JOBS WORKING PAPER Issue No. 79 Heading Towards 1.5ºC – Impacts on Labor Demand in Selected Countries Ulrike Lehr and Hector Politt HEADING TOWARDS 1.5⁰C – IMPACTS ON LABOR DEMAND IN SELECTED COUNTRIES ULRIKE LEHR AND HECTOR POLITT 1 © 2024 International Bank for Reconstruction and Development / The World Bank. 1818 H Street NW, Washington, DC 20433, USA. Telephone: 202-473-1000; Internet: www.worldbank.org. Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Background and literature .................................................................................................................... 7 3. Modeling approach and data ................................................................................................................ 9 3.1. The MINDSET model ..................................................................................................................... 9 3.2. Scenario description.................................................................................................................... 11 3.2.1. Policy basket and country selection.................................................................................... 11 3.2.2. Economic structure in selected countries and readiness for clean energy transition........ 12 3.2.3. Clean energy investments ................................................................................................... 14 4. Discussion of results............................................................................................................................ 16 4.1. Ambitious investment and high potential demand for jobs – winners under 1.5C.................... 19 4.1.1. Egypt ................................................................................................................................... 19 4.1.2. India..................................................................................................................................... 21 4.1.3. Pakistan ............................................................................................................................... 24 4.2. Coal mining winners.................................................................................................................... 25 4.2.1. Indonesia ............................................................................................................................. 25 4.2.2. South Africa ......................................................................................................................... 27 4.3. Muddling through ....................................................................................................................... 30 4.3.1. Mexico ................................................................................................................................. 30 4.3.2. Thailand ............................................................................................................................... 31 4.3.3. Philippines ........................................................................................................................... 32 4.4. Challenged by underinvestment ................................................................................................. 34 4.4.1. Brazil.................................................................................................................................... 34 4.4.2. Türkiye................................................................................................................................. 36 4.4.3. Colombia ............................................................................................................................. 37 1 The authors would like to thank the MINDSET modeling team Ira Dorband, Gilang Hadardi and especially Noe Reidt for the support of this work. The authors would also like to thank Penelope Ann Mealy (Senior Economist, Climate Change, World Bank), and Elizabeth Ruppert Bulmer (Lead Economist, Jobs Group, World Bank) for their reviews and valuable contributions to this publication. 3 5. Summary and conclusions .................................................................................................................. 37 6. References .......................................................................................................................................... 39 Figures Figure 1: Output shares of aggregated sectors in percent. ........................................................................ 12 Figure 2: Sectoral shares of employment, percent..................................................................................... 13 Figure 3: Employment shares in sectors relevant for the clean energy transition ..................................... 14 Figure 4: Investment in clean energy, USD billion ...................................................................................... 15 Figure 5: Employment effects by sector, relative difference to base. ........................................................ 17 Figure 6: Employment effects by country, absolute and relative difference to baseline........................... 18 Figure 7: Sectoral output by driver, absolute (in MM USD) and relative difference (in %) to the baseline – Egypt ........................................................................................................................................................... 20 Figure 8: Additional labor demand by skill level - Egypt, absolute and relative differences to baseline. .. 21 Figure 9: Additional labor demand by gender – Egypt, absolute and relative differences to baseline...... 21 Figure 10: Sectoral output by driver, absolute and relative difference to the baseline - India.................. 22 Figure 11: Additional labor demand by skill level – India, absolute and relative differences to baseline. 23 Figure 12: Additional labor demand by gender – India, absolute and relative differences to baseline..... 23 Figure 13: Additional labor demand by skill level – Pakistan, absolute and relative differences to baseline. .................................................................................................................................................................... 24 Figure 14: Additional labor demand by gender – Pakistan, absolute and relative differences to baseline25 Figure 15: Sectoral output by driver, absolute and relative difference to the baseline - Indonesia .......... 26 Figure 16: Additional labor demand by skill level – Indonesia, absolute and relative differences to baseline. .................................................................................................................................................................... 27 Figure 17: Additional labor demand by gender – Indonesia, absolute and relative differences to baseline. .................................................................................................................................................................... 27 Figure 18: Sectoral output by driver, absolute and relative difference to the baseline – South Africa ..... 28 Figure 19: Additional labor demand by skill level – South Africa, absolute and relative differences to baseline. ...................................................................................................................................................... 29 Figure 20: Additional labor demand by gender – South Africa, absolute and relative differences to baseline. .................................................................................................................................................................... 29 Figure 21: Sectoral output by driver, absolute and relative difference to the baseline - Mexico .............. 30 Figure 22: Additional labor demand by skill level – Mexico, absolute and relative differences to baseline. .................................................................................................................................................................... 31 Figure 23: Additional labor demand by gender – Mexico, absolute and relative differences to baseline . 31 Figure 24: Additional labor demand by skill level – Thailand, absolute and relative differences to baseline. .................................................................................................................................................................... 32 Figure 25: Additional labor demand by gender – Thailand, absolute and relative differences to baseline. .................................................................................................................................................................... 32 Figure 26: Sectoral output by driver, absolute and relative difference to the baseline - Philippines ........ 33 Figure 27: Additional labor demand by skill level – Philippines, absolute and relative differences to baseline. ...................................................................................................................................................... 33 Figure 28: Additional labor demand by gender – Philippines, absolute and relative differences to baseline. .................................................................................................................................................................... 34 4 Figure 29: Sectoral output by driver, absolute and relative difference to the baseline - Brazil ................. 35 Figure 30: Additional labor demand by skill level – Brazil, absolute and relative differences to baseline. 35 Figure 31: Additional labor demand by gender – Brazil, absolute and relative differences to baseline. ... 36 Figure 32: Sectoral output by driver, absolute and relative difference to the baseline - Türkiye.............. 