# Fighting Climate Change: International Attitudes toward Climate Policies

Antoine Dechezleprêtre, Adrien Fabre, Tobias Kruse, Bluebery Planterose,

Ana Sanchez Chico, Stefanie Stantcheva

► To cite this version:

Antoine Dechezleprêtre, Adrien Fabre, Tobias Kruse, Bluebery Planterose, Ana Sanchez Chico, et al.. Fighting Climate Change: International Attitudes toward Climate Policies. American Economic Review, 2025, 115 (4), pp.1258-1300. 10.1257/aer.20230501. hal-05459604

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Fighting Climate Change:
International Attitudes Toward Climate Policies*

Antoine Dechezleprêtre, Adrien Fabre, Tobias Kruse,
Bluebery Planterose, Ana Sanchez Chico, and Stefanie Stantcheva

March 23, 2022

[UNDER EMBARGO: PLEASE DO NOT POST ONLINE. COMMENTS WELCOME!]

**Abstract**

Using new surveys on more than 40,000 respondents in twenty countries that account for 72% of global CO2 emissions, we study understanding and attitudes toward climate change and climate policies. We show that, across countries, support for climate policies hinges on two key factors: the perceived effectiveness of the policies in reducing emissions and their perceived distributional impacts on one's own household (self-interest) and on lower-income households more generally (inequality concerns). We also show that information that specifically addresses these two key concerns (effectiveness and distributional impacts) can substantially increase the support for climate policies in many countries. Furthermore, we identify which socioeconomic and lifestyle factors are most predictive of policy views. We show that respondents' own experience and energy usage characteristics affect their policy views directly by influencing their expected financial gains or losses. But in addition, they also shape people's views about the overall effectiveness and distributional consequences of policies, thus further influencing their support for climate policies.

**JEL codes:** Q54, Q58, D78, H23, P48

**Keywords:** Climate change, climate policies, carbon tax, green energy, experiment

<footnote>*Corresponding author: Stantcheva: Harvard, CEPR, and NBER (e-mail: sstantcheva@fas.harvard.edu). Dechezleprêtre: OECD and LSE (e-mail: antoine.dechezlepretre@oecd.org); Fabre: ETH Zürich (e-mail: fabre.adri1@gmail.com); Kruse: OECD and LSE (e-mail: tobias.kruse@oecd.org); Planterose: Social Economics Lab and EU Tax Observatory (email: bluebery.planterose@sciencespo.fr); Sanchez Chico: Harvard and Opportunity Insights (e-mail: anasanchezchico@g.harvard.edu). We are grateful for financial support from the OECD Center for Tax Policy and Administration, the OECD Environment Directorate, the French Ministry of Foreign Affairs, the French Conseil d'Analyse Economique and the Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO). We thank Laurence Boone, Aida Caldera, Tomasz Kozluk, Philippe Martin and Hélène Paris for valuable comments and feedback. The project is approved by IRB at Harvard University (IRB21-0137), and was preregistered in the AER RCT Registry (AEARCTR-0007300).</footnote>
# Contents

<table><tr><td>1</td><td>Introduction</td><td>3</td></tr><tr><td>2</td><td>The survey</td><td>7</td></tr><tr><td>2.1</td><td>Survey data collection and sample</td><td>7</td></tr><tr><td>2.2</td><td>The questionnaire</td><td>9</td></tr><tr><td>2.3</td><td>Logic of the analysis</td><td>13</td></tr><tr><td>3</td><td>Knowledge about climate change</td><td>13</td></tr><tr><td>3.1</td><td>What do people know?</td><td>14</td></tr><tr><td>3.2</td><td>Who has better knowledge?</td><td>15</td></tr><tr><td>3.3</td><td>Expectations about climate change</td><td>15</td></tr><tr><td>4</td><td>Who supports climate action?</td><td>16</td></tr><tr><td>4.1</td><td>Cross-country comparisons</td><td>16</td></tr><tr><td>4.1.1</td><td>Climate policies</td><td>16</td></tr><tr><td>4.1.2</td><td>Willingness to adopt climate-friendly behaviors</td><td>17</td></tr><tr><td>4.2</td><td>Individual characteristics correlated with support for climate policies</td><td>18</td></tr><tr><td>5</td><td>How do respondents reason about climate policies?</td><td>19</td></tr><tr><td>5.1</td><td>Perceived distributional and efficiency impacts</td><td>19</td></tr><tr><td>5.2</td><td>How do different groups of respondents reason about climate policies?</td><td>20</td></tr><tr><td>6</td><td>What reasoning underlies support for climate policies?</td><td>21</td></tr><tr><td>6.1</td><td>Factors correlated with policy support</td><td>21</td></tr><tr><td>6.2</td><td>Information treatments</td><td>21</td></tr><tr><td>7</td><td>Conclusion</td><td>24</td></tr><tr><td>8</td><td>References</td><td>38</td></tr></table>
# 1 Introduction

Climate change is an existential threat. Temperatures are reaching record highs, the word “unprecedented” keeps being added in front of extreme climate events like droughts or wildfires, and sea levels are threatening the areas of residence and livelihoods of millions of people around the world (IPCC 2021; NOAA 2021). Economic losses incurred from large weather- and climate related events amounted to over USD$ 740 billion between 2017 and 2021 in the United States, the highest five-year average on record, with damages of USD$ 145 billion in 2021 alone (NCEI 2022). In Europe, climate-related extremes already accounted for about 80% of total economic losses from natural hazards between 1980 and 2020 (EEA 2022), and the impacts of climate change are expected to rise with temperature increases (IPCC 2022). Between 2030 and 2050, climate change is expected to cause 250,000 additional deaths annually, mostly in developing countries (WHO 2021). To address climate change, countries are adopting more ambitious environmental policies and targets. In the 2015 Paris Agreement and at the 2021 Glasgow Climate Summit, countries pledged to limit the average temperature increase to well below 2°C (and to pursue efforts to limit warming to 1.5°C). However, with the currently implemented policies, average temperatures are still expected to rise to about 2.7°C by 2100, increasing the likelihood of catastrophic impacts for societies and economies (Climate Action Tracker 2021; IPCC 2022). To prevent a disaster, further policies are required.

Yet, despite these frightening prospects, climate policies have often been difficult to pass, even when the objective of limiting global warming is broadly accepted. Our new large-scale international survey across 20 countries shows that while in each country at least three-quarter of respondents (and often much more) agree that “climate change is an important problem” and that their country “should take measures to fight climate change” (see Figure 1), this may not (and often, does not) translate into tangible support for climate policies. Relative to other policy areas, public acceptability of measures to fight climate change is further complicated by the public good and global nature of the problem, the uncertainties about future impacts, and the long time horizon over which the impacts of any action taken today play out.

In this paper, we seek to understand what drives support for or opposition to various important climate policies across the world. Does opposition to climate policies simply stem from a lack of knowledge of the impacts of climate change? Are citizens concerned with the impacts of policies on their own budget and lifestyle? Do they hold broader concerns about the effects of climate policies on others and on the economy? Or do they misunderstand how any given policy will reduce climate change?

Understanding the conditions of public acceptability is key for addressing citizens' concerns and misconceptions, and to design successful climate policies. Our goal is to contribute cross-country and country-specific evidence on people's perceptions, understanding, and preferences regarding climate change and climate policy. Climate change is a global issue, but
<figure><img src="image_2.png" /><figcaption>Figure 1: Share of respondents who somewhat to strongly agree that “Climate change is an important problem” and that their country “should take measures to fight climate change.”</figcaption></figure>

with very different impacts on different regions and people. Thus, studying the underlying attitudes that shape support for climate action needs to be done across the world.

We conduct large-scale international surveys on over 40,000 respondents in twenty countries, shown in Figure 2. These countries span all continents, different levels of income, and different social and economic contexts. Taken together, they account for 72% of global CO₂ emissions, and include 18 out of the 21 largest emitters of greenhouse gas (GHG). The surveys are standardized to allow for cross-country comparability, while also adapting to country-specific conditions. We elicit respondents’ knowledge and understanding of climate change, their own lifestyle and energy usage characteristics, and their views on a broad range of climate policies. Importantly, we ask specific questions about their understanding and perceptions of how these policies work, in terms of their effectiveness, economic impacts, distributional consequences for various groups and their own household. A random sub-sample of respondents is shown pedagogical information videos on the impacts of climate change in their country and on how three key climate policies – a ban on combustion-engine cars, a carbon tax with cash transfers, and a green infrastructure program – work.

Our main findings are as follows. First, we shed light on the factors that foster people’s support for more climate action. There are two key beliefs that are major predictors of whether people support a given climate policy: i) its perceived effectiveness in reducing emissions and ii) its perceived distributional impacts, both on themselves (self-interest) and on lower-income households overall (inequality concerns). Worrying about climate change or knowing more about it are not strongly correlated with support for climate policies.

Second, information works, but only if it actually addresses these two key beliefs. Respondents who see the informational video on the impacts of climate change do not significantly change their views on climate policies. On the contrary, respondents who see pedagogical information about how the three main policies work and what their distributional implications are show stronger support for these and related climate policies.

Third, we explore what personal socioeconomic characteristics, and lifestyle, as well as energy usage patterns are correlated with policy views. There are clear differences by levels of education and political affiliation, and mixed patterns across countries by age and income. The strongest predictors of opposition to climate policies across the board is a lack of available
public transportation, car usage, and, to a lesser extent, gas expenses.

