Policy Research Working Paper 10552 Greener Is Not Always Pricier Ecolabeling and Price Premium in the Tourism Industry Ridwan Bolaji Bello Olanrewaju Kassim Sodiq Oladayo Bello MIGA Economics and Sustainability Unit August 2023 Policy Research Working Paper 10552 Abstract Voluntary ecolabeling programs have gained popularity in percent in the full sample. However, point estimates of the tourism industry as initiatives for promoting ecofriendly this premium vary across cities, from a low of 1 percent practices among tourism firms. Yet, for these programs to in Venice to a high of 22 percent in London. As a novel appeal to firms, it is crucial that they generate positive contribution, the paper shows that the ecolabel delivers a market benefits for ecolabeled firms. This paper studies the quantitatively and statistically significant price premium effect of a sustainable tourism label on prices of hotel firms. only in cities where tourism (destination) competitiveness is It uses hotel listing data collected from Booking.com and high and ecolabel attainment is low. The paper discusses the covering more than 6,000 hotels across 10 popular Euro- implications of these findings for firms and policymakers pean cities. The paper finds that the presence of the ecolabel in the industry. is associated with a price premium of approximately 10 This paper is a product of the MIGA Economics and Sustainability Unit. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at okassim@worldbank.org, rbello@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Greener Is Not Always Pricier: Ecolabeling and Price Premium in the Tourism Industry Ridwan Bolaji Bello, 1 World Bank Group, Washington DC, USA. Email: rbello@worldbank.org Olanrewaju Kassim World Bank Group, Washington DC, USA. Email: okassim@worldbank.org Sodiq Oladayo Bello University of Ilorin, Nigeria. Email: Sodiqoladayobello@gmail.com JEL Codes: D40, L83, Q50 Keywords: Ecolabel, sustainable tourism, hotel, hedonic pricing model, tourism competitiveness 1 Corresponding author. 1. Introduction Ecolabeling programs have gained popularity in the tourism industry as initiatives for encouraging tourism firms to adopt ecofriendly practices. The fundamental idea is that a credible ecolabel differentiates ecolabeled firms from competitors by signaling to consumers that these firms meet the needs of tourists while limiting detrimental impacts on the natural environment and local communities. This differentiation increases the appeal of ecolabeled firms to consumers, offering a competitive advantage often reflected as a price premium in the market (Asche & Bronnmann, 2017; Blackman & Rivera, 2010; Galarraga Gallastegui, 2002; Sipic, 2017). This idea, though intuitively simple, is contested in the literature. The main counterargument is that whereas the environmental measures that firms must undertake to qualify for an ecolabel often require non-zero implementation costs, consumers’ willingness to pay for ecofriendly services may not be sufficiently strong to deliver a positive and commensurate price differential (Sedjo & Swallow, 2002; Yenipazarli, 2015). Indeed, survey evidence shows that, despite consumer preferences in the tourism sector shifting towards more sustainable services, low cost remains the primal consideration for many tourists when booking tourism trips (Jones, 2022; Lupu et al., 2013). In this paper, we investigate the price implication of an ecolabel among hotel firms across 10 popular European cities. First, we assess whether the presence of an ecolabel on a hotel’s listing generates a price premium. Then, we analyze whether the ecolabel premium varies systematically across cities— and if so, why. As our main contribution, we show that while the ecolabel does command a price premium in all cities, this premium is only quantitatively and statistically significant in cities where tourism competitiveness is high and attainment of the ecolabel is low. The tourism industry is a fast-growing, export-oriented, trillion-dollar industry. In 2019, the industry contributed 7% of global exports and 5% of global gross domestic product (UNWTO, 2022), while international tourist arrivals and receipts grew at an annual rate of 3%–5%, outpacing the growth of international trade (Lenzen et al., 2018). However, this boom has been accompanied by adverse environmental and socioeconomic impacts. International tourism accounts for 8% of global CO2 emissions (Lenzen et al., 2018) and continued growth in the industry increases pressure on infrastructure, natural resources, and local communities (OECD, 2020). Greater awareness of these negative externalities, along with rising concerns about climate change, has in recent years led to increased consumer preferences for more sustainable tourism services (Jarvis et al., 2010). 2 Cognizant of these shifting consumer preferences, the industry is making concerted efforts to improve its social and environmental performance. Part of these efforts is the growing use of voluntary ecolabeling or eco-certification programs aimed at motivating businesses to incorporate eco-friendly practices into their operations. As an illustration, a quick scan of Ecolabel Index – a global directory of ecolabels – returns 15 different ecolabel programs targeted at addressing various environmental concerns in the tourism sector at national and global levels (Ecolabel Index, 2022). In a report based on 27,000 hotels across 54 countries, the World Travel & Tourism Council estimates that 40% of full service hotels have a third-party sustainability certification (Green Lodging Trends Report, 2022). Despite this increasing popularity of ecolabeling in the industry, empirical evidence on the effect of ecolabeling on prices in the hospitality sector is limited and inconclusive. On one hand, some studies present a positive view of the effect of ecolabeling on room rates (Rivera, 2002, Kuminoff et al., 2010; Sipic, 2017; Soler et al., 2016). Other studies present a nuanced view, reporting that ecolabeling generates market premiums for hotel firms, only in certain contexts (Segarra-Oña et al., 2012; Soler et al., 2016). We review extant literature in the next section. Our study leverages a sustainability recognition