36 Figure 33: Additional labor demand by gender - Colombia, absolute and relative differences to baseline. .................................................................................................................................................................... 37 Tables Table 1: Overview of investment under the scenario and 2019 emission shares ...................................... 16 Table 2: Overview of results from scenario 1.5C ........................................................................................ 16 5 1. Introduction Climate change already has led to severe adverse impacts and is one of the biggest challenges to development. The latest IPCC Assessment Report (AR6) (IPCC 2023) states with a high confidence level that human-caused climate change is already affecting many weather and climate extremes in every region across the globe and has led to widespread adverse impacts and related losses and damages to nature and people. If no immediate action is taken, projections show that no matter which future warming level, risks will multiply, losses and damages will combine to compound and higher risks will be increasingly difficult to manage. The current reachable level of emissions for 2030, based on the pledges in the NDCs, are not on track with emission levels needed to maintain temperatures below 1.5°C in this century (IPCC 2023). Climate change is not only a threat to human well-being and planetary health, but also seen as the biggest challenge to development (World Bank Group 2021). The Sixth Assessment Report (IPCC 2023) concludes that deep, rapid and sustained mitigation will reduce projected losses and damages and deliver many co-benefits, while delayed action will lead to further locked-in high-emissions infrastructure, including for developing countries which need to expand their respective infrastructure in the next decades. A green transition is considered inevitable but is still seen more as a challenge than an opportunity for development, although benefits in terms of more and better jobs arise if climate policies are designed carefully. In terms of policies, developing countries often prefer direct investments in technologies that ensure additional benefits besides GHG mitigation. They assume co-benefits from energy savings, or a decrease in local air pollution besides GHG reduction from the power sector (Timilsina 2022). And, of course, subsidized finance (e.g., grants, low-interest loans) from development partners such as multilateral development banks, provides additional incentives for the focus on direct investment instruments. However, the massive efforts needed to combat climate change, the strained budgets of many developing countries and the need for additional fiscal space, will necessitate an approach that combines fiscal policies with targeted investment in clean energy. Moreover, carbon taxes might address several issues caused by the high share of informality in many developing countries’ labor markets (Timilsina 2022). If designed revenue neutral, it could expand fiscal space, because it is much more difficult to evade that other tax instruments and does not incentivize workers to leave the formal sector as often observed for income taxes. Shifting the tax burden towards energy and decreasing labor costs could further possibly create incentives to shift from the informal to the formal sector (Bento, Jacobsen, and Liu 2018). All in all, the potential benefits regarding emission reduction and economic effects seem to call for a diversified policy basket which is tailored to the economic structure, endowment and development stage of a country or region. Next to the policy design, a country’s endowment with natural resources, its economic structure a nd the capacity of its labor force determine the jobs outcome of a climate policy package and a clean energy transition. Fossil fuel rich countries will suffer losses in exports and along the oil, coal and gas value chain, while fuel importing countries benefit from lower expenses and often are able to decrease fuel subsidies and therefore unburden public budgets. For many developing countries, the capital stock in the energy sector is currently being built or expanded, and if the shift to clean energy is not happening timely, the danger of technology lock-in is high. The impacts of a climate policy basket on employment have been studied globally and for developed countries, among other in IRENA’s World Energy Outlook (IRENA 2021a) or the IEA’s World Energy Outlook. Overall employment effects are positive but differ in size and 6 sign by region and country. This paper focuses in the analysis on ten developing countries2, representing different stages of development, endowment, and level of ambition in climate change pledges and policies. The next chapter gives an overview of the relevant literature and identifies the research gap. Section 3 describes the data set, the modeling approach and the scenarios analyzed, section 4 describes results and section 5 concludes with policy recommendations. 2. Background and literature The literature on the employment impacts of deep emission reductions tends to reflect two different traditions of assessment. Bottom-up analyses of the potential jobs created by new technologies suggest that large numbers of ‘green’ jobs could be created during the transition (Garrett-Peltier 2017; Pai et al. 2021). In contrast, macroeconomic analyses typically suggest that any new jobs will be offset by job losses in other economic sectors, leading to either no change in overall employment or a net economy-wide reduction. Most assessments emphasize the role differences in economic structure, geography, economic sectors, and skills play for labor market outcomes under a clean energy transition. The IPCC’s Sixth Assessment Report (AR6) summarizes the potential impacts of decarbonization on jobs (Working Group III, Section 3.6.4.1). It finds that there may be substantial heterogeneity in outcomes across geographies, sectors, and skills categories. There is no specific discussion about employment effects in developing countries, but strong differences in outcomes for fuel-exporting and fuel-importing countries are noted. Overall, aggregate impacts on employment are typically small. AR6 does not include a formal quantitative assessment of jobs in the low carbon transition, but Working Group III does present a broad set of model results for global GDP impacts in decarbonization scenarios (Panel B on Figure 3-34). The results uniformly suggest that decarbonization will lead to losses of GDP over 2030-2100 and, given that the report also states that ‘aggregate employment impacts of mitigation pathways mainly depend on the aggregate macroeconomic effect of mitigation’ (p3-96), it could be inferred that employment levels would at best stagnate over this period. Model selection is starkly related to the questions asked and the answers obtained in any analysis . Theory based models such as computable general equilibrium (CGE) models tend to overemphasize the notion of a market system that builds on neoclassical theory and aim at obtaining equilibria in the long run. Policy decision making is often calibrating itself on short run or mid run results, where restrictions limit market clearing, as models inclined towards empirical observations reflect. In the short run, options for structural and technological change are more limited, technologies are more locked-in and less prone to flexible substitution solutions. The duration of short term varies across economic sectors. For energy, e.g., for power plants and energy-intensive factories, the normal re-investment cycle is 40 years or more, while for heating systems and aircraft the life cycle spans 25 years, in the case of cars roughly 15 years and for electronic media devices only few years. 2 The contribution is based on a simulation for the World Bank Groups 2023 Flagship Report on Jobs. The report focused on ten selected country cases, for the selection process see (Merotto 2022). The results found that countries can be grouped into four groups: Ambitious winners, coal mining winners, muddling through and challenged by underinvestment. This paper picks one illustrative example for each group, more material can be found in an online appendix. 7 Equilibrium oriented models exhibit smaller effects due to perfect functioning of markets, instantaneous price adjustments and perfect substitution, while models allowing for rigidities and sticky prices tend to exhibit double dividend effects and positive net employment results. While results from the modelling used in the IPCC report fall firmly into the macroeconomic category of analyses, they can be explained by assumptions in the models about optimizing behavior, perfect markets, fixed technologies, and a limited money supply. These are assumptions that have been challenged from several quarters, including by the modelers themselves (e.g. Peng et al. (2021); Trutnevyte (2016); Trutnevyte et al. (2019); Pollitt and Mercure (2018); Stern (2013)). Where these assumptions have been relaxed, the models may find ‘double dividend’ impacts in which the introduction of climate change mitigation pol icy can either draw upon spare capacity in the economy, or increase capacity through technological advance (Jean-Francois Mercure et al. 2019; Barker et al. 2016). Similarly, scenario analyses comparing more ambitious scenarios to less ambitious business-as-usual (BAU) cases or a counterfactual scenario often show positive efects for gross domestic product (GDP) and employment (Lutz, Becker, and Kemmler 2021)Model comparison exercises by the European Commission have highlighted some of the differences between the different modelling approaches (European Commission 2017).. The literature on jobs impacts of decarbonization is