2020: Argentina, Brazil), Trend Economy (estimated based the import values in 2020 for Egypt, Ghana, India, Lao PDR, and Mexico). Health case for better vehicle standards at the country level The selected countries have a combined population of nearly 2 billion or one-quarter of the world’s population. These countries account for a total of 291,768 traffic crash fatalities—24.5 percent of the global traffic crash-related fatalities (table 3.2). Although serious injury estimates are not reliably reported in emerging and developing economies, it is estimated that for each fatality, approximately 10 seriously injured victims of traffic crashes can be added. Considering the country’s population, the average fatality rate per 100,000 population is 14.9 for these eight countries. In comparison, the global average fatality rate per 100,000 population in 2021 was 15. Table 3.2 Selected statistics on road crashes, 2021 Fatalities (annual deaths) Fatality rate per 100,000 pop. Argentina 3,983 8.8 Brazil 33,586 15.7 Egypt 10,263 9.4 Ghana 8,494 25.9 India 216,618 15.4 Kazakhstan 2,341 12.2 Lao PDR 1,217 16.4 Mexico 15,267 12 Source: WHO 2023. Safe and Clean Vehicles for Healthier 31 and More Productive Societies Road transport NO2 (% of total NO2) Road transport NO2 (% of total NO2) 50% 50% 46 The population-weighted annual ambient PM2.5 in the eight countries in 2019 ranged from 12-15 micrograms per 40% 40% meter in Ghana, India and Egypt. This pollution cubic meter in the three LAC countries to 50-68 micrograms per cubic 33 34 concentration is estimated to cause 1.2 million premature deaths from ambient PM2.5 exposure in the eight countries 30% 26At the country level, deaths from road transport ranged 30% 25 in 2021. from 2,361 in Lao 25 million in India PDR to nearly one (GBD 2021). 19 19 20% 20% 12 11 11 Global averages mask significant country disparities in how road transport emissions contribute to aggregate 10% 10% ambient PM2.5 pollution. According to GBD major air pollution sources (GBD MAPS) estimates for 2017 (McDuffie et al. 2021a,b), the contribution of motorized road transport to ambient 0% 0% PM2.5 levels ranges from 3.1 percent in Ghana to 12.3 percent in Mexico. In absolute terms, the contribution ranges from World LI LMI UMI HI SA 0.8 micrograms EAP SSAper cubic MNAmeter in Brazil ECA LAC to 3.4 micrograms per cubic meter in India and 3.9 micrograms per cubic meter in Egypt (figure 3.1). Figure 3.1 Motorized road transport’s contribution to population weighted ambient PM2.5 Percent of ambient PM2.5 Ambient PM2.5 contribution (μg/m3) Argentina 9.0 Argentina 1.3 Brazil 6.3 Brazil 0.8 Egypt 5.8 Egypt 3.9 Ghana 3.1 Ghana 1.5 India 5.7 India 3.4 Kazakhstan 5.1 Kazakhstan 1.1 Lao PDR 5.7 Lao PDR 1.3 Mexico 12.3 Mexico 1.8 0% 2% 4% 6% 8% 10% 12% 14% 0 1 2 3 4 5 Source: Original figure for this publication, based on estimates reported by McDuffie et al. 2021a,b. Although the contribution from motorized road transport to PM2.5 levels may seem small, it caused 71,685 premature deaths and the loss of 17.4 million IQ points in children in eight countries in 2021 (table 3.3). Table 3.3 Deaths and IQ losses from PM2.5 attributable to motorized road transport, 2021 IQ point losses (million) Premature deaths Argentina 0.3 1,374 Brazil 0.74 3,339 Egypt 1.44 6,708 Ghana 0.27 342 India 13.18 54,012 Kazakhstan 0.15 647 Lao PDR 0.07 135 Mexico 1.23 5,128 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 32 and More Productive Societies NO2 emissions from road transport accounted for 25 percent of total NO2 emissions in emerging and developing economies in 2017. In the eight selected countries, the share from motorized road transport ranged from 13–15 percent in Kazakhstan and India to 44–60 percent in the three LAC countries and Lao PDR. The high share in the LAC countries is associated with a high degree of motorized transport. The high share in Lao PDR is due to low NO2 emissions from sectors, primarily because most of electricity in the country is generated from hydropower. Exposure to ambient other2,500 NO2 attributed to motorized road vehicles caused an estimated 40,600 premature deaths in the eight countries in 2021. The number 2,000 of deaths from road transport NO2 was significantly higher than from road transport PM2.5 in the three LAC countries because of their relatively moderate ambient PM2.5 levels. 1,500 Table 3.4 Ambient NO2 and mortality in eight select countries 1,000 Road transport NO2 emissions Premature deaths from Premature deaths from road transport (% of total NO2 emissions), road transport NO2, NO2 (% change relative to road 500 2017 2021 transport deaths from PM2.5) Argentina 44 1,830 133 0 Brazil 48 6,772 203 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Egypt 17 2,204 33 East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa Ghana 26 N/A N/A North America South Asia Sub-Saharan Africa India 15 20,771 38 Kazakhstan 13 144 22 Lao PDR 48 N/A N/A 450 0.6 450 420 0.7 Mexico 420 60 0.54 8,877 173 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 400 400 0.59 0.6 0.5 Source: 350 by McDuffie et al. 2020a,b. 350 Original table for this publication based on NO2 emissions estimates reported 0.44 0.43 0.5 300 0.4 300 0.45 (US$ billion) (US$ billion) Economic 250 250 at the country level cost of suboptimal vehicle standards 0.35 257 0.44 0.4 0.3 200 200 0.3 The 156 crashes in the selected countries ranged from $1 billion in Lao PDR to $105 estimated annual cost of road traffic 150 0.2 150 123 0.22 0.2 100 in India. The estimated annual cost as a share of the country’s billion 100 GDP ranges from 2.4 percent in Egypt to 5.0 0.09 0.1 0.1 0.1 50 in Brazil (figure 3.2a). percent 50 16 16 0.01 0.1 7 3.3 4.1 0.1 0.01 0 0 0 0 World Figure 3.2 Selected LI LMI health cost UMI associated HI to road World EAP transport by country ECA as of 2021 LAC MNA SA SSA a. Road traffic crashes b. PM2.5 from road transport 120 6 12 0.6 0.55 105 Share of GDP equivalent (percent) Share of GDP equivalent (percent) 5.0 10.2 100 5 10 0.5 4.2 4.2 0.42 84 80 4 8 0.4 0.35 (US$ billion) (US$ billion) 3.3 0.31 0.32 60 3.3 56 3 6 5.4 0.3 3.1 3.2 40 2.4 2 4 0.16 0.2 2.6 2.3 20 15 1 2 0.08 0.14 0.1 10 1.5 6 0.7 3 1 0.06 0.02 0 0 0 0 Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Note: Including fatalities and serious injuries. Source: Authors estimates, based on GBD 2021 data. Safe and Clean Vehicles for Healthier 33 and More Productive Societies The cost of mortality and morbidity attributed to PM2.5 from motorized road transport totaled $22.9 billion in 2021 in the sampled countries and went from 0.08 percent of GDP in Ghana to 0.55 percent in Egypt in 2021. The annual cost of IQ losses in children from PM2.5 traceable to road transport totaled $15.5 billion and ranged from 0.11 percent of GDP in Brazil to 0.26-0.29 percent in Ghana, India and Egypt. On average, the cost of mortality and morbidity was equivalent to 0.3 percent of GDP (figure 3.2b) and the cost of IQ losses to 0.2 percent of GDP. Cost of mortality and morbidity attributed to NO2 from road transport totaled $21.5 billion and ranged from 0.08 percent of GDP in Kazakhstan to 0.71 percent in Mexico (table 3.5). Deaths and cost of mortality and morbidity from road transport NO2 are far higher than from road transport PM2.5 in the three LAC countries (table 3.5). These countries have relatively high ambient NO2 from road transport while PM2.5 from the sector is relatively moderate. In contrast, ambient PM2.5 from road transport is particularly high in Egypt and India while ambient NO2 from the sector is moderate, with cost of road transport PM2.5 being substantially higher than the cost of road transport NO2 . In Kazakhstan ambient NO2 from road transport is moderate with a relatively small share of the population exposed to ambient NO2 above the WHO annual AQG. Thus the low cost of road transport NO2 relative to PM2.5. Table 3.5 Health impacts and costs associated to road transport by country as of 2021 Deaths from ambient air Cost of mortality and morbidity Cost of mortality and morbidity pollution* (US$ million) (% of GDP) Road transport Road transport Road transport Road transport Road transport Road transport PM2.5 NO2 PM2.5 NO2 PM2.5 NO2 Argentina 1,374 1,830 1,476 1,966 0.31 0.40 Brazil 3,339 6,772 2,619 5,312 0.16 0.32 Egypt 6,708 2,204 2,324 764 0.55 0.18 Ghana 342 N/A 67 N/A 0.08 N/A India 54,012 20,771 10,248 3,941 0.32 0.12 Kazakhstan 647 144 679 151 0.35 0.08 Lao PDR 135 N/A 27 N/A 0.14 N/A Mexico 5,128 8,877 5,417 9,378 0.42 0.71 * Estimates of deaths from ambient NO2 and PM2.5 should not be considered additive. It is plausible that estimates of deaths from PM2.5 include deaths that are caused by NO2 and vice versa. What the estimates of deaths from NO2 does demonstrate, however, is that NOx is a pollutant of public health concern and importance. Note: Impacts of ambient NO2 from road transport could not be estimated due to data constraints. Source: Original table for this publication. Safe and Clean Vehicles for Healthier 34 and More Productive Societies 3.2 Evaluating the health impact of vehicle policies at the country level This section uses the analytical model discussed in chapter 2 to estimate the health burden of traffic crash fatalities and serious injuries, and air pollutants mortality and morbidity over the period from 2025 to 2050. The analysis is at the country level and will represent the proof of concept of the model proposed to quantify the impact of vehicle standard adoption under alternative scenarios. The mandate for a specific technology or standards is country specific and applies in cases where the technology is voluntary for vehicle types in that country. The model simulates the technology equipment levels of the in-use vehicle fleet, starting from the status quo in 2025 and projecting forward to the end of the assessment period. The state of adoption of various motorization policies is diverse throughout the sample (table 3.6). Argentina, Brazil, Egypt, and India have national policies on complete or near-complete ban on the import of used vehicles, whereas motorization growth in Ghana, Lao PDR, Kazakhstan, and Mexico is influenced by import of used vehicles. In the adoption of UN vehicles safety mandates linked to active safety systems, India has adopted most except for the vehicle stability function for heavy vehicles (UNECE, 2025). Egypt, Ghana, Kazakhstan, and Lao PDR, on the other hand, lack all the advanced safety technologies analyzed in this study. Table 3.6 State of analyzed motorization policies as of 2024 Adoption of vehicle safety technology Argentina Brazil Egypt Ghana India Kazakhstan Lao PDR Mexico Electronic Stability Control ü ü x x ü x x ü Advanced Emergency Breaking : x x x x ü x x x LDV Vehicles Advanced Emergency Breaking: x x x x ü x x x pedestrians & cyclists Front and Rear Underrun ü x x x ü x x x Protective Devices HDV Vehicle Stability Function ü x x x ü* x x x MTW Anti-lock Brake System ü x x x ü x x x * excluding trucks Adoption of vehicle emission standards Argentina Brazil Egypt Ghana India Kazakhstan Lao PDR Mexico Euro 4 x x ü ü x ü ü ü Euro 5 ü ü x x x x x x Euro 6 x x x x ü x x x Adoption of rules for imported vehicles Argentina Brazil Egypt Ghana India Kazakhstan Lao PDR Mexico Used vehicles x x x ü x ü ü ü New vehicles ü ü ü ü ü ü ü ü Source: Original table for this publication. Safe and Clean Vehicles for Healthier 35 and More Productive Societies The scenarios are defined to address the impact of motorization management policies with focus on vehicle standards becoming mandatory in the year 2030 for: • New vehicles entering the fleet • Used vehicles entering the fleet • 20 years and older vehicles not meeting air quality and safe standards exiting the fleet. Two additional scenarios showing the uptake of electric vehicles (EVs) as part of diversifying in-country vehicle fleet were modeled for all study countries The modeling results were estimated for the following five scenarios summarized in table 3.7: • Scenario 1: New vehicles—vehicles being registered for the first time as new—that are entering the fleet are mandated by 2030 to meet study-prescribed safety and air quality standards whereas used vehicle imports may or may not meet new standards. • Scenario 2: All first-time registered vehicles—new, and used imports—are mandated by 2030 to meet study- prescribed safety and air quality standards. • Scenario 3: All first-time registered vehicles—new, and used imports—are mandated to meet study-prescribed safety and air quality standards, and in addition, vehicles older than 20 years that do not meet prescribed standards are required to exit circulation, this latter is being referred to as “fleet retirement” in the report. • Scenario 4: EV 30x30 scenario illustrates 30 percent of new cars, buses and minibuses and 70 percent of new motorcycles are electric by 2030. This scenario does not include higher EV uptake of vans or trucks. EV growth is held at a constant two percent for cars and buses, 10 percent for motorcycles and two percent for vans. • Scenario 5: In this EV 50x50 scenario, 50 percent of new cars, buses, minibuses, motorcycles and vans are electric by 2050, and 50 percent of new trucks are EVs by 2055. EV growth is held at a constant two percent for cars and buses, 10 percent for motorcycles and two percent for vans. Table 3.7 Motorization management scenarios New Vehicles Used Vehicles Vehicle Scrappage EV 30x30 EV 50x50 Scenario 1 ü x x x x Scenario 2 ü ü x x x Scenario 3 ü ü ü x x Scenario 4 ü x x ü x Scenario 5 ü x x x ü Note: Scenarios 1-3 include the adoption of Euro 6 and/or Euro 5 emission standards as applicable to the country. Source: Original table for this publication. The results are estimated at the national level and benefits quantified for crash safety and air quality outcomes by comparing fleet growth and composition under a business-as-usual (BAU) scenario against scenarios resulting from projecting fleet behavior under alternative motorization management policies. The estimates run from 2025 to 2050, with policies impacting emissions in five-year intervals. The estimates consider that newer vehicles are used more than older vehicles and hence have higher annual mileage than older vehicles. Motorization polices are broadly categorized for the three vehicle types: MTWs, LDVs, buses and HDVs. All projections were prepared for the period 2025–2050 based on five years of historic data, and cumulative results compared with the BAU scenario. Safe and Clean Vehicles for Healthier 36 and More Productive Societies Additional key assumptions were made to transform a complex real-world situation with the best available data into a coherent model. The model considered incomplete data and ensured a consistent approach in each country. These assumptions are: • The best exposure metric available for all countries was the vehicle fleet size. Calculations performed were based on the number of fatalities and serious injuries per vehicle circulating on the roads. Other metrics that were not available, such as miles driven, would have enabled the modelling of more complex effects of differing usage profiles of new and old vehicles. • The future fatalities and serious injuries baseline extrapolation assumes continued improvements in vehicle and infrastructure design and road user behavior. This is modeled by reducing the fatalities and serious injuries rate per vehicle from a country’s existing level to the level of a European country with good road safety outcomes to be reached in 2050. • The level and quality of accidentology data vary from country to country. Where certain breakdowns of data, such as vehicle category pairing or impact directions, were not available, these were substituted with proportions from proxy countries applied to the country studied. • The available information was limited about prevailing safety technology adoption rates in the in-use vehicle fleet, as well as in new or imported used vehicles. The authors applied expert estimates based on known adoption curves for other countries and the main markets from which the used vehicles were imported. These estimates can be substituted with more definitive data when available. Another underlying assumption is that these technologies remain operational throughout the vehicle’s life, which may require periodic technical inspections to ensure full benefits are realized. • Limited information was forthcoming on the types of vehicles leaving the fleet or (scrapped vehicles. The model therefore assumed that only the oldest vehicles, which were usually those not equipped with the safety technologies, were scrapped. • The technology effectiveness studies selected were from high quality published research, but the underlying research was largely based on US or European accidentology. Although safety technology effectiveness values should be expected to vary depending on infrastructure and traffic environment, applying this research provided the best estimate in absence of country specific studies. • Health benefits of adopting the various safety standards were quantified in saved fatalities and serious injuries. Owing to the lack of reliable serious injury data in the countries studied, and in emerging and developing economies in general, a ratio approach of serious injuries to fatalities was used. This ratio varied based on the definition and level of reporting of serious injuries, as well as on the crash type, vehicle type, and road user involved. The study examined several high income countries, and concluded to use a ratio of 10 for scenarios involving LDVs, MTWs, and pedestrians, and a ratio of five for HDVs scenarios. • The model aims to insulate the impact of mandating stricter emission standards. For that reason, in some cases the emission reduction benefits of vehicle retirement represent a lower bound. For countries already with a mandate equivalent to Euro 6 or higher, emission reduction gains due to adopting Euro 6 are reported as Non- Applicable (N/A) for all scenarios even when namely, Scenario 3 considers retirement of non-complaint old vehicles which render benefits unto itself regardless of the emission standard in place. That is the case of India (tables table 3.8, 3.10 and 3.12). Similarly, for