program launched in 2021 by Booking.com, a popular hotel reservation website. The Travel Sustainable Program, as it is called, assesses the operations of self-enrolled hotels and displays a “Travel Sustainable Property” badge on the listing of hotels that adopt sustainability measures. In January 2022, we visited the Booking.com website and searched for hotel options for two adult guests for a one-night stay exactly one month away from the search date. For all the hotels returned in the search results, we used webscraping techniques to extract data on room rate, ecolabel status, and several other relevant hotel attributes. Performing this procedure for the following 10 European cities: Amsterdam, Barcelona, Brussels, Copenhagen, Lisbon, London, Paris, Stockholm, Venice, and Vienna, we collected data on over 6,000 hotels. Then, we used regression analysis to examine the relationship between room rate and ecolabel status. As a preview of our results, we find that the ecolabel is associated with a price premium of approximately 10% in the full sample. However, heterogeneity tests across cities show that the point estimate of the premium ranges from a low of 1% in Venice to a high of 22% in London, but in only 5 cities – Barcelona, Lisbon, London, Paris, and Vienna – is this estimate statistically significant at the 10% threshold. Crucially, we observe that the premium is negatively correlated with ecolabel ubiquity within cities but positively correlated with cities’ tourism competitiveness. 3 The rest of the paper proceeds as follows. In section 2, we review related literature and present our research hypotheses. Section 3 describes the research context and data sources. Our analytical approach is described in Section 4, followed by results and discussion in Section 5. Section 6 concludes with implications of our results for firms and policy makers in the tourism industry. 2. Related literature and research hypothesis 2.1. Determinants of hotel room prices The determinants of hotel room pricing have been the subject of vast research interest. Sánchez- Pérez et al. (2019, 2020) provide excellent reviews of this literature, but a summary of current research is that room rates are a function of four main sets of factors: location, product differentiation, value-added services, and temporal factors. Concerning location, evidence suggests that location influences hotel room rates via its implication for agglomeration and spatial competition as hotels located near one another tend to adjust their pricing in response to their competitors’ pricing (Kim et al., 2017; Lee & Jang, 2013; Sánchez-Pérez et al., 2020; Silva, 2016). A hotel’s location also determines its accessibility which in turn influences it pricing (Yang et al., 2016). Other locational factors, such as proximity to tourist attractions and infrastructure, have also proven to be robust predictors of room rates (Lee & Jang, 2013; Pawlicz & Napierala, 2017; Soler et al., 2019). Hotel room rates are also determined by the presence of product-differentiating attributes. Perceived to be indications of superior quality by consumers, these attributes are valued higher by the market. These product-differentiating attributes include high hotel category or star rating (Espinet et al., 2003; Israeli, 2002; Pawlicz & Napierala, 2017), positive online reputation or “electronic word of mouth” (Abrate & Viglia, 2016; Nieto-García et al., 2017; Ye et al., 2009) as well as brand or chain affiliation (Becerra et al., 2013). Existing literature also indicates that hotel prices are driven by the availability of optional amenities or value-added services that are in demand by prospective guests, such as breakfast, pools, spa, Wi-Fi, children’s facilities, etc. Finally, there is growing evidence that hotels follow dynamic pricing strategies such that hotel prices vary with temporal factors such as seasonality (peak vs off-peak tourism seasons) or day of check-in (weekday vs weekend) (Wang et al., 2019). Table 1 presents the determinants of hotel room rates as reported in past literature. 2.2. Ecolabeling and price premium in the hospitality sector Although research on the economics of ecolabeling is extensive, the evidence base on ecolabeling in the tourism industry is limited. A large chunk of extant studies on ecolabeling focus on the 4 agriculture, forestry, and manufacturing sectors (Blackman & Rivera, 2010), and have often reported mixed effects of ecolabeling on firms’ prices, demand or profit (Anould et al., 2009; Delmas et al., 2008; Dragusanu et al., 2018; Feuß et al., 2022; Fort & Ruben, 2009; Lyngbaek et al., 2001; Ruben & Fort, 2012; Sáenz-Segura & Zúñiga-Arias, 2008; Zúñiga-Arias & Sáenz Segura, 2008). Table 1: Determinants of hotel room pricing from selected studies Determinants Examples in the literature Studies Location Proximity to competitors, proximity Lee & Jang, 2011, 2013; Pawlicz & Napierala, to key infrastructure (e.g., train 2017; Sánchez-Pérez et al., 2019, 2020; Silva, stations and airports), and to 2016; Yang et al., 2016; Zhang et al., 2011 tourist attractions (e.g., beach, popular landmarks, city centre). Product Star ratings, hotel brand, chain Becerra et al., 2013; García-Pozo et al., 2013; differentiation affiliation, quality certification, Israeli, 2002; Jiang & Taylor, 2020; Pawlicz & online ratings and reviews Napierala, 2017; Sánchez-Ollero et al., 2014; Sánchez-Pérez et al., 2019, 2020; Wang et al., 2019; Yang et al., 2016; Zhang et al., 2011 Value-added Breakfast, bed type, room size, García-Pozo et al., 2013; Hung et al., 2010; Kim et services housekeeping, pool, spa, airport al., 2017; Sánchez-Ollero et al., 2014; Wang et al., transfer, children facility, free 2019 cancellation, number of rooms Temporal Seasonality, booking day of week Jiang & Taylor, 2020; Silva, 2016; Wang et al., factors 2019 Source: Authors’ compilation. In the hospitality sector, studies examining the relationship between ecolabeling and market prices also report inconclusive findings. On one hand, some studies present a positive view of the effect of ecolabeling on room rate. Rivera (2002) examined the Certification for Sustainable Tourism program (CST) in Costa Rica and found that high environmental