largely looking at developed countries . Relatively few studies have considered the specificities of labor markets in the developing world, for example accounting for informal labor or remittances. The World Bank’s Country Climate and Development Reports (World Bank Group 2022a) give perhaps the best overview but do not focus on employment impacts. At the EU level, transformation scenarios are also assessed in terms of their socioeconomic consequences by soft linking energy system models with macroeconomic models (Fragkos, Fragkiadakis, and Paroussos 2021, Vrontisi et al. 2020). Nieto et al. (2020) analyze different pathways and find positive employment effects. Other modeling results include Pai et al. (2021), who find an overall increase of energy jobs globally of 7.7 million by 2050, using an integrated assessment model augmented with country specific labor productivity data. One area that is not generally well considered in any model-based analysis is the mobility of labor (Spencer et al. 2018). Models either assume perfect labor mobility (equilibrium tools) or constraints based on historical trends (macro-econometric models). The scale of structural change in the low-carbon transition may, however, run up against skills and location-based constraints that have not previously been seen. Methods for how to assess these constraints are only now being developed (e.g., Mealy and Coyle 2022). The gender dimension of the transition also should not be ignored, particularly in countries with limited female labor market participation (Pearl-Martinez and Stephens 2016). 8 3. Modeling approach and data 3.1. The MINDSET model The MINDSET model is a tool with price-endogenous technology that combines the strengths of Input Output (IO) analysis with responses to exogenous price changes. The IO approach yields short- to medium-term economic responses to exogenous demand changes, and accounts for all multiplier effects from intermediate demands along the value chains in a consistent framework. Connecting countries globally by bilateral trade extends the framework to capture trade effects from (intermediate and final) demand changes in one country on its trading partners and their trading partners. Combining it with the Leontief price model allows for changes in the intermediate and final demands as a reaction to exogenous price changes. The database comprises trade information, interindustry flows, labor market data and a detailed representation of the energy sector. In practice, the model used in the simulations for this paper is based on the Global Trade Analysis Project (GTAP)-Power Data Base (Peters 2016). It covers a total of 76 sectors across 141 countries and regions. Augmented with the Gender-Disaggregated Labor Data Base (GDLD) and other labor force survey data the model disaggregates employment outcomes by gender and skill stemming from changes and differences in output outcomes by economic sector in response to demand changes and price changes. The main transmission mechanism translates climate policies into price and demand changes and simulates the response of the economy to different climate policy scenarios. The results then are typically given as scenario differences, such as percentage difference of employment, output, or other economic indicators. The approach needs assumptions regarding the responses of industries and households to the scenario’s price level and labor tax level. These responses are determined by the respective elasticities, with own- and cross-price elasticities for energy carriers as intermediate inputs to production and price elasticities of final demand from households and Governments. The same holds true for labor taxes. The core elements of the model are the interindustry final demand vectors by country connected by respective bilateral trade. Carbon taxes then make energy inputs more expensive, proportional to their respective carbon content. Industries substitute away from certain energy carriers, either into other energy carriers if possible or into capital or labor, depending on the respective production technology and potential additional incentives. Short-term (price) elasticities tend to be lower than long-term elasticities, reflecting that substitution is often severely limited by technology choice and rigid production processes. These rigidities limit potential responses to a price change. Vulnerable groups, for instance, have limited capacity to adjust technologies which provide energy services to them, such as heating, cooling, transport or running appliances. The adjustment period depends on the technology. Small household appliances are easier to replace than energy processes tied to large capital stock. Hence, the lifetime of capital stock matters for the substitution potential. Power plants or energy-intensive production sites often look at a lifetime of 40 years or more, heating systems or aircraft 25 years, cars live for roughly 15 years and communication technology tends to be rather short lived (e.g. IEA Energy Technology Perspectives (IEA 2020) for an overview). As intermediate and final demands change in response to prices, these changes give rise to changes along the value chain lastly to total output of a sector. 9 The transition towards clean energy and carbon emissions at levels compatible with global warming of maximal 1.5C needs massive investment in renewable energy and energy efficiency and leads to additional demand for some goods in the economy, such as parts for solar installations or wind farms, electric vehicles, solar water heaters, heat pumps or insulation of homes and heat recovery in production processes. Less demand will occur for goods and services related to conventional fuels. This shift introduces a shift in outputs, either from abroad or domestically, depending on the structure of the respective economy. If produced domestically, multipliers along the value chain apply. Utilities need to update their capital stock over time switching from fossil fuel based generation to modern energy uses. They finance the investment up front and then recoup the costs through charging higher prices, which exert pressures in the directions as described above for the tax. Employment demand enters the picture via two channels. Firstly, sectoral output is related to employment through sector specific labor intensities. Additional, or less, demand for a certain sectoral output will lead to more or less hours worked and eventually to more or less people needed. Secondly, payroll tax cuts make labor cheaper compared to other inputs and hence might yield a so-called double dividend of an evironmental tax reform, with the environmental (in this case climate change mitigating) dividend from reducing harmful externalities and the second dividend from positive labor effects. For discussions on the the double dividend of environmental tax reforms, see for instance (Freire-González 2018; Karydas and Zhang 2019; Kirchner et al. 2019; Koschel 2001; Maxim and Zander 2019; Maxim, Zander, and Patuelli 2019; Pearce 1991; Pigato 2019; Tuladhar, n.d.; World Bank 2022; Wesseh and Lin 2019) Net results of the clean energy policy then indicate if accounting for positive and negative pressures and drivers leads to additional or less labor demand. However, distributional effects often even matter more, especially for public acceptance of the clean transition policies and agreements. The sector specific view allows to target policiy measures so that job opportunities can be reaped, by having the right skills at the right point in time and the right place, but also to design support where often well paid, organized and culturally esteemed jobs are lost. Next to macroeconomic indicators such as GDP and employment, structural change and distribution aspects increasingly enter the public debate (Oei et al. 2020). Social aspects need to be addressed to make the energy transition just and inclusive and increase acceptance in the population. Assumptions on pathways and scenarios in physical units define where we need to be from a climate change avoiding perspective and need to be translated into investment and costs. Simulating the impacts on jobs and growth requires model coupling or application of different models’ results from the literature to calibrate parameters. 10 3.2. Scenario description 3.2.1. Policy basket and country selection A clean energy policy basket consists of different elements, such as carbon prices, fossil fuel subsidy removal, private investment incentives, public investment and procurement and command and control regulation. The scenario applied in this paper consists of a few basic elements. Rather than represent a prediction of countries’ policies and activities to reach the 1.5C target, the scenario uses a simplified approach. The main purpose is to illustrate the potential of a double dividend from carbon prices and a labor tax reduction for selected countries and the potential added benefits from pursuing a clean energy investment pathway. Given the medium-term time horizon of the MRIO as described above, results are reported for 2030. The clean energy transition will not be completed by then, but the pathway needs to be set, because of the energy system’s investment cycles and to avoid stranded investment in a not 1.5C compatible energy system. Global carbon prices are set to 80 $/tCO2, in 2019 prices and to illustrate the potential of achieving a double dividend, tax revenues are recycled to reduce payroll taxes. In selected countries for country deep dives, revenues are partly used to finance efficiency investments, which are determined following countries’ commitments and pathways. In addition, clean energy investment is included in ten country-deep-dives. A selection of ten developing countries with different aspirations and