scenarios estimating impact of Euro 5 adoption, the model will report Non-Applicable (N/A) as the potential gain for countries already with Euro 5 or higher. Again, these results would admittedly underestimate emission benefits rendered by vehicle retirement. That is the case of Argentina, Brazil and India for scenarios considering upgrade to the Euro 5 mandate (tables 3.9, 3.11 and 3.13). Safe and Clean Vehicles for Healthier 37 and More Productive Societies Scenario 1: Safety and emission mandates for new vehicles by 2030 In Scenario 1, the selected countries mandate that only new vehicles meet the prescribed safety and air emission standards by 2030. The results show that the adoption of safety technologies reduces fatalities and serious injuries by up to 3.5 percent in Egypt and 2.3 percent in Brazil, with marginal benefits in Argentina, Ghana, India, Kazakhstan, and Lao PDR.1 The adoption of AEBS prevents the most fatalities and serious injuries, followed by use of ESC. The mandated exhaust emissions standards for air pollution result in a significant reduction of nitrogen oxide emissions compared to the BAU scenario, ranging from a 15.7 percent reduction in Argentina to a 26.6 percent decrease in Lao PDR (table 3.8). The scenario results in comparatively low reductions in PM2.5 emissions because a significant share of these emissions is caused by tire, brake, and road wear—non-exhaust emissions—which are covered by the Euro emissions standards. Notably, in Brazil, which has a significant overall crash fatalities and serious injuries burden, a 2.3 percent reduction translates to 137,943 fatalities and serious injuries prevented. Safety model outcomes for Scenario 1 In countries where all six safety technologies are voluntary—Egypt, Ghana, Kazakhstan, and Lao PDR—most fatalities and serious injuries were prevented with the adoption of AEBS followed by ESC. For Egypt and Ghana, these two safety technologies accounted for over 90 percent of fatalities and serious injuries prevented. In Kazakhstan, they accounted for approximately 78 percent of fatalities and serious injuries prevented and about 25 percent in Lao PDR. In Lao PDR, about 42 percent of fatalities and serious injuries prevented came from mandating ABS for MTWs, which represent a large share of vehicles in the country. In Brazil and Mexico, which have four voluntary safety technologies, the most impactful technology was ABS, which resulted in 58 percent of fatalities and serious injuries prevented in Brazil and 63 percent in Mexico (figure 3.3). Table 3.8 Impact of motorization Scenario 1, cumulative impact over 2025–2050 compared to BAU Crash fatalities and serious PM2.5 reduction Nitrogen oxides reduction injuries (FSI) reduction FSI % kt % kt % Argentina 5,953 0.7 7 3 732.4 15.7 Brazil 137,943 2.3 77.6 5 7,139 20.2 Egypt 54,728 3.5 34 9.2 2,135 20.6 Ghana 2,307 0.5 8 6.9 663 20 India 107,009 0.5 N/A N/A N/A N/A Kazakhstan 1,479 0.3 5 5.1 462 17.6 Lao PDR 189 0.1 6 16.3 245 26.6 Mexico 44,618 1.1 118 10.7 3,132 17.8 Notes: The modeling, by design, aims to isolate the benefits of adopting stricter emissions standards by excluding India—which already adopted Euro 6—from estimations. kt=kiloton. Source: Original table for this publication. Safe and Clean Vehicles for Healthier 38 and More Productive Societies Figure 3.3 Prevented FSIs by safety technology mandates per vehicle type (Scenario 1) 100% 75% 50% 25% 0% Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Clean model outcomes for Scenario 1 100% For countries which have Euro 4 or lower standards, adopting Euro 5 and or Euro 6 lead to substantial improvements 80% in air quality because of reduced pollutant emissions from the fleet. This is the case for Egypt, Ghana, Kazakhstan, Lao PDR and Mexico. Euro 6 emission standards are stricter than Euro 5 standards, particularly for HDVs, such as buses and60% trucks. Within the same Euro standard, decreases in nitrogen oxides emission are generally significantly higher than those of PM2.5 (table 3.9). The comparatively low reduction in PM2.5 emissions is because a significant share of 40% these emissions is caused by tire, brake and road wear, or non-exhaust emissions, which are not included in the Euro emission standards. 20% Table 3.9 Pollutant percentage reduction Scenario 1, cumulative impact over 2025–2050 compared to BAU 0% Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Euro 5 Euro 6 LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices PM2.5 NOx PM2.5 NOx LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function Argentina N/A N/A 3 15.7 LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Brazil N/A N/A 5 20.2 Egypt 3.1 1.1 9.2 20.6 Ghana 1.4 1.2 6.9 20 100% India N/A N/A  N/A  N/A Kazakhstan 0.9 4.7 5.1 17.6 Lao75% PDR 12.3 6.5 16.3 26.6 Mexico 8.3 3.6 10.7 17.8 50% Source: Original table for this publication. 25% 0% Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function Safe and Clean Vehicles for Healthier 39 LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System and More Productive Societies Scenario 2: Safety and emission mandates for new vehicles and imported used vehicles by 2030 In Scenario 2, countries mandate that both new and used road motor vehicles adopt safety and emission standards by 2030. Including used imported vehicles in the mandate results in greater percentages of fatalities and serious injuries prevented compared to the BAU scenario, surpassing those observed in Scenario 1. AEB for pedestrians and cyclists, and ESC for LDVs significantly contribute to preventing fatalities and serious injuries in Ghana and Kazakhstan. In Lao PDR and Mexico, ABS for MTWs is the technology responsible for the highest prevented fatalities and serious injuries. The reduction of air pollutants relative to the BAU scenario is higher than in Scenario 1 in the countries that permit imports of used vehicles, such as in Ghana, where the reduction of air pollution almost doubles in Scenario 2, relative to Scenario 1. Euro 6 emission standards provide the maximum reduction in pollutant emissions from 2025 to 2050. In this scenario, reductions of nitrogen oxides emissions under Euro 6 are greater than those of PM2.5 and sulfur in the four countries where the vehicle fleet comprises both new and imported used vehicles. Results from Scenario 2 demonstrate the influence of new and imported used vehicles on safety and air quality outcomes in study countries that permit the imports of used vehicles—Ghana, Kazakhstan, Lao PDR, and Mexico. A mandate that includes used imported vehicles shows greater percentages of fatalities and serious injuries prevented compared to the BAU scenario than those observed in Scenario 1. In Ghana, this percentage is as high as eight percent, corresponding to 38,238 fatalities and serious injuries prevented. A similar trend, though with marginal changes, can be observed in the reduction of air pollutants relative to the BAU scenario. Mandating air quality standards on all vehicles entering the fleet for the first time results in the following emissions reductions in Mexico, which has a large volume of both new and used imported vehicles: 17 percent reduction in PM2.5,13.8 percent reduction in sulfur and 39 percent reduction in nitrogen oxides compared to BAU. Safety model outcomes for Scenario 2 An assessment of the impact of mandates on required safety technology for vehicles under Scenario 2 shows that more fatalities and serious injuries are prevented when such mandates are required for both used and new vehicles (table 3.10). Similar to Scenario 1, AEB for pedestrians and cyclists, as well as ESC for light duty vehicles significantly contribute to preventing fatalities and serious injuries in Ghana and Kazakhstan (figure 3.4). In Ghana, these two technologies account for about 95 percentage of fatalities and serious injuries prevented, whereas in Kazakhstan, they account for approximately 75 percent. A combination of AEB for pedestrians and motorcyclists accounts for 38 percent of fatalities and serious injuries prevented in Lao PDR. Due to the large number of MTWs in Lao PDR, a mandate of ABS for MTWs will contribute to preventing 32 percent of fatalities and serious injuries. This is followed by the vehicle stability function for heavy trucks, which contributes to 20 percent of fatalities and serious injuries prevented. Mexico, with its large population of new and imported used vehicles, presents a larger sample size to model the impact of mandating four vehicle technologies on all vehicles entering the fleet. The percentage of prevented fatalities and serious injuries in Mexico per technologies mandated under Scenario 2 are: ABS for MTWs: 57 percent; advanced emergency braking systems for pedestrians and cyclists: 30.5 percent; front and rear underrun protective devices for heavy duty vehicles: eight percent; vehicle stability function: three percent; and AEBS for LDVs: 1.5 percent. Safe and Clean Vehicles for Healthier 40 and More Productive Societies Table 3.10 Impact of motorization Scenario 2, cumulative impact over 2025–2050 compared to BAU Crash FSI reduction PM2.5 reduction NOx reduction FSI % kt % kt % Argentina 5,953 0.7 7 3 732.4 15.7 Brazil 137,943 2.3 77.6 5 7,139 20.2 100% Egypt 54,728 3.5 34 9.2 2,135 20.6 Ghana 38,238 8 15.9 13.6 998 30 75% India 107,009 0.5 N/A N/A N/A N/A Kazakhstan 9,679 2.1 5.1 5.5 489 18.6 50% Lao PDR 5,905 3.6 9.1 23 278 30.2 Mexico 44,618 1.5 186.7 17 6,824 38.9 25% kt=kiloton Note: Source: Original figure for this publication. 0% Policy benefits are largely Argentina driven by Brazil travel mode Egypt and crash distribution Ghana India for a specific Laos Kazakhstan Mexico which have country. Countries none of the safety technology mandated—Egypt, Ghana, Kazakhstan, and Lao PDR—get maximum benefit from LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices adoption of active braking for pedestrians and cyclists and ESC in LDVs. In addition, the high proportion of MTWs in LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function Lao PDR correlates the anticipated benefits associated with adopting ABS for MTWs, unlike the African countries. LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Figure 3.4 Prevented FSI by safety technology mandates per vehicle type (Scenario 2) 100% 80% 60% 40% 20% 0% Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Source: Original figure for this publication. 100% model outcomes for Scenario 2 Clean The trends for air quality outcomes in Scenario 2 (table 3.11) are similar to those in Scenario 1. Euro 6 emission 75% standards provide the maximum reduction in pollutant emissions from 2025 to 2050. In this scenario, reductions of nitrogen oxides emissions under Euro 6 are greater than those of PM2.5 in the four countries where the vehicle fleet comprises 50% both new and imported used vehicles. 25% 0% Safe and Clean Vehicles for Healthier 41 and More Productive Societies Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico Table 3.11 Pollutant percentage reduction Scenario 2, cumulative impact over 2025—2050 compared to BAU Euro 5 Euro 6 PM2.5 NOx PM2.5 NOx Argentina  N/A  N/A 3 15.7 Brazil  N/A  N/A 5 20.2 Egypt 3.1 1.1 9.2 20.6 Ghana 1.7 1.6 13.6 30 India  N/A  N/A  N/A  N/A Kazakhstan 1.3 3.8 5.5 18.6 Lao PDR 19.1 10.1 23 30.2 Mexico 11.5 5.3 17 38.9 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 42 and More Productive Societies Scenario 3: Safety and emission mandates for new vehicles and imported used vehicles by 2030, with retirement of vehicles older than 20 years that do not meet regulated standards Scenario 3 shows the model outcomes when all vehicles, new and used imports, are mandated to meet the safety and air quality standards, and vehicles older than 20 years that do not meet these standards must exit circulation. The mandates in Scenario 3 lead to a more significant reduction of pollutant emissions and prevented fatalities and serious injuries than those in scenarios 1 and 2. For example, in Ghana and Egypt, Scenario 3 resulted in a nine percent increase in fatalities and serious injuries prevented and substantial reductions of nitrogen oxides emissions in both countries (table 3.12). This scenario results in many old polluting and unsafe vehicles exiting the country fleet in a short timeframe. Safety model outcomes for Scenario 3 Mandating the fitment of safety technology for new and imported used vehicles, combined with the removal of unsafe vehicles over 20 years old, results in the most significant prevention of fatalities and serious injuries in all eight countries (figure 3.5). Mandating AEB for pedestrians and cyclists leads to the most fatalities and serious injuries prevented in Argentina, Kazakhstan, and Mexico. In Ghana and Egypt, ESC for LDVs contributes the most to fatalities and serious injuries prevention. In Brazil and Lao PDR, ABS for MTWs are most effective in reducing fatalities and serious injuries. Finally, in India, the adoption of the sole technology of vehicle stability function for heavy duty vehicles, prevents approximately 158,200 fatalities and serious injuries over the 25-year period. Table 3.12 Impact of motorization Scenario 3, cumulative impact over 2025–2050 compared to BAU Crash FSI prevented PM2.5 reduction NOx reduction FSI % kt % kt % Argentina 8,045 0.93 17 7.4 1,300 28 Brazil 168,061 3.0 157.6 10.1 11,386 32.2 Egypt 140,921 9.1 72 19.4 4,203 40.6 Ghana 44,793 9.4 29 25.3 1,444 43.5 India 158,183 0.8 N/A N/A N/A N/A Kazakhstan 28,908 6.4 11 12.1 1,079 41.1 Lao PDR 6,515 4.1 10 25.7 320 34.8 Mexico 75,691 2.3 227 20.6 8,160 46.5 Note: kt=kiloton Source: Original table for this publication. Safe and Clean Vehicles for Healthier 43 and More Productive Societies LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Figure 3.5 Prevented FSI by safety technology mandates per vehicle type (Scenario 3) 100% 75% 50% 25% 0% Argentina Brazil Egypt Ghana India Kazakhstan Laos Mexico LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices LDV Advanced Emergency Braking - Vehicles HDV Vehicle Stability Function LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Source: Original figure for this publication. Clean model outcomes for Scenario 3 As in scenarios 1 and 2, the adoption of Euro 6 standards in Scenario 3 leads to the maximum reductions of PM2.5 , which are relatively lower than those of nitrogen oxides. (table 3.13). Table 3.13 Pollutant percentage reduction Scenario 3, Cumulative impact over 2025–2050 compared to BAU Euro 5 Euro 6 PM2.5 NOx PM2.5 NOx Argentina N/A N/A 7.4 28 Brazil N/A N/A 10.1 32.2 Egypt 11.4 15.3 19.4 40.6 Ghana 13.1 12.2 25.3 43.5 India N/A N/A N/A N/A Kazakhstan 6.1 20.2 12.1 41.1 Lao PDR 21.5 13.1 25.7 34.8 Mexico 9.6 14.7 20.6 46.5 Notes: For Argentina, Brazil and India, the estimation of emission benefits excludes those traceable to vehicle retirement unto itself. The modeling, by design, aims to isolate benefits of adopting stricter emission standards by excluding Argentina and Brazil—that already adopted Euro 5—as well as India—that already adopted Euro 6—from estimates. These three countries are reported as N/A for this reason. Source: Original table for this publication. Safe and Clean Vehicles for Healthier 44 and More Productive Societies Scenario 4: Electric vehicle (EV) 30x30 uptake as part of diversifying vehicle fleet In Scenario 4, the safety impact of technology is considered the same for all vehicles in LDV, HDV, and MTW categories, regardless of whether they are EVs or not. Therefore, the fatalities and serious injuries prevented in Scenario 4 are assumed to be equivalent to those prevented in Scenario 1 (table 3.14). The EV 30x30 scenario for air quality introduces motorized road vehicles with no exhaust emissions in most of the case study countries. Shifting a significant share of the vehicle fleet to electric results in notable reductions in nitrogen oxides exhaust emissions. The reductions in PM2.5 emissions are comparatively smaller because PM2.5 is largely emitted through non-exhaust activities such as breaking and tire wear. The results show that the adoption of Euro 6 standards for vehicles results in more significant reductions of PM2.5 and nitrogen oxides than the EV 30x30 scenario. Crashworthiness tests conducted by the Insurance Institute for Highway Safety (IIHS) and National Highway Traffic Safety Administration (NHTSA) in the US did not find significant differences between the safety performance of EVs and of similar sized internal combustion engine vehicles. Table 3.14 Impact of motorization Scenario 4, cumulative impact over 2025–2050 compared to BAU Crash FSI prevented PM2.5 reduction NOx reduction FSI % kt % kt % Argentina 5,953 0.7 13 5.6 633 13.6 Brazil 137,943 2.3 89 5.7 4,964 14 Egypt 54,728 3.5 26 7.1 1,628 15.7 Ghana 2,307 0.5 7 5.8 383 11.5 India 107,009 0.5 195 6.6 6,411 22.8 Kazakhstan 1,479 0.3 6 6.3 257 9.8 Lao PDR 189 0.1 1.1 2.9 48 5.2 Mexico 44,618 1.1 90 8.2 2,417 13.8 Note: kt=kiloton Source: Original table for this publication. Safe and Clean Vehicles for Healthier 45 and More Productive Societies Scenario 5: Electric vehicle (EV) 50x50 uptake as part of diversifying vehicle fleet Although a higher percentage and more types of vehicles transition to EVs in Scenario 5 compared to Scenario 4, this transition occurs over a longer timeframe. Consequently, air pollutant emissions in the EV 50x50 scenario are lower than in the BAU scenario, but higher than in Scenario 4, EV 30x30. This finding underscores that the early and sustained introduction of EVs is critical to achieving significant air pollution reductions by 2050. For the same reasons explained under Scenario 4, the crash fatalities and serious injuries prevented in Scenario 5 are assumed to be equivalent to those prevented in Scenario 1 (table 3.15). Table 3.15 Impact of motorization Scenario 5, cumulative impact over 2025—2050 compared to BAU Crash FSI prevented PM2.5 reduction NOx reduction FSI % kt % kt % Argentina 5,953 0.7 7 3.2 331 7.1 Brazil 137,943 2.3 55 3.5 2,741 7.8 Egypt 54,728 3.5 18 4.8 810 7.8 Ghana 2,307 0.5 4 3.5 196 5.9 India 107,009 0.5 118 4 3,253 11.6 Kazakhstan 1,479 0.3 6 6.3 257 9.8 Lao PDR 189 0.1 2 4.7 56 6.1 Mexico 44,618 1.1 32 2.9 839 4.8 Note: kt=kiloton Source: Original table for this