ratings obtained in the CST program increased hotel room rates by up to $30 per night. Kuminoff et al. (2010) showed that hotels participating in a government-administered eco-certification scheme in the US State of Virginia enjoyed a 23% premium on the minimum price of a standard room. Kim et al. (2017), also studying US hotels, reported that the presence of a green badge on hotels’ listing on TripAdvisor had a positive effect, not just on room rate and hotel revenue, but also on guests’ overall rating and revisit intentions. García-Pozo et al. (2013) investigated the link between environmental practices and hotel room pricing. They found that the implementation of ecofriendly measures along with the use of environmental messaging in marketing campaigns was associated with approximately 5% increase in hotel room prices. Sipic (2017) studied the Blue Flag certification in Croatia and documented a 48% price differential of the ecolabel on hotel room rate. 5 On the other hand, some studies present a nuanced view, reporting that ecolabeling generates market premiums for hotels only in certain contexts. For example, Segarra-Oña et al. (2012) analyzed the relationship between the ISO 14001 environmental certification and revenues among hotels in urban, beachfront and rural tourist destinations in Spain. They found that while hotels in urban and beachfront locations experienced greater revenues due to the eco-certificate, hotels in rural areas recorded no such revenue increases. They explain the null effect of the eco-certification in rural areas by speculating that hotel guests tend to perceive hotels in rural areas as ecofriendly, with or without the possession of an eco-certificate; therefore, eco-certification did not confer any product- differentiating benefits on hotels in rural areas. In another example, Soler et al. (2016), using data from TripAdvisor, found that a green tag on hotels’ listings is associated with a 14% increase in average hotel room rate in Barcelona but is unrelated to room rate among hotels in Madrid. They contend, albeit without empirical evidence, that the absence of an ecolabel price premium in Madrid is an indication that hotel guests in Madrid are more sensitive to prices than to environmental quality of hotel rooms. 2.3. Research hypotheses The review of relevant literature in the previous section raises a yet unanswered question: why does the ecolabeling premium vary across contexts? In particular, under what conditions does ecolabeling generate price-raising benefits for hotel firms? Tackling these questions with empirical evidence is the main contribution that this study makes to the literature. A priori, we make two predictions underpinned by the axiomatic theory of demand and supply. First, we predict that the price premium generated by an ecolabel decreases with the ecolabel’s ubiquity. The intuition is that a ubiquitous ecolabel signifies that the underlying eco-friendly services that the ecolabel represents are available in abundant supply and will therefore be valued less expensively by market forces. Second, we predict that the price premium generated by an ecolabel increases with the tourism competitiveness of the destination where the ecolabeled hotel is located. The intuition here is that destinations with better developed and more competitive tourism sectors have stronger capacities to attract prospective tourists and therefore generate greater tourism arrivals and receipts (Cao et al., 2022). In turn, this translates to a potentially larger pool of consumers with higher willingness-to-pay for ecofriendly services, which drives up the ecolabel premium. Figure 1 represents these predictions with a simple framework. 6 In the context of the present study, these predictions translate to two testable hypotheses about the “Travel Sustainable Property” ecolabel: Hypothesis 1: The price premium of the ecolabel is negatively related to the proportion of hotels that have adopted it. Hypothesis 2: The price premium of the ecolabel is positively related to the tourism competitiveness of the city destination where it is adopted. Figure 1: Conceptual framework Source: Author’s conception 3. Context and data 3.1. Booking.com and the Travel Sustainable Program This study derives its context from the initiatives of Booking.com, the website of a popular travel and tourism company. Available in 43 languages and listing more than 28 million hotel firms including hostels, hotels, aparthotels, and resorts, Booking.com is one of the world’s leading reservation platforms for tourists seeking travel and hospitality services. The website displays copious details on listed hotels and prospective guests utilize these details to inform their reservation choices. Hotels have an incentive to keep the information provided on their listing accurate given that Booking.com operates a guest review and rating system. Upon completing a stay in a hotel, guests are asked to provide a voluntary review and rating of the hotel’s services. Often included in these reviews are guests’ assessments of the veracity of the information displayed on hotels’ listing at the time of reservation. These reviews and ratings are then published on Booking.com for prospective guests to consider when making reservations. 7 In 2021, Booking.com launched a Travel Sustainable Program, an initiative to recognize and promote hotel firms that incorporate sustainability measures into their operations. In this program, the recognition process involves assessing the operations of self-enrolled hotel firms against 32 measures across five social and environmental topics including: waste, energy and greenhouse gases, water, supporting local communities, and protecting nature. Specific examples of these measures include switching to energy-efficient lighting equipment, eliminating single-use plastic toiletries, running on 100% renewable energy sources, as well as investing a certain percentage of profits into local community and conservation projects. Firms that are verified to adopt appropriate selections of these 32 measures have a “Travel Sustainable Property” badge added to their listing. 