different levels of success in development (Merotto 2022) is analyzed in-depth to illustrate how different structural effects affect the respective labor demand outcomes. The countries are selected according to their performance along four dimensions of development, i.e. the sectoral dimension shifting from less efficient traditional agriculture to manufacturing with higher productivity and eventually to services, the spatial dimension with movement from rural to urban spaces to benefit from agglomeration economies, especially in manufacturing and services sectors, the occupational dimension shifting towards formal employment and as firms diversify, deepen capital per worker, and implement new technologies, the task complexity of occupations in firms changes and the organizational dimension which enables firms to reap the benefits of scale. The selection includes successful transformers and unsuccessful transformers, and countries which are somewhat neither. Some improved along the dimensions without much growth and poverty reduction, grew without improving dimensions or reducing poverty, which is often the case with resource rich countries, or improved along one dimension or another without much growth or poverty reduction. Successfully transforming countries show progress over all indicators with increased economic growth, reduced poverty, and progressed more rapidly along the dimensions than the average for their country income grouping. Unsuccessfully transforming countries have grown, reduced poverty, and progressed more slowly along the dimensions than the average for their country income grouping. Among countries with low starting per capita GDP level, India (LMIC) is selected and a successful country with annual percentage growth rates3 of 5% since 1990, a reduction of poverty by 1.4 percentage and improvement above average in capital deepening and increase of waged employment as part of the organizational dimension, urbanization in the spatial dimension and sectoral change our of agriculture. In the country group with medium starting per capita GDP level, Thailand (UMIC) and Indonesia (UMIC) were successful, with annual average growth rates of 3.1% each and poverty reduction by 1.3 percentage points (p.p.) in 3 Average annual growth, GDP measured in PPP, 2017 USD 11 the case of Thailand and 3.4 percentage points in Indonesia. Moreover, Thailand scored positively in all five dimensions and Indonesia in four but the skills level. Egypt (LMIC) with 2.4% average annual per capita growth 0.5 p.p., and above average progress only in one development dimension (capital deepening). Turkey (UMIC) with 3.4% growth and 1.4 p.p. poverty reduction scores above average in all five dimensions. In the group of countries having started with high levels of GDP, South Africa (UMIC) with 1.5% growth and 0.5 p.p., Colombia (UMIC) with 2.1% growth and 0.5 p.p. poverty reduction and Mexico (UMIC) with 1.0% and 0.6 p.p. poverty reduction have stalled in their development in most indicators. The Philippines (LMIC) and Brazil (UMIC) are selected because of their mixed performance. The Philippines have done well on growth and improved along other dimensions, but poverty reduction lags behind. Brazil has done well on poverty reduction (-1.5p.p.) but grew rather slowly (1.45 percent). 3.2.2. Economic structure in selected countries and readiness for clean energy transition The clean energy transition induces structural change and its impacts on output and jobs depend on the economic structure of a country and the structure of the workforce. The clean energy transition will shift demands away from carbon intensive sectors and towards clean energy, energy efficient production, climate friendly technologies and processes and cleaner means of transportation. This creates large potential job demands, but reaping these benefits requires the right set of resources, endowments, and policies. Few developing countries currently manufacture the technologies needed, except for solar PV, and the learning curves to establish a clean energy sector are quite steep. However, having an industrial base with industries such as metal industries, automotive parts, or electrical equipment and with relevant services sectors can facilitate spillovers under the right circumstances. Figure 1: Output shares of aggregated sectors in percent. 100% 9% 9% 8% 6% 13% 13% 10% 10% 11% 14% 90% 8% 11% 15% 9% 9% 14% 14% 15% 15% 80% 6% 3% 10% 10% 10% 6% 6% 7% 7% 9% 13% 5% 70% 5% 8% 11% 9% 5% 6% 6% 60% 11% 11% 2% 3% 3% 9% 17% 11% 4% 3% 3% 50% 23% 25% 25% 25% 21% 6% 24% 40% 23% 14% 29% 29% 30% 20% 39% 36% 35% 35% 35% 31% 34% 34% 10% 24% 24% 0% Brazil Colombia Egypt India Indonesia Mexico Philippines South Thailand Türkiye (UMIC) (UMIC) (LMIC) (LMIC) (LMIC) (UMIC) (LMIC) Africa (UMIC) (UMIC) (UMIC) Manufacturing Private Services and Transport Agriculture Mining, utilities, water Construction Wholesale, retail and household employees Public services Source: GTAP10-Energy 12 Figure 2: Sectoral shares of employment, percent. 100% 13% 16% 16% 11% 15% 22% 22% 26% 21% 30% 7% 80% 4% 17% 17% 20% 15% 11% 11% 3% 15% 2% 2% 11% 4% 1% 3% 3% 11% 11% 11% 13% 60% 7% 35% 8% 2% 7% 2% 3% 1% 6% 6% 21% 24% 24% 8% 16% 15% 40% 16% 16% 16% 12% 12% 23% 14% 25% 14% 22% 20% 20% 24% 20% 25% 25% 19% 23% 25% 20% 16% 15% 15% 15% 0% Brazil Colombia Egypt India Indonesia Mexico Philippines South Thailand Türkiye (UMIC) (UMIC) (LMIC) (LMIC) (LMIC) (UMIC) (LMIC) Africa (UMIC) (UMIC) (UMIC) Manufacturing Private Services and Transport Agriculture Mining, utilities, water Construction Wholesale, retail and household employees Public services Source: GTAP10-Energy Countries differ with regards to their economic structure and labor intensities differ across sectors and countries. Two Latin American Upper Middle Income Countries Brazil and Colombia have the highest shares of private sector services in their total economic output (29%), followed by India, Indonesia and Mexico each with 25%. Egypt has the highest share of manufacturing with 39% followed by India with 36%. Figure 1 gives an overview of the respective economic structure by sector, disaggregated into manufacturing, private services and transport, agriculture, mining and utilities, construction, wholesale and retail and public services. Agriculture shares vary between 3% in Brazil and 17% in South Africa. Pakistan and India have the largest share of jobs in agriculture, with more than 35%; Brazil and South Africa have a low share of employment in agriculture. Services contribute most to the country’s employment in Brazil, South Africa, Mexico, the Philippines, Columbia and Egypt. The share of jobs in industry is around 20% in most countries. A closer look into the industrial sector reveals differences, which have an influence on how a country can handle the uptake of clean energy technologies. Food production employs a large share of workers in manufacturing with over 20% in most countries, except for Thailand and South Africa and over 30% in Egypt, Pakistan, and the Philippines. In most countries textile and apparel production is the second largest industry, in Honduras, the textile and apparel industry are the largest single sectors in terms of jobs. The clean energy transition needs inputs from machinery, electrical equipment, electronics, the vehicle industry, plastic products, and construction. Collecting these sectors’ shares to economy wide production, reveals that in Mexico, Thailand, and Brazil forty percent or more of all workers are employed in sectors matching the demand profile of the transition towards clean energy. Thirty percent of all workers are employed in relevant sectors in India, Indonesia, and South Africa (Figure 3) 13 Figure 3: Employment shares in sectors relevant for the clean energy transition 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Source: GTAP10-Energy 3.2.3. Clean energy investments The carbon tax alone has been found not to induce enough investment in clean energy and energy efficiency to keep temperature increases within the boundaries agreed in Paris. A review by (O’Mahony 2020) illustrates that carbon prices exhibit a persistent “implementation gap”, between the aspirational carbon prices, and those that can practically be enforced. To meet the Paris goals, this gap needs to be closed with other investment incentives to attract investment at the required level. Clean energy, however, in most countries has a compelling business case. Evidence is mounting that renewable energy is the least cost option for many developing countries which need to expand their power generation capacity to keep up with the growing need for electricity and the development dividend from electrification. For Pakistan, for instance, the World Bank team commissioned a study in mid-2018 to help understand how much solar and wind could—and should—be added to the Pakistan grid considering its cost and variability (Knight 2021). Increasing solar and wind capacity to at least 30% of total installed capacity by 2030 is found to be a "least-cost" expansion scenario, resulting in fuel savings equal to $5 billion over 20 years, increased energy security, and reduced greenhouse gas emissions. Investment in clean energy depends on a country’s ambition, commitment, and capacity. To show the effect on sector specific outputs and labor demand the 1.5C-scenario includes investment in renewable energy and energy efficiency in combination with the carbon tax. Tax revenues are spent on energy efficiency and remaining revenues are used to lower payroll taxes as outlined above. The amounts invested in clean energy and efficiency are taken from the literature and follow the respective country’s plans as outlined in their NDC or in clean energy policies and regulations. Globally, the world needs to invest USD 5.7 trillion per year until 2030 to stay on track with the 1.5C scenario (IRENA 2021b). Emissions, and investment in clean energy technologies have been unevenly distributed across regions in the past. For years, China has led East Asia and Pacific to the pole position in clean energy investments (Figure 4) followed by Western Europe and lately OECD Americas. 