publication. Aggregate results While all scenarios result in reduced fatalities and serious injuries and air pollution relative to BAU, the change in these outcomes varies significantly depending on the vehicles that are required to comply with them (table 3.16). Requiring imported used vehicles to comply with safety standards, Scenario 2, prevents more fatalities and serious injuries that only mandating new vehicles in all countries that permit such imports—Scenario 1— but also banning of older vehicles that do not meet safety standards, Scenario 3, is required to achieve the maximum fatalities and serious injuries prevention. In countries such as Egypt and Kazakhstan, Scenario 3 results in approximately a threefold increase in prevented fatalities and serious injuries compared in Scenario 2. Safe and Clean Vehicles for Healthier 46 and More Productive Societies Table 3.16 Cross-country comparision of safety and emissions outcomes (in % reduction compared to BAU) Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Crash FSI Argentina 0.7 0.7 0.9 0.7 0.7 Brazil 2.3 2.3 3.0 2.3 2.3 Egypt 3.5 3.5 9.1 3.5 3.5 Ghana 0.5 8.0 9.4 0.5 0.5 India 0.5 0.5 0.8 0.5 0.5 Kazakhstan 0.3 2.1 6.4 0.3 0.3 Lao PDR 0.1 3.6 4.1 0.1 0.1 Mexico 1.1 1.5 2.3 1.1 1.1 PM2.5 Argentina 3.0 3.0 7.4 5.6 3.2 Brazil 5.0 5.0 10.1 5.7 3.5 Egypt 9.2 9.2 19.4 7.1 4.8 Ghana 6.9 13.6 25.3 5.8 3.5 India N/A N/A N/A 6.6 4.0 Kazakhstan 5.1 5.5 12.1 6.3 6.3 Lao PDR 16.3 23.0 25.7 2.9 4.7 Mexico 10.7 17.0 20.6 8.2 2.9 NOx Argentina 15.7 15.7 28 13.6 7.1 Brazil 20.2 20.2 32.2 14 7.8 Egypt 20.6 20.6 40.6 15.7 7.8 Ghana 20 30 43.5 11.5 5.9 India N/A N/A N/A 22.8 11.6 Kazakhstan 17.6 18.6 41.1 9.8 9.8 Lao PDR 26.6 30.2 34.8 5.2 6.1 Mexico 17.8 38.9 46.5 13.8 4.8 Notes: All scenarios assume raising the emission standards to Euro 6. The modeling, by design, aims to isolate benefits of adopting stricter emission standards excluding from estimations India, that already adopted Euro 6. Those emission gains are reported as N/A for that reason Source: Original table for this publication. Similarly, adding the requirement that older, non-compliant vehicles exit the fleet—Scenario 3—results in the most significant reductions of PM2.5 and nitrogen oxides across countries, often twice as large as those achieved by requiring new and imported used vehicles to meet emission standards but not including older in-use vehicles in the mandate, Scenario 2. The prescribed Euro standards only limit tailpipe emissions. Consequently, electrification scenarios show a significant reduction in nitrogen oxides emissions, but relatively minor reductions in PM2.5, which is largely emitted from non-exhaust activities. The adoption of Euro 6 standards for vehicles results in more significant reductions of PM2.5 and nitrogen oxides than the increased participation of EVs in the fleet. The impact from adoption of safety technology could be significant—a comprehensive policy adoption on LDV, HDV, and MTW, could reduce FSI up to nine percent in the medium term. Countries that rely on used import vehicles stand to gain the most, for instance, Egypt, Ghana. Safe and Clean Vehicles for Healthier 47 and More Productive Societies 3.3 Quantifying the potential economic benefits of improved vehicle standard adoption For illustrative purposes, the present value (PV) of the economic benefits from the adoption of safety technology was calculated for Scenario 3, as it is the most comprehensive policy change among all scenarios affecting new, used, and older vehicles. Remaining scenarios would yield fewer economic benefits. Table 3.17 compares the PV economic benefit from safety technology adoption, Scenario 3, for the selected countries. Based on the analysis, Brazil shows the maximum potential for economic benefits on account of a combination of three factors: relatively high gross national income per capita, high magnitude of fatalities and serious injury, and finally compliance on several advanced safety technology yet to be in place. India on the other hand, which has a very high magnitude of fatalities and serious injuries, has relatively less potential for economic savings due to low gross national income per capita and as most safety technologies have already been mandated. Such estimates of economic benefits from adoption of safety technology can be used for cost–benefit analysis and policy prioritization. Table 3.17 PV of the safety benefit estimated for Scenario 3 over the period 2025–2050   GNI per capita (2020) Economic benefit from FSI saved - (Scenario 3, PV)   USD billion USD Argentina 9,040 17.1 Brazil 8,080 68.8 Egypt, Arab Rep. 2,960 23.5 Ghana 2,250 19.9 India 1,900 14.2 Kazakhstan 8,380 11.8 Lao PDR 2,470 2.8 Mexico 8,920 27.7 Source: Original table for this publication. In an indicative manner rather than precise quantification, this report estimates economic benefits from adopting safety and vehicle emission standards. The numbers are only indicative for two reasons. First, because the gains in saved lives and avoided disabilities assumed that adoption comes in tandem with enforcement, that it is the exception rather than the norm in practice, and that their assessment was out of the scope of this study. It is assumed that once adopted, a standard is enforced. Second, and perhaps more complex, is that the model estimates reductions of pollutants, PM2.5 and nitrogen oxides. However, estimating improved health outcomes from improved air quality requires an understanding how pollutant reductions translate into air pollutant exposure. To better understand how this should be done, it is important to consider the difference between primary and secondary PM2.5 emissions and their relationship with exposure. Primary PM2.5 emissions are directly emitted from vehicles. These include tailpipe emissions—particles released directly from the exhaust of cars, trucks, and other vehicles—consisting of soot, metals, and other particulate matter. Primary emissions also include non-exhaust emissions, such as particles re-entrained from the road surface, including dust from tire and brake wear. Secondly, PM2.5 emissions are formed through chemical reactions in the atmosphere. They originate from precursor gases emitted by vehicles, such as sulfur dioxide, nitrogen oxides, and volatile organic compounds. These reactions Safe and Clean Vehicles for Healthier 48 and More Productive Societies typically occur downwind from the emission source and are influenced by factors such as sunlight, temperature, and humidity. Secondary PM2.5 often constitutes the majority of total PM2.5 levels in urban and regional areas, especially during pollution peaks (Kuang et al, 2022; Rattanapotanan et al, 2023; Zhang et al, 2022). Modeling both pollution levels and human exposure is essential to assess the health impacts of air pollution. Pollution modeling is required to estimate the percentage of precursor gases (sulfur dioxide, nitrogen oxides, and volatile organic compounds) that is converted into secondary PM2.5 and the percentage that remains as other pollutants, like nitrogen dioxide. However, the team preparing this report was unable to find publicly available data on such modeling for the transport sector. In the absence of this data, estimates must rely on bold assumptions, particularly regarding the conversion percentages to estimate how the reductions in nitrogen oxides affect secondary PM2.5 and in nitrogen dioxide emissions (figure3.6). Modeling the relationship between air pollution emissions and air pollution exposure —the amount of pollution that people breathe—is extremely complex and also out of the scope of this report. This would include the use of chemical transport models that integrate emissions, meteorological, geographical, and land use data and reaction pathways for atmospheric chemistry to simulate the dispersion and chemical transformation of pollutants in the atmosphere. These data would need to be combined with information on the number of people living in specific areas, country level or airshed, to estimate exposure levels. The study did not contemplate such modeling because it is quite costly and labor intensive. Figure 3.6 Primary and secondary PM2.5 emissions PM2.5 SOX NOX Primary PM2.5 Secondary PM2.5 Secondary PM2.5 NO from NOX from SOX NO2 Secondary PM2.5 from other sources Notes: Simplified figure for illustrative purposes. The image does not intend to illustrate the chemical reactions leading to the formation of different air pollutants. It excludes key chemicals and pollutants, such as ground-level ozone or volatile organic compounds. Source: Original figure for this publication. With all these caveats, a ballpark envelope of the potential economic benefits of adopting safety and emission standards are estimated and presented in table 3.18. The economic benefits of vehicle emission reductions are not additive—as addition would likely involve double counting (e.g. between PM2.5 and NO2) —and because the economic benefit of avoided IQ losses is estimated as income gains rather than using the value of statistical life approach. Notably the benefits of NOx or NO2 emission reductions are substantially larger than the benefits of PM2.5 reductions in the three LAC countries and of a similar order of magnitude in the other countries. Safe and Clean Vehicles for Healthier 49 and More Productive Societies Table 3.18 Envelope of potential economic benefits due to improved vehicle standards, cumulative benefit over 2025-2050 Road crash FSI PM2.5 prematue deaths NO2 premature deaths PM2.5- IQ Losses Country US$ million % of GDP equivalent US$ million % of GDP equivalent US$ million % of GDP equivalent US$ million % of GDP equivalent Argentina 2,625 0.5 1,107 0.2 7,705 1.6 742 0.2 Brazil 44,850 2.9 3,274 0.2 26,849 1.6 2,233 0.1 Egypt 8,750 2.1 5,345 1.3 3,950 0.9 2,820 0.7 Scenario 1 Ghana 375 0.5 116 0.1 N/A N/A 362 0.4 India 12,500 0.4 N/A N/A N/A N/A N/A N/A Kazakhstan 450 0.2 866 0.4 657 0.3 444 0.2 Lao PDR 25 0.1 110 0.6 N/A N/A 163 0.9 Mexico 14,575 1.2 14,490 1.1 41,703 3.2 6,733 0.5 Argentina 2,625 0.5 1,107 0.2 7,705 1.6 742 0.2 Brazil 44,850 2.9 3,274 0.2 26,849 1.6 2,233 0.1 Egypt 8,750 2.1 5,345 1.3 3,950 0.9 2,820 0.7 Scenario 2 Ghana 6,000 8.4 228 0.3 N/A N/A 714 0.9 India 12,500 0.4 N/A N/A N/A N/A N/A N/A Kazakhstan 3,150 1.7 934 0.5 695 0.4 479 0.2 Lao PDR 900 2.9 155 0.8 N/A N/A 230 1.2 Mexico 19,875 1.6 23,022 1.8 91,137 7.1 10,697 0.8 Argentina 3,375 0.7 2,731 0.6 13,742 2.9 1,830 0.4 Brazil 58,500 3.8 6,613 0.4 42,798 2.6 4,510 0.3 Egypt 22,750 5.5 11,271 2.7 7,784 1.8 5,946 1.4 Scenario 3 Ghana 7,050 9.9 424 0.5 N/A N/A 1,328 1.6 India 20,000 0.7 N/A N/A N/A N/A N/A N/A Kazakhstan 9,600 5.3 2,054 1.1 1,535 0.8 1,053 0.5 Lao PDR 1,025 3.3 173 0.9 N/A N/A 257 1.3 Mexico 30,475 2.4 27,898 2.2 108,943 8.4 12,962.6 1.0 Safe and Clean Vehicles for Healthier 50 and More Productive Societies Road crash FSI PM2.5 prematue deaths NO2 premature deaths PM2.5- IQ Losses Country US$ million % of GDP equivalent US$ million % of GDP equivalent US$ million % of GDP equivalent US$ million % of GDP equivalent Argentina 2,625 0.2 2,066 0.4 6,674 1.4 1,385 0.3 Brazil 44,850 0.1 3,732 0.2 18,608 1.1 2,545 0.2 Egypt 8,750 0.7 4,125 1.0 3,010 0.7 2,176 0.5 Scenario 4 Ghana 375 0.7 97 0.1 N/A N/A 305 0.4 India 12,500 1.9 16,909 0.5 22,197 0.7 13,908 0.4 Kazakhstan 450 2.9 1,069 0.6 366 0.2 548 0.3 Lao PDR 25 0.4 20 0.1 N/A N/A 29 0.2 Mexico 14,575 0.5 11,105 0.9 32,331 2.5 5,160 0.4 Argentina 2,625 0.5 1,181 0.2 3,484 0.7 791 0.2 Brazil 44,850 2.9 2,292 0.1 10,367 0.6 1,563 0.1 Egypt 8,750 2.1 2,789 0.7 1,495 0.4 1,471 0.3 Scenario 5 Ghana 375 0.5 59 0.1 N/A N/A 184 0.2 India 12,500 0.4 10,248 0.3 11,293 0.4 8,429 0.3 Kazakhstan 450 0.2 1,069 0.6 366 0.2 548 0.3 Lao PDR 25 0.1 32 0.2 N/A N/A 47 0.2 Mexico 14,575 1.2 3,927 0.3 11,246 0.9 1,825 0.1 Notes: The benefits of Scenario N (N=1,…,5) are calculated as: cost of health impacts in 2021 from road transport emissions multiplied by percent reduction in road transport emissions in Scenario N divided by GDP in 2021 and multiplied by 100. All scenarios assumed raising the emission standards to EURO 6. The modeling, by design, aims to isolate benefits of adopting stricter emission standards excluding from estimations India, that already adopted EURO 6. Those emission gains are reported as N/A for that reason. Source: Original table for this publication. Safe and Clean Vehicles for Healthier 51 and More Productive Societies Notes 1. Scenario 1 corresponds to new vehicles entering the fleet are mandated by 2030 to meet study prescribed safety and air quality standards while used vehicle imports may or may not meet new standards. References GBD. 2021. Risk Factors Collaborators. 2024. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet; 403(10440):2162-2203. doi: 10.1016/S0140-6736(24)00933-4. Kuang, B., F. Zhang, J. Shen, Y. Shen, F. Qu, L. Jin. Q. Tang, X. Tian, and Z. Wang. 2022. Chemical characterizatio , formation mechanisms and source apportionment of PM2.5 in north Zhejiang Province: The importance of secondary formation and vehicle emission. Science of Total Envronment 851, Part 2, 158206.  McDuffie E, Martin R, Yin H, Brauer M. 2021a. Global Burden of Disease from Major Air Pollution Sources (GBD MAPS): A Global Approach. Health Effects Institute. Research Report 210. Boston. USA. McDuffie E, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. Source sector and fuel contributions to ambient PM2.5 and attributable mortality. Rattanapotanan, T.,  T. Thongyen, S. Bualert, P. Choomanee, P. Suwattiga, T. Rungrattanaubon, T. Utavong, J. Phupijit, N. Changplaiy. 2023. “Secondary sources of PM2.5 based on the vertical distribution of organic carbon, elemental carbon, and water-soluble ions in Bangkok.” Environmental Advances 11. UNECE. 2025. World Forum for Harmonization of Vehicle Regulations (WP.29). UNECE. https://unece.org/transport/ vehicle-regulations/world-forum-harmonization-vehicle-regulations-wp29 Zhang, H., Li, N., Tang, K., Liao, H., Shi, C., Huang, C., Wang, H., Guo, S., Hu, M., Ge, X., Chen, M., Liu, Z., Yu, H., and Hu, J.: Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach, Atmos. Chem. Phys., 22, 5495–5514, https://doi.org/10.5194/acp-22-5495-2022. Safe and Clean Vehicles for Healthier 52 and More Productive Societies Chapter 4. Policy Implications for Decision Makers This chapter explores strategic approaches to adopting safety and emission standards for alternative scenarios. It provides evidence-based recommendations to inform decision making utilizing the model’s results and highlighting the patterns and benefits of five alternative policy options. (i) Cumulative adoption of standards for new vehicles, for used vehicle imports, and old fleet retirement. (ii) Alternative of either adoption of vehicle standards in new vehicles together with fleet retirement, or adoption of vehicle standards in new vehicles together with targets for electric vehicle adoption. (iii) Alternative of either adoption of vehicle standards in new vehicles together with standards for used vehicle imports or adoption of vehicle standards in new vehicles together with targets for electric vehicle adoption (iv) Alternative of adopting either emission standard Euro 5 or emission standard Euro 6 (v) Alternative option of electric vehicle adoption between either thirty percent of electric new vehicles for 2030 or 50 percent of electric new vehicles for 2050. The proposed policy options are clearly a starting point to bring out specific and evidence-supported recommendations to countries in adoption of safety and emission standards for vehicles. The goal is that this model can be replicated for other countries and policy scenarios to document the enormous human capital implications of regulating fleet and related motorization management policies. Safe and Clean Vehicles for Healthier 53 and More Productive Societies Policy option 1: Cumulative adoption of standards in new vehicles, standards in used vehicle imports, and old fleet retirement Policy makers may consider mandating vehicle standards incrementally: (i) require safety and emission standards for new vehicles, scenario 1; (ii) require safety and emission standards for both new vehicles and used imports, scenario 2, or (iii) require safety and emission standards for both new vehicles and used imports while also mandating the retirement of non-roadworthy vehicles, scenario 3. The model’s results indicate that regulating new vehicles alone has limited impact if there is no requirement for fleet retirement, as the largest gains tend to be achieved when old (over twenty-year old), non-roadworthy vehicles are removed from the fleet, particularly in what pertains to emission reduction (Table 4.1). In practice this involves mandating vehicle standards but also setting in place vehicle periodic technical inspection procedures and incentives for scrapping when needed. For health benefits linked to air pollution, the most impactful policy is the retirement of old fleet. The reductions of PM2.5 and NOx increase in order of magnitude when retirement is added to improving vehicle standards. The most impactful focus to increased road safety is projected to materialize when improving standards of used imports and fleet retirement. For instance, in Kazakhstan, Ghana and Laos the safety benefits were 6, 15 and 35 times higher respectively, if standards are mandated for new vehicles, used import and fleet retirement as opposed to adopting standards for new vehicles only. This necessarily involves period vehicle inspections to enforce the mandated standards and to manage the fleet retirement program. Good vehicle inspection practices also make sure benefits of stricter standards of air pollution emissions percolate throughout a country’s vehicle stock. The benefits of emissions standards fully depend on the proper and sustained use of relevant technologies like diesel particle filters for PM2.5 and selective catalytic reduction systems for nitrogen oxides. Ultra-low sulfur diesel, less than 10 parts per million, is generally required by Euro 5 and Euro 6-compliant technology. Higher sulfur content in diesel can reduce the potential benefits of stricter emission standards. Safe and Clean Vehicles for Healthier 54 and More Productive