2 3.2. Data 3.2.1. Hotel room pricing and attributes In January 2022, we visited Booking.com and searched for accommodation options for two adult guests for a one-night stay exactly one month away from the date of the search. Once the browser returned the search results, we used Python to extract information on all the hotel listing in the search results. Performing this data collection procedure for Amsterdam, Barcelona, Brussels, Copenhagen, Lisbon, London, Paris, Stockholm, Venice, and Vienna, we collected data on a total of 6,262 hotels. These cities represent important international tourism markets and are among the top 100 travel destination cities in the world (Yasmeen et al., 2021). For each hotel firm in our data, the information we collected includes core attributes such as name of the hotel, room rate (in USD per night), ecolabel status, bed type, bed count, number of online reviews, and aggregate online rating. We also extracted geographical attributes such as city, distance to city center, and metro access. Other attributes captured from the website reflect value-added services such as breakfast option, prepayment waiver, and free cancellation option. Figure A1 (in the Annex) represents a sample listing showing the extracted hotel attributes and Table 2 presents definitions of the variables used in our analysis. In addition to hotel attributes, we also extracted the names of the hotel brands or chains operating in each city in our sample. We then use these brand names to construct a variable indicating chain- affiliation of the hotel firms. Essentially, we applied string similarity techniques on hotel names such that a hotel is chain-affiliated if its name matches that of any hotel brand in our data. 2 Details of the badge administration process are available at https://www.sustainability.booking.com/travel-sustainable. 8 Two features of our data are worth highlighting. First, 28% of the hotels in our data offered a price discount at the time of our search. For these hotels, the booking.com website displays both the undiscounted and discounted room rates. In such cases, our analysis utilizes the discounted price since this is the price that consumers would pay if they reserved a room at that moment. Second, at the time of our search, the Booking.com website did not display information on “distance to city center” for any hotels located in London. This is likely because we limited our hotel search in London to Central London rather than the Greater London Area. The Greater London Area includes several boroughs outside of the city of London, and observations from these outer boroughs may create noise in the London sample. As we discuss in Section 5, we address this issue in our analysis by running two versions of our regression model: one that includes the “distance to city center” as a covariate but excludes observations from London, and another that excludes “distance to city center” as a covariate but includes observations from London. Table 2: Variable definitions Variable Definition Room rate Price (in USD per night) that consumers pay for the right to stay in a hotel. If hotel offers a discount, room rate is the discounted price. Ecolabel status Dummy = 1 (labeled) if hotel property has attained the “Travel Sustainable Property” badge; 0 (unlabeled) otherwise. City City where the hotel is located. Property type Dummy = 0 if property is a hotel and 1 if aparthotel. Chain-affiliated Dummy = 1 if the hotel is part of a hotel chain; 0 otherwise. Total online reviews Total number of consumers that have reviewed the quality of a hotel’s services on booking.com. Aggregate online rating A number between 1 (worst) and 10 (best) computed as an average of all consumer ratings a hotel has received on booking.com. Distance to city center Distance (in km) between hotel location and the city center. Metro access Dummy = 1 if location of hotel is proximal to an intra-city transport terminal such as a metro, train or bus stop; 0 otherwise. Breakfast included Dummy = 1 if breakfast is included in the room rate; 0 otherwise. Prepayment waiver Dummy = 1 if hotel does not require consumers to pay a deposit at the time of booking to secure their reservation; 0 otherwise. Free cancellation option Dummy = 1 if hotel allows consumers to cancel a reservation free of charge; 0 otherwise. Tourism demand City ranking ranging based on index of overall (domestic and international) tourism demand. Share of ecolabel adopters Share of hotels in city that obtained the “Travel Sustainable Property” badge. Source: Authors’ compilation 9 3.2.2. Tourism competitiveness We obtained data on cities’ tourism competitiveness from the Top 100 City Destination Index, a metric which ranks cities based on their performance as hubs for tourism activity and investment. Provided by Euromonitor International, a leading market research firm, the index combines 54 indicators across six key pillars to compute an overall tourism competitiveness score for cities. These pillars are: (1) economic and business performance, (2) tourism performance, (3) tourism infrastructure, (4) tourism policy and attractiveness, (5) health and safety, and (6) sustainability (Yasmeen et al., 2021). After selecting the pillars and associated indicators, the index weighs these pillars to reflect their relative importance to determining cities’ attractiveness for tourism businesses and visitors. The index then ranks the top 100 cities based on their computed scores. Details of each pillar, weights and underlying indicators are provided in Table A1 (in the Annex), while further details on the index’s methodology are available in (Yasmeen et al., 2021). Although other measures of tourism competitiveness exist (e.g., the World Economic Forum’s Travel & Tourism Development Index), we use the Top 100 City Destination Index because it provides city- level data. 3 This is important because cities are often the centers of tourism activity within countries (Bock, 2015; Traykov & Naydenov, 2017; World Tourism Organization, 2012), and a localized measure like the Top 100 City Destination Index is a better reflection of tourism competitiveness within cities than country-level measures like the Travel & Tourism Development Index. 