14 Figure 4: Investment in clean energy, USD billion Source: (Irena 2021) Ten focus countries, who contributed 17% to global emissions, invest 10% of the USD 5.7 trillion needed in 2030. Global total emissions are dominated by China, followed by the United States, India, and the EU. However, when it comes to emission reduction and development, the fair shares of the globally needed reductions have been discussed widely, without coming to agreed guidelines yet. Mapping per capita emissions of the top ten emitters ranks the US, Russia, South Korea, and Iran as top countries with China coming in as fifth and India last. Per unit of GDP puts Iran on top followed by Indonesia, India and Russia, with HICs ranking low4. Table 1 compares the share in global emissions for the eleven selected countries with the share of total investment into clean energy, a measure which reflects a country’s ambition regarding the transition to clean energy. 4 https://www.climatewatchdata.org/ghg- emissions?calculation=PER_GDP&chartType=line&end_year=2020&gases=all-ghg®ions=TOP§ors=total- including-lucf&start_year=2000 15 Table 1: Overview of investment under the scenario and 2019 emission shares Contribution to the sum of 10 Share of emissions of all selected countries’ investment countries Brazil 3.5% 7.6% Colombia 2.3% 1.4% Egypt, Arab Rep. 7.5% 4.4% India 45.6% 42.9% Indonesia 16.7% 10.8% Mexico 7.7% 7.8% Pakistan 1.6% 3.3% Philippines 3.8% 2.5% South Africa 5.0% 7.7% Thailand 3.4% 4.7% Türkiye 2.9% 6.9% Own calculation. 4. Discussion of results The carbon tax with payroll tax reduction revenue recycling has small positive or negative job impacts, which are enhanced, alleviated, or mitigated by additional clean energy investments depending on a country’s endowment and readiness for the clean energy transition. For most countries, except for Brazil and South Africa, labor demand is higher under the carbon tax scenario than before (Table 2). India and Egypt show the largest gains in labor demand, followed by Thailand. Adding clean energy investment to the scenario enhances the positive effects for Egypt and India and flips the sign for South Africa. Table 2: Overview of results from scenario 1.5C BRA COL EGY IDN IND MEX PAK PHL THA TUR ZAF carbon tax plus payroll tax reduction jobs relative difference -0.14% 0.52% 1.47% 0.60% 1.52% 0.38% 0.90% 0.41% 1.18% 0.35% -1.57% carbon tax plus payroll tax reduction plus green investment output rel. difference -3.1% -0.7% 7.1% 2.2% 3.4% -1.7% 1.5% 0.5% -2.7% -0.4% -1.2% jobs relative difference -0.02% 0.5% 5.8% 3.3% 5.1% 1.0% 1.2% 3.2% 1.2% 1.1% 2.4% shift to female jobs yes yes no no no some no some yes no some green investment/ GDP 0.3% 0.5% 8.6% 7.7% 6.7% 3.1% 4.2% 2.2% 2.7% 0.9% 6.8% share of potentially 40.9% 20.6% 13.1% 30.3% 35.5% 52.0% 19.2% 27.8% 48.3% 29.2% 34.5% green manufacturing Source: Own table. Jobs impacts differ across countries and across economic sectors . Disaggregation of the results by economic sectors reveals the propagation of impacts within the respective economies (Figure 5)5. The mining and quarrying sectors in all countries have lower labor demand under the carbon tax scenario, with South Africa and Indonesia seeing the largest losses of more than 25%. The utility sector is the second 5 Note that the figure does not include results for India, due to scale. The results are reported in the country specific section. 16 sector where labor demand decreases across most countries, though to different degrees. Other sectors see losses in some countries and gains in other countries. Mining and utilities demand inputs from transport and storage, professional scientific services, administrative services and information and communication. The stronger the vertical integration, the larger the losses of jobs in these interconnected sectors. Examples are South Africa, Indonesia, and Brazil. Some private services, agriculture, and all public services, such as health and education benefit from payroll tax decreases and increased household incomes. Figure 5: Employment effects by sector, relative difference to base. Source: Own results, MINDSET model. 17 Figure 6: Employment effects by country, absolute and relative difference to baseline. Source: Own results, MINDSET model. The effects within each country are distributed across all economic sectors to differing degrees. Clean energy investments go to the power sector and to energy efficiency. The employment impact depends on the technology selected, the capacities in the country to manufacture and install the technologies and operate and maintain them. Figure 6 shows total employment effects by country, with the red dots showing total relative effects and the bars summing the negative and positive effects by sector. The bars reflect what drives total effects, and how losses in one sector may be compensated by gains in another sector. For all countries the labor demand results are improved by targeted investment in clean energy, reflected in additional labor demand in the construction sector, in manufacturing and in several services. South Africa manages to turn the negative effects into positive labor market outcomes, other countries, such as Egypt or India increase the positive effects by large. Several similarities between countries benefitting to a large extent from the energy transition and countries with low impacts point to the need for context-specific conclusions and policy recommendations. The subsequent section examines countries both collectively and individually, focusing on sector-specific outcomes regarding relative and absolute disparities in output and labor demand based on gender and skill level. The results are further categorized according to the driving forces behind the scenario, such as investments in clean energy, shifts in household energy consumption, additional household incomes resulting from energy savings and increased employment, price responses from households, indirect investments from various sectors, the transition to cleaner electricity generation technologies, and the effects on trade, including reduced imports of fossil fuels or decreased exports of oil, coal, or natural gas. By linking the analysis of these driving forces to economic sectors, it 18 becomes possible to anticipate the expected effects on output and labor demand, thereby enabling governments to adequately prepare for the corresponding impacts. 4.1. Ambitious investment and high potential demand for jobs – winners under 1.5C Egypt, India, and Pakistan see high gains from a transition to clean energy, both in terms of output and employment. Although the countries differ in economic structure and income level, they have similar ambitions regarding clean energy investment. Pakistan, Egypt, and India share common energy policies aimed at diversifying energy sources, promoting renewable energy, improving energy efficiency, attracting private sector investment, ensuring energy access and affordability, and prioritizing environmental sustainability. They committed to reducing dependence on a single fuel, increasing the share of renewable energy, implementing energy-efficient practices, expanding energy access, and mitigating environmental impacts. While specific approaches may vary, their collective goal is to achieve a sustainable and secure energy future. 4.1.1. Egypt Egypt’s has managed to decouple economic and emissions growth, and the country has set ambitious targets for both the integration of renewable energy and adoption of energy efficiency measures in Egypt’s Integrated Sustainable Energy Strategy ISES 2035 (World Bank Group 2022b). The scenario presented in this analysis assumes that the measures outlined in the strategy will be executed in accordance with the predetermined plan. In this context, Egypt emerges as the country with the most significant relative increase in output, amounting to 7.1%. This growth is primarily observed in the manufacturing sector and the construction industry. One of the key factors contributing to Egypt’s economic progress is its large potential for renewable energy, particularly in solar and wind power. The country has set ambitious targets to generate 42% of its total power from renewable sources by 2030, with a further aim to achieve this goal by 2035 (Lewis and Safety, 2022). This commitment to renewable energy aligns with Egypt’s abundant solar energy resources and its potential for harnessing wind power. Additionally, Egypt has already established domestic manufacturing capabilities for solar photovoltaic (PV) modules. This means that the country is not only able to generate renewable energy but also produce the necessary equipment for its installation. This domestic production of solar PV modules contributes to the overall growth of the manufacturing sector in Egypt. 