Societies Table 4.1 Gains of adoption of alternative standards: policy option 1 Incremental Gains from BAU by Adoption of Standards (% Reduction) Scenario 3 Scenario 2  Scenario 1 Impact Country Standards in New Vehicles Standards in Used Vehicle Import Retirement of old fleet Argentina 0.7 N/A 0.2 Brazil 2.3 N/A 0.7 Egypt 3.5 N/A 5.6 Ghana 0.5 7.5 1.4 FSI  India 0.5 N/A 0.3 Kazakhstan 0.3 1.8 4.3 Lao PDR 0.1 3.5 0.5 Mexico 1.1 0.4 0.8 Argentina 3.0 N/A 4.4 Brazil 5.0 N/A 5.1 Egypt 9.2 N/A 10.2 Ghana 6.9 6.7 11.7 PM2.5 India N/A N/A N/A Kazakhstan 5.1 0.4 6.6 Lao PDR 16.3 6.7 2.7 Mexico 10.7 6.3 3.6 Argentina 15.7 N/A 12.3 Brazil 20.2 N/A 12.0 Egypt 20.6 N/A 20.0 Ghana 20.0 10.0 13.5 NOx India N/A N/A N/A Kazakhstan 17.6 1.0 22.5 Lao PDR 26.6 3.6 4.6 Mexico 17.8 21.1 7.6 Notes: a. Aspirational Safety Standards: (i) Electronic Stability Control in LDVs, (ii) Advanced Emergency Braking for vehicles in LDVs, (iii) Advanced Emergency Braking for pedestrians and cyclists in LDVs; (iv) Front- and Rear-Underrun Protective Devices in HDVs, (v) Vehicle Stability Function in HDVs; and (vi) Anti-lock Brake System in MTWs. b. Aspirational Emission Standard: Euro 6 Source: Original table for this publication. Ghana, Kazakhstan, and Lao PDR benefit less than one percent reduction in FSI by only focusing on new vehicles entering the fleet, however, when a comprehensive policy is adopted for used vehicle entering the fleet and retiring old vehicles, the benefits improve approximately by four to nine percent. For countries which have a domestic automotive manufacturing industry but have not mandated the adoption of advanced technology, incremental benefits can be achieved by focusing on new vehicles, used vehicles and vehicle scrappage. Safe and Clean Vehicles for Healthier 55 and More Productive Societies Policy option 2. Alternative of either adoption of vehicle standards in new vehicles together with fleet retirement or adoption of vehicle standards in new vehicle together with targets for electric vehicle adoption Policy makers in countries that have prohibited the import of used vehicles such as Argentina, Brazil, Egypt, and India, may consider two strategic options: require that new vehicles comply with established safety and emission standards, while also mandating the retirement of non-compliant vehicles over 20 years old, as in Scenario 3, or (ii) apply the same requirement for new vehicles, combined with the adoption of an EV 30x30 target—aiming for 30 percent of new cars, buses, and minibuses, and 70 percent of new motorcycles to be electric by 2030, as in Scenario 4. While both approaches aim to modernize the vehicle fleet and reduce emissions, the findings suggest that developing and emerging economies derive significantly higher safety and emission reduction benefits from retiring older, high- emitting vehicles than from focusing on ambitious electrification targets. In all countries analyzed—except India— the benefits from circulation outweigh those of implementing the EV target. India is a notable exception due to its adoption of the Euro 6 standard to improve the emissions profile of its vehicle fleet. As a result, further gains from fleet renewal are limited in comparison to electrification (table 4.2). Table 4.2 Gains of adoption of alternative standards: policy option 2 Gains with Respect to BAU by Adoption of Standards (% Reduction) Scenario 3 Scenario 4 Standards in New Vehicles together with Standards in New Vehicles together Impact Country Fleet Retirement with Electric Vehicles 30x30 Argentina 0.9 0.7 Brazil 3.0 2.3 FSI Egypt 9.1 3.5 India 0.8 0.5 Argentina 7.4 5.6 Brazil 10.1 5.7 PM2.5 Egypt 19.4 7.1 India N/A 6.6 Argentina 28.0 13.6 Brazil 32.2 14.0 NOx Egypt 40.6 15.7 India N/A 22.8 Notes: a. Aspirational Safety Standards: (i) Electronic Stability Control in LDVs, (ii) Advanced Emergency Braking for vehicles in LDVs, (iii) Advanced Emergency Braking for pedestrians and cyclists in LDVs; (iv) Front- and Rear-Underrun Protective Devices in HDVs, (v) Vehicle Stability Function in HDVs; and (vi) Anti-lock Brake System in MTWs. b. Aspirational Emission Standard: Euro6 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 56 and More Productive Societies Policy option 3. Alternative of either adoption of vehicle standards in new vehicle together with standards in used vehicle imports or adoption of vehicle standards in new vehicles together with targets for electric vehicle adoption In countries that still allow the importation of used vehicles—such as Ghana, Kazakhstan, Lao PDR, and Mexico— policy may compare two strategic options: (i) mandate that all imported vehicles, both new and used, comply with established safety and emission standards as in Scenario 2, or (ii) apply these standards only to new imported vehicles, while simultaneously adopting the EV 30x30 target, as in Scenario 4. The results show that the safety and emissions benefits of Scenario 2 are significantly greater—often by orders of magnitude—than those under Scenario 4. While promoting EVs may be politically appealing, it is the regulation of used vehicle imports that offers the most immediate and significant gains in air quality and road safety (table 4.3). Table 4.3 Gains of adoption of alternative standards: policy option 3 Gains with Respect to BAU by Adoption of Standards (% Reduction) Scenario 2 Scenario 4 Standards in New Vehicles and Standards in New Vehicles Impact Country Standards in Imported Used Vehicles together with Electric Vehicles 30x30 Ghana 8.0 0.5 Kazakhstan 2.1 0.3 FSI Lao PDR 3.6 0.1 Mexico 1.5 1.1 Ghana 13.6 5.8 Kazakhstan 5.5 6.3 PM2.5 Lao PDR 23.0 2.9 Mexico 17.0 8.2 Ghana 30.0 11.5 Kazakhstan 18.6 9.8 NOx Lao PDR 30.2 5.2 Mexico 38.9 13.8 Notes: a. Aspirational Safety Standards: (i) Electronic Stability Control in LDVs, (ii) Advanced Emergency Braking for vehicles in LDVs, (iii) Advanced Emergency Braking for pedestrians and cyclists in LDVs; (iv) Front- and Rear-Underrun Protective Devices in HDVs, (v) Vehicle Stability Function in HDVs; and (vi) Anti-lock Brake System in MTWs. b. Aspirational Emission Standard: Euro 6 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 57 and More Productive Societies Policy option 4. Alternative of adopting emission standard Euro 5 or emission standard Euro 6 (or even fleet electrification) Countries such as Egypt, Ghana, Kazakhstan, Lao PDR, and Mexico operate under the Euro 4 or lower vehicle emission standard. Policy makers in these countries face a critical decision—whether to adopt Euro 5 incrementally or to leapfrog directly to the more stringent Euro 6 standard. Adopting Euro 6 offers significantly greater reductions in harmful emissions—particularly nitrogen oxides—compared to Euro 5. This leapfrogging strategy presents a powerful opportunity to maximize public health benefits, including reductions in premature mortality, morbidity, and cognitive impairments linked to vehicle-related air pollution. Vehicles must be equipped with advanced emissions control technologies to comply with these standards, such as diesel particle filters to reduce PM2.5 emissions and selective catalytic reduction systems to control nitrogen oxide emissions. These technologies require the use of ultra-low sulfur diesel, with sulfur content below 10 parts per million. Therefore, the success of stricter emission standards hinges on two critical enablers: (i) upgrade national fuel quality standards to ensure the availability of ultra-low sulfur diesel, and (ii) implement robust vehicle inspection and maintenance programs to verify the functionality of emissions control systems (table 4.4). Policy option 4-1 compares Euro 5 with Euro 6 emission standards, while policy option 4-2 compares Euro 5 with Euro 6 emission standards as well as with the EV 30x30 emission scenario. Table 4.4 Gains of adoption of alternative emission standards: policy option 4-1 (Ratio Gains Euro 6 over Gains Euro 5) Country PM2.5 NOx Egypt 2.97 18.73 Standards in New Ghana 4.93 16.67 Scenario 1 Vehicles Kazakhstan 5.67 3.74 Lao PDR 1.33 4.09 Mexico 1.29 4.94 Egypt 2.97 18.73 Standards in Used New Vehicles and Ghana 8.00 18.75 Scenario 2 Vehicles Kazakhstan 4.23 4.89 Lao PDR 1.20 2.99 Mexico 1.48 7.34 Egypt 1.70 2.65 Used Vehicles and and Standards in New Vehicles Ghana 1.93 3.57 Standards in Retirement Scenario 3 Kazakhstan 1.98 2.03 Lao PDR 1.20 2.66 Mexico 2.15 3.16 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 58 and More Productive Societies An additional policy question is whether for new fleet emission mandates, one should think about leapfrogging even further and embrace an ambitious electrification agenda. The answer is not totally clear without analyzing the out- of-pocket and total cost of operation. What does emerge from the analysis is that –without any other consideration such as used-vehicle regulation or scrapping—in countries with Euro 4 and lower, the largest emission savings come from leapfrogging to Euro 6. Electrification brings emission gains significantly larger than mandating an incremental standard improvement to Euro 5, but not yet as high as moving to Euro 6 (table 4.5) Table 4.5 Gains of adoption of alternative emission standards: policy option 4-2 Changes from BAU by Adoption of Standards (% Reduction) Euro 5 Euro 6 EV 30x30 PM2.5 Egypt 3.1 9.2 7.1 Ghana 1.4 6.9 5.8 Kazakhstan 0.9 5.1 6.3 Lao PDR 12.3 16.3 2.9 Mexico 8.3 10.7 8.2  NOx Egypt 1.1 20.6 15.7 Ghana 1.2 20 11.5 Kazakhstan 4.7 17.6 9.8 Lao PDR 6.5 26.6 5.2 Mexico 3.6 17.8 13.8 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 59 and More Productive Societies Policy Option 5. Alternative rates of electric vehicle adoption between either 30 percent of electric new vehicles for 2030 or 50 percent of electric new vehicles for 2050 Policy makers face a choice between two electrification scenarios differing timelines and levels of ambition. EV 30x30 targets 30 percent electrification of new cars, buses, and minipacebuses, and 70 percent of new motorcycles by 2030, as in Scenario 4. EV 50x50 aims for 50 percent of new cars, buses, minibuses, motorcycles, and vans to be electric by 2050, and 50 percent of new trucks by 2050, as in Scenario 5. While EV50x50 covers a broader range of vehicles and achieves higher electrification rates, its longer implementation timeline results in lower cumulative emission reductions compared to the more immediate targets of EV 30x30. This comparison highlights the importance of early and sustained policy implementation to achieving greater air quality and health benefits over the coming decades (table 4.6). Table 4.6 Gains of cumulative adoption of standards: policy option 5 Gains with Respect to BAU by Adoption of Standards (% Reduction) Scenario 4 Scenario 5 Impact Country Standards in New Vehicles together Standards in New Vehicles together with with Electric Vehicles 30x30 Electric Vehicles 50x50 Argentina 5.6 3.2 Brazil 5.7 3.5 Egypt 7.1 4.8 Ghana 5.8 3.5 PM2.5 India 6.6 4 Kazakhstan 6.3 6.3 Lao PDR 2.9 4.7 Mexico 8.2 2.9 Argentina 13.6 7.1 Brazil 14 7.8 Egypt 15.7 7.8 Ghana 11.5 5.9 NOx  India 22.8 11.6 Kazakhstan 9.8 9.8 Lao PDR 5.2 6.1 Mexico 13.8 4.8 Note: a. Aspirational Safety Standards: (i) Electronic Stability Control in LDVs, (ii) Advanced Emergency Braking for vehicles in LDVs, (iii) Advanced Emergency Braking for pedestrians and cyclists in LDVs; (iv) Front- and Rear-Underrun Protective Devices in HDVs, (v) Vehicle Stability Function in HDVs; and (vi) Anti-lock Brake System in MTWs. b. Aspirational Emission Standard: Euro 6 Source: Original table for this publication. Safe and Clean Vehicles for Healthier 60 and More Productive Societies Appendix A: Countries-At-A-Glance Argentina Country context and motorization characteristics Region Latin America Income category Upper middle GDP per capita (current US$) 14,187 Population (million) 45.5 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 26,413,085 [583] Ambient PM2.5 (µg/m3) 17 PM2.5 9.0 Ambient air pollutant contribution from road transport (%), 2017 NO2 44 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) Fatality (annual) - IRTAD Road Safety Country Profile - Argentina 2023 GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O’Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles x Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control ü LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices ü HDV Vehicle Stability Function ü MTW Anti-lock Brake System ü Euro 4 x Adoption of Vehicle Emission Standards Euro 5 ü Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 3,983 Fatality rate per 100,000, 2021 (number) 8.8 PM2.5 1,374 Premature deaths per air pollutant from road transport, 2021 (number) NO2 1,830 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 0.3 Source: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data) Safe and Clean Vehicles for Healthier 61 and More Productive Societies Prevented FSIs by Technology and Vehicle Type (cumulative 2025-50 as a difference from BAU) Scenario 1: New vehicle only Scenario 2: New + Used Scenario 3: New + Used + Scrappage 7,000 7,000 7,000 5,918 6,000 6,000 6,000 4,470 5,000 4,470 5,000 FSI prevented FSI prevented FSI prevented 5,000 4,000 4,000 4,000 7,000 7,000 7,000 3,000 3,000 3,000 2,127 5,918 6,000 1,483 6,000 1,483 6,000 2,000 2,000 2,000 4,470 5,000 4,470 5,000 FSI prevented FSI prevented FSI prevented 5,000 1,000 1,000 1,000 - - - - - - - - - - - - 4,000 0 4,000 0 4,000 0 3,000 a b c d e f 3,000 a b c d e f 3,000 a b c d e f 2,127 1,483 1,483 HDV Front and Rear Underrun LDV Electronic 2,000 Stability Control 2,000 2,000 Protective Devices 1,000 1,000 HDV Vehicle 1,000 LDV Advanced - - Braking Emergency - - Vehicles - - - - -Stability Function - - - - 0 0 0 LDV Advanced a b Emergency c e - Pedestrians d Braking f & Cyclists a b c MTW d f Brake System Anti-lock e a b c d e f Argentina 2050: Reduction in emissions, by scenario Note: Dashes represent that this technology/vehicle type is not applicable. 30 on BAU 25 Argentina 2050: Argentina Reduction 2050: in Reduction inemissions, by emissions, by scenario scenario 20 on reduction 30 15 BAU 25 10 Percentage 20 Percentage reduction 5 15 0 10 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 5 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 100,000 100,000 100,000 79,672 79,672 PM 2.5 Nox 79,672 80,000 80,000 80,000 FSI prevented FSI prevented FSI prevented 56,868 60,000 60,000 60,000 42,699 42,699 100,000 100,000 100,000 40,000 79,672 40,000 79,672 40,000 80,000 10,897 12,865 79,672 80,000 80,000 20,000 20,000 20,000 FSI prevented FSI prevented FSI prevented 7,069 5,340 7,069 5,340 56,868 7,759 - 3,163 - 3,163 - 60,000 60,000 60,000 0 42,699 0 42,699 0 a b c d e f a b c d e f a b c d e f 40,000 Note: For 40,000 a detailed explanation of each scenario, see Chapter 40,000 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). 10,897 12,865 20,000 7,069 5,340 20,000 7,069 5,340 20,000 7,759 - 3,163 - 3,163 - 0 0 0 a b c d e f Brazil 2050: Reduction a bin emissions, c dby scenario e f a b c d e f 40 on BAU 30 Brazil 2050: Reduction in emissions, by scenario on reduction 40 20 BAU 30 Percentage 10 Percentage reduction 20 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: 10 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Safe and Clean Vehicles for Healthier 62 and More Productive Societies Brazil Country context and motorization characteristics Region Latin America Income category Upper middle GDP per capita (current US$) 10,295 Population (million) 211.1 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 111,446,870 [520] Ambient PM2.5 (µg/m3) 16 PM2.5 6.3 Ambient air pollutant contribution from road transport, 2017 (%) NO2 48 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles x Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control ü LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 x Adoption of Vehicle Emission Standards Euro 5 ü Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 33,586 Fatality rate per 100,000, 2021 (number) 15.7 PM2.5 3,339 Premature deaths per air pollutant from road transport, 2021 (number) NO2 6,772 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 0.74 Source: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data) Safe and Clean Vehicles for Healthier 63 and More Productive Societies Argentina 2050: Reduction in emissions, by scenario on BAU reduc 15 30 10 Percentage 25 5 20 Percentage reduction 0 15 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New New + Used FSIs Prevented Used + Scrappage by+Technology EV 30Type and Vehicle X 30 EV 50 X 50 10 (cumulative 2025-50 as a difference from BAU) PM 2.5 Nox 5 Scenario 1: New vehicle only Scenario 2: New + Used Scenario 3: New + Used + Scrappage 0 100,000 Scenario 1: 2: Scenario 100,000 Scenario 3: Scenario 4: Scenario 5: 100,000 New vehicle only 79,672 New + Used New + Used + Scrappage 79,672 EV 30 X 30 EV 50 X 50 80,000 79,672 80,000 80,000 FSI prevented FSI prevented FSI prevented 56,868 60,000 PM 2.5 Nox 60,000 60,000 42,699 42,699 40,000 40,000 40,000 100,000 100,000 100,000 10,897 12,865 20,000 7,069 5,340 20,000 7,069 5,340 20,000 7,759 - 3,16379,672 - 3,163 79,672 - 79,672 80,000 0 80,000 0 80,000 0 FSI prevented FSI prevented FSI prevented a b c d e f a b c d e f a b 56,868 c d e f 60,000 60,000 60,000 42,699 42,699 LDV Electronic Stability Control 40,000 40,000 40,000 Protective Devices HDV Front and Rear Underrun HDV Vehicle Stability Function 10,897 12,865 7,069Emergency LDV Advanced 20,000 5,340Braking - Vehicles 20,000 7,069 5,340 20,000 7,759 3,163 Brazil 2050: Reduction in emissions, by scenario - - - 3,163 LDV 0 Advanced Emergency Braking - Pedestrians & Cyclists 0 MTW Anti-lock Brake System 0 40 a b c d e f a b c d e f a b c d e f Note: Dashes represent that this technology/vehicle type is not applicable. on BAU reduction on BAU 30 Brazil 2050: Brazil Reduction 2050: in Reduction inemissions, by emissions, by scenario scenario 20 40 Percentage 10 30 Percentage reduction 0 20 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Note: For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). Safe and Clean Vehicles for Healthier 64 and More Productive Societies Arab Republic of Egypt Country context and motorization characteristics Region Middle East Income category