3.3. Descriptive statistics Table 3 and Table 4 respectively present hotel-level and city-level descriptive statistics from our study data. Figure 2 shows the distribution of room rates in each city. Figure 3 shows the difference between median room rate of ecolabeled versus unlabeled hotel firms across cities. In the full sample, room rates per night range from a low of USD21 to USD21,739, with mean of USD170 and median of USD125 (Table 3). Across all 10 cities in our sample, median hotel room rates are highest in Paris and lowest in Lisbon (Figure 2). Meanwhile, based on tourism competitiveness, Paris ranks highest (rank number 1) and while Copenhagen ranks lowest (rank number 47) among the 10 cities in the study (Table 4, column 5). Of the hotels in the full sample, 14% attained the ecolabel, but this proportion varies across cities from 6% in Paris to 48% in Stockholm (Table 4, column 4). Interestingly, at a descriptive level, the 3 The 100 City Destinations Index builds on and replaces Euromonitor International’s Top 100 Cities Destinations report, which ranked leading travel city destinations based on the number of international arrivals and was published annually until 2020 (Yasmeen et al., 2021). 10 unconditional median room rate of ecolabeled hotels exceeds that of unlabeled hotels in all 10 cities, although the magnitude of the price differential varies noticeably across cities (Figure 3). Table 3: Hotel-level descriptive statistics Variable Observations Mean Min Median Max Room rate (USD per night) 5873 170.17 21 125 21,739 Ecolabel status (labeled = 1) 5873 0.14 0 0 1 Property type (hotel = 0, aparthotel = 1) 5873 0.30 0 0 1 Chain-affiliated (Yes = 1) 5873 0.14 0 0 1 Number of online reviews 5873 1004.09 3 557 20,398 Online rating (1 = worst, 10 = best) 5873 8.24 1 8.4 10 Distance to city center (km) 5077 2.01 0 1.45 39.10 Metro access (Yes=1) 5873 0.69 0 1 1 Breakfast included (Yes=1) 5873 0.13 0 0 1 Prepayment waiver (Yes=1) 5873 0.47 0 0 1 Free cancellation (Yes=1) 5873 0.92 0 1 1 Notes: 1. Authors calculation based on data obtained from Booking.com 2. The variable “Distance to city center” has fewer number of observations because this variable is missing for hotel observations in London. Figure 2: Distribution of hotel room rates, by city Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com 2. Lower and upper whiskers represent 5th and 95th percentile respectively. 11 Table 4: City-level descriptive statistics Hotels Hotels Share of Tourism City All with no with ecolabel competitiveness hotels ecolabel ecolabel adopters rank (1) (2) (3) (4) (5) Paris 879 825 54 6% 1 Lisbon 841 779 62 7% 17 Vienna 817 694 123 15% 11 London 796 692 104 13% 8 Venice 708 654 54 8% 27 Barcelona 679 565 114 17% 10 Amsterdam 503 358 145 29% 3 Brussels 314 265 49 16% 29 Stockholm 187 98 89 48% 40 Copenhagen 149 94 55 37% 47 Notes: 1. Authors’ calculations based on data obtained from Booking.com and Top 100 City Destination Index 2021. 2. Tourism competitiveness rank indicates cities’ performances as hubs for tourism activity and investment. Lower is better. 3. Table is sorted in decreasing order of the number of all hotels. Figure 3: Median room rates of ecolabeled vs. unlabeled hotels, by city Notes: 1. Each bar is annotated with the median hotel room rate in the city it represents. 2. For aesthetic purposes only, the bars are arranged in increasing order of the room rate of unlabeled hotels. 4. Econometric approach The aim of this analysis is to evaluate the effect of the “Travel Sustainable Property” badge on room rate. In line with the Hedonic Pricing Model commonly used in the hospitality research literature, our 12 empirical approach models room rate as a function of several determinants of hotel room pricing already identified in the literature, while including the ecolabel as a regressor in the model. Our regression specification is as follows: = 0 + µ + 1 + 2 + 3 + + Ɛ (1) In Equation 1, the dependent variable is the price per night of a room in hotel i located in city c. We utilize a logarithmic transformation of this variable to address potential issues relating to outliers and skewness. , the independent variable of interest, is a binary variable defined as 1 if a hotel i located in city c has the ecolabel badge on its listing on Booking.com and 0 otherwise. is a vector of product-differentiating variables controlling for observable attributes that may increase a hotel’s pricing by signaling superior quality to prospective guests. Earlier findings in the literature suggest that such product-differentiating variables include online ratings (Sánchez-Pérez et al., 2019, 2020) and chain-affiliation (García-Pozo et al., 2013; Israeli, 2002; Sánchez-Ollero et al., 2014; Wang et al., 2019)—we include these variables in our model. Hotel star rating is another common indicator of product differentiation in the literature (Pawlicz & Napierala, 2017; Zhang et al., 2011), but this attribute is not included in our model given the multi-country nature of our analysis. Official hotel rating systems often differ across countries such that hotels of reasonably similar qualities may get different star ratings in different countries (López Fernández & Serrano Bedia, 2004; Núñez-Serrano et al., 2014). In addition, there is significant quality overlap between adjacent levels of the star rating hierarchy (Sánchez-Pérez et al., 2019). Hence, we reckon that these issues significantly undermine the reliability of star rating as a measure of hotel quality in this study. is a vector of optional amenities or value-added services offered by hotels, including breakfast offer, prepayment waiver, and free cancellation option. represents a set of locational factors that past studies have identified as robust determinants of room rate, including distance to city center (Lee & Jang, 2011; Pawlicz & Napierala, 2017) and metro/subway access (Lee & Jang, 2011; Zhang et al., 2011). We also include city fixed effects ( ) to account for city-level differences in the hotel sector that may influence room pricing of all hotel firms within the same city. As each city in the study sample is in a different country, city fixed effects also capture country-level differences in macroeconomic conditions, such as consumer price index and exchange rates, that may influence hotel pricing strategy. 