19 Figure 7: Sectoral output by driver, absolute (in MM USD) and relative difference (in %) to the baseline – Egypt Source: MINDSET simulations. Despite the overall increase in output, the relative change in labor demand resulting from additional investment in Egypt is comparatively lower. This can be attributed to the significant rise in large-scale solar installations, which are less labor-intensive in terms of both manufacturing and installation when compared to other clean energy technologies like wind energy. The labor demand in these solar installations is relatively lower due to their scale and the level of automation involved. The advanced automation technologies employed in the manufacturing process reduce the need for a large workforce, resulting in a lower impact on labor demand. Furthermore, it is important to note that even though Egypt has the potential for domestic manufacturing, there are still certain components of the clean energy technologies required under the clean energy pathway that will need to be imported within the specified time frame of the simulations. This means that despite the capability to manufacture some parts of the technologies domestically, there are specific components or parts that cannot be produced within the country and must be sourced from international suppliers. The reasons for this reliance on imports despite domestic manufacturing potentials vary, starting from lack of specialization in the workforce, the necessary raw materials, or productions costs. Other sectors, such as wholesale and trade, transport, and construction, experience a more substantial impact on labor demand. This is primarily due to the combined effects of clean energy investment, payroll tax reduction, and increased household demand resulting from higher income levels. These sectors are more labor-intensive and therefore experience a greater increase in labor demand compared to manufacturing. By skills level, lower skill levels benefit, and male workers take most of the additional labor demand. The distribution of skills in additional labor demand reflects the dominant skills and the skill distribution in the respective sector. If the scenario favors the manufacturing sector, then the skill composition of the manufacturing sector determines the result. Hence, the clean energy transition does not initiate a transition towards different skills in the economy in this medium-term analysis. The introduction of new firms producing new technologies and the respective education and training will take time. The analysis reported here does not reflect a deep structural change but rather building on the existing skills mix. 20 However, new knowledge and new tasks will be necessary to respond to the demands of the new jobs under the clean energy transition. The largest job impacts are in male dominated sectors, and the low female labor force participation rate in Egypt (22% before Covid, dropping to 14% in 2020, and now slowly recovering) is reflected in the results in Figure 9. Figure 8: Additional labor demand by skill level - Egypt, absolute and relative differences to baseline. Source: MINDSET simulations. Figure 9: Additional labor demand by gender – Egypt, absolute and relative differences to baseline. 4.1.2. India India has set ambitious climate change goals and ratified the Paris Agreement early on, presenting major mitigation goals in its Nationally Determined Contribution (NDC). These goals include reducing the emissions intensity of its GDP, increasing the share of non-fossil-fuel-based energy resources in its 21 electric power installed capacity, creating additional carbon sinks through forest and tree cover, and promoting a sustainable way of living based on conservation and moderation. The country is experimenting with a mix of market mechanisms, fiscal instruments, and regulatory interventions to attract investments and support its climate change efforts. The scenario reflects these ambitious goals assuming India spends close to seven percent of its GDP annually on the energy transition. India sees a positive change in output and labor but sees a higher change in labor demand than in output. Wind energy and PV manufacturers, Suzlon and Tata being the largest companies, as well as producers of intermediate goods for the clean energy transition and a favorable policy environment, India is in a good position to reap the benefits of the energy transition. Clean energy investment triggers the largest positive effect in the manufacturing sector and in construction, smaller effects are seen in wholesale and trade and in the transport sector. Mining and quarrying sector is losing output and will be shedding labor. However, the effect is rather small in relative and absolute terms. The net balance in output is a plus of more than three percent, and the 1.5C scenario sees an additional five percent in labor demand. Figure 10: Sectoral output by driver, absolute and relative difference to the baseline - India India has a beneficial economic structure to reap the benefits of the energy transition. In India, 35.5% of the workers work in sectors which may become relevant for the clean energy transition. With large domestic investment in the transition, under the 1.5C scenario India invests 6.7% of today’s GDP in the energy sector, producers of goods and services for the transition have a pipeline of projects which provides some security also to potentially expand. However, the modeling framework used to reach the reported results currently assumes a rigid structure of domestic production and integration along the respective value chains. Given the sectors where additional demand occurs, more low skilled labor will be needed, and male workers will find new job opportunities, with little change and opportunities for female workers. 22 Figure 11: Additional labor demand by skill level – India, absolute and relative differences to baseline. Figure 12: Additional labor demand by gender – India, absolute and relative differences to baseline. 23 4.1.3. Pakistan Pakistan has ambitious goals for mitigation and adaptation, laid out in its 2021 update of the NDCs. For renewable energy, the target is set to 60 % of all energy produced in the country to be generated from renewable energy resources including hydropower by 2030. By the same year, 30 % of all new vehicles sold in Pakistan in various categories will be Electric Vehicles. Moreover, since 2020, Pakistan holds a moratorium for new coal power plants, and no generation of power through imported coal is allowed (Government of Pakistan 2021). Pakistan’s labor market is coined by high levels of underemployment and informality. Many workers in Pakistan experience chronic and seasonal underemployment, particularly in the informal and agricultural sectors. Additionally, there is a significant number of "discouraged workers" who have given up looking for work and are not included in the official unemployment statistics. This is especially true for women, who often face barriers to labor force participation. Informality is a feature of the labor market in Pakistan, with 94.9% of wage workers being informally employed. Pakistan benefits from the scenario with labor demand being more than 3% higher than under the reference scenario. Negative effects on the Pakistani economy from higher prices due to the carbon tax are weighing on the mining sector, agriculture and manufacturing. The substitution away from carbon intensive production technologies results in output losses in manufacturing, mining and also in agriculture, which is one of the dominant sectors in the country. The positive impacts from savings in net trade, higher household income and hence higher demand and clean energy investment, compensate these output losses more than fully, with a balance of slightly above zero. In terms of employment, the positive effects are widely spread and favor higher skills in the business services, in manufacturing, and in administrative and support services, as well as in education and in the financial sector (Figure 13). This translates into higher employment. Female employment increases only marginally, with the exception of the agricultural sector. However, in this sector unskilled labor dominates, hence the chances of finding better jobs for women in rural areas seem dim (Figure 14). Figure 13: Additional labor demand by skill level – Pakistan, absolute and relative differences to baseline. 24 Figure 14: Additional labor demand by gender – Pakistan, absolute and relative differences to baseline 4.2. Coal mining winners For coal mining countries, the clean energy transition poses challenges, but Indonesia and South Africa exhibit net gains under the 1.5C scenario. The challenges of a just transition6 ((Malerba 2022; Ruppert Bulmer et al. 2022) are well documented. They involve the local concentration of often well-paying jobs, the lack of diversification in coal mining regions, the high level of organization and social security of these jobs and the lack of alternatives which provide the same job quality. Meeting these challenges requires very country-specific approaches tailored to the respective region, ranging from transfers to support early retirement, relocation benefits, upskilling and reskilling, domestic value creation requirements for clean energy tech companies, local content regulation and other support mechanisms for workers and municipalities. However, new job opportunities will arise from investment in clean energy and in the case of Indonesia and South Africa, will outweigh the negative impacts. 4.2.1. Indonesia Indonesia’s coal mining sector is large, matters and suffers under the clean energy transition. The major coal regions in Indonesia are South Kalimantan, East Kalimantan, and North Kalimantan provinces. These provinces account for the bulk of coal employment in the country. During the period of rapidly expanding coal production, particularly between 2007 and 2012, the economies of South Kalimantan and East and North Kalimantan added a total of 726,000 net jobs, with nearly 110,000 of those jobs being in coal mining. This reflects a significant annual average growth rate of 21 percent in coal mining employment. While the number of coal jobs created is smaller compared to total job creation in non-coal sectors, coal mining jobs can have large spillover effects on the local economy, or, in some cases also crowd out other manufacturing sectors (Ruppert Bulmer et al. 2022). If the global or local demand for coal decreases, these spillovers will extend negative effects to other sectors. 