Lower middle GDP per capita (current US$) 3,457 Population (million) 114.5 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 10,909,456 [100] Ambient PM2.5 (µg/m3) 60  PM2.5 5.8 Ambient air pollutant contribution from road transport, 2017 (%) NO2 17 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles x Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control x LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 ü Adoption of Vehicle Emission Standards Euro 5 x Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 10,263 Fatality rate per 100,000, 2021 (number) 9.4 PM2.5 6,708 Premature deaths per air pollutant from road transport, 2021 (number) NO2 2,204 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 1.44 Sources: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data). Safe and Clean Vehicles for Healthier 65 and More Productive Societies Prevented FSIs by Technology and Vehicle Type (cumulative 2025-50 as compared to BAU) Scenario 1: New vehicle only Scenario 2: New + Used Scenario 3: New + Used + Scrappage 30,000 26,089 30,000 26,089 100,000 24,957 24,957 78,149 25,000 25,000 80,000 57,276 FSI prevented FSI prevented FSI prevented 20,000 20,000 60,000 15,000 15,000 30,000 26,089 30,000 26,089 100,000 40,000 24,957 10,000 10,000 24,957 78,149 25,000 25,000 80,000 2,454 5,000 2,454 20,000 5,000 1,143 214 2,454 1,143 2,758 57,27670 FSI prevented FSI prevented FSI prevented 20,000 19 66 20,000 19 66 0 0 60,000 0 15,000 a b c d e f 15,000 a b c d e f a b c d e f 40,000 10,000 LDV Electronic Stability Control 10,000 HDV Front and Rear Underrun Protective Devices 2,454 2,454 20,000 5,000 5,000 1,143 Emergency 1,143 214 2,454 LDV Advanced 19 Braking 66 - Vehicles 19 HDV 66Vehicle Stability Function 2,758 70 0 0 0 LDV a Advanced b Emergency c d Braking e f - Pedestrians & Cyclists a b emissions, c d MTW e Anti-lock f Brake System a b c d e f Egypt 2050: Reduction in by scenario 50 Percentage reduction on BAU 40 Egypt, Arab Rep. Egypt 2050: 2050: Reduction Reduction in emissions, in emissions, by scenario by scenario 30 50 Percentage reduction on BAU 20 40 10 30 0 20 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: 23,495 5: Scenario 1,400 1,199 25,000 25,000 EV New vehicle only New + Used New + Used + Scrappage EV 30 X 30 50 X 50 1,200 19,632 19,093 988 20,000 20,000 1,000 PM 2.516,551 Nox FSI prevented FSI prevented FSI prevented 800 15,000 15,000 1,400 600 1,199 25,000 10,000 25,000 23,495 10,000 1,200 400 19,632 19,093 988 20,000 5,000 16,551 20,000 5,000 1,000 200 71 1,236 879 73 1,231 FSI prevented FSI prevented FSI prevented 1 5 43 757 16 46 22 800 0 15,000 0 15,000 0 600 a b c d e f a b c d e f a b c d e f 10,000 10,000 Note: For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). 400 5,000 5,000 200 71 1 5 43 757 16 46 1,236 879 22 73 1,231 0 0 0 a b c d e Ghana f 2050: Reduction a in emissions, b c by scenario d e f a b c d e f 50 Percentage reduction on BAU 40 Ghana 2050: Reduction in emissions, by scenario 30 50 20 Percentage reduction on BAU 40 10 30 0 20 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Safe and Clean Vehicles for Healthier 66 and More Productive Societies Ghana Country context and motorization characteristics Region Sub-Saharan Income category Lower middle GDP per capita (current US$) 2,260 Population (million) 33.8 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 3,314,215 [101] Ambient PM2.5 (µg/m ) 3 63  PM2.5 3.1 Ambient air pollutant contribution from road transport, 2017 (%) NO2 26 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) Fatality (annual) – GRSF (2025) Personal communication GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles ü Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control x LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 ü Adoption of Vehicle Emission Standards Euro 5 x Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 8,494 Fatality rate per 100,000, 2021 (number) 25.9 PM2.5 342 Premature deaths per air pollutant from road transport, 2021 (number) NO2 No data IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 0.27 Source: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data) Safe and Clean Vehicles for Healthier 67 and More Productive Societies Percentage reduction 30 Egypt 2050: Reduction in emissions, by scenario 50 20 Percentage reduction on BAU 40 10 30 0 Prevented FSIs by Technology and Vehicle Type Scenario 1: Scenario 2: (cumulative Scenario 2025-50 3: as compared Scenario to BAU) 4: Scenario 5: 20 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 vehicle only Scenario 1: New Scenario New + Used 2:2.5 PM Nox Scenario 3: New + Used + Scrappage 1,400 1,199 0 25,000 25,000 23,495 1,200 Scenario 1: Scenario 2: 19,632 Scenario 3: Scenario 4: Scenario 5:19,093 988 New vehicle only New20,000 + Used New + Used + Scrappage 16,551 EV 30 X 30 20,000 EV 50 X 50 1,000 FSI prevented FSI prevented FSI prevented 800 15,000 PM 2.5 Nox 15,000 600 10,000 10,000 400 1,400 1,199 25,000 25,000 23,495 5,000 5,000 200 1,200 71 5 43 19,632 757 16 46 1,236 879 19,093 22 73 1,231 9881 20,000 16,551 20,000 0 1,000 0 0 FSI prevented FSI prevented FSI prevented 800 a b c d e f 15,000 a b c d e f 15,000 a b c d e f LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices 600 10,000 10,000 LDV Advanced Emergency Braking - Vehicles 400 HDV Vehicle Stability Function 5,000 5,000 200 71 Emergency Braking LDV Advanced 1 - 43 5 GhanaPedestrians 757 & Cyclistsin emissions, 2050: Reduction MTW 16 Anti-lock by scenario46 1,236 Brake System 879 22 73 1,231 0 0 0 50a b c d e f a b c d e f a b c d e f Percentage reduction on BAU 40 Ghana 2050: Reduction in emissions, by scenario 30 Ghana 2050: Reduction in emissions, by scenario 50 20 Percentage reduction on BAU 40 10 30 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: 20 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Note: For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). Safe and Clean Vehicles for Healthier 68 and More Productive Societies India Country context and motorization characteristics Region South Asia Income category Lower middle GDP per capita (current US$) 2,481 Population (million) 1,438.10 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 326,300,000 [232] Ambient PM2.5 (µg/m3) 62  PM2.5 5.7 Ambient air pollutant contribution from road transport, 2017 (%) NO2 15 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Adoption of Rules for Imported Used Vehicles x Vehicles New Vehicles ü Electronic Stability Control ü LDV Advanced Emergency Breaking - Vehicles ü Advanced Emergency Breaking - pedestrians & cyclists ü Front and Rear Underrun Protective Devices ü HDV Vehicle Stability Function ü* MTW Anti-lock Brake System ü Euro 4 x Adoption of Vehicle Emission Euro 5 x Standards Euro 6 ü * excluding trucks Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 216,618 Fatality rate per 100,000, 2021 (number) 15.4 PM2.5 54,012 Premature deaths per air pollutant from road transport, 2021 (number) NO2 20,771 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 13.18 Sources: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data). Safe and Clean Vehicles for Healthier 69 and More Productive Societies Prevented FSIs by Technology and Vehicle Type (cumulative 2025-50 as a difference from BAU) Scenario 1: New vehicle only Scenario 2: New + Used Scenario 3: New + Used + Scrappage 200,000 200,000 200,000 158,18 150,000 150,000 150,000 FSI prevented FSI prevented FSI prevented FSI prevented FSI prevented FSI prevented 107,009 107,009 100,000 100,000 100,000 200,000 200,000 200,000 50,000 50,000 50,000 158,18 150,000 - - - - - 150,000 - - - - - 150,000 - - - - - 0 107,009 0 107,009 0 100,000 a b c d e f 100,000 a b c d e f 100,000 a b c d e f LDV Electronic Stability Control HDV Front and Rear Underrun Protective Devices 50,000 50,000 50,000 LDV Advanced Emergency Braking - - Vehicles HDV Vehicle Stability Function - - - - - - - - - - - - - - 0 India 2050:& LDV Advanced Emergency Braking - Pedestrians 0 Cyclists Reduction MTW in emissions, by Anti-lock Brake System 0 scenario a b c d e f a b c d e f a b c d e f Note: Dashes represent 40 that this technology/vehicle type is not applicable. reduction on BAU 30 India 2050: Reduction in emissions, by scenario India 2050: Reduction in emissions, by scenario 40 20 on BAU Percentage 30 10 Percentage reduction 20 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox 2,000 10,000 20,000 15,113 8,000 1,500 6,064 15,000 FSI prevented FSI prevented FSI prevented FSI prevented FSI prevented FSI prevented 927 6,000 1,000 Note: 10,000 For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of policies vehicle 6,711 at the country level (page 35). 6,275 2,000 4,000 10,000 20,000 1,986 500 223 310 2,000 1,227 5,000 15,113 8,000 528 185 1,500 10 4 5 6,064192 25 185 15,000 96 0 0 6,000 0 927 1,000 a b c d e f a b c d e f 10,000 a b c d e f 6,7116,275 4,000 1,986 500 223 310 2,000 1,227 5,000 10 5 192 25 185 528 96 185 4 0 0 0 a b c d e f a b c d e f a b c d e f Kazakhstan 2050: Reduction in emissions, by scenario 50 reduction on BAU 40 Kazakhstan 2050: Reduction in emissions, by scenario 30 50 on BAU 20 40 Percentage 10 entage reduction 30 0 20 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: Safe and Clean Vehicles for Healthier 70 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50and More Productive Societies 10 Kazakhstan Country context and motorization characteristics Region Eastern Europe Income category Upper middle GDP per capita (current US$) 12,919 Population (million) 20.3 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 4,338,639 [226] Ambient PM2.5 (µg/m3) 17  PM2.5 5.1 Ambient air pollutant contribution from road transport, 2017 (%) NO2 13 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles ü Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control x LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 ü Adoption of Vehicle Emission Standards Euro 5 x Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 2,341 Fatality rate per 100,000, 2021 (number) 12.2 PM2.5 647 Premature deaths per air pollutant from road transport, 2021 (number) NO2 144 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 0.15 Sources: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data). Safe and Clean Vehicles for Healthier 71 and More Productive Societies reducti 10 Percenta 20 Percentage 0 10 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 0 PM 2.5 Nox Scenario 1: 2: FSIs by Technology Prevented Scenario Scenario 3: and Vehicle Type4: Scenario Scenario 5: New vehicle only New + Used (cumulative New + Used 2025-50 as a Scrappage from +difference EV BAU) 30 X 30 EV 50 X 50 PM No Scenario 1: New vehicle only Scenario 2: New 2.5 + Used x Scenario 3: New + Used + Scrappage 2,000 10,000 20,000 15,113 8,000 1,500 6,064 15,000 FSI prevented FSI prevented FSI prevented 2,000 927 6,000 10,000 20,000 1,000 10,000 6,711 6,27515,113 4,000 8,000 1,500 1,986 6,064 15,000 FSI prevented FSI prevented FSI prevented 500 223 310 2,000 1,227 5,000 927 10 6,000 192 185 528 96 185 4 5 25 1,000 10,000 6,711 0 0 0 6,275 4,000 a b c d e f a 1,986 b c d e f a b c d e f 500 223 310 2,000 1,227 5,000 10 Control LDV Electronic Stability 5 192 HDV 25 Front 185 and Rear Underrun Protective 528 Devices 96 185 4 0 LDV Advanced Emergency Braking - Vehicles 0 0 HDV Vehicle Stability Function a b c d e f a b c d e f a b c d e f LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System Kazakhstan 2050: Reduction in emissions, by scenario 50 BAU on BAU Kazakhstan Kazakhstan Reduction in 2050:Reduction 2050: inemissions, emissions,by by scenario scenario 40 50 reduction 30 40 reduction on 20 Percentage 30 10 20 Percentage 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: 10 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 0 PM 2.5 Nox Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Note: For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). Safe and Clean Vehicles for Healthier 72 and More Productive Societies Lao PDR Country context and motorization characteristics Region East Asia and Pacific Income category Lower middle GDP per capita (current US$) 2,067 Population (million) 7.7 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 1,850,020 [274] *2016 data Ambient PM2.5 (µg/m3) 29  PM2.5 5.7 Ambient air pollutant contribution from road transport, 2017 (%) NO2 48 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles ü Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control x LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 ü Adoption of Vehicle Emission Standards Euro 5 x Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 1,217 Fatality rate per 100,000, 2021 (number) 16.4 PM2.5 135 Premature deaths per air pollutant from road transport, 2021 (number) NO2 No data IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 0.07 Source: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data). Safe and Clean Vehicles for Healthier 73 and More Productive Societies Prevented FSIs by Technology and Vehicle Type (cumulative 2025-50 as compared to BAU) Scenario 1: New vehicle only Scenario 2: New + Used Scenario 3: New + Used + Scrappage 1,849 1,849 100 2,000 2,000 79 1,532 80 1,338 1,500 1,301 1,500 FSI prevented FSI prevented FSI prevented 1,161 60 49 1,012 921 1,000 1,000 40 619 25 527 1,849 1,849 100 21 2,000 2,000 10 79 500 500 1,532 165 20 5 146 80 1,338 1,500 1,301 1,500 FSI prevented FSI prevented FSI prevented 0 0 1,161 0 1,012 60 a b c 49d e f b a 921 c d e f a b c d e f 1,000 1,000 40 619 25 LDV Electronic Stability Control 527 HDV Front and Rear Underrun Protective Devices 21 500 500 20 5 Advanced Emergency Braking - Vehicles LDV 10 146 HDV Vehicle Stability Function 165 0 0 LDV Advanced Emergency Braking - Pedestrians & Laos 2050: Reduction in emissions, Cyclists by scenario MTW Anti-lock Brake0System a b c d e f a b c d e f a b c d e f 40 Percentage reduction on BAU 30 Lao PDR 2050: Laos Reduction 2050: inemissions, Reduction in emissions,byby scenario scenario 40 20 Percentage reduction on BAU 30 10 20 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 35,000 35,000 35,000 31,154 27,982 PM 2.5 Nox 28,942 28,942 30,000 30,000 30,000 25,000 FSI prevented FSI prevented FSI prevented 25,000 25,000 20,000 20,000 15,666 20,000 15,000 15,000 12,021 15,000 35,000 10,000 35,000 10,000 35,000 10,000 7,186 31,154 28,942 4,110 5,602 28,942 30,000 Note: For a detailed 27,982 explanation 30,000 of each scenario, see Chapter 5,000 3, section 2: Evaluating 30,000 the health impact 5,000 policies at the country2,807 of vehicle level (page 35). 5,000 2,399 1,170 731 1,524 - 1,046 - - 25,000 FSI prevented FSI prevented FSI prevented 25,000 0 25,000 0 0 20,000 a b c d e f 20,000 a b c d e f 20,000 a b c d e f 15,666 15,000 12,021 15,000 15,000 10,000 10,000 10,000 7,186 4,110 5,602 5,000 2,399 1,170 1,046 5,000 1,524 5,000 2,807 - - 731 - 0 0 0 a b c d e f a b c d e f a b c d e f Mexico 2050: Reduction in emissions, by scenario 50 Percentage reduction on BAU 40 Mexico 2050: Reduction in emissions, by scenario 30 50 20 Percentage reduction on BAU 40 10 30 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: 20 New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 10 PM 2.5 Nox Safe and Clean Vehicles for Healthier 74 and More Productive Societies 0 Mexico Country context and motorization characteristics Region Latin America Income category Upper middle GDP per capita (current US$) 13,790 Population (million) 129.7 Motorization level / ownership (inc. 2/3 wheelers) (Total registered [veh/1000 pop]) 53,115,396 [419] Ambient PM2.5 (µg/m3) 31  PM2.5 12.3 Ambient air pollutant contribution from road transport, 2017 (%) NO2 60 Sources: GDP per capita, Population – World Bank Open Data (latest data available: 2023) Motorization level/ownership – WHO Global Status Report on Road Safety 2023 (2021 data) GBD 2021 Risk Factors Collaborators. 2024. McDuffie E, Martin R, Yin H, Brauer M. 2021a. McDuffie EE, Martin RV, Spadaro JV, Burnett R, Smith SJ, O'Rourke P, Hammer MS, van Donkelaar A, Bindle L, Shah V, Jaeglé L, Luo G, Yu F, Adeniran JA, Lin J, Brauer M. 2021b. State of analysed vehicle standards as of 2024 Used Vehicles ü Adoption of Rules for Imported Vehicles New Vehicles ü Electronic Stability Control ü LDV Advanced Emergency Breaking - Vehicles x Advanced Emergency Breaking - pedestrians & cyclists x Front and Rear Underrun Protective Devices x HDV Vehicle Stability Function x MTW Anti-lock Brake System x Euro 4 ü Adoption of Vehicle Emission Standards Euro 5 x Euro 6 x Source: Original table for this publication. Health and cognitive impacts of motorization Road fatality, 2021 (number) 15,267 Fatality rate per 100,000, 2021 (number) 12.0 PM2.5 5,128 Premature deaths per air pollutant from road transport, 2021 (number) NO2 8,877 IQ points lost from air pollutants from road transport, 2021 (million) PM2.5 1.23 Sources: Original table for this publication, based on WHO Global Status Report on Road Safety 2023 (2021 data). Safe and Clean Vehicles for Healthier 75 and More Productive Societies 30 Percentage reduction Percentage r 20 10 10 0 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 0 Prevented FSIs by Technology and Vehicle Type Scenario 1: Scenario (cumulative PM 2.5 2: 2025-50Scenario 3: as compared No Scenario to BAU)x 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 Scenario 1: New vehicle only New Scenario 2:PM 2.5 + UsedNox Scenario 3: New + Used + Scrappage 35,000 35,000 35,000 31,154 27,982 28,942 28,942 30,000 30,000 30,000 25,000 FSI prevented FSI prevented FSI prevented 25,000 25,000 35,000 35,000 35,000 31,154 20,000 20,000 15,666 28,942 20,000 28,942 30,000 27,982 30,000 30,000 15,000 12,021 15,000 15,000 25,000 FSI prevented FSI prevented FSI prevented 25,000 25,000 10,000 10,000 10,000 7,186 20,000 20,000 4,110 20,000 5,602 2,399 5,000 15,666 5,000 2,807 5,000 1,170 1,046 1,524 15,000 - 12,021 15,000 - 731 15,000 - 0 0 0 10,000 a b c d e f 10,000 a b 4,110 c d e f 10,000 7,186 a c b5,602 d e f 5,000 2,399 1,170 5,000 1,524 5,000 2,807 - 1,046 Control LDV Electronic Stability - 731 HDV Front and Rear Underrun - Protective Devices 0 0 0 a b Advanced LDV c dEmergency e f Braking - Vehicles a b c d e HDV a f Vehicle Stability Function b c d e f LDV Advanced Emergency Braking - Pedestrians & Cyclists MTW Anti-lock Brake System is not2050: Mexico Note: Dashes represent that this technology/vehicle type Reduction in emissions, by scenario applicable. 