13 Although hotel firms have been known to utilize inter-temporal dynamic pricing strategies (Abrate et al., 2012), our model does not account for temporal factors such as time, day or month of booking or check-in. Nor does it need to. During data collection, we searched for hotel rooms to check in on the same day and the data was extracted for all 10 cities within a matter of minutes. Hence, the data collection method has effectively removed temporal variation from the data. Overall, Equation 1 is estimated using an OLS model and, conditional on the controls in the model, µ is the estimate of the price premium due to the “Travel Sustainable Property” badge. 5. Results and discussion 5.1. Effect of ecolabel on hotel room rates Table 5 presents the results of running different specifications of equation 1. As a baseline, we begin by regressing the log-transformed room rate only on the ecolabel status. The result yields a coefficient of 0.157, indicating that average room rate for a standard double room is approximately 15.7% higher in ecolabeled hotel firms than in unlabeled counterparts (Table 5, column 1). In simple terms, this baseline estimate represents the average effect of the ecolabel on room rate plus any bias due to differences in the attributes of ecolabeled and unlabeled hotels in the sample. We sequentially partial out this potential bias first by controlling for product-differentiating, locational factors and value added services, as defined in section 4. In this expanded model, the coefficient of the ecolabel reduces to 0.091, but remains statistically significant at the 1% level. Next, we include city fixed effects to capture the influence of locational factors that are common to hotels operating within the same city (column 3). The marginal effect of the ecolabel status remains stable at 0.107 and statistically significant at 1% level. Finally, we include the “distance from city center” variable as a covariate in the model, thereby excluding observations from London from the regression sample. The marginal effect of the ecolabel, though slightly lower in magnitude (0.090) remains consistent with earlier results in direction and statistical significance. Overall, these results show that, holding other relevant factors constant, the average room rate of a standard double room in an ecolabeled hotel is approximately 10% higher than in an unlabeled hotel. Given that the average room rate in the full sample is USD170, this ecolabel premium amounts to approximately USD17 per night. The coefficients of several of the other covariates in our regression results have the expected signs. A standard double room is on average pricier in aparthotels than in regular hotels. The effect of chain 14 affiliation, online rating, and metro access on room rate are all plausibly positive and statistically significant. As expected, distance to city center, when included in the model, is negatively correlated with room rates. One interesting result is the positive effect of prepayment waiver on room rate. This suggests that all else equal, hotels charge higher room rates when they offer guests the option to defer payment, either in part or in full, at the time of booking. Table 5: Determinants of hotel room rates Room rate (ln) Baseline Extended Extended model Extended model, model model with city FE excl. London VARIABLES (1) (2) (3) (4) Ecolabel status (labeled = 1) 0.157*** 0.091*** 0.107*** 0.090*** (0.023) (0.022) (0.019) (0.020) Property type (hotel = 0, aparthotel = 1) 0.035* 0.152*** 0.160*** (0.021) (0.018) (0.019) Chain-affiliated (Yes = 1) 0.172*** 0.168*** 0.177*** (0.022) (0.018) (0.020) Number of online reviews (ln) -0.073*** -0.076*** -0.068*** (0.005) (0.005) (0.005) Online rating (in std units) 0.218*** 0.261*** 0.242*** (0.007) (0.006) (0.007) Metro access (Yes=1) 0.226*** 0.115*** 0.035* (0.016) (0.017) (0.019) Breakfast included (Yes=1) -0.004 0.104*** 0.141*** (0.022) (0.019) (0.021) Prepayment waiver (Yes=1) 0.254*** 0.161*** 0.150*** (0.017) (0.014) (0.015) Free cancellation (Yes=1) 0.004 -0.063*** -0.051** (0.028) (0.024) (0.024) Distance to city center (ln) -0.093*** (0.008) Constant 4.865*** 4.977*** 5.164*** 5.162 *** (0.009) (0.042) (0.041) (0.043) City fixed effects No No Yes Yes Observations 6,262 5,873 5,873 5,071 R-squared 0.007 0.222 0.456 0.445 Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com 2. Standard error in parenthesis. *** p<0.01, ** p<0.05, * p<0.1 5.2. Heterogeneity by city In this section, we assess whether the ecolabel premium varies across cities by estimating equation 1 separately for each city in our sample, including all covariates in the Equation 1 (except city fixed 15 effects). Figure 4 plots the marginal effects of ecolabel status on room rate, as obtained from these city-specific regressions. Figure 4 shows that the ecolabel has a positive effect on room rate in all 10 cities in our sample, with point estimates ranging from a low of approximately 1.4% in Venice to a high of 22% in London. However, only in 5 cities – Vienna, Barcelona, Lisbon, Paris, and London – is the marginal effect large or precise enough to be statistically significant at 10% level or lower. Figure 4: Effect of ecolabel on hotel room rate, heterogeneity by city Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com 2. The round marker represents the point estimate of the marginal effect of ecolabel status on (log of) room rate in each city and the whiskers represent the 95% confidence intervals. 3. Names of cities in the legend are arranged in the same order as the coefficients in the plot. What do Vienna, Barcelona, Lisbon, Paris and London have in common that is driving the significant ecolabel premium on room rate in these cities? To answer this question, we test the hypotheses outlined in section 2.2 by re-estimating equation 1 on the full sample, while including interaction terms of the ecolabel status with measures of tourism competitiveness and ecolabel ubiquity. As these two measures are observed at the city level, we cluster the standard errors at the city level to account for correlation between residuals of observations located in the same city (Abadie 2022). Table 6 presents the results of these regressions. 