6 Note that the term in itself has been challenged over time, since it seems to include a normative setting for ‘just’. It will be used in the following because the literature which is referred here is centered around this term. 25 Renewable energy is expected to contribute increasing shares to the Indonesian energy mix. Indonesia has set a target of achieving 23 percent of new and renewable energy in its national energy mix by 2025. This target includes various renewable energy sources such as solar, wind, geothermal, hydro, bioenergy, and ocean wave energy. Indonesia is a largely resource rich country, in terms of fossil fuels, minerals, agricultural products and renewable energy potential. The geographical structure of the Indonesian archipelago makes distributed energy generation even more feasible. Indonesia's economy is stable, and the country performs well in macroeconomic management, public sector management, and infrastructure development, with challenges in human capital development (World Bank 2024). Indonesia has consistently exceeded the long-term average growth for middle- income countries and achieved upper-middle-income status in 2023. The country aims to become a high- income country by 2045. However, Indonesia's structural transformation has been associated with falling productivity and growth potential. The dominance of low-complexity industries and services, high levels of informality, and low-skilled jobs pose challenges for achieving higher growth rates and reducing economic insecurity. Under the 1.5C scenario, labor demand losses in the fossil fuel sectors are compensated by potential gains in other sectors, resulting in a 3.25 percent higher labor demand compared to the baseline. Figure 15 shows differences to base for output by driver. The largest drivers of the economic effects are the clean energy investment itself, and the losses due to the shift away from fossil fuels. The mining sector decreases output and has very little to gain from clean energy investment. Manufacturing on the other hand, gains from clean energy investment, which together with increased household income from more employment outweighs the losses. Households in employment need more often a household help, but price levels increase and weigh on households’ budgets. Figure 15: Sectoral output by driver, absolute and relative difference to the baseline - Indonesia Employment impacts differ by sector, gender, and skills level. Indonesia sees even higher increases with 3.25%, with high changes also in the agriculture sector, in financial services, and professional and business services. All sectors benefit from the various aspects of the scenario, i.e., from additional household incomes, payroll tax reductions and clean energy activities. In Indonesia, employment effects are 26 crosscutting throughout the whole economy, and across skill levels but with a stark dominance on male workers. Figure 16: Additional labor demand by skill level – Indonesia, absolute and relative differences to baseline. Figure 17: Additional labor demand by gender – Indonesia, absolute and relative differences to baseline. 4.2.2. South Africa South Africa is an upper-middle income country, which is struggling with high inequality, increasing unemployment, and increasing challenges in the electricity sector and from climate change. Mining is one of the main industries in South Africa, with it being the largest producer of gold, and platinum, and a large exporter of platinum, iron products, coal, manganese, and diamonds. Coal is mined for exports and for domestic electricity generation. Electricity supply by the state-owned utility, however, is unreliable and insufficient and is seen as an important barrier to development by South African firms. In 2020, more 27 than 54 percent of all firms in the World Bank Enterprise Survey confirmed electricity to be the biggest obstacle7. South Africa has set renewable energy targets and implemented policies to promote the development and use of renewable energy sources. A multi-faceted policy mix supports South Africa’s clean transition, with a competitive bidding program, a feed-in tariff, renewable power purchase agreements to guarantee a market for clean electricity and a carbon tax. South Africa sees output drop under the 1.5C scenario, but employment will be higher than under the baseline. In the 1.5C scenario, South Africa’s ambition is even higher, and the scenario does not assume any implementation gap. Hence, the mining sector sees large losses, dragging down the respective intermediate goods production and services supporting the trade with coal, such as transport and trade itself, as well as coal fired electricity production. Total output change amounts to -1.18%. Jobs are created in more labor-intensive sectors, such as construction and services. Figure 18: Sectoral output by driver, absolute and relative difference to the baseline – South Africa The sectoral distribution of additional labor demand under the 1.5C scenario determines and preserves the need for skill levels and the opportunities for women. Since the positive labor effects are driven mainly by construction, and certain services and manufacturing, there will be more demand for low skill levels. Jobs lost in mining are also predominantly low skilled, while the electricity sector sheds a lot of high skilled jobs, too. Gains in retail, education and health, driven by gains in household income, add demand for high skilled labor to the picture (Figure 19). The latter also drive opportunities for female workers (Figure 20). Except for education and health, the additional labor demand is predominantly filled by male workers. 7 Data from the World Bank Enterprise Survey data base, https://www.enterprisesurveys.org/en/data/exploretopics/biggest-obstacle 28 Figure 19: Additional labor demand by skill level – South Africa, absolute and relative differences to baseline. Figure 20: Additional labor demand by gender – South Africa, absolute and relative differences to baseline. This reflects what the Department of Statistics for the Republic of South Africa stated in 2022: “Globally, women who are looking for work and are available to work have a tougher time finding work than men. This phenomenon is more pronounced in South Africa, with higher unemployment rates for the general population as compared to the rest of the world. In 2022, 47,0% of South African women were recorded as economically inactive. This means that almost half of the working age women in South Africa are out of labour force compared to 35,6% of their male counterparts. The latest global labour force participation rate for women is about 47% compared to 72% for men (ILO,2022). The productive potential of South Africa women in the labour market remains unused.” (Statistics South Africa 2022) 29 4.3. Muddling through Mexico, the Philippines, and Thailand have moderate but unambitious investment in clean energy and energy efficiency, leading to marginal gains from clean energy investment. Mexico and Thailand have strong potentially green economic sectors, hence more investment could yield better labor market outcomes in either country. The following analyses show, how these countries still yield small positive net jobs outcomes. 4.3.1. Mexico Mexico has shown slack growth over the past decades and its climate pledges are increasingly unambitious. Mexico submitted an updated NDC in 2022, which aims for a 35% unconditional emission reduction by 2030 compared to business as usual (BAU). However, the BAU scenario has been updated and the latest pledge falls behind the earlier 2016 pledge8. Clean energy, which in Mexico includes natural gas cogeneration, nuclear, carbon capture and storage and renewables, contributed 23.2 percent to total electricity generation. Mexico loses output under the 1.5C scenario compared to baseline, and labor demand only benefits marginally from clean energy investment. The losses in manufacturing are driven by the increased price of fossil fuels for industry, services and households, the mining sector additionally gets negative impacts from losing foreign demand and export opportunities under the 1.5C scenario. Global demand for fossil fuels is lower under this scenario, and Mexico cannot maintain a competitive position on the fossil fuel market. Construction and manufacturing are positively impacted by investment in clean energy, but to a lesser extent compared to other countries in the sample (Figure 21). Figure 21: Sectoral output by driver, absolute and relative difference to the baseline - Mexico Mexico sees small amounts of additional labor demand, mostly from increased spending on health, education, and consumption goods by households. Additional labor demand happens in sectors with high rates of female employment ( 8 https://climateactiontracker.org/countries/mexico/targets/ 30 Figure 23). Overall, Mexico has increased its female labor force participation rate to 45% in 2019, with a sharp reduction in 2020, and stepwise recovery almost to the 2019 peak value. It still falls behind the regional average with 52 percent or the average of OECD countries with 53 percent. Most skilled jobs are added in the health and education sector (Figure 22), benefitting educated women typically in the education sector. Figure 22: Additional labor demand by skill level – Mexico, absolute and relative differences to baseline. Figure 23: Additional labor demand by gender – Mexico, absolute and relative differences to baseline 4.3.2. Thailand Clean energy investment changes the pattern of sector specific economic effects from the payroll tax and the redistribution through payroll tax reductions only marginally. Clean energy investment shows output effects in construction and to a lesser extent in manufacturing. Employment gains stem mainly 31 from payroll tax cuts and induced spending from higher household income. This gives a very broad range of sectors winning and leads to increased demand for skilled labor and high shares of female workers in the winning sectors. Figure 24: Additional labor demand by skill level – Thailand, absolute and relative differences to baseline. Figure 25: Additional labor demand by gender – Thailand, absolute and relative differences to baseline. 