Finally, we show that respondents’ own experience and material self-interest – as captured by their energy usage and life style characteristics – not only directly shape their own perceived gains and losses from climate policies, and, hence their policy support, but also indirectly influences their whole reasoning about how effective and progressive climate policies are – the two key factors driving policy support.

<figure><img src="image_3.png" /><figcaption>Figure 2: The 20 countries covered in the survey.</figcaption></figure>

This project is part of an on-going effort to collect extensive cross-country, standardized, and rigorous evidence on people's views on climate change and climate action over time. The survey data is shared and will be updated on the dedicated research website for researchers and other people from all over the world who are interested in exploring attitudes toward climate policies.

## Related Literature

Our work adds to a growing literature that studies the determinants of support for climate action. Our key contributions are to offer comparable cross-country evidence covering 20 countries, to explore a range of distinct and comprehensive climate policies, and to dig into the underlying understanding and reasoning that people have about climate change and climate policies.

Our paper builds on the theoretical literature on the political economy of climate change. Climate change mitigation policies are complicated by the lack of first-hand experience of
climate change impacts, the time lag between damaging actions and the consequences, and the complex and diffuse nature of the problem (Millner and Ollivier 2016). The literature has shown that some of the most efficient climate policies according to economists, such as a comprehensive pricing of carbon emissions, often face political obstacles that can lead to the adoption of less efficient policies. Hahn and Stavins (1992) and Anthoff and Hahn (2010) review political obstacles and discuss trade-offs among policy objectives, including policy efficiency, political feasibility, and policy clarity to the general public. As a result, less efficient and sometimes regressive policies can be implemented, including for example the regulation of vehicle fuel efficiency (Davis and Knittel 2019; Levinson 2019) or of residential energy use (Fowlie, Greenstone and Wolfram 2018). Across countries, Shapiro (2021) shows that import tariffs and non-tariff barriers are substantially lower on emissions-intensive industries than on clean industries, which the authors attribute to stronger lobbying power of upstream sectors – an important political economy barrier to efficient policies.

Our paper also contributes to the empirical environmental economics literature seeking to understand drivers of support for climate policies. Existing studies largely focus on Nordic- and Western European countries or the United States and on carbon taxes or carbon pricing. They show that people often reject carbon pricing because they perceive it as ineffective (Sælen and Kallbekken (2011) for Norway; Carattini et al. (2017) for Switzerland), because they overestimate the costs, while underestimating its benefits (e.g., Thalmann (2004) for Switzerland; Jagers and Hammar (2009) for Sweden), or because they perceive it to be regressive (Brannlund and Persson (2012) for Sweden; Dietz and Atkinson (2010) for the U.K.; Sommer, Mattauch and Pahle (2022) for Germany). Douenne and Fabre (2022) show that opposition to carbon pricing in France during the Yellow Vests is driven by misperceptions of how the policy will impact respondents, its effectiveness, and distributional impacts.

Few studies have focused on cross-country comparisons, and most studies focus on a narrow set of primarily pricing policies. Various methodologies have been employed, ranging from the collection of voluntary responses (UNDP 2021), non-academic opinion polls (Ipsos 2020; Stokes, Wike and Carle 2015), semi-representative surveys ran through Facebook (Leiserowitz et al. 2021), and representative surveys which focus on a small subset of policies or countries (Bechtel, Scheve and van Lieshout 2020; Umit and Schaffer 2020). Umit and Schaffer (2020) use one question from the European Social Survey conducted in 2016 (ESS 2016), showing that support for an increase in fossil fuel taxation varies across European countries. Bechtel, Scheve and van Lieshout (2020) analyse public support for different carbon pricing trajectories across four high-income countries (France, Germany, the United Kingdom and the United States), showing that carbon price trajectories which gradually increase over time are less popular than stable carbon prices.

Building upon this branch of the literature, our paper makes several contributions to existing work. We investigate preferences across a comprehensive set of varied climate policies that have different characteristics. We cover 20 countries with different levels of income,
across several continents and provide standardized, detailed, and rigorous survey questions to elicit not only policy views, but also the underlying reasoning of respondents. Existing cross-country surveys mostly include high-level questions about climate change rather than precise questions on policy understanding and attitudes (e.g., Ipsos 2020; UNDP 2021) (for detailed reviews of the literature, see Carattini, Carvalho and Fankhauser (2018); Drews and van den Bergh (2016)). Thanks to our questionnaire, we can identify which individual characteristics and beliefs impact policy preferences. We also design pedagogical video treatments to causally test the effects of information on climate change policy and the impacts of climate change. We can thus provide causal evidence that information about policies' characteristics can impact people's willingness to support the policies covered and related ones. Our findings contribute to the ongoing discussions surrounding the importance of information for the acceptance of climate policies (e.g., Boon-Falleur et al. 2022; Kahan 2015; Sunstein et al. 2017).

Methodologically, our work draws from the literature on the formation of political beliefs and preferences for public policies. Using large-scale representative online survey experiments existing work has looked at public preferences for redistribution (Alesina, Stantcheva and Teso 2018; Kuziemko et al. 2015), immigration (Alesina, Miano and Stantcheva 2022) and taxation (Stantcheva 2021). Our paper builds in particular upon Stantcheva (2021), which analyses how people understand mechanisms of tax policy in the United States, showing that the perceived policy efficiency, distributional effects and fairness considerations shape support for and opposition to policies.

The rest of the paper is organized as follows. Section 2 describes the data collection, the sample, and the questionnaires. The subsequent sections present our main results: Section 3 focuses on knowledge about climate change; Section 4 describes the support for policies across respondents and countries; Section 5 analyzes the beliefs about the main climate policies; and Section 6 studies the factors that shape support for climate change action. The main Online Appendix provides core additional information on the survey and results. In addition, all the analyses are replicated for each country in the sample in each of the country-specific Online Appendices.

# 2 The survey

## 2.1 Survey data collection and sample

### Data collection

We collected our survey data between March 2021 and March 2022 using the survey companies *Dynata* and *Respondi*. The survey companies maintain panels of respondents, send survey links to panelists with targeted socio-demographic characteristics, and compensate the respondents who fully complete the survey. Compensation takes various forms, ranging from cash to frequent flyer points or hotel points, thanks to the partnerships of the survey
company with other companies. In total, we collected around 40,000 responses, with the number of respondents per country ranging from 1,600 to 2,300.

The respondents are first channeled through screening questions that ensure that the final sample is representative along the dimensions of gender, age, income, region, and place of residence (urban versus rural). Appendix 2.1 gives more detail on our sampling procedure.

## Sample

Tables 1-5 show that our sample is relatively representative in high-income countries. Some exceptions are Italy, Japan, South Korea, and Spain, where the share of college-educated respondents is 15 to 25 p.p. higher than the actual population share, which often happens in online survey samples (see for instance Alsan et al. (2021) or Stantcheva (2021)).

In middle-income countries (Brazil, China, India, Indonesia, Mexico, South Africa, Turkey, and Ukraine), we faced constraints due to the online nature of the survey and the pandemic-related restrictions on door-to-door surveys. College-educated people are over-represented and respondents aged 50 and more or living in rural areas tend to be under-represented. For these countries, the results should thus be interpreted with caution as they do not accurately reflect the attitudes of the population at large, but rather those of the “online population,” that tends to be skewed towards the middle and upper classes, mainly in urban areas.

Throughout the paper, we re-weight the samples within each country along the dimensions of gender, age, income, region, urbanity, education, and employment.

## Data Quality

We took several steps to ensure the best possible data quality. Native speakers translated and reviewed the survey into the main national languages of each country and ensured it was in line with local culture and understanding.

On the introductory consent page, we appeal to people's social responsibility by asking them to answer carefully and honestly for the sake of research. We also warn them that monetary compensation would be withheld if their answers did not pass our quality checks. We record time spent on different blocks and on the survey overall: the median time is 28 minutes (see Appendix A-2 for the full distribution of survey times).

We added a question to screen out inattentive respondents. The representative samples (as shown in Tables 1-5) are obtained after excluding the inattentive respondents and those who rush to complete the survey (i.e., who take less than 11 minutes). *Ex post*, we checked that there were few careless response patterns (such as choosing the same answer for all items in a matrix of questions).

At the end of the survey, we ask whether our survey was politically biased and allow respondents to leave some feedback. Across all countries, 74% of respondents found the survey unbiased. 15% found it left-wing biased, and 11% found it right-wing biased.
## 2.2 The questionnaire

As shown in Figure 3, the questionnaire is structured in four parts, described below: questions on household characteristics, information experiments, questions on climate change, and elicitation of views on climate policies. We kept the questionnaires as similar as possible across countries, while allowing for some appropriate variations. For instance, in some countries, questions about specific policies were added (e.g., asking about a ban on deforestation in Brazil and Indonesia). Other questions were omitted if deemed inappropriate (e.g., asking about heating in tropical countries or about cattle-related policies in India). Finally, necessary adjustments were made to country-specific figures and examples (e.g., the gasoline price increase implied by a carbon tax).

Appendix A-5 provides the full questionnaire as well as links to each country's questionnaire in the original languages.

<figure><img src="image_4.png" /><figcaption>Figure 3: Survey outline.</figcaption></figure>

**Household characteristics.** We ask respondents about their basic socio-economic information, such as their age, income, gender, zipcode, size of agglomeration, household composition, highest level of education achieved, occupation, wealth, whether they are homeowners. Political leanings are measured by several questions: voting behavior in the latest national election, general interest in politics, leaning on economic policy issues, and interest and participation in environmental causes.