16 Table 6: Effect of ecolabel on room rate, heterogeneity by tourism competitiveness ecolabel ubiquity Room rate (ln) Original Modified Modified model model model VARIABLES (1) (2) (3) Ecolabeled status (labeled = 1) 0.107*** 0.128*** 0.159** (0.019) (0.030) (0.061) Property type (hotel = 0, aparthotel = 1) 0.152*** 0.153** 0.151** (0.018) (0.066) (0.066) Chain-affiliated (Yes = 1) 0.168*** 0.173*** 0.169*** (0.018) (0.040) (0.041) Number of online reviews (ln) -0.076*** -0.076*** -0.076*** (0.005) (0.018) (0.018) Online rating (in std units) 0.261*** 0.261*** 0.261*** (0.006) (0.026) (0.026) Metro access (Yes=1) 0.115*** 0.115*** 0.116*** (0.017) (0.014) (0.014) Breakfast included (Yes=1) 0.104*** 0.106* 0.108* (0.019) (0.053) (0.054) Prepayment waiver (Yes=1) 0.161*** 0.161*** 0.161*** (0.014) (0.017) (0.017) Free cancellation (Yes=1) -0.063*** -0.062 -0.064 (0.024) (0.065) (0.064) Share of ecolabeled hotels (in std units) 0.223*** (0.019) Ecolabeled hotel x Share of ecolabeled hotels -0.048** (0.015) Tourism competitiveness rank (smaller is better) -0.041*** (0.004) Ecolabeled hotel x Tourism competitiveness rank -0.003 (0.002) Constant 5.164*** 4.822*** 5.274*** (0.041) (0.124) (0.141) City fixed effects Yes Yes Yes Observations 5,873 5,873 5,873 R-squared 0.456 0.457 0.456 Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com and Top 100 City Destination Index 2021. 2. Standard error, clustered at city level, in parenthesis. *** p<0.01, ** p<0.05, * p<0.1 The table shows that interaction of ecolabel status and ecolabel ubiquity has a negative and statistically significant effect on room rate. The effect of the interaction of ecolabel status and tourism competitiveness rank is also negative, but statistical non-significant at the 10% threshold (p = 0.154). These trends are shown more clearly in figures 5 and 6, both of which graphically presents the results of the interaction effects from Table 6. Figure 5 shows that the ecolabel premium decreases as the 17 ecolabel becomes more widely adopted. In fact, the model predicts that the premium becomes statistically indistinguishable from zero for values of the share of ecolabeled hotels greater than 2 standard deviations above its mean value. This result confirms hypothesis 1. Similarly, figure 6 shows that the ecolabel premium decreases as the city tourism competitiveness rank rises (meaning cities’ touristic attractiveness reduces). Crucially, the estimated premium is statistically positive in cities with competitiveness rank below 35. This result confirms hypothesis 2. Overall, both these findings imply that the ecolabel premium reduces as the ecolabel becomes more commonplace and as cities’ tourism competitiveness rank increases (meaning touristic attractiveness reduces). 5.3. Discussion As consumer preferences shift towards more ecofriendly tourism services, better understanding of how the provision of such services influence market advantage and prices is important for firms in the tourism industry. In this study, we use hotel listing data from 10 European cities to investigate the effect of offering ecofriendly tourism services, as signaled by the attainment of a sustainable tourism ecolabel, on room rates among hotel firms. We find that the ecolabel does command a positive price premium, implying a competitive advantage for ecolabeled firms. We also find that the magnitude of the ecolabel premium is moderated by the ubiquity of the ecolabel as well as destination competitiveness of the cities where hotel firms are located. Simply put, the market benefits of eco-friendliness are greatest when hotel firms are uniquely ecofriendly and located in a city with high tourism destination competitiveness. These results complement earlier findings in the literature. One study closely related to ours is Segarra-Oña et al. (2012) which examined the effect of eco-certification on hotel firms in urban, beachfront, and rural tourist destinations in Spain. They found that eco-certification delivers price premium for hotel firms in urban and beachfront destinations but not in rural destinations. We contend that their results support our argument by way of deductive logic—there are no ecolabel price premiums in rural destinations because (1) rural areas attract lower volumes of tourist arrivals than urban and beachfront destinations (UNWTO, 2020), and (2) pro-environmental attitudes and behaviors tend to be stronger in rural than urban areas (Berenguer et al., 2005) and thus, rural destinations may have a higher proportion of hotel firms that adopt ecofriendly practices. Another closely related study is Sipic (2017) which studied the effect of the Blue Flag ecolabel on tourism prices in Croatia. This study espoused the theoretical view that ecolabels deliver competitive 18 advantages for businesses when three conditions are exist: (1) customer willingness-to-pay for labeled service, (2) label credibility, and (3) label exclusivity. Our findings resonate with the first and third conditions. Figure 5: Effect of ecolabel on room rate, heterogeneity by ecolabel ubiquity Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com 2. The line represents the linear prediction of the model, and the shaded area represents the 95% confidence intervals. Figure 6: Effect of ecolabel on room rate, heterogeneity by tourism competitiveness Notes: 1. Source: Authors’ calculations based on data obtained from Booking.com 2. The line represents the linear prediction of the model, and the shaded area represents the 95% confidence intervals. 