4.3.3. Philippines In the Philippines most effects under the 1.5C scenario are small. The substitution away from fossil fuel technologies and the losses of investment in fossil fuels as well as households’ responses to energy prices 32 presses manufacturing slightly downward, compensated by the positive impacts of clean energy investment and payroll tax reductions. The largest effect is seen in construction, particularly in relative terms. This leads to a large effect also on demand for construction workers and a low to negligible effect on additional jobs for female workers. Figure 26: Sectoral output by driver, absolute and relative difference to the baseline - Philippines Figure 27: Additional labor demand by skill level – Philippines, absolute and relative differences to baseline. 33 Figure 28: Additional labor demand by gender – Philippines, absolute and relative differences to baseline. 4.4. Challenged by underinvestment Brazil, Colombia, and Türkiye spend less than 1% of GDP on clean energy and this expenditure does not suffice to counterbalance negative effects from the carbon prices. The reasons differ, with Brazil already using hydropower, and the other two countries are lagging in their pledges. 4.4.1. Brazil Brazil already generates the largest share of its electricity from renewable energy sources, predominantly from hydropower, and its climate change mitigation policies are less focused on investment. The largest contribution to climate change from Brazil is the deforestation of the Amazonas rainforest. Sinks and nature-based solutions are thus far not captured in the MINDSET model. Hence, the main negative effects in Brazil are from the technology substitution away from carbon intensive technologies and outputs stimulated by the carbon tax. Households respond negatively to higher prices, too and demand for transport and expenditures of households as employers are lower under the 1.5C scenario. 34 Figure 29: Sectoral output by driver, absolute and relative difference to the baseline - Brazil Figure 30: Additional labor demand by skill level – Brazil, absolute and relative differences to baseline. Employment responses are mixed and average basically to a zero net effect . Health and education benefit from the payroll tax decrease, reducing a known distortion on the labor market in Brazil. Reflecting the crosscutting nature of the effects, sectors with high shares of female workers see a little additional labor demand and the losing sectors are more male dominated. 35 Figure 31: Additional labor demand by gender – Brazil, absolute and relative differences to baseline. 4.4.2. Türkiye Türkiye sees an overall output loss (Figure 32), and small gains in labor demand. The reason can be mostly seen in the low climate change mitigation ambitions of the country as from their latest NDC. Fossil fuels continue to drive Türkiye’s economy, with a heavy dependence on imports, especially oil and gas (93% and 99%, respectively). Türkiye has prioritized an expansion of domestic exploration and production to help reduce its oil and gas import dependence (IEA 2021). Figure 32: Sectoral output by driver, absolute and relative difference to the baseline - Türkiye However, the country has overperformed targets set for electricity generation from renewable sources and seen growth in renewables in the past decade (notably solar, wind and geothermal). Renewable electricity generation almost tripled in the last decade (IEA 2021), and Türkiye has already exceeded its target of 38.8% of power generation from renewables set out under the Eleventh Development Plan 36 (2019-2023). Hence, if this pace is maintained, increasing investment in renewables, and especially increasing energy efficiency for some energy intensive manufacturing sectors could lead the country to reaping the benefits of the clean energy transition. Labor demand effects sum to one percent, more labor is demanded in construction and in several other sectors across the economy due to the clean energy transition and the incentive from labor tax cuts. 4.4.3. Colombia Negative impacts from the reduction of global demand for fossil fuels weigh on Colombia’s mining sector and on its inputs to the fossil fuel sectors providing industries. Clean energy investment only leads to some additional output in construction, but the effect on output remains negative under the 1.5C scenario. Total effect on employment is positively influenced by the payroll tax reductions, leading to few additional job opportunities in most sectors. Colombia has high female labor force participation rates and hence female workers see additional demand in many sectors (Figure 33). Figure 33: Additional labor demand by gender - Colombia, absolute and relative differences to baseline. 5. Summary and conclusions The results are based on simulations with MINDSET, a price endogenous MRIO to see the effects of a policy basket of a $80 carbon tax and revenue recycling via payroll tax reductions combined with detailed investment in renewable energy and energy efficiency in selected countries . The scenario simulates the start phase of a path towards 1.5C, the global pathway towards a world which keeps global warming at bay at 1.5 degrees Celsius by 2100 compared to pre-industrial levels. The approach takes the capacities of the respective country in terms of sectoral structure into consideration and shows the underlying differences and similarities in the responses of the selected countries to the 1.5C compatible pathway. The model used is rigid in technologies, domestic value added, import and export shares. If countries engage more in the clean energy transition, new regional hubs, domestic markets and trading partners might emerge. The above analysis provides a snapshot for the year 2030. 37 The analysis focuses on the medium-term future, for two reasons. Firstly, the modeling approach is better suited for short- to medium-term analysis and would need to be complemented by assumptions regarding technological change and productivity increases if driven further to the future. Secondly, as (IRENA 2021b) put it: ‘we have no time’. The time to act and start on the investment pathway which is 1.5C degree compatible is now, or at least in the very near future. The results show positive values for labor demand changes under a 1.5C compatible scenario are possible. The size of the effect depends on the country’s willingness to invest in clean energy, to price carbon emissions and use the revenues for labor friendly outcomes. These pillars have been suggested in earlier literature on the double dividend of an ecological tax reform. Empirical evidence in earlier adopters shows positive impacts of such a policy basket. Potential additional labor demand only translates into more and better jobs if the right skills are at the right place and the right time. Any caveats on spatial or skills mismatch also persist under a climate friendly scenario. Hence, this analysis warrants going deeper and show on a better spatial, structural and occupational resolution what needs to be done to turn additional labor demand into a beneficial story on domestic jobs and development. The main drivers of positive labor outcomes differ between ambitious and less ambitious countries, leading to different results under the structural changes in each economy. Ambitious clean energy investment leads to additional demand for certain manufactured goods, supporting services, construction, and for intermediate goods and services along their respective value chains. If these can be provided domestically, the effect is positive. For countries with long-term ambitious plans, the recommendation is to integrate domestic value chains and support the domestic workforce to be able to match the resulting additional labor demand. Construction sector employment benefits significantly from the clean energy transition, be it in constructing energy infrastructure, renewable energy installations, or improving energy efficiency in residential and commercial buildings. The construction sector is labor intensive and has often a domestic workforce, although in many countries dominated by migrant workers. However, construction has high shares of informal workers in many economies. This may cause a challenge to ensure and enforce quality regulation, standards and building codes. Training, including for informal workers, is needed to deliver to the technical requirements of buildings under the clean energy transition. The modeling results give a first orientation and manage expectations on impacts. To develop policies for the clean energy transition, simulations help to map the potential future pathways and to answer the ‘what-if’ question of policy design. Results obtained with a global model support analysis of drivers, allowing to group countries, by income level, resource endowment, demographic status or regions. Trade implications only play out in a full model including global trade. Country-level results need to be complemented by detailed analyses of regional impacts, labor markets and workers capacities. The net changes in labor demand are the result of negative and positive impacts, which neither need to coincide spatially, nor skill-wise, nor timewise. 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