An important set of questions centers around energy usage and life style as related to climate change. The answers to these questions allow us to assess the extent to which respondents may personally be affected by climate policies. We ask households about their housing (their heating source and expenses and the quality of insulation), transportation (fuel expenditures, modes of transport used, availability of public transportation, frequency of flying), as well as about their beef consumption.
<figure><img src="image_5.png" /><figcaption>Figure 4: Climate policies video</figcaption></figure>

**Information experiments.** In the experimental part of the paper, respondents in randomly selected subsamples are shown none, one, or both of two videos. The *Climate impacts* video, which is 2-3 minutes long, centers on the impacts of climate change, with information that is tailored to the country of the respondent. The *Climate policies* video (5 minutes long) focuses on three major climate policies and is also adapted to each country's specifics.<fnref n="1" /> The objective of these treatments is to understand how perceptions change after receiving salient information on the effects of climate change or climate policies, and how these perceptions and beliefs causally translate into policy support. Both videos were followed by comprehension and attention check questions. Appendix A-5 contains the scripts and links to the videos; Appendix A-8 contains the data sources used.

The video on *Climate impacts* starts by explaining that climate change is anthropogenic and presents possible country-specific impacts and possible death tolls from local air pollution if nothing is done to reduce it. Some of the impacts presented, as expected by scientists in absence of ambitious measures to stop climate change are more severe heatwaves, frequent forest fires, and growing numbers of areas at risk of being permanently flooded due to sea-level rise (see Figure 4a).<fnref n="2" /> The video concludes with the necessity of bringing greenhouse gas (GHG) emissions close to zero to tackle climate change.

The video on *Climate policies* focuses on three major climate policies, that have been implemented in many countries over the past years, describing their advantages, but also some drawbacks. First, it presents a ban on the production and sale of new combustion-engine cars that produce more than a given amount of CO₂. The limit is progressively lowered over time so that only electric (or hydrogen) vehicles may be sold by 2030, similar to fuel economy standards that have been implemented in many countries including the United States, the European Union, as well as China and India among others (Anderson and Sallee 2016). It alerts respondents to the fact that electric vehicles have a lower range and may be more expensive. Second, the video describes a carbon tax with cash transfers that taxes all

<footnote n="1">¹Because all descriptive statistics are calculated on the control group, it is 25% larger than the other groups. It contains 29.4% of the sample, while the three treatment branches each contain 23.5% of the sample.</footnote>

<footnote n="2">²In Canada and Denmark, some positive effects are mentioned as well, following scientific predictions.</footnote>
products that emit greenhouse gases. This tax would increase the price of gasoline by $0.40 per gallon. The video explains that with such a tax, companies and people would reduce their emissions as those become costlier. It also shows that redistributing the revenues equally means that low-income earners will on average receive more in cash transfers than they pay in taxes, while the reverse holds for high-income earners (see Figure 4b). We choose a lump-sum revenue-recycling mechanism as one of our three main policies, because this is the earmarking mechanism advocated by a group of leading economists in the “Economists’ Statement on Carbon Pricing” (WSJ 2019). Third, the video discusses the effects of a large program of public investment in green infrastructure in transport, power, insulation, and agriculture financed by public debt. It provides estimates of the number of jobs created in green sectors and of jobs lost in brown sectors. Green infrastructure investment programs have been advocated by economists for years to accelerate the transition towards a low-carbon economy (Hepburn et al. 2020; High Level Commission on Carbon Prices 2017). Over the past years, many governments have started to launch such programs, including the E.U.’s Green Deal (EC 2019), and programs adopted in the aftermath of the COVID-19 pandemic, such as the Next Generation E.U. fund (EC 2020) and the U.S. Infrastructure Investment and Jobs Act (US Congress 2021) with provisions for investments in green infrastructure.

Finally, the video recalls that other policies would also be useful to fight climate change and recapitulates the pros and cons of climate policies: On the one hand, the world will be safer for future generations and less polluted, on the other, people will have to change their habits and some will have to change job.

After the respondent has seen a video, we check that there was no technical issue (which holds true in 97% of cases) and whether the respondent has been attentive, by asking two questions about the video content. 29% of respondents had both answers correct (27% for the *Climate impacts* treatment, and 31% for the *Climate policies* one), while 29% had both answers incorrect (34% and 24%, respectively).

**Knowledge and understanding of climate change.** We measure the respondent's knowledge and understanding of climate change by asking a series of more general and more technical questions. For instance, we ask them about the extent to which they believe climate change is human-caused, which GHG cause it, whether cutting emissions by half would suffice to stop temperatures from rising, the ranking of different activities, modes of transportation, foods, and world regions in terms of GHG emissions, and the likelihood of various possible impacts.

We also elicit their attitudes on private climate action, by asking how climate change affects their lifestyle, the extent to which they are willing to adopt different climate-friendly behaviors, and what factors would facilitate this adoption.

**Attitudes on climate policies.** One of our core contributions is to elicit detailed reasoning about climate change policies. In the final block of the survey, we explore how respondents think about the three main climate policies that were covered in the videos seen by the treatment group (ban on combustion-engine cars, investment in green infrastructure,
and a carbon tax with cash transfers), as well as a range of other policies.

Importantly, rather than only asking respondents about their support for the main policies, we also elicit their perceived effectiveness of the policy (Will it reduce emissions? Will it impact the economy?); their perceived distributional impacts (Which groups will lose or win?); the perceived impacts on their own household (Will they lose or win?); and the perceived fairness of the policy. We also ask them about variations on the policy in terms of sources of funding (in the case of the green infrastructure program), how the revenue is spent (in the case of the carbon tax), or what policy bundles are put in place (e.g., in the case of the ban on combustion-engine cars, whether alternative modes of transportation are made available).

The choice of policies that we test builds upon the literature and policy debate. We intentionally do not limit the policies to only cover first-best instruments because of potential trade-offs between policies being efficient and publicly acceptable. In addition to the three main policies described above, we test a number of additional policies.

First, we assess support for several variants of carbon taxes, which differ in the way the revenues are earmarked. Carbon taxes have been implemented in several countries or sub-national jurisdictions, including in Sweden (Andersson 2019), France (Dussaux 2020), and British Columbia (Murray and Rivers 2015). Yet carbon taxes have often been politically difficult to implement, as seen by the Yellow Vest movement in France (Douenne and Fabre 2022). We show that the way in which the revenues are earmarked is a key determinant of support for carbon taxes across countries.

Second, we include several variants of bans of combustion-engine cars, motivated by existing bans or restrictions for combustion-engine cars for example in Mexico City (Davis 2008), or cities across Germany (Wolff 2014). We include several policy variants, including a ban of polluting vehicles in city centers, or banning combustion engine cars generally while increasing the availability of public transport.

A third group of policies includes support for investments in low-carbon technologies and green infrastructure. The IEA (2021) estimates that half of the global reductions in energy-related CO₂ emissions through 2050 will have to come from technologies that are currently at the demonstration or prototype phase. Hence, policies that support innovation in low-carbon technologies will be key to meet emission reduction targets. Moreover, to decarbonise the energy sector, worldwide clean energy investment will need to more than triple by 2030 to around USD $4 trillion annually. To reach this target, large-scale increase in public and private investments in energy infrastructure are necessary (IEA 2021).

Fourth, we elicit support for policies to reduce emissions from residential energy use. In the United States (Goldstein, Gounaridis and Newell 2020) and in the E.U. (Eurostat 2020) households account for about 20% of total greenhouse gas emissions. Policies to improve energy efficiency and reduce greenhouse gas emissions from residential energy use are key to reach net-zero emissions by mid-century. Building upon existing weatherization programs we assess support for a mandatory and subsidized weatherization policy. The evidence on existing weatherization programs shows that uptake is often limited despite generous subsidies and support measures (Fowlie, Greenstone and Wolfram 2018; Nauleau 2014).
Fifth, we test support for policies to reduce emissions from the agricultural sector, in particular cattle farming. Globally, livestock accounts for nearly 15% of greenhouse gas emissions with beef and cattle milk production accounting for the majority of livestock emissions, contributing 41% and 20% respectively (Gerber et al. 2013). We test support for three policies on cattle farming, a high tax on cattle products that would double beef prices, a ban of intensive cattle farming and a removal of subsidies for cattle farming. In addition, we also include a policy to subsidise organic farming.

Furthermore, we also assess support for an tax on flights (increasing ticket prices by 20%). While, CO₂ emissions from aviation currently account for only about 3% of global CO₂ emission, they have been growing rapidly over the past decades (at about 2% per year), and by 2045 fuel consumption from aviation is expected to triple compared to 2015 levels (IEA 2020). Further policies are necessary to reduce emissions from aviation to reach net-zero emissions by mid-century.

In addition to self-reported policy support, we also ask two “real-stakes” questions: a donation and a petition question. In the donation question, respondents are informed that they are automatically entered in a lottery to win $100 (or the equivalent in their local currency). Before they know whether they have won the lottery, they have to decide which share of their potential win, if any, to donate to the non-profit *Gold Standard*, which fights against deforestation. The petition for climate action expresses the view that *“immediate action for climate change is critical”* and the respondent is told that the country’s head of state’s office will be informed of the share of respondents who support this petition.