50 Mexico 2050: Reduction in emissions, by scenario Mexico 2050: Reduction in emissions, by scenario Percentage reduction on BAU 40 50 Percentage reduction on BAU 30 40 20 30 10 20 0 10 Scenario 1: Scenario 2: Scenario 3: Scenario 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 0 Scenario 1: Scenario 2: PM 2.5 Scenario 3: No Scenario x 4: Scenario 5: New vehicle only New + Used New + Used + Scrappage EV 30 X 30 EV 50 X 50 PM 2.5 Nox Note: For a detailed explanation of each scenario, see Chapter 3, section 2: Evaluating the health impact of vehicle policies at the country level (page 35). Safe and Clean Vehicles for Healthier 76 and More Productive Societies Appendix B: Global Calibration Parameters B.1 Road Safety Cross-Country Parameters Collision Geometry - Impact directions for FSI casualties (Share of Casualties) Description Share Front-to-rear in two-vehicle LDV & LDV impacts 15.6% Front-to-rear in two-vehicle LDV & HDV impacts 17.6% Front-to-rear and front-to-front in two-vehicle LDV & HDV impacts 49.4% Note: Used to define target population for technologies LDV AEB for vehicles and HDV FRUPD. Source: Great Britain’s Stats19 database (average of years 2011–2015) Safety effectiveness values assumed for each technology in relation to the target population quoted Technology Safety effectiveness Source Electronic Stability 25% of fatal single vehicle Hoye (2011) The effects of electronic stability control (ESC) Control collisions on crashes–an update Advanced Emergency 56% of injurious car-front to Cicchino (2017) Effectiveness of forward collision warning Braking vehicle motor vehicle-rear collisions and autonomous emergency braking systems in reducing front-to-rear crash rates Advanced Emergency 26% of injurious car-to-pedestrian Cicchino (2022) Effects of automatic emergency braking Braking ped./cycl. and car-to-cyclist collisions systems on pedestrian crash risk Cicchino (2019) Effects of a bicyclist detection system on police-reported bicycle crashes Front- and Rear-Underrun 28% of fatal car occupants in Average effectiveness reported by (weighted by number Protective Devices car-front to truck-front or rear of FSI casualties occurring in corresponding impact collisions configurations): Govardhan et al. (2020) Effectiveness of rear underrun protection devices in trucks for reducing passenger car fatalities and serious injuries in India Robinson & Riley (1991) Improving HGV Safety – Front Underrun Guards and Antilock Braking Systems Vehicle Stability Function 19% of injurious HGV-single- Teoh et al. (2017) Crash risk factors for interstate large vehicle collisions trucks in North Carolina Anti-lock Brake System 30% of all FSI motorcycle collisions Elvik et al. (2024) The Handbook of Road Safety Measures – Online revision Safe and Clean Vehicles for Healthier 77 and More Productive Societies B.2 Air Quality Cross-Country Parameters Dataset Source Purpose Emission factors for different European Environment Agency/EMEP Air Emission factors in g/veh-km are applied vehicle types by Euro Pollutant Emission Inventory Guidebook 2023 – to the activity (veh-kms) for each mode Standard. Both exhaust and Update 2024 by the composition of vehicles of each non-exhaust emissions vintage by powertrain and Euro standard. Propensity for older vehicles Kaneko, M. & Kagawa, S. 2021. Driving Informs the shape of curves such that to have a lower annual propensity and vehicle lifetime mileage: A older vehicles have a lower mileage than mileage quantile regression approach. Journal of new vehicles, mode specific Environmental Management, Vol 278. https:// doi.org/10.1016/j.jenvman.2020.111499 Weighting of emission factors European Environment Agency/EMEP Air Informs the shape of curves such that by vehicle age Pollutant Emission Inventory Guidebook 2023 – older vehicles have higher emissions due Update 2024 to engine degradation, pollutant and powertrain specific Safe and Clean Vehicles for Healthier 78 and More Productive Societies Appendix C: Country-Specific Datasets Emissions Dataset Argentina Brazil Egypt Ghana India Kazakhstan Lao PDR Mexico Historic stock Prepared by the Obtained from Egypt Ministry of Motor Vehicle India Ministry of Road UNECE 2024 - Lao PDR PDR, Mexico INEGI 2025 of vehicles, by Directorate of Road Ministry of Transport 2024, Registration: Transport & Highways 2020 Road vehicle fleet Dept of Transport Motor Vehicles mode (units) Statistics – National Transport and Egypt CAPMAS 1995-2023; Source - Road Transport Year Book at 31 December & Accumulating Registered In-Use Directorate of Road SENATRAN Statistical – Driver and Vehicle 2019-20 Kazakhstan Statistics: Land Observatory (2025) Databook 2018 Licensing Authority https://morth.nic.in/ National Bureau Motor Vehicle based on data sites/default/files/RTYB_ of Statistics 2024 Registration from reported by the Publication_2019_20%20 – On the number 2000 to 2023 jurisdictions & (1).pdf & of vehicles Miller & Braun 2020 – Guttikunda 2024 - India Cost Benefit analysis Vehicle Stock Numbers & of Euro VI Heavy-Duty Survival Functions & Emission Standards in Argentina & Data for India 2025 - Vehicle Ownership https:// OICA 2020 World www.dataforindia.com/ vehicles in use vehicle-ownership/ Average annual Grassei et al 2021 - Argentina used as World Bank 2020 Keyna & Egypt used Goel et al 2014 - Tajikistan and O’Neill 2024 Generic mileage mileage per Fleet Characterization proxy - Egypt E-mobility as proxy Benchmarking vehicle and European proxies - Air pollutant used from ICCT vehicle (km/ & Assessment Strategy White passenger travel in Delhi used emissions and Roadmap model year) emission inventory paper for emissions sources in Lao of Latin American Baidya, S. and Borken- PDR & World Intermediate City Kleefeld, J. (2009) Bank 2018 - “Atmospheric emissions Transport Costs from road transportation and Prices in Lao in India”, Energy Policy, PDR 37(10), 3812- 3822 Powertrain Grassei et al 2021 - Calibrated to TPA 2024, World Source – Driver and In-country response to Kazakhstan O’Neill 2024 Calibrated to split, by Fleet Characterization match the IEA Bank 2020 Vehicle Licensing request for info National Bureau - Air pollutant match the IEA vintage, by & Assessment estimate of fuel Egypt E-Mobility Authority of Statistics 2024 emissions and estimate of fuel mode (% of emission inventory use. IEA 2024 Strategy White – On the number sources in Lao use. IEA 2024 vehicles) of Latin American Brazil country Paper, JICA of vehicles PDR Mexico country Intermediate City profile 2012 MiNTS, profile MISR National Transport Study Safe and Clean Vehicles for Healthier 79 and More Productive Societies Dataset Argentina Brazil Egypt Ghana India Kazakhstan Lao PDR Mexico Age of vehicles UNEP 2020 Used UNEP 2020 Used Egypt Ministry of UNEP 2020 Used UNEP 2020 Used Vehicles UNEP 2020 Used GIZ 2014 - US Commercial when joining Vehicles and the Vehicles and the Transport 2024, Vehicles and the and the Environment Vehicles and the Transport and Service 2018 the fleet (year) Environment Environment UNEP 2020 Used Environment, Environment Logistics in Lao Regulations of the Vehicles and the calibrated to match; PDR: Impact Importation of Environment Motor Vehicle of the ASEAN used Vehicles and Registration: Economic Trucks into Mexico, 1995-2023; Source Community – Driver and Vehicle Licensing Authority Age profile of Miller & Braun 2020 – ICCT 2012 – Global Egypt Ministry of Motor Vehicle Guttikunda 2024 - India Kazakhstan India used as a IDF 2023, LATAM the existing Cost Benefit analysis Transportation Transport 2024, Registration: Vehicle Stock Numbers & National Bureau proxy VIO TRENDS fleet, and/or of Euro VI Heavy-Duty Roadmap The International 1995-2023; Source Survival Functions of Statistics 2024 Edition 1. Olguin, the survival Emission Standards in Council on Clean – Driver and Vehicle – On the number F., Iskakov G. & curve of Argentina Transportation Licensing Authority of vehicles Kendall, A. 2023 vehicles. 2012. Roadmap US-Mexico second- Model Version hand electric 1-0, Held et al vehicle trade: 2021 Lifespans of Battery circularity passengers cars in and end-of-life Europe policy implications Euro standard UNEP 2020 Used UNEP 2020 Used UNEP 2015 Air UNEP 2020 Used UNEP 2020 Used Vehicles UNEP 2020 Used ICCT Lao PDR UNEP 2020 Used of vehicles Vehicles and the Vehicles and the Quality Policies Vehicles and the and the Environment Vehicles and the 2019 - Current Vehicles and the Environment & Miller Environment in Egypt, Daily Environment Environment Status of Emission Environment & Braun 2020 – Cost Egypt News 2018 The Regional and Fuel Efficiency Benefit analysis of EOS issues new Environmental in Lao PDF Euro VI Heavy-Duty Egyptian fuel Centre for the Emission Standards in specifications Caucasus 2008 - Argentina Fuel Quality and Vehicle Emission Standards Overview Sulphur UNEP 2024 CCAC 2016 – CEDARE 2015 UNEP 2024 Global UNEP 2024 Global Diesel UNEP 2024 UNEP 2024 TransportPolicy.net content of fuel Global Diesel Fuel Cleaning up the Fuel Quality Diesel Fuel Sulphur Fuel Sulphur Levels & Global Diesel Fuel Global Diesel Fuel Mexico Fuels Sulphur Levels & Global on-road Roadmap for Levels & – West Bharat stage emission Sulphur Levels Sulphur Levels transportpolicy.net – diesel fleet & Arab States African Ministers standards & The Regional & O’Neill 2024 Argentina Heavy-duty Transportpolicy. adopt cleaner fuels Environmental - Air pollutant emissions net Brazil: Fuels: & vehicles standards Centre for the emissions and Diesel and Caucasus 2008 - sources in Lao Gasoline Fuel Quality and PDR Vehicle Emission Standards Overview: Safe and Clean Vehicles for Healthier 80 and More Productive Societies Vehicle regulatory standards in selected countries Criteria Egypt Mexico India Ghana Brazil Argentina Kazakhstan Lao PDR Age limit for Ban on 9 year + Ban on 10-year age Ban on Ban on 5-year age Unknown imported used all used all used limit on all all used all used limit on all vehicles (year) vehicles vehicles used vehicles vehicles vehicles used vehicles imported imported Existing emission Euro 4 Euro 4 Euro 6 Euro 2 (but Euro 5 Euro 5 Euro 4 Euro 4 standard for most imports vehicles Euro 4)/Euro IV Assumed long 4% 3% 5% 5% 2.5% 2.5% 2.5% 3% term GDP growth rate (World Bank) Notes: GDP (2015 Constant US$) [World Bank Open Data] was used for Gompertz curve for each country. Forecast GDP growth [IMF to 2029: World Economic Outlook. 2030 onwards a fixed growth rate assumed.] informs the forecast increase in transport demand, hence fleet size. Population (Historic and forecast) [UN population division: World Population Prospectus, middle forecast] informs GDP per capita which drives an increase in car ownership through the calibrated Gompertz curve. Source: Original table for this publication. Safe and Clean Vehicles for Healthier 81 and More Productive Societies Appendix D: Technical Estimations for Air Quality Impact APPENDIX APPENDIX K K– Technical Estimations – Technical for Air Estimations for Quality Impact Air Quality Impact Commented [KN12]: Commented a referencelist areference @LeslieNii [KN12]: @Leslie listfor thisappendix forthis appendix NiiOdart Odar K.1APPENDIX D.1 IQ losses from ambient PM2.5 exposure K.1 IQ IQ losses K – Technical losses from ambient Estimations ambient PM for Air Quality Impact 2.5 exposure Commented [KN12]: @Leslie Nii Odartey M from PM2.5 exposure Commented Commented a reference [KN13R12]: [KN13R12]: list for this To Duina: We appendix To Duina: W referencelist reference listat thevery atthe lastpage verylast page K.1 The IQ losses Thefollowing followingPM from ambient PM2.5 exposure Commented [KN13R12]: To Duina: We ma 2.5 exposure PM2.5 exposure– IQresponse –IQ response function appliedfor isapplied functionis forchild basedon childiibased onexpanded expanded The following PM2.5 exposure – IQ response function is applied for child i based on expanded number of studies and sub- reference list at the very last page number number of of studies studies and and sub-analyses sub-analyses of of the the meta-analysis meta-analysis by by Alter Alter et et al. al. (2024): (2024): The following PM exposure – IQ response function is applied for child i based on expanded analyses of the meta-analysis2.5 by Alter et al. (2024): number of studies and ΔΔsub-analyses IQ =0 IQi = 0 of the meta-analysis for for X ≤X Xi ≤ by X0 µg/m µg/m 33 Alter et al. (2024): i i 0 Δ IQ Δ i=0 ΔIQ IQi i = -β11 (X (Xi i -- X = -β X00)) for Xi ≤ X 0 µg/m for for X0 < X 3 30 i> 30µg/m 3 µg/m33 i 1 0 2 3 i i where whereΔ ΔIQ IQi iisisIQ Δ IQi =lost IQpoints -β1 (11 by bychild - X0 ) - β ii;; X2 (30 i iis isPM - 11) - β3 (Xi – 30) for Xi > 30 µg/m 3 points lost child X PM 2.5 exposure 2.5 exposure experienced experienced bychild by childii;;X X00isisaalower lower PM PM2.5 where Δexposure 2.5 exposure IQi is IQ points threshold thresholdlost by below below child which i; Xi isIQ which IQ PM loss loss 2.5 is is exposureassumed assumed to to be be experienced zero; zero; by and and child ββ is iis ; X IQ IQ 0 ispoints points a lower lost lost where Δ IQi is IQ perpoints per µg/m µg/m lost 33 of PM of by2.5 PM child i; Xi is with exposure, exposure, PM with β 2.5β exposure =0.69 =0.69 (95% (95% experienced CI: CI: 0.22-1.17) 0.22-1.17) byfor child for X X ≤ ≤ i11X0 ;11 is a 3 µg/m µg/m lower 3;; β β =0.31PM(95% =0.31 (95% 2.5 exposure threshold PM2.5 exposure threshold below which IQ loss is assumed to be zero; 2.5 11 i i and β is IQ points lost22 below which IQCI: loss is 0.21-0.40)assumed for 11to 11 < be 30 i>30 µg/m per µg/m 3 of PM exposure, withon β1=0.69 (95% CI: for..X X 0 is set to 5 ; µg/m based on based 3 3 3 perCI: 0.21-0.40) µg/m 3 of PM2.5 for exposure, ≤with µg/m β1=0.69 3 and (95% =0CI: for0.22-1.17) µg/m 3 Xi≤ 0 is 11 set µg/mto 5 3 2.5 βµg/m 3 2=0.31 (95% 0.22-1.17) for Xthe ithe CI: ≤ 11 lowerµg/m lower 0.21-0.40) tail tail 3 ; of for β11 of =0.31 2PM PM Xiin <2.5 2.5 (95% in ≤ the 30the CI: 0.21-0.40) studies studies µg/m 3 ; and in the in the β3=0 MA.for forThe MA. 11 The X < i>30 X exposure-response exposure-response µg/m i ≤ 303 µg/m3; and . X0 is set tofunction β3=0 3for function 5 µg/m is is assumed based Xi>30onµg/m . X0 is set to 5 assumed 3 µg/m based on 3 flat the the flat lowerlower for for PM PM tail of 2.5tail 2.5 exposures exposures PMof2.5PMin 2.5 the instudies above abovethe30studies 30 µg/m µg/m in the 3 in 3 in inthe MA.order order MA. The to to The be exposure-response be conservative conservative exposure-response due due to limited to function function limited numberis assumed number is assumed of of flat for PM2.5 exposures above studies flat 30 PM studies for µg/m of IQ of IQ 3 losses in 2.5 exposures orderat losses at tothese above exposures. be conservative these 30exposures. µg/m in order 3 due to tobe limited number conservative dueof studies to limited of number IQ losses ofat these exposures. Total of studies IQ losses annual in exposures. at these IQ losses children are estimated by first calculating the children’s PM2.5 Total annual IQTotal annual IQ losses in children are estimated by first calculating the children’s PM2.5 losses in children are estimated by first calculating the children’s PM2.5 exposure distribution. The exposure exposure distribution. distribution. The The proportion proportion of ofchildren children((PPi) i) with with PM2.5exposure PM2.5 exposurein inthe rangex therange i-1 to xi-1 to proportion ofTotal annual children (Pi) IQ losses with in PM2.5 children exposure are in estimated the range xi-1by tofirst calculating xi (xi-1 < xi) is: the children’s PM2.5 xi i (x x exposure 0.999. such )>0.999. losses, with ninterval that – x,i-1µ, Fi (x of x = σ)>0.999. 0.5 µg/m3 is applied to estimate IQ losses, with n such that FX(xn, µ, X n σ)>0.999. 