19 One improvement that this research makes over these earlier studies is that, unlike its predecessors which focused on a single country, the current research utilizes a larger sample of hotel firms in cities across multiple countries. This allows us to test the robustness of the ecolabeling-room rate relationship across multiple contexts, thus increasing the external validity of our results. Additionally, it allows us to go beyond theoretical postulations and empirically assess whether certain theorized factors or conditions moderate the relationship between ecolabeling and prices. Yet, two concerns with our methodological approach are noteworthy. First, our regression model fails to control for managerial capabilities of hotel firms, an unobservable characteristic that may affect both ecolabel attainment and room rate (Blackman & Rivera, 2010). We did control for service quality (online rating) and chain affiliation, which some literature indicate can act as proxies for hotels’ managerial capability (Sipic, 2017). But this is admittedly second-best to statistical techniques that explicitly account for this unobservable attribute. Second, one may wonder whether the effects reported in our study are driven by reverse causality – that is whether hotel firms with higher prices are more likely to adopt the ecolabel. Yet, we submit that this reverse causal effect, though impossible to rule out categorically given the cross-sectional nature of our data, is unlikely to play a significant role in our findings. This is because the decision to obtain an ecolabel is mainly driven by non-price attributes, crucially corporate structure, financial resources, and firm size (Bowen, 2002; Buunk & van der Werf, 2019), and room rate is merely a consequence of these underlying attributes. Having controlled for chain-affiliation which we consider as an observable and reasonable proxy for these attributes, we expect any reverse effect of room rate on ecolabel status to be minimal. 6. Policy implications The agenda for the hospitality sector to become more ecofriendly is an important one, and our study offers lessons relevant to this agenda, both for firms and policy makers in this sector. According to the Global Hotel Decarbonization Report (2017), the global hotel industry must reduce its greenhouse gas emissions per room per year by 66% of 2010 levels by 2030 and 90% by 2050, in order to align with the Paris Climate Agreement. Such ambitious targets would require hotel firms to undertake significant operational changes to achieve the required environmental standards. Given profit-making objectives, hotel firms are more likely to subscribe to this agenda if there are clear indications that eco-friendliness yields market rewards, either in terms of higher prices or reduced costs. Like its predecessors, this study provides evidence that providing ecofriendly services, signaled by the attainment of a sustainable tourism ecolabel, delivers market benefits for hotel firms, in the form of higher hotel room rates. But unlike its predecessors, this study highlights the 20 conditions under which hotel firms might expect to reap such market benefits. These insights are valuable for hotel firms seeking to position themselves in a world in which consumer and industry preferences are shifting towards greener, more sustainable services. For policy makers, our results demonstrate that improving cities’ tourism destination competitiveness can support hotel firms to adopt ecofriendly measures by increasing the economic value that these firms are able to derive from implementing such measures. As ecolabels – and the ecofriendly services they represent – become more commonplace, improving tourism destination competitiveness can also offset the downward pressures that such ubiquity will exert on the price premiums of ecofriendly services. Our results are also relevant for audiences within the World Bank Group, particularly in IFC and MIGA, where technical teams engage with the private sector on decarbonization and climate change. 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Consumer expenditure on transport Consumer expenditure on leisure and recreation Total population Share of population 15–64 in total population Percentage of households with access to Internet Possession of mobile telephone Employment in trade; motor vehicles repair, Personal and household goods; Hotels and restaurants; Transport Unemployment rate Tourism This pillar encompasses the overall tourism demand in the International arrivals by city performance (20%) city by international and domestic tourists from the Inbound length of stay country as well as value generated by these visitors. Average daily spend by arrival Domestic trips per capita Average spend per domestic trip Tourism This pillar measures the presence of physical Hotels Infrastructure infrastructure and attractions needed to support visitor Other lodging (25%) growth in a city. Number of airport passengers Passenger cars in use Sports Clubs Index Most visited top 100 museums Number of Universities in the top 200 Top things to do Places to shop Number of shopping malls Number of foodservice outlets Tourism policy and This pillar explores regulatory aspects such as ease of Visa requirements attractiveness travel, price stability of a destination and social media Tax free shopping (20%) presence as these contribute to the attractiveness of a city Reviews of the top city attractions on social media platforms destination given changing consumer lifestyles. City popularity on Google trends Health and Safety This pillar captures health and safety in cities as it relates Total cases per capita in the country (10%) to tourism including visa policies and traveller Deaths per capita in the country immunisation. covid vaccination in the country Political Stability and Absence of Violence Index Corruption Perceptions Index Road injury accidents Global Terrorism Index Retail and recreation Parks Grocery & pharmacy Transit stations Sustainability The pillar looks at city performance on aspects such as Mean temperature growth (10%) pollution and levels of over-tourism as these reshape PM2.5 tourist preference for destinations. Population: tourism ratio Population: tourism density UNESCO heritage sites Sustainable Travel Index Notes: 25 1. According to the index’s methodology, available in Yasmeen et al., 2021, the selection of indicators, pillars, and weights is based on wide-ranging consultations with industry experts within and outside Euromonitor International. 26 Figure A1: A sample hotel listing on booking.com Notes: 1. A = hotel name; B = location; C = distance to city center; D = metro access; E = Total online reviews; F = online rating; G = sustainable badge; H = room type; I = bed type; J = breakfast offer; K = free cancellation option; L = prepayment waiver; M = Undiscounted room rate; N = Discounted room rate. 27