## 2.3 Logic of the analysis

All variables constructed based on the questionnaire and used in the analysis are defined in Appendix A-1. The descriptive statistics shown in Sections 3, 4, and 5 use the control group sample only, i.e., respondents who see no information video. In the analysis, we usually correlate individual views and reasoning with two sets of individual covariates: i) individual socio-economic characteristics (e.g., age, gender, or income) and ii) lifestyle and energy usage characteristics (e.g., car usage or heating source), “energy usage” for short. Whenever the effects of these covariates are relatively homogeneous across countries, we only show one figure and point out possible exceptions in the text. If patterns are heterogeneous, we do not show the main effect of a given variable, but rather zoom in on the coefficients in different countries. The results by country for our main analyses are in Appendix A-4. The full analysis is repeated for each country in the country-specific online Appendices.

# 3 Knowledge about climate change

This section describes respondents’ knowledge about and understanding of climate change, starting with cross-country patterns and then considering individual-level heterogeneity.
## 3.1 What do people know?

Less than one tenth of people outright deny the existence of climate change, except in the U.S. and in Australia (where the share is 13%). Most people believe that climate change is anthropogenic: 40% of them know that “most” (if not all) of it is due to human activity, and, depending on the country, 60% to 90% of respondents believe that human activity causes “a lot” or “most” of climate change.

**Consequences of climate change.** Most respondents (80-90%) correctly foresee some of the consequences of unabated climate change such as severe sea-level rise or droughts and heatwaves (see Figure 5). At the same time, most also believe that climate change will entail more frequent volcanic eruptions, which is not in line with climate science. People seem to bundle several types of disasters together.

**Sources of greenhouse gas emissions.** Respondents are generally too optimistic about the decarbonization needed. One half of high-income countries respondents correctly believes that cutting GHG emissions by half would *not* suffice to stop global warming, and this share is less than one third in middle-income countries.

Respondents are relatively well aware of the factors causing climate change, and more so in high-income countries. About 80% correctly recognize that CO₂ is a greenhouse gas, 60% that methane is one, and 75% that particulate matter is not. Most rankings for different types of food and power generation in terms of GHG footprint are also correct, although a non trivial share of respondents, especially in middle-income countries, believe that nuclear power has a higher footprint than gas or coal.

Answers are less accurate concerning transportation modes, but especially so in countries where the difference in emissions between trains and cars is smaller because of the lack of low-carbon trains. Respondents are asked to imagine a family journey between two large cities in their country and to rank the possible modes of transportation according to their greenhouse gas emissions. The options are *Plane*, *Car*, and *Train* (or *Bus*, in countries lacking railroads: Australia, Brazil, Canada, Mexico, South Africa, Turkey, the U.S.).<fnref n="3" /> Respondents rank options more accurately in countries like Denmark or Germany where trains are very low-carbon, and less so in countries such as Indonesia or India, where trains are not an unambiguously less carbon-intensive option.

**Ranking countries of the world by emissions.** The respondents were also asked to rank China, the U.S., the E.U., and India<fnref n="4" /> by total and per capita emissions. Respondents rank regions and countries quite accurately in terms of total emissions, but many overestimate the footprint of the average Chinese resident and underestimate that of the average European.

<footnote n="3">³In countries such as Indonesia, where trains rely on coal, the advantage of trains is less clear. Respondents are thus asked about a couple (traveling 800 km from Surabaya to Jakarta) instead of a family of four, since a fully occupied car would be more efficient than the train. Featuring two passengers instead of four also blurs the comparison between the GHG footprint per passenger of plane and car, as the two are comparable when there is only one passenger in the car.</footnote>

<footnote n="4">⁴The respondent's country was also added for the GHG footprint, except for E.U. countries.</footnote>
## 3.2 Who has better knowledge?

To summarize a respondent's knowledge about climate change, we construct a *Knowledge index*, that summarizes the aforementioned variables and is increasing the more accurate a respondent's answers are (see Appendix A-1). All indices in the paper are constructed by transforming each underlying variable into a z-score (by subtracting the control group mean and dividing by the control group standard deviation) and then standardizing again using the sample mean and standard deviation. In Figure 6a, we regress the *Knowledge index* on respondents' socio-demographic characteristics and variables that proxy for their energy usage.

Across most countries, having a college degree is significantly associated with more accurate knowledge. Also consistent across many countries is that respondents with left-leaning economic views have more accurate perceptions. On the other hand, women are generally less accurate, except in Australia, China, Indonesia, South Korea, the U.K., the U.S., and Ukraine (where there are no clear differences by gender). These inaccuracies are generally due to the more technical questions about greenhouse gases, carbon footprints, and to the fact that women tend to perceive more negative impacts of climate change (which are not always accurate, such as, e.g., more frequent volcanic eruptions). The association between income and knowledge, conditional on education, is either significantly positive or insignificant, except in China (having higher income is associated with higher knowledge in Australia, Brazil, Canada, Germany, India, Italy, Poland, Spain, Ukraine, and the United Kingdom, while higher income does not matter in Denmark, France, Indonesia, Japan, Mexico, South Africa, South Korea, Turkey, the U.S.).

The effect of age varies across countries (see Figure 6b): while age is positively correlated with knowledge in Canada, Denmark, Germany, Spain, Poland, India, Turkey, Ukraine, the U.K., and the U.S., the correlation is negative in South Korea. Finally, respondents living with young children are somewhat less accurate too.

## 3.3 Expectations about climate change

The share of people who think that climate change affects or will affect their personal life is generally higher in countries more vulnerable to climate change, e.g., 79% in India compared to 16% in Denmark. The correlation of this subjective belief and the University of Notre Dame vulnerability index (Chen et al. 2015) is 0.68 and is significant. Thus, subjective beliefs about the impacts of climate change are related to the country's actual vulnerability.

Overall, expectations about the future are relatively bleak. Only a minority of respondents believe that it is technically feasible to stop GHG emissions by the end of the century while sustaining satisfactory standards of living. Very few people think that the world will be richer than it is today in a hundred years. A bit more than half of all respondents think that it is likely that climate change will cause the extinction of humankind.

The vast majority of people think that ambitious climate policies would negatively affect their lifestyle at least "*a little*", but in most countries a small fraction believe they would affect them "*a lot*" (22% in high-income countries). Opinions are mixed about whether ambitious
climate policies would have positive or negative effects on the economy and employment. Only in Japan is there clearly less people believing in positive effects: 38% when we exclude people who foresee no noticeable effects, in other high-income countries this relative share is between 47% and 64%. Also, only few people think that ambitious climate policies will lead to "very negative" economic effects: at most 15%, in the U.S.

# 4 Who supports climate action?

In this section, we describe support for climate policies across countries and respondents. One complication is that individual policies (e.g., carbon taxes) may generate quite different levels of support than bundles of policies (e.g., revenues from a carbon tax used to fund low carbon technologies). While it would be convenient to think of the tax side as separate from the revenue side, respondent's views on each revenue-generating policy very much depend on the use of the revenue. Vice-versa, for each costly policy, the funding source matters too. Policy bundles are complicated to study because there are so many different combinations. Our approach is therefore as follows. First, we provide evidence on several key types of policies. Second, we hold a major policy constant and shed light on the possible uses of revenue (from carbon taxes) and on the possible sources of funding (for green infrastructure program). Third, we explain in Section 5 what factors shape support for policies, thus providing guidance about evaluation and predicting support for other combinations and types of policies.

## 4.1 Cross-country comparisons

### 4.1.1 Climate policies

We start with a cross-country comparison in support for climate action. Comparing levels of support for alternative policies across countries is clearly difficult because other policies already in place differ and may shape preferences. For instance, the status quo level of taxes may influence how much appetite there is for more taxation.

Nevertheless, we can see some clear patterns in the rankings of policies (see Figure 7). The most popular policies are subsidies for low-carbon technologies, as well as public investments in green technologies and infrastructure. Many respondents across countries also support banning polluting vehicles in city centres or dense areas. There is lower support for generalized bans on combustion-engine cars even when alternatives are available, but support is even lower for generalized bans without alternatives. The importance of having alternative transportation modes available for support for climate policies overall will be highlighted in Section 6. Banning polluting vehicles in city centers or dense areas may carry additional benefits such as reduced air pollution.

A priori, carbon taxes and taxes on fossil fuels appear to be among the least popular policies, even if the revenue is used for cash-transfers to households. However, the use of revenue matters a lot. In fact, carbon taxes whose revenue is used to fund environmental infrastructure, subsidize low-carbon technologies, or reduce income taxes see a lot of support.
Carbon taxes with transfers to the poorest or to the most constrained households enjoy medium levels of support in higher-income countries and very high levels of support in middle-income countries.

Finally, there is high support for the mandatory and subsidized insulation of residential buildings across countries. On the contrary, policies that aim to curb the pollution from cattle production are highly unpopular.

**Support and opposition versus indifference.** An important point when deciding to map these survey findings to real-world support for a policy is that around one-third of respondents say that they neither support nor oppose a given policy.

Appendix Figure A3 shows the share of respondents who support a policy out of all respondents who express either support or opposition (but not indifference). Among respondents that are not indifferent, a majority – and often a large one – is in favor of the policies.

These patterns suggest that indifference to climate policies is a critical aspect to take into account and it is important to also focus on the citizens who express a lack of opinion on these issues. This expression may reflect lack of interest, lack of knowledge, or actual ambiguity about climate action. In that sense, indifferent respondents may be akin to “swing voters” and may be those whose views are most malleable. Their views could change if a policy is actually proposed, discussed, and they are asked to vote on it. Section 6 highlights the factors shaping people’s support for various policies and can be informative about what pieces of information are needed to sway people’s views on average.