2 of IQ losses D.2 Valuation 2 2 An individual’s IQ has an effect on lifetime income. This has long been established by for instance, Schwartz (1994) and Salkever (1995). The first study estimates that a loss of one IQ point is associated with a 1.76 percent decline in lifetime income. A little over one-quarter is the direct effect on earnings while nearly three-quarters is the indirect effect through reduced schooling, and through reduced lifetime work or labor force participation. The second study estimates that a loss of one IQ point reduces male and female lifetime income by 1.9 and 3.3 percent, respectively, including the effect on work participation. Safe and Clean Vehicles for Healthier 82 and More Productive Societies Subsequently Johnson and Neal (1998) estimated an overall direct and indirect income effect of about 2.8 percent per IQ point with a much larger effect for females than for males as also found by Salkever (1995). The estimate does not include work or labor force participation effect. Zax and Rees (2002) find a much smaller direct and indirect effect of 0.39-1.39 percent per IQ point for white males. Salkever (2014a) argues, however, that this size effect is not representative as the study does not include females and minority groups for which size effects are found to be much larger by Salkever (1995) and Johnson and Neal (1998). The size of the effect of IQ on lifetime income continues to be debated. Grosse (2014) argues that the effect is smaller than found by Salkever (1995) while Salkever (2014a,b) defends the estimates on the basis that other studies often omit females and minorities and do not include the effect on work or labor force participation. Attina and Trasande et al (2013) and Larsen and Sanchez-Triana (2023) applied a lifetime income effect of 2 percent per IQ point for their estimate of the cost of lead exposure in children in LMICs and globally. Grosse and Zhou (2021) applied an effect of 1.4 percent of lifetime income per IQ point as a conservative estimate based on a review of the literature that includes recent studies by Lin et al. (2018) and Lundborg et al. (2014). USEPA (2020) was able to undertake a reanalysis of Salkever (1995) using a more recent version of the data set used by Salkever. The reanalysis found a lifetime income effect per IQ point of 1.86 percent for males (slightly below Salkever 1995) and 3.4 percent for females (somewhat above Salkever 1995). In this study an effect of 2.0 percent decline in lifetime income per lost IQ point is applied to all individuals participating in the labor force. This is slightly above the value used by Grosse and Zhou (2021) but substantially below the value in Johnson and Neal (1998), Salkever (1995), and USEPA (2020). Lifetime income for a person that is or will be in the labor force is calculated as follows: Lifetime income for a person that is or will be in the labor force is calculated as follows: " =! Lifetime income for a person that is or will be in the labor force is calculated as follows: '(% $ & ! != & % $" + % ! " #$" + $ ! " " =! (D2.1) (K2.1) Lifetime income for a person where PV0 (I) is the present '( $ & ! != % that is or & #" + % ! " #$" + $ ! " " =$ (K2.1) % will be in the labor force is calculated as follows: value in year 2021 of lifetime income, I0 is annual income in year " =! " =# where PV0 (I) the is2021, present value in year 2021 of lifetime income, I0 is annual income in year 2021, is annual growth g The where PV 0 isis g(I) the annual present '( growth % $& ! = ! value in & in year real " 2021 of income, % $" + % ! #$" + $ ! and" lifetime r is the income, discount (K2.1)I0 is annual rate income of future in year income. in real income, 2021, and equation allows for income to start from year k, and end in year n. The present value of year k, and g r is is the annual discount growth rate in real " = #of future income, income. and r isThe the equation discount allows rate of for income future to income. start The from where n. The end in yearequation PV0present lifetime(I) is the value allows income present for is income ofvalue toin lifetime calculated year startincome for from a 2021 child of is lifetime calculated year at ageincome, k, and the end offor 2.5 I0 is annual child ayears, in year at n. theat income The i.e., age present the in of 2.5 year years, value mid-point i.e., at the mid- of 0-5 of 2021, g is annual growth in real income, and r is the discount rate of future income. The point of 0-5lifetime years years income during during is calculated which which IQIQ losses losses for are aassumed. are child assumed.at the age of 2.5 Therefore, Therefore, kkyears, isisthe the i.e., age age at of the mid-point ofentering entering the of 0-5 the labor force less than 2.5 force equation years allows during for which income IQ losses to start n are from assumed. year k, and end Therefore, k isinthe year age n.of The present entering valueforce of the labor years, and n is less the than age 2.5 of years, retirement and less is the than 2.5of age retirement years. less than 2.5 years. lifetime income is calculated for a child at less than 2.5 years, and n is the age of retirement less than 2.5 years. the age of 2.5 years, i.e., at the mid-point of 0-5 years A person’s during which annual income in the year 2021 is calculated as follows: A person’s annual income inIQ the year are losses 2021 assumed. is calculated Therefore, k is the age as follows: of entering the labor force A person’s annual income less than 2.5 years, and n is the Iage in the year 2021 is calculated of retirement less than 2.5 years. as follows: 0 = GDP0 s / L0 (K2.2) A person’s where annual GDP isincome inIthe 0 = GDP the country’syear0 2021 total s / L0 is calculated as follows: (K2.2) (D2.2) GDP, L is the total labor force, and s is labor compensation where GDP share of is the country’s GDP. S is from total PENN I0 = GDP GDP, World 0 s / L0 L is the total Table, labor Version force, 10.(K2.2)and s is labor compensation where GDPshare is the of Cost GDP. S is country’s from total PENN inGDP, L World is Table, the total Version labor 10. and s is labor compensation share of GDP. S is from force, where GDPof isIQ thelosses country’s each country total GDP, Lin is2021 is then: the total labor force, and s is labor compensation PENN World Table, Cost share ofof Version IQ GDP. losses 10. in each S is from PENN country World in 2021 is then: 10. Table, C= α PV (I)Version p ΔQ 0 T (K2.3) Cost of IQ losses in losses each C= α PV 0(I) pis ΔQ (K2.3) Cost of IQ α iscountry in each in 2021 country is in then: 2021 then: T where the eaect of IQ on lifetime income (here 2.0 percent per IQ point), PV0(I) is the where α is the present valueeaect of of IQ= on lifetime C α PVlifetime income 0(I) p ΔQT income in 2021, (here p is percent 2.0the probability (K2.3) (D2.3) per IQ point), of futurePV0(I) is the labor force present value of participation lifetime (LFP), and ΔIQ income in 2021,total T is a country’s p is IQthe probability points lost in 2021 of due future laborexposure. to PM2.5 force where α is the eaect participation (LFP), of IQ and ΔIQon lifetime income (here 2.0 percent per IQ point), PV0(I) is the T is a country’s total IQ points lost in 2021 due to PM2.5 exposure. where α is the effect The of IQ parameter lifetime on values income for equations(hereK2.1-3 2.0 percent per IQ point), PV0(I) is the present value of lifetime present value of lifetime income in 2021, p isare the presented probability table in of K2.1. future Future labor income force income in 2021, p is the parameter Thegrowth participation probability values in high-income (LFP), and ΔIQ for of future labor equations countries K2.1-3 is set force atare participation presented recent historic (LFP), inGDP table per T is a country’s total IQ points lost in 2021 due to PM2.5 exposure. and ΔIQT K2.1. capita is a country’s Future growth income rates, attotal IQ points due lost in 2021growth toinPM somewhat high-income 2.5 exposure. lower than countries historic is set rates at recent in historic middle-income GDP per countriescapitaasgrowth these rates, at economies The parameter somewhat gradually values lower mature, than for and equations historic at somewhat K2.1-3 rates in are presented middle-income greater than historic in rates K2.1. table countries Future as these in low-income income economies countries as growth in high-income gradually these mature, and countries maycountries atbe somewhat is set expected at achieve greater to recentthan historic historic greater GDP rates “catch perin capita up” growth growth low-income countries rates. rates, atas The discount somewhat these lower countries than historic rates in middle-income countries as these economies rate of futuremay income be expected to achieve is set at twice the per greater capita “catch income growthrate up”growth rates. as The discount proposed by the gradually rate mature, of future World Bank and income for at projectis somewhat set at twice economic greater the per analysisthan historic capita (World rates income Bank in low-income growth 2016). rate The countries as proposed probability byas theLFP of future these Worldcountries is Bank set for at the may be project LFP rateexpected economic toanalysis in 2021 reported achieve by greater (World the World “catch Bank up” 2016). Bank growth (2024). rates. The The probability ofdiscount future LFP rate of future is set at theincome LFP rateis inset2021 twice theby at reported per thecapita World income growth rate as proposed by the Bank (2024). Bank K.1 WorldTable Parameters for project economic for estimation analysis (World of theBankcost 2016). of IQ losses The probability of future LFP Safe and Clean Vehicles for Healthier 83 Table K.1 Parameters for estimation of the Parameter cost of IQ losses Middle-income High-income and More Productive Societies Low-income is set at the LFP rate in 2021 reported by the World Bank (2024). Parameter countries Low-income countries Middle-income countries High-income The parameter values for equations D2.1-3 are presented in table D2.1. Future income growth in high-income countries is set at recent historic GDP per capita growth rates, at somewhat lower than historic rates in middle- income countries as these economies gradually mature, and at somewhat greater than historic rates in low-income countries as these countries may be expected to achieve greater “catch up” growth rates. The discount rate of future income is set at twice the per capita income growth rate as proposed by the World Bank for project economic analysis (World Bank 2016). The probability of future LFP is set at the LFP rate in 2021 reported by the World Bank (2024). Table D2.1 Parameters for estimation of the cost of IQ losses Low-income Middle-income High-income Parameter countries countries countries Effect of lifetime income per IQ point α 2.0% 2.0% 2.0% Future income growth per year g 2.5% 2.5% 1.5% Rate of discounting of future income r 5% 5% 3% Future labor force participation rate p Current rate Current rate Current rate Labor force participation (age) 15-60 years 18-65 years 21-65 years Source: Original table for this publication. D.3 Cognitive Impacts of Ambient PM2.5 on Children's Cognitive Development. Annual IQ point losses in children (million), 2021 1,200 1,126 400 350 341 331 1,000 300 800 234 623 250 600 200 150 400 1,200 1,126 203 252 100 400 83 200 38 51 48 50 350 341 331 1,000 0 0 300 800 World LI LMI UMI HI EAP 234 ECA LAC MNA SA SSA 623 250 600 200 150 UMI=upper middle-income; HI=high-income. SA=South Notes: Only LMICs are included in regions. LI=Low-income; LMI=Lower middle-income; 400 Asia; EAP=East 10 Asia and Pacific; SSA=Sub-Saharan 9.0 9.2 Africa;252 12 Africa; ECA=Europe and Central Asia; LAC=Latin America MNA=Middle East and North IQ loss (oints) oer child 8.4 203 100 83 IQ loss (points) per child and Caribbean. 8.0 51 10.0 200 10 50 38 9.4 9.0 8 48 8.8 Source: Original figures for this publication. 0 0 8 6 World 6.8 LI LMI UMI HI EAP ECA LAC MNA SA SSA Average IQ point 6 5.4 3.9 loss per child 4 4 10 9.0 9.2 12 IQ loss (oints) oer child 2 8.4 2 IQ loss (points) per child 8.0 10.0 10 9.4 9.0 8 0 0 8.8 World LI LMI UMI HI 8 EAP ECA LAC MNA SA SSA 6 6.8 6 5.4 3.9 4 4 2 2 0 0 World LI LMI UMI HI EAP ECA LAC MNA SA SSA Cardiovascular disease Respiratory disease Lung cancer 1.25 Original figures for this publication, based Source: 1.14 1.18 on national datasets reported by the Health Effects Institute (HEI 2024). 1.12 1.16 1.20 1.14 1.10 1.12 1.15 1.08 1.10 1.10 1.06 1.08 1.04 1.06 Safe and Clean Vehicles for Healthier 84 1.05 Cardiovascular disease 1.02 Respiratory disease 1.04 Lung cancer and More Productive Societies 1.02 1.25 1.00 1.14 1.00 1.18 1.00 K.4 Welfare cost of premature mortality The predominant measure of the welfare cost of a premature death used by economists is D.4 Welfare cost of premature mortality the value of statistical life (VSL). VSL is based on valuation of mortality risk. Everyone in societymeasure The predominant is constantly facing of the costrisk a certain welfare ofpremature of a death of dying. Examples such used risks by are occupational economists is the value of statistical fatality risk, risk of traaic accident fatality, and environmental mortality risks. It has been life (VSL). VSL is based on valuation of mortality risk. Everyone in society is constantly facing a certain risk of dying. observed that individuals adjust their behavior and decisions in relation to such risks. For Examples of such risks are occupational fatality risk, risk of traffic accident fatality, and environmental mortality instance, individuals demand a greater wage (a wage premium) for a job that involves a risks. It has been greaterobserved that individuals occupational adjust risk of fatal their accident behavior than and in other decisions jobs, in relation individuals to such risks. For instance, may purchase individuals demand a greater wage (a wage premium) for a job that involves a greater safety equipment to reduce the risk of death, and/or individuals and families may be occupational willing risk of fatal accident than to in other pay jobs, individuals a premium or greater rentpurchase may safety for properties equipment (land to reduce and buildings) the in a risk ofand cleaner death, lessand/or individuals may be location. and familiespolluted willing to pay a premium or greater rent for properties (land and buildings) in a cleaner and less polluted location. Through the observation of individuals’ choices and willingness to pay (WTP) for reducing mortality risk (or minimum amounts that individuals require to accept a higher mortality Through the observation of individuals’ choices and willingness to pay (WTP) for reducing mortality risk (or minimum risk), it is possible to estimate the value to society of reducing mortality risk, or, equivalently, amounts that individuals measure require the social to cost ofaccept a higher a particular mortality risk. risk), mortality it is possible For instance, it may to be estimate observed the value to society of that reducing mortality a certain risk, or, equivalently, health measurerisk hazard has a mortality theof social cost of a 2.5/10,000. particular This mortality means that, risk. For on average, oneinstance, it may be observed that a certain individual health dies fromhazard has afor this hazard mortality risk of every 4,000 2.5/10,000. individuals This means exposed. If each that, on average, on one individual individual dies from this hazard average for every is willing to pay4,000 individuals US$ 40 exposed. to eliminate If each this mortality individual risk, then everyon4,000 average is willing to pay $40 to individuals eliminate this aremortality collectively risk, then to willing pay 4,000 every individuals US$ 160,000. Thisare the VSL, or willing is collectively the value to pay that $160,000. individualsThis is the VSL, or collectively the value that individuals arecollectively willing to pay avoid one towilling are death. to pay Mathematically to avoid one death.it can be expressed Mathematically as be expressed as it can follows: follows: VSL = WTPAve * 1/ R (D4.1) (K4.1) where WTPAve is the average willingness-to-pay per individual for a mortality risk reduction of where WTPAve magnitude R. In the is the average illustration above, willingness-to-pay R=2.5/10,000 per (or a individual for R=0.00025) mortality and WTPAve= US$ risk reduction of 40. magnitude R. In the Thus, if 10 illustration above, R=2.5/10,000 (or R=0.00025) and WTPAve= $40. Thus, if 10 individuals die VSL individuals die from the health risk illustrated above, the cost to society is 10* from the health risk = 10* US$ 0.16 million = US$ 1.6 million. illustrated above, the cost to society is 10* VSL = 10* $0.16 million = $1.6 million. The main approaches to estimating VSL are through revealed preferences and stated The main approaches preferencestoof estimating areathrough VSL for people’s WTP revealed reduction preferences in mortality risk. Mostand stated of the preferences studies of people’s WTP for of revealed in mortality are a reduction preferences risk.hedonic Most ofwage the studies revealed ofwhich studies, preferences estimate are hedonic labor market wage wage studies, which estimate diaerentials labor market wage differentials associated associated with diaerences with differences in occupational mortalityin occupational risk. Most of themortality risk. Most of the stated stated preference studies rely preference studies oncontingent rely on contingent valuation valuation methods methods (CVM), (CVM), which which in various in various formsforms ask individuals about their ask individuals about their WTP for mortality WTP for mortality risk reduction. risk reduction. Studies of WTP for a reduction in risk of mortality have been carried out in numerous Studies of WTP for a reduction in risk of mortality have been carried out in numerous countries. A commonly used countries. A commonly used approach to estimate VSL in a specific country without such WTP approach to estimate studies a therefore VSL inis a benefitsuch to use without specific country transfer WTP (BT) based studies is on meta-analyses therefore to use aof WTP transfer (BT) benefit studies from other countries. Many meta-analyses have been conducted in the last6two based on meta-analyses of WTP studies from other countries. Many meta-analyses have been conducted in the last decades. These meta-analyses find that VSL is strongly associated with income level. two decades. These meta-analyses find that VSL is strongly associated with income level. A meta-analysis prepared for the OECD was exclusively based on stated preference studies, A meta-analysis prepared arguably for the of greater OECD was relevance exclusively for valuation based onrisk of mortality stated from preference environmentalstudies, arguably factors than of greater hedonic wage studies (Navrud and Lindhjem 2010; Lindhjem et al. 2011; OECD 2012). These relevance for valuation of mortality risk from environmental factors than hedonic wage studies (Navrud and Lindhjem 2010; Lindhjemstated preference et al. 2011; OECD studies are from 2012). These a database stated preference more than of studies 1,000aVSL are from estimates database from of more than 1,000 VSL multiple studies in over 30 countries, including in developing countries. estimates from multiple studies in over 30 countries, including in developing countries. World Bank and IHME (2016), Sanchez-Triana et al (2021)and World Bank (2022) applied a Deleted: World Bank and IHME transfer benefit (2016), function Sanchez-Triana et al (2021)and for estimating World VSL that draws onBank OECD (2022) applied (2012). a benefit The benefit transfer function transfer for estimating VSL that draws function is: on OECD (2012). The benefit transfer function is: #$,& ,,) = ./"0 ∗ (# '()* )1 (D4.2) (K4.2) where VSLc,n is the where estimated for country VSLestimated VSLc,n is the VSLc for in year n; VSL country year c inOECD isn;the VSL average VSL OECD is the in 2011 average VSLinin the sample of OECD 2011 in the countries with VSL sample studies of OECD ($3.83 countries million); with YOECD VSL is the studies ($ average 3.83 GDP per capitaY million); for OECD is the the average sample of GDP OECD countries in 2011 ($37,000); Yc,ncapita per is GDP for perthe sample capita of OECD in country c countries in year n;in 2011 and ɛ is($an incomeYelasticity 37,000); c,n is GDP ofper 1.2capita in and middle- for low- country c in year n ; and ɛ is income countries and 0.8 for