Although we focus on climate policies at the national level, when asked the level at which climate policies need to ideally be put in place, 73% to 93% of people choose the global level. Less than half of all respondents think that policies should be enacted mainly at the federal (or European), the national (or state), or the local levels.

### 4.1.2 Willingness to adopt climate-friendly behaviors

Our paper focuses on people's understanding of and support for climate policies. However, climate action can also take the form of individual behavior changes, and it is interesting to compare and contrast respondents' willingness to adopt climate friendly behaviors versus to support wider-ranging climate policy.

Given current incentives, just around half of respondents say they are “*a lot*” willing to have a fuel-efficient car or electric vehicle or to limit flying. Furthermore, except in Italy, India, and to a lesser extent, China, respondents are not very willing to limit beef or meat consumption, perhaps because of a lack of satisfactory substitute foods. Few are willing to limit driving, or heating or cooling their homes.

We also asked people about their willingness to adopt these behaviors under different circumstances. It is important to people that others, and especially the most well-off, also change their behaviors and that they receive enough financial support to make these changes.
## 4.2 Individual characteristics correlated with support for climate policies

To summarize support for climate policies, we construct a *Support for Main Climate Policies index* based on the three main policies studied (see Appendix A-1 for details). In Figure 9a, we regress the *Support for Main Climate Policies index* on the full set of individual covariates, as well as a range of variables measuring the lifestyle of respondents and country fixed effects. When we do not discuss differences across countries, it is because the average effects shown in the main figure are quite representative of the overall patterns.

**Individual characteristics.** In Figure 9b, we zoom-in on several significant patterns of heterogeneity across countries. Political affiliation (even conditional on other personal characteristics) is one of the strongest predictors of views on climate action. In most countries, left-leaning respondents also support more climate action. The exceptions are China, Indonesia, Mexico, Turkey, Ukraine, and South Africa. In addition, respondents who live with children below the age of 14 are more supportive of climate policies.

In several countries, college-educated respondents are more likely to support climate action (Australia, China, Denmark, Spain, Indonesia, India, the United Kingdom, and the United States). Income has mixed effects. Higher-income respondents support more climate action in Brazil, China, India, Indonesia, Italy, Japan, Poland, South Korea, and Ukraine. There are no clear patterns by income for the other countries. Age also has mixed effects. Older respondents in Brazil, China, India, Indonesia, Japan, Mexico, Poland, South Korea, Turkey, and Ukraine are more supportive of climate action. Bear in mind, however, that in online-representative samples, older respondents, most notably above 65 years old represent only a small and possibly quite selected share of the sample. In higher-income countries such as Australia, France, and the U.S., younger respondents are more likely to be favorable to climate policies. There is no significant heterogeneity by age in the U.K. or other E.U. countries.

**Lifestyle and energy usage factors.** The right panel of Figure 9a shows respondent lifestyle characteristics which are correlated with support for main climate policies. One of the strongest predictors of support is having access to public transportation (the only exceptions are Japan and Mexico). Conditional on access to public transportation, living in a large agglomeration only has significantly positive effects in Denmark, the U.K., Turkey, and the U.S. Thus, availability of public transport seems to be the first-order concern and perhaps somewhat of a “sufficient statistic” for the place of residence. For all high-income countries except the U.S., using a car regularly is associated with lower support for climate action. However, in China, India, and Indonesia, car usage is positively associated with policy support, perhaps because of the selected nature of these respondents. Conditional on car usage, high gas expenses per se matter only marginally in Canada, Denmark, and Germany. Frequent flyers tend to support more climate action. One exception (see Appendix Figure A4) is support for a tax... on flying! Eating beef frequently is also associated with lower support in Australian, Canada, Germany, Denmark, Spain, and France.
Appendix Figure A4 shows support for a range of other climate policies. Overall, the correlations with individual characteristics are similar to those for the main policies. Appendix Figures A9 and A10 zoom-in on the individual policies and further heterogeneity patterns.

Car-dependent respondents are less supportive of bans on combustion engines (regardless of whether those are overall bans, with alternatives, and in dense areas). They also exhibit lower support for taxes on fossil fuels and carbon taxes with cash transfers (only in Australia, France, Japan, Poland, and the United Kingdom). They do not have different views though on taxes on flying, green infrastructure programs, subsidies for low-carbon technologies, or mandatory and subsidized insulation of buildings. Home owners and landlords are less supportive of mandatory insulation, but not less supportive of other climate change action.

# 5 How do respondents reason about climate policies?

In this section, we study respondents’ understanding of climate policies, in particular, their perceptions of the efficiency and distributional impacts. This section and the next thus dive into the factors that shape support for climate action.

## 5.1 Perceived distributional and efficiency impacts

Figure 10 summarizes the reasoning on the efficiency and distributional effects of the three main policies, separating countries into high-income ones; China, India, and Indonesia (which have similar responses); and other middle-income ones (for a country-by-country plot, see Appendix Figures A6 - A8).

**Perceived environmental benefits.** The environmental benefits of climate policies are largely acknowledged: in both high-income and middle-income countries, a majority of respondents agree that the three policies would reduce air pollution and reduce GHG emissions. Respondents in high-income countries are more divided about the behavioral effects induced by the policies, such as driving less or using more public transportation. In France and Germany for instance, only 40-42% of respondents believe that a carbon tax would encourage people to drive less.

**Perceived economic effects.** Few respondents think that climate policies will have positive impacts on the economy and employment. When asked about whether each of the policies is a cost-effective versus costly way to fight climate change, most respondents rank the carbon tax as most costly, followed by the green infrastructure program, and then the ban on combustion-engine cars.<fnref n="5" />

**Perceived distributional impacts.** In high-income countries, only around one fifth of respondents believe that their household would financially gain from the main climate policies. The share that believes that low-income earners, the middle class, and those living in rural

<footnote n="5">⁵We check for acquiescence bias to these questions in Appendix A-2.2.</footnote>
areas would gain is similarly low. The largest perceived losses are for the ban on combustion-engine cars, where close to 85-90% of respondents think that all these groups will lose on balance. On the contrary, around 40% of respondents believe that high-income earners will come out net positive from these three policies. In middle-income countries, a larger share of respondents believes that various groups will gain, especially in India, Indonesia, and China.

To sum up, these three key policies are thus seen as environmentally effective but regressive and against one's own financial interest.

## 5.2 How do different groups of respondents reason about climate policies?

Appendix Figure A14 regresses the various perceived efficiency and distributional effects of the main policies – summarized in indices – on the key individual socio-economic characteristics and lifestyle variables.

Higher income respondents tend to be more optimistic about policies' effectiveness in reducing emissions. In some few countries, they are somewhat less likely to think that they or low-income earners will lose from them, but these effects are neither consistent nor large.

Respondents with young children are less likely to think that they personally or low-income earners will lose from these policies.

Age has mixed effects. In middle-income countries, older respondents tend to be more likely to believe that policies reduce emissions, and less likely to think they or low-income earners will lose. In some high-income countries (Australia, Canada, Denmark, France, Germany, the U.K., and the U.S.), older respondents are more likely to think that they or low-income earners will lose. Gender has very small and typically insignificant effects.

Although not consistently significant, having a college degree typically makes respondents more optimistic about the effectiveness of policies to reduce emissions and less pessimistic about the impacts on themselves and lower income people.

In high-income countries, there is a clear political gradient for most perceptions: left-leaning respondents are more likely to believe that policies will have positive economic impacts, that they will reduce emissions, and less likely to believe that they themselves or the low-income earners would lose. Differences by political leaning are not significant in middle-income countries.

Some lifestyle and energy usage characteristics strongly influence respondents' views on the effectiveness and distributional impacts of policies: Respondents with public transportation available are quite consistently more optimistic about the environmental, economic, and distributional impacts of the policies. The same goes for respondents that often fly. The opposite holds for respondents who use cars and who have high gas expenses (conditional on car usage). People in large agglomerations are marginally more optimistic, but the availability of public transportation is, once again, the major predictor of perceptions.
# 6 What reasoning underlies support for climate policies?

## 6.1 Factors correlated with policy support

To determine which beliefs are correlated with support for climate policy, we regress support for each of the three main climate policies on the socio-economic characteristics and on a set of indices measuring different beliefs about climate change, all standardized and defined in Appendix A-1. Figure 11 shows the regression coefficients on the belief variables.

The perceived distributional impacts of climate policies are strongly correlated with policy support. First comes self-interest: those who think they will themselves lose from a given policy are much less likely to support it. Second, the overall progressivity matters too, as respondents who believe that low-income earners will lose are less supportive of the policy. In a few countries (France, Spain, and Ukraine) the belief that the high-income earners will lose is actually positively associated with support for it.

The perceived effectiveness of a policy is also strongly correlated with support for it, especially the belief that it will reduce emissions and, to a lesser extent, pollution (which is perhaps more indirect). Beyond the environmental effectiveness and perceived distributional effects, broader perceived economic effects are not strongly correlated with support. Worries about the impacts of climate change are much more weakly correlated with policy support. Similarly, knowledge about climate change is not a strong predictor of favoring climate policies, with some marginal effect for those who believe that climate change is human-made.

Our results for 20 countries confirm some of the patterns for specific countries, where the importance of perceived fairness, effectiveness, and self-interest has been highlighted (Carattini, Carvalho and Fankhauser 2018; Douenne and Fabre 2022; Klenert et al. 2018).

Considering the broader range of climate policies (see Appendix Figure A13), the same factors matter. Opposition to bans on combustion-engine cars (with alternatives available and in dense areas) and taxes on fossil fuels is particularly strongly correlated with respondents' belief that they will personally lose from these measures.

Respondents' willingness to adopt climate-friendly behaviors is driven mainly by worries about the consequences of climate change and that they will suffer from the main climate policies (see Appendix Figure A12). It is much more weakly correlated with the perceptions of efficiency or distributional impacts of those policies, which makes sense when it comes to individual-level behaviors.

## 6.2 Information treatments

The correlations just outlined are confirmed experimentally by the effects of the video treatments.

In the cross-country pooled data, the *Climate impacts* treatment alone has the smallest effects on support for each of the policies, as well as on the willingness to adopt climate-
friendly behaviors.<fnref n="6" /> It is significant in very few individual countries. The effects of the *Climate policies* treatment in the pooled sample are much stronger, especially for the carbon tax with cash transfers and, to a lesser extent, for the ban on combustion-engine cars. The strongest impacts are from the combination of the *Climate impacts* and *Climate policies* treatments, which are roughly equal to the sum of the two treatments' impacts. In the pooled data, the treatment effects are largest for the carbon tax with cash transfers, followed by the ban on combustion-engine cars, and the green infrastructure program. All three treatments have significant and large effects on the perceived fairness of the three policies.

For the green infrastructure program, for which the treatment effects are the smallest, only the combination of the *Climate impacts* and *Climate policies* treatments shifts support for it in Australia, Canada, Denmark, India, Spain, and the U.K., by on average 14% of the control group mean support in these countries. This impact needs to be interpreted in light of the fact that this program enjoys the highest level of baseline support. The apparently small treatment effect is actually equivalent to 54% of the share of those who oppose the program in the control group.

Turning to the ban on combustion-engine cars, seeing the *Climate policies* alone is significant only in few countries (France, Italy, and South Africa). But seeing both treatments is significant in Australia, Brazil, China, Denmark, France, Italy, Japan, Spain, South Africa, and the U.K., as is the pooled effect across countries. In those countries, the effect of both treatments is equivalent to 24% of the control group mean on average, ranging from 14% in China (which starts with a high level of baseline support) to 43% in Australia. The magnitude also corresponds to 60% of the opposition on average and to 102% of the gap in support between left- and right-wing.

Finally, on the carbon tax with transfers, seeing the *Climate policies* increases support significantly in all countries except India, Mexico, South Africa, and South Korea. The magnitudes correspond to 29% of the control group mean (ranging from 11% in China to 50% in Germany), 66% of the share who oppose this program, and on average to 40% of the gap between left- and right-wing respondents in countries where it is significant. Seeing both the *Climate impacts* and *Climate policies* treatments has even stronger effects in all countries (except India and Turkey). The effects are equivalent to 35% of the control group mean (ranging from 9% in China to 65% in Denmark) and to 74% of the opposition in countries where the effect is significant.

Thus, the treatments have a larger effect on policies that start with a lower support and that have more room for improving support. They sway sizable shares of those who oppose each policy in the control group. Furthermore, the effects of the combined treatment are the strongest, but typically close on average to those of the *Climate policies* treatment alone, suggesting that a critical information is provided about policies and that there isn't necessarily an amplification of the effects of each individual treatment when shown together.

Note also that the *Climate policies* treatment – which only provides information on the three main policies, but not on climate change per se – has barely any effect on the willingness

<footnote n="6">⁶With the exception of Denmark, where its effect is nevertheless very small.</footnote>
to adopt climate-friendly behaviors, and has much stronger effect on the support for the main policies. This suggests that the effects of the treatment are due to their informational content rather than to the framing or experimenter demand.

**Treatment effects on other policies' support.** There are significant treatment effects for other policies too, specifically those that are related to the three main ones covered in the videos. The *Climate policies* and the combination treatment both significantly increase support for the tax on fossil fuels, with an effect size equal to around 30% of the control group mean and of the opposition. The treatments also increase support for a tax on flying, presumably because it is also associated with reducing fuel usage.

The ban on combustion-engine cars with alternatives made available and to some extent the ban on polluting vehicles in city centers – which are both much more popular than the overall ban on combustion-engine cars – see some treatment effect as well. On the other hand, policies that are not closely related to the ones showcased in the video, such as the mandatory insulation of buildings, do not see higher levels of support in the treatment group than in the control group.

**Interpretation of the treatment effects.** The interpretation for these treatment effects is that the *Climate policies* and the combination treatment shift exactly those margins that we just showed to be most relevant: the perceived distributional impacts and effectiveness of the policies. The *Climate impacts* treatment alone does not increase worry about climate change that much – perhaps because these worries are already well-pronounced – and in any case, worries about climate change are not a strong predictor of support for more climate policies. This treatment does not shift the key mechanisms that matter for policy support, namely perceived effectiveness and distributional impacts. Thus, explaining how policies work and who can benefit from them (or how losers can be compensated) is critical to foster policy support. Simply making people more worried about climate change is not an effective strategy to foster policy support.

Furthermore, as can be seen from the weaker effects on support for other policies, not covered in the videos, it is important to provide information about and explain the workings of a given specific or closely related policy. It is not immediate to extrapolate from one policy's effect to another.

**Private action versus public policy.** The treatment effects of the *Climate policies* on willingness to change one's own behavior is insignificant in all countries, except Australia. The *Climate impacts* or the combination treatment have small effects in only a few countries (Australia, Japan, Mexico, Poland, Spain, and the U.K.). There is also no effect on the real stakes questions (donation and petition). This confirms again that simply informing people about *Climate impacts* is not effective in stimulating demand for private action and discussing *Climate policies* generates demand for policies, not private action. Recall from Section 4 that two of the most important factors for respondents to be willing to adapt their own behavior were if others adapted too and if they received financial support. Hence, without changing these two key factors, there is no reason to expect that private behaviors should change.
# 7 Conclusion

Our international survey documents widespread concern for climate change, but disagreement about which measures should be taken to fight it. In many countries, there is strong majority baseline support for green infrastructure programs, subsidies for low-carbon technologies, carbon taxes with a strongly progressive use of revenues (such as cash transfers to the poorest or most impacted households), and policies that bring other benefits such as bans on polluting vehicles from city centers or dense areas, and the mandatory insulation of buildings.

Our major results center around the factors that make people support more climate action. We show that generating support for a given policy action requires convincing people of two key aspects: i) That the policy is effective in reducing emissions (and more broadly, that it can reduce pollution); ii) that it does not have adverse distributional impacts. This concern comes in two shapes: First, a concern that the policy does not hurt lower-income households overall, and, second, that it will not financially hurt the respondent's own household. Differences in perceived effectiveness and in perceived distributional impacts of a policy best predict policy views and support for climate action. Per se, worrying about climate change or knowing more about it are not strong predictors of support for climate action.

These findings are supported experimentally. Providing people with information about the implications of climate change in their country does not shift views significantly. On the contrary, providing them with concrete information about the effectiveness and distributional implications of a policy (e.g., how will it be paid for) does significantly improve support. The three main policies we address in our informational treatments – a green infrastructure program, a ban on combustion-engine cars, and a carbon tax with cash transfers – show different treatment effect magnitudes, but in all cases, a significant share of the baseline opposition can be swayed by explanations of how the policies work and who they impact.

The differences between groups that support climate change action more and those that support it less lies in the different reasoning on exactly these two dimensions. For instance, college-educated respondents are generally more supportive of climate action and we can trace this back to their belief that climate policies will be effective in reducing emissions and that they or lower income households will not lose from them as much. Left-wing respondents in most countries are significantly more supportive of climate action because they believe it is both more effective and it does not hurt them or low-income earners as much as right-wing respondents believe it does.

Respondents' own experience and material self-interest, as measured by their energy usage and life style characteristics affects not only how much they believe they can gain or lose from policies, but also how much they think others benefit and how effective these policies are overall. Most important are the availability of public transport, car usage, and, to a lesser extent, gas expenses. In that sense, people's own self-interest shapes their whole reasoning about the impacts of climate policies.

The policy lessons emerging from these international surveys and experiments are, first, that the specific policies proposed need to be progressive and that citizens need to be made aware of that and of their effectiveness. Explanations work, if they address those two key con-
cerns. Second, information on the dangers of climate change alone, without a corresponding explanation of the policies that can help has only limited impacts on policy support. Third, some of the key concerns people have relate to their own possible losses from the implementation of climate action. Their own experience shapes their broader perceptions and beliefs of climate action. The availability of public transportation and the alternatives to polluting technologies made available or subsidized seem critical as well.