high income countries. an income elasticity of 1.2 for low- and middle-income countries (LMICs) and 0.8 for high income countries (HICs). This benefit transfer function is used in this paper to estimate VSLc,n in each LMIC and HIC for the year n=2019. All values in equation K4.2 are in 2011 purchasing power parity (PPP) 85 prices. VSLc,n is converted from 2011 to 2021 prices by the rate of inflation and from PPP Safe and Clean Vehicles for Healthier and More Productive Societies prices to US dollars using country-specific PPP exchange rates for 2021 from the World where VSLc,n is the estimated VSL for country c in year n; VSLOECD is the average VSL in 2011 in the sample of OECD countries with VSL studies ($ 3.83 million); YOECD is the average GDP per capita for the sample of OECD countries in 2011 ($ 37,000); Yc,n is GDP per capita in country c in year n; and ɛ is an income elasticity of 1.2 for low- and middle-income countries (LMICs) and 0.8 for high income countries (HICs). This benefit benefitfunction transfer This is used in transfer function is this used paper to in this estimate paper VSLc,n in to estimate each VSL LMIC and HIC for the year n=2019. All c,n in each LMIC and HIC values in equation D4.2 for the year are in 2011 n=2019. purchasing All values power in equation are(PPP) parity K4.2 in 2011prices. VSLc,n is purchasing converted power parityfrom (PPP)2011 to 2021 prices of inflation by the rateprices. VSLc,nand from PPPfrom is converted prices to US 2011 to dollars country-specific usingby 2021 prices PPP exchange the rate of inflation and from rates PPP for 2021 from the prices to US World Development dollars using Indicators (World Bank 2024). PPP exchange rates for 2021 from the World country-specific Development Indicators (World Bank 2024). Total global welfare cost (W) of global premature deaths from exposure to an environmental risk factor is calculated Total global welfare cost (W) of global premature deaths from exposure to an environmental for the year 2019 in 2021 prices as follows: risk factor is calculated for the year 2019 in 2021 prices as follows: ) = ∑, ,,) ,,)2 (D4.3) (K4.3) where Mc,nwhere estimated Mc,n is the number is the estimated number of premature of premature deaths c deaths in country in incountry c in year 2021 year from 2021 from exposure to the environmental exposure to the environmental risk factor; and VSLc,n’ is VSLc,n in 2021 prices. Regional risk factor; and VSLc,n’ is VSLc,n in 2021 prices. Regional welfare cost and welfare cost by country income classification welfare cost and welfare cost by country income classification is calculated likewise by is calculated likewise by summing country welfare cost over the relevant group of countries. summing country welfare cost over the relevant group of countries. 7 Safe and Clean Vehicles for Healthier 86 and More Productive Societies D.5 Valuation of Morbidity Two valuation techniques are commonly used to estimate the cost of morbidity or illness. The cost-of-illness (COI) approach includes cost of medical treatment and value of income and time lost to illness. The second approach equates the cost of illness to individuals’ willingness-to-pay (WTP) for avoiding an episode of illness. Therefore, the latter includes the welfare cost of pain and suffering from illness. Studies in many countries have found that individuals’ WTP to avoid an episode of an acute illness is generally much greater than the cost of treatment and value of income and time losses (Alberini and Krupnick 2000; Cropper and Oates 1992; Dickie and Gerking 2002; Wilson 2003). The OECD, in its report on the global economic consequences of outdoor air pollution, includes the cost of both mortality and morbidity (OECD 2016). Mortality is valued using VSL, and the cost of morbidity is estimated both in terms of i. Market impacts or COI (reduced labor productivity and increased health expenditures associated with bronchitis, asthma, hospital admissions, and restricted activity days from illness); and ii. Nonmarket impacts (welfare cost of pain and suffering from illness). Globally, the OECD estimated the cost of market impacts or COI to about 0.2 percent of GDP or equivalent to 4 percent of the cost of mortality. Expressed in terms of welfare, using the equivalent variation of income, the cost was 0.4 percent of GDP or 8 percent of the cost of mortality. The nonmarket impacts or welfare cost was equivalent to 0.5 percent of GDP or 9 percent of mortality cost. Thus, the total cost of morbidity was estimated at 0.7–0.9 percent of GDP or 13–17 percent of the cost of mortality according to the OCED report. Estimating the cost of morbidity requires much more data—and less accessible data, including baseline health data— than estimating the cost of mortality. Therefore, a simplified approach is applied in this report using the following steps: i. YLD from PM2.5 exposure from GBD 2021 are converted to days of illness by applying the disability weights from GBD. ii. The cost of a day of illness is then approximated as a fraction of the average daily wage rates to reflect income losses from illness, health expenditure, time losses, and the welfare cost of pain and suffering. iii. The cost of a day of illness is also applied to individuals without income, because illness prevents most of these individuals from undertaking household work and other activities with a social value, as well as involving all the non-income impacts of illness. The cost of morbidity is thus estimated as follows. First, annual disease days (M) in country, The cost of morbidity is thus k, are as: as follows. First, annual disease days (M) in country, k, are calculated as: estimated calculated 3 = ∑) ) $*+ 3$ = ∑$*+(3$ ∗ 365/3$ ) (D5.1) (K5.1) where YLD is years lost to disease, i, from exposure to PM , and d is the disability weight where YLDki is years lost to disease, ki i, from exposure to PM2.5, and dki is the disability 2.5 ki weight for disease, i, in country, for disease, i, in country, k. The disability weights are from GBD 2021 for each of the diseases k. The disability weights are from GBD 2021 for each of the diseases associated with PM2.5 exposure. associated with PM2.5 exposure. The disability weight Theis a measure disability used in weight is GBD to calculate a measure used YLDs in GBDfrom to days of illness, calculate YLDsdisease, or injury. from days The weighted of illness, average global disability disease, weights forThe or injury. major diseases theweighted averageassociated with exposure global disability weights forto PM the major 2.5 range from 0.016 for diseases ischemic heart disease (IHD) to associated 0.169 with for lung exposure cancer to PM (table 2.5 range fromD5.1). 0.016 for ischemic heart disease (IHD) to 0.169 for lung cancer (table K5.1). Table K5.1 Disability Weights Associated with PM2.5 Air Pollution Average disability weights Diabetes type 2 0.078 COPD 0.070 Stroke 0.162 Cataract 0.065 Safe and Clean Vehicles for Healthier 87 IHD 0.016 and More Productive Societies LRI 0.059 3 = ∑) ) $*+ 3$ = ∑$*+ (3$ ∗ 365/3$ ) (K5.1) 3 = ∑) ) $*+ 3$ = ∑$*+(3$ ∗ 365/3$ ) (K5.1) where YLDki is years lost to disease, i, from exposure to PM2.5, and dki is the disability weight where YLDki is years lost to disease, i, from exposure to PM2.5, and dki is the disability weight for disease, i, in country, k. The disability weights are from GBD 2021 for each of the diseases for disease, i, in country, k. The disability weights are from GBD 2021 for each of the diseases associated with PM2.5 exposure. associated with PM2.5 exposure. The disability weight is a measure used in GBD to calculate YLDs from days of illness, The disability Table D5.1 Disability weights associated weight isPM a measure used in GBD to calculate YLDs from days of illness, disease, or injury.with The weighted 2.5 air pollution average global disability weights for the major diseases disease, or injury. The weighted average global disability weights for the major diseases associated with exposure to PM2.5 range from 0.016 for ischemic heart disease (IHD) to 0.169 associated with exposure to PM2.5 range from 0.016 for ischemic heart disease (IHD) to 0.169 for lung cancer (table K5.1). Average disability weights for lung cancer (table K5.1). Diabetes type 2 Table K5.1 Disability Weights Associated with PM2.5 Air Pollution 0.078 Table K5.1 Disability Weights Associated with PM2.5 Air Pollution COPD Average 0.070 disability weights Average disability weights Stroke Diabetes type 2 0.162 0.078 Diabetes type 2 0.078 COPD 0.070 Cataract COPD 0.065 0.070 Stroke 0.162 IHD Stroke Cataract 0.016 0.162 0.065 Cataract 0.065 LRI IHD 0.059 0.016 IHD 0.016 LRI 0.059 Lung cancer LRI 0.169 0.059 Lung cancer 0.169 Neonatal disorders Lung cancer Neonatal disorders 0.142 0.169 0.142 Neonatal disorders Source: Original table for this publication, based on GBD 2021 data (IHME 2021). 0.142 Source: Original table for this publication, based Source: Original on table GBD for this 2021 data (IHME publication, 2021). based on GBD 2021 data (IHME 2021). cost of a day The disease, The cost of a day lived with , orlived with disease, i, or a disease day, in country, k, is thus: The cost of aiday a disease lived day, in country, with disease, k, is thus: i, or a disease day, in country, k, is thus: 3$ = 3 3$ / (D5.2) (K5.2) 3$ = 3 3$ / (K5.2) where wk and dki are average daily wage rate and disability weight for disease, i, in country, k, where wk and where wk and dki are average dki are average daily wage rate and disability weight for disease, i, inDcountry, k, and D daily wage is a disability rate weight andthat disability weight corresponds tofor disease, a severity of in country, i,disease k, and for which is a of disability the cost a weight that corresponds and D is a to a severity disability weight of disease that for which corresponds the cost to a severity of a rate. disease of disease for which the cost of a disease day is assumed equal to the average wage D isday here is assumed set equal at 0.4. This is ato the average disability disease at 0.4.(DW) wage rate. D is here setweight day is assumed is a disability This associated equal to weight the average (DW) wage associated rate. D is here set at 0.4. This is a disability with severely restricted workwith severely and leisure restricted activity work and from disease andleisure weight (DW) associated with severely restricted work and leisure activity from disease and activity from disease and substantial substantial medical medical cost,cost, forfor example, example, severe severe COPD COPD(DW (DW = 0.41), = 0.41), distance-vision distance-vision blindness blindness substantial medical cost, for example, severe COPD (DW = 0.41), distance-vision blindness (DW = 0.19) and Stage 5(DW = 0.19) chronic kidney and Stage disease 5 chronic (DW =kidney disease 0.57) due (DW = 0.57) to diabetes, and due to diabetes, stroke and stroke with severity levelwith 3 (DW = (DW = 0.19) and Stage 5 chronic kidney disease (DW = 0.57) due to diabetes, and stroke with severity level 3 (DW = 0.32) 0.32) and 4 (DW = 0.55).severity level 3 (DW = 0.32) and 4 (DW = 0.55). and 4 (DW = 0.55). Cost of morbidity (C) in country, k, is calculated as follows: Cost of morbidity (C) in country, k, is calculated Cost of morbidity as follows: (C) in country, k, is calculated as follows: ) 3 = ∑$*+ ( 3$ 3$ ) (K5.3) 3 = ∑) $*+(3$ 3$ ) (D5.3) (K5.3) 9 Average daily wage rate is dailyas estimated Average follows: wage rate is estimated as follows: 9 wk = GDPk / Lk / 250 * sk (D5.4) (K5.4) where GDP is the country’s total GDP, L is the total labor force, s is labor compensation share where GDP is the country’s total GDP, L is the total labor force, s is labor compensation share of GDP, and annual of GDP, and annual working days is averaging 250. GDP and L are from the World working days is averaging 250. GDP and L are from the World Development Indicators by the World Bank and s is Development Indicators by the World Bank and s is from PENN World Table, version 10. from PENN World Table, version 10. Safe and Clean Vehicles for Healthier 88 and More Productive Societies D.6 Mortality from ambient NO2 Seven meta-analyses of the relationship between long-term NO2 exposure and mortality outcomes are presented in table A5.1. Atkinson et al. (2018) report the smallest hazard ratios for all-cause, CVD, respiratory and lung cancer mortality from NO2 based on dozens of cohort studies from North America, Europe and Asia. This report applies the hazard ratios and 95 percent confidence intervals (CI) from Atkinson et al. for CVD, respiratory, and lung cancer mortality to provide conservative Mortality K.6estimates offrom ambient global NO2 The ratios are applied to the population aged health effects. 25+ years to be consistent with the studies in the meta-analyses. Seven meta-analyses of the relationship between long-term NO2 exposure and mortality outcomes are presented in table A5.1. Atkinson et al. (2018) report the smallest hazard for mortality Table D6.1 Hazard ratiosratios outcomes for all-cause, and NO from annual CVD, respiratory lung exposure 2 cancer mortality from NO2 based on dozens of cohort studies from North America, Europe and Asia. This report applies the hazard ratios and 95 percent confidence Mortality hazard intervals (CI) from ratio Increment (HR) et al. for CVD, respiratory, Atkinson and lung cancer mortality to provide conservative All-cause CVD estimates Respiratoryof global Lunghealth eaects. The ratios are cancer applied to the population aged 25+ years to be consistent with the studies in the meta- Faustini et al. 2014 1.04 1.13 1.03 per 10 µg/m3 analyses. Atkinson et al. 2018 1.02 1.03 1.03 1.05 per 10 µg/m3 1.02 ratios for mortality outcomes Table K6.1 Hazard Huangfu and Atkinson 2020 1.03 from annual NO2 exposure per 10 µg/m3 Mortality hazard ratio (HR) Increment Huang et al. 2021 1.06 1.11 All- 1.05 CVD Respiratory Lung cancer per 10 ppb Stieb et al. 2021 1.047 cause 1.058 1.062 1.083 per 10 ppb Faustini et al. 2014 1.04 1.13 1.03 per 10 µg/m3 HEI 2022 Atkinson et al.1.04 2018 1.05* 1.02 1.03 1.05 1.03 per 10 µg/m3 1.05 1.04 per 10 µg/m3 Chen et al. 2024 1.03 2020 Huangfu and Atkinson 1.07 1.02 1.03 1.03 per 10 µg/m3 per 10 µg/m3 Huang et al. 2021 1.06 1.11 1.05 per 10 ppb * Ischemic heart disease. Stieb et al. 2021 1.047 1.058 1.062 1.083 per 10 ppb HEI Note: 10 ppb = 18.8 µg/m3 of NO . 2022 1.04 1.05* 1.05 1.04 per 10 µg/m3 2 Chen et al. 2024 1.03 1.07 1.03 per 10 µg/m3 Source: Original table for this publication. Source: Original table for this publication. Note: 10 ppb = 18.8 µg/m3 of NO2. * Ischemic heart disease. The continuous mortality risk function from exposure to annual NO2 is: The continuous mortality risk function from exposure to annual NO2 is: +,-./01 4$ = #2 ("! 6"2 ) for ci ≥ c0 (D6.1a) (A5.1a) 4$ = 1 for ci < c0 (D6.1b) (A5.1b) where ci is annual NO2 concentration (µg/m3), c0 is the TMREL or annual NO2 concentration (µg/m3) below(µg/m3), where ci is annual NO2 concentration which it isc0 is the TMREL assumed or annual that there NO2 concentration are no mortality eaects; and(µg/m3) HRj is thebelow which it increase is assumed that there are risk inno of mortality mortality from effects; disease and HRj isjthe 10 µg/m in perincrease 3 of annual risk NO2 exposure of mortality (table K6.2). from disease The j per 10 µg/m3 TMREL of annual NO2 exposure (table is set at D6.2). Thethe WHO’s TMREL isannual guideline set at the WHO’s value of 10 annual µg/m (WHO 3 guideline value of 10The 2021). risk µg/m 3 function (WHO 2021). is presented graphically in figure A5.1 for each of the three mortality outcomes. The risk function is presented graphically in figure D6.1 for each of the3 three mortality outcomes. Table K6.2 Mortality hazard ratios per 10 µg/m of annual NO2 exposure HR (central estimate) 95% Confidence Interval (CI) Table D6.2 Mortality hazard ratios per Cardiovascular 10 µg/m3 of annual NO2 exposure disease 1.03 1.02-1.05 Respiratory disease 1.03 1.01-1.05 Lung cancer 1.05 1.02-1.08 HR Source: Original table for this (central publication, estimate) based on Atkinson et al. (2018). 95% Confidence Interval (CI) Cardiovascular disease 1.03 1.02-1.05 11 Respiratory disease 1.03 1.01-1.05 Lung cancer 1.05 1.02-1.08 Source: Original table for this publication, based on Atkinson et al. (2018). Safe and Clean Vehicles for Healthier 89 and More Productive Societies 0 0 World LI LMI UMI HI EAP ECA LAC MNA SA SSA Figure D6.1 Relative risk of mortality from annual NO2 exposure Cardiovascular Figure disease fromdisease Respiratory K6.1 Relative risk of mortality annual NO exposure Lung cancer 2 1.25 1.14 1.18 1.12 1.16 1.20 1.14 1.10 1.12 1.15 1.08 1.10 1.10 1.06 1.08 1.04 1.06 1.05 1.04 1.02 1.02 1.00 1.00 1.00 10 15 20 25 30 35 40 45 50 10 15 20 25 30 35 40 45 50 10 15 20 25 30 35 40 45 50 Annual NO2 (µg/m3) Annual NO2 (µg/m3) Annual NO2 (µg/m3) Source: Original figures for this publication, based on Atkinson et al. (2018). Note: Solid line is central estimate. Dotted lines are the 95% confidence interval (CI). Note: Solid line is central estimate. Dotted lines are the 95% confidence interval (CI). Source: Original figures for this publication, based on Atkinson et al. (2018). The population attributable fraction (PAF) of mortality from disease j from exposure to annual NO2 is: fraction (PAF) of mortality from disease j from exposure to annual NO2 is: The population attributable & ∑!3# 9! :0! 6+ 4 = & (D6.2) (A5.2) ∑!3# 9! :0! where i is one of four population exposure groups; and Pi is the share of the national adult population where i is one of four in exposure population exposure groups;i.and category PAFPi calculated isis of each the sharefor countryadult and for the national each disease population j in exposure and is multiplied category i. PAF is calculated for each by baseline country number and of deaths for each disease from j in j and is each country multiplied byin 2021 from baseline the GBD number of deaths 2021 to arrive at an estimate of annual deaths from NO from j in each country in 2021 from the GBD 2021 to arrive at an estimate of annual deaths from NO exposure in 2 exposure in each country in 2021. 2 each country in 2021. 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