Future research could shed light on other policy proposals, including some that are unusual and less discussed in the debate. It would also be fruitful to expand the sample of countries to reflect more of the international differences in reasoning about climate change.
<table><caption>Table 1: Summary Statistics – High-income countries 1</caption><thead><tr><th rowspan="2">Sample size</th><th colspan="2">Australia</th><th colspan="2">Canada</th><th colspan="2">Denmark</th><th colspan="2">France</th></tr><tr><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th></tr></thead><tbody><tr><td>Male</td><td>0.49</td><td>0.56</td><td>0.49</td><td>0.45</td><td>0.50</td><td>0.50</td><td>0.48</td><td>0.44</td></tr><tr><td>18-24 years old</td><td>0.11</td><td>0.10</td><td>0.10</td><td>0.09</td><td>0.11</td><td>0.09</td><td>0.12</td><td>0.10</td></tr><tr><td>25-34 years old</td><td>0.19</td><td>0.19</td><td>0.17</td><td>0.14</td><td>0.16</td><td>0.12</td><td>0.15</td><td>0.15</td></tr><tr><td>35-49 years old</td><td>0.26</td><td>0.27</td><td>0.24</td><td>0.25</td><td>0.23</td><td>0.25</td><td>0.24</td><td>0.25</td></tr><tr><td>More than 50 years old</td><td>0.44</td><td>0.44</td><td>0.48</td><td>0.52</td><td>0.50</td><td>0.54</td><td>0.49</td><td>0.50</td></tr><tr><td>Income Q1</td><td>0.25</td><td>0.45</td><td>0.25</td><td>0.25</td><td>0.26</td><td>0.29</td><td>0.25</td><td>0.31</td></tr><tr><td>Income Q2</td><td>0.25</td><td>0.31</td><td>0.25</td><td>0.28</td><td>0.23</td><td>0.25</td><td>0.25</td><td>0.31</td></tr><tr><td>Income Q3</td><td>0.25</td><td>0.17</td><td>0.25</td><td>0.28</td><td>0.28</td><td>0.26</td><td>0.25</td><td>0.23</td></tr><tr><td>Income Q4</td><td>0.25</td><td>0.07</td><td>0.25</td><td>0.20</td><td>0.22</td><td>0.19</td><td>0.25</td><td>0.14</td></tr><tr><td>Region 1</td><td>0.33</td><td>0.30</td><td>0.07</td><td>0.06</td><td>0.32</td><td>0.30</td><td>0.19</td><td>0.19</td></tr><tr><td>Region 2</td><td>0.20</td><td>0.23</td><td>0.06</td><td>0.07</td><td>0.23</td><td>0.23</td><td>0.22</td><td>0.24</td></tr><tr><td>Region 3</td><td>0.07</td><td>0.10</td><td>0.26</td><td>0.23</td><td>0.10</td><td>0.10</td><td>0.20</td><td>0.22</td></tr><tr><td>Region 4</td><td>0.28</td><td>0.28</td><td>0.39</td><td>0.39</td><td>0.14</td><td>0.16</td><td>0.25</td><td>0.20</td></tr><tr><td>Region 5</td><td>0.11</td><td>0.09</td><td>0.23</td><td>0.24</td><td>0.21</td><td>0.21</td><td>NA</td><td>NA</td></tr><tr><td>Urban</td><td>0.72</td><td>0.76</td><td>0.83</td><td>0.89</td><td>0.53</td><td>0.53</td><td>0.60</td><td>0.59</td></tr><tr><td>College education (25-64)</td><td>0.49</td><td>0.46</td><td>0.60</td><td>0.56</td><td>0.36</td><td>0.44</td><td>0.40</td><td>0.42</td></tr><tr><td>Inactivity rate (15-64)</td><td>0.22</td><td>0.22</td><td>0.23</td><td>0.29</td><td>0.21</td><td>0.28</td><td>0.29</td><td>0.25</td></tr><tr><td>Unemployment rate (15-64)</td><td>0.07</td><td>0.12</td><td>0.10</td><td>0.12</td><td>0.06</td><td>0.12</td><td>0.08</td><td>0.10</td></tr><tr><td>Employment rate (15-64)</td><td>0.73</td><td>0.69</td><td>0.70</td><td>0.63</td><td>0.74</td><td>0.63</td><td>0.65</td><td>0.67</td></tr></tbody></table>

*Note:* This table displays summary statistics of the samples alongside nationally representative statistics. For *College education (25-64)*, the sample statistics are provided for respondents aged between 25 and 64 years old. For *Inactivity rate (15-64)*, the sample statistics include the share of respondents aged between 15 and 64 years old who indicated being either “Inactive (not searching for a job),” a “Student,” or “Retired.” For *Unemployment rate (15-64)*, the sample statistics include the share of respondents aged between 15 and 64 years old who indicated being “Unemployed (searching for a job)”, among active people (“Unemployed (searching for a job),” “Full-time employed,” “Part-time employed,” or “Self-employed”). For *Employment rate (15-64)*, the sample statistics include the share of respondents aged between 15 and 64 years old who indicated being either “Full-time employed,” “Part-time employed,” or “Self-employed.” Detailed sources for each variable and country, as well as the definitions of regions, college education, and urban categories are available in Appendix A-8.
<table><caption>Table 2: Summary Statistics – High-income countries 2</caption><thead><tr><th rowspan="2">Sample size</th><th colspan="2">Germany</th><th colspan="2">Italy</th><th colspan="2">Japan</th><th colspan="2">Poland</th></tr><tr><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th></tr></thead><tbody><tr><td>Male</td><td>0.49</td><td>0.48</td><td>0.48</td><td>0.49</td><td>0.48</td><td>0.54</td><td>0.48</td><td>0.44</td></tr><tr><td>18-24 years old</td><td>0.09</td><td>0.06</td><td>0.08</td><td>0.09</td><td>0.08</td><td>0.08</td><td>0.09</td><td>0.09</td></tr><tr><td>25-34 years old</td><td>0.15</td><td>0.16</td><td>0.12</td><td>0.13</td><td>0.12</td><td>0.13</td><td>0.17</td><td>0.18</td></tr><tr><td>35-49 years old</td><td>0.22</td><td>0.22</td><td>0.24</td><td>0.26</td><td>0.24</td><td>0.27</td><td>0.28</td><td>0.30</td></tr><tr><td>More than 50 years old</td><td>0.54</td><td>0.56</td><td>0.56</td><td>0.52</td><td>0.56</td><td>0.53</td><td>0.46</td><td>0.42</td></tr><tr><td>Income Q1</td><td>0.25</td><td>0.25</td><td>0.25</td><td>0.29</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.22</td></tr><tr><td>Income Q2</td><td>0.25</td><td>0.25</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.27</td></tr><tr><td>Income Q3</td><td>0.25</td><td>0.23</td><td>0.25</td><td>0.23</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.27</td></tr><tr><td>Income Q4</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.21</td><td>0.25</td><td>0.19</td><td>0.25</td><td>0.25</td></tr><tr><td>Region 1</td><td>0.10</td><td>0.10</td><td>0.20</td><td>0.20</td><td>0.17</td><td>0.18</td><td>0.12</td><td>0.10</td></tr><tr><td>Region 2</td><td>0.15</td><td>0.16</td><td>0.11</td><td>0.12</td><td>0.18</td><td>0.19</td><td>0.14</td><td>0.13</td></tr><tr><td>Region 3</td><td>0.18</td><td>0.16</td><td>0.19</td><td>0.17</td><td>0.35</td><td>0.38</td><td>0.23</td><td>0.21</td></tr><tr><td>Region 4</td><td>0.29</td><td>0.27</td><td>0.27</td><td>0.30</td><td>0.11</td><td>0.10</td><td>0.29</td><td>0.33</td></tr><tr><td>Region 5</td><td>0.28</td><td>0.31</td><td>0.23</td><td>0.21</td><td>0.20</td><td>0.16</td><td>0.22</td><td>0.23</td></tr><tr><td>Urban</td><td>0.80</td><td>0.76</td><td>0.83</td><td>0.89</td><td>0.70</td><td>0.76</td><td>0.57</td><td>0.66</td></tr><tr><td>College education (25-64)</td><td>0.31</td><td>0.32</td><td>0.20</td><td>0.38</td><td>0.53</td><td>0.72</td><td>0.33</td><td>0.46</td></tr><tr><td>Inactivity rate (15-64)</td><td>0.21</td><td>0.23</td><td>0.36</td><td>0.19</td><td>0.20</td><td>0.22</td><td>0.29</td><td>0.18</td></tr><tr><td>Unemployment rate (15-64)</td><td>0.04</td><td>0.07</td><td>0.09</td><td>0.17</td><td>0.03</td><td>0.05</td><td>0.03</td><td>0.09</td></tr><tr><td>Employment rate (15-64)</td><td>0.76</td><td>0.72</td><td>0.58</td><td>0.67</td><td>0.77</td><td>0.74</td><td>0.69</td><td>0.75</td></tr></tbody></table>

*Note*: This table displays summary statistics of the samples alongside nationally representative statistics. See notes to Table 1. Detailed sources for each variable and country, as well as the definitions of regions, college education, and urban categories are available in Appendix A-8.
<table><caption>Table 3: Summary Statistics – High-income countries 3</caption><thead><tr><th rowspan="2"></th><th colspan="2">South Korea</th><th colspan="2">Spain</th><th colspan="2">U.K.</th><th colspan="2">U.S.</th></tr><tr><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th></tr></thead><tbody><tr><td>Sample size</td><td>NA</td><td>1,874</td><td>NA</td><td>2,268</td><td>NA</td><td>2,008</td><td>NA</td><td>2,218</td></tr><tr><td>Male</td><td>0.50</td><td>0.56</td><td>0.49</td><td>0.49</td><td>0.50</td><td>0.53</td><td>0.50</td><td>0.47</td></tr><tr><td>18-24 years old</td><td>0.10</td><td>0.09</td><td>0.08</td><td>0.10</td><td>0.10</td><td>0.08</td><td>0.12</td><td>0.12</td></tr><tr><td>25-34 years old</td><td>0.16</td><td>0.19</td><td>0.12</td><td>0.14</td><td>0.17</td><td>0.19</td><td>0.18</td><td>0.18</td></tr><tr><td>35-49 years old</td><td>0.27</td><td>0.32</td><td>0.28</td><td>0.29</td><td>0.24</td><td>0.24</td><td>0.24</td><td>0.25</td></tr><tr><td>More than 50 years old</td><td>0.47</td><td>0.40</td><td>0.51</td><td>0.48</td><td>0.49</td><td>0.48</td><td>0.46</td><td>0.45</td></tr><tr><td>Income Q1</td><td>0.25</td><td>0.29</td><td>0.25</td><td>0.26</td><td>0.25</td><td>0.27</td><td>0.20</td><td>0.26</td></tr><tr><td>Income Q2</td><td>0.25</td><td>0.29</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.24</td><td>0.24</td><td>0.28</td></tr><tr><td>Income Q3</td><td>0.25</td><td>0.30</td><td>0.25</td><td>0.23</td><td>0.25</td><td>0.21</td><td>0.24</td><td>0.26</td></tr><tr><td>Income Q4</td><td>0.25</td><td>0.13</td><td>0.25</td><td>0.23</td><td>0.25</td><td>0.27</td><td>0.31</td><td>0.20</td></tr><tr><td>Region 1</td><td>0.25</td><td>0.24</td><td>0.19</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.21</td><td>0.20</td></tr><tr><td>Region 2</td><td>0.34</td><td>0.37</td><td>0.30</td><td>0.28</td><td>0.13</td><td>0.13</td><td>0.17</td><td>0.18</td></tr><tr><td>Region 3</td><td>0.19</td><td>0.22</td><td>0.11</td><td>0.10</td><td>0.24</td><td>0.23</td><td>0.38</td><td>0.39</td></tr><tr><td>Region 4</td><td>0.22</td><td>0.17</td><td>0.13</td><td>0.15</td><td>0.11</td><td>0.10</td><td>0.24</td><td>0.23</td></tr><tr><td>Region 5</td><td>NA</td><td>NA</td><td>0.28</td><td>0.26</td><td>0.31</td><td>0.33</td><td>NA</td><td>NA</td></tr><tr><td>Urban</td><td>0.92</td><td>0.95</td><td>0.70</td><td>0.75</td><td>0.82</td><td>0.84</td><td>0.73</td><td>0.72</td></tr><tr><td>College education (25-64)</td><td>0.51</td><td>0.76</td><td>0.40</td><td>0.57</td><td>0.49</td><td>0.62</td><td>0.61</td><td>0.60</td></tr><tr><td>Inactivity rate (15-64)</td><td>0.31</td><td>0.17</td><td>0.28</td><td>0.18</td><td>0.21</td><td>0.24</td><td>0.27</td><td>0.26</td></tr><tr><td>Unemployment rate (15-64)</td><td>0.04</td><td>0.08</td><td>0.16</td><td>0.14</td><td>0.05</td><td>0.09</td><td>0.08</td><td>0.13</td></tr><tr><td>Employment rate (15-64)</td><td>0.66</td><td>0.77</td><td>0.62</td><td>0.71</td><td>0.75</td><td>0.69</td><td>0.67</td><td>0.64</td></tr></tbody></table>

*Note:* This table displays summary statistics of the samples alongside nationally representative statistics. See notes to Table 1. For *College education (25-64)* in the U.S., the sample statistics is provided for all respondents and not only respondents aged between 25 and 64 years old. Detailed sources for each variable and country, as well as the definitions of regions, college education, and urban categories are available in Appendix A-8.
<table><caption>Table 4: Summary Statistics – Middle-income countries 1</caption><thead><tr><th rowspan="2"></th><th colspan="2">Brazil</th><th colspan="2">China</th><th colspan="2">India</th><th colspan="2">Indonesia</th></tr><tr><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th></tr></thead><tbody><tr><td>Sample size</td><td>NA</td><td>1,860</td><td>NA</td><td>1,526</td><td>NA</td><td>1,925</td><td>NA</td><td>1,998</td></tr><tr><td>Male</td><td>0.49</td><td>0.45</td><td>0.51</td><td>0.54</td><td>0.51</td><td>0.54</td><td>0.50</td><td>0.46</td></tr><tr><td>18-24 years old</td><td>0.15</td><td>0.16</td><td>0.10</td><td>0.13</td><td>0.18</td><td>0.19</td><td>0.17</td><td>0.17</td></tr><tr><td>25-34 years old</td><td>0.22</td><td>0.23</td><td>0.20</td><td>0.27</td><td>0.24</td><td>0.26</td><td>0.23</td><td>0.23</td></tr><tr><td>35-49 years old</td><td>0.30</td><td>0.32</td><td>0.28</td><td>0.37</td><td>0.29</td><td>0.25</td><td>0.31</td><td>0.31</td></tr><tr><td>More than 50 years old</td><td>0.34</td><td>0.29</td><td>0.42</td><td>0.23</td><td>0.28</td><td>0.30</td><td>0.29</td><td>0.29</td></tr><tr><td>Income Q1</td><td>0.25</td><td>0.24</td><td>0.25</td><td>0.09</td><td>0.25</td><td>0.21</td><td>0.25</td><td>0.25</td></tr><tr><td>Income Q2</td><td>0.25</td><td>0.30</td><td>0.25</td><td>0.26</td><td>0.25</td><td>0.26</td><td>0.25</td><td>0.25</td></tr><tr><td>Income Q3</td><td>0.25</td><td>0.24</td><td>0.25</td><td>0.33</td><td>0.25</td><td>0.26</td><td>0.25</td><td>0.25</td></tr><tr><td>Income Q4</td><td>0.25</td><td>0.22</td><td>0.25</td><td>0.33</td><td>0.25</td><td>0.26</td><td>0.25</td><td>0.25</td></tr><tr><td>Region 1</td><td>0.08</td><td>0.07</td><td>0.29</td><td>0.29</td><td>0.27</td><td>0.23</td><td>0.08</td><td>0.08</td></tr><tr><td>Region 2</td><td>0.09</td><td>0.04</td><td>0.12</td><td>0.18</td><td>0.26</td><td>0.28</td><td>0.30</td><td>0.30</td></tr><tr><td>Region 3</td><td>0.27</td><td>0.28</td><td>0.08</td><td>0.05</td><td>0.13</td><td>0.14</td><td>0.13</td><td>0.12</td></tr><tr><td>Region 4</td><td>0.14</td><td>0.15</td><td>0.29</td><td>0.23</td><td>0.20</td><td>0.21</td><td>0.21</td><td>0.22</td></tr><tr><td>Region 5</td><td>0.42</td><td>0.45</td><td>0.22</td><td>0.25</td><td>0.14</td><td>0.15</td><td>0.27</td><td>0.27</td></tr><tr><td>Urban</td><td>0.69</td><td>0.77</td><td>0.63</td><td>0.58</td><td>0.36</td><td>0.40</td><td>0.57</td><td>0.58</td></tr><tr><td>College education (25-64)</td><td>0.20</td><td>0.64</td><td>0.10</td><td>0.65</td><td>0.09</td><td>0.90</td><td>0.13</td><td>0.55</td></tr><tr><td>Inactivity rate (15-64)</td><td>0.34</td><td>0.12</td><td>0.23</td><td>0.10</td><td>0.46</td><td>0.16</td><td>0.30</td><td>0.20</td></tr><tr><td>Unemployment rate (15-64)</td><td>0.14</td><td>0.11</td><td>0.03</td><td>0.01</td><td>0.09</td><td>0.02</td><td>0.06</td><td>0.05</td></tr><tr><td>Employment rate (15-64)</td><td>0.57</td><td>0.79</td><td>0.75</td><td>0.88</td><td>0.49</td><td>0.82</td><td>0.66</td><td>0.76</td></tr></tbody></table>

*Note*: This table displays summary statistics of the samples alongside nationally representative statistics. See notes to Table 1. Detailed sources for each variable and country, as well as the definitions of regions, college education, and urban categories are available in Appendix A-8.

<table><caption>Table 5: Summary Statistics – Middle-income countries 2</caption><thead><tr><th rowspan="2"></th><th colspan="2">Mexico</th><th colspan="2">Turkey</th><th colspan="2">South Africa</th><th colspan="2">Ukraine</th></tr><tr><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th><th>Population</th><th>Raw sample</th></tr></thead><tbody><tr><td>Sample size</td><td>NA</td><td>1,970</td><td>NA</td><td>1,932</td><td>NA</td><td>2,003</td><td>NA</td><td>1,564</td></tr><tr><td>Male</td><td>0.48</td><td>0.50</td><td>0.49</td><td>0.43</td><td>0.49</td><td>0.46</td><td>0.45</td><td>0.61</td></tr><tr><td>18-24 years old</td><td>0.18</td><td>0.19</td><td>0.16</td><td>0.18</td><td>0.21</td><td>0.21</td><td>0.08</td><td>0.12</td></tr><tr><td>25-34 years old</td><td>0.23</td><td>0.25</td><td>0.21</td><td>0.24</td><td>0.28</td><td>0.29</td><td>0.18</td><td>0.25</td></tr><tr><td>35-49 years old</td><td>0.30</td><td>0.32</td><td>0.30</td><td>0.34</td><td>0.28</td><td>0.28</td><td>0.28</td><td>0.40</td></tr><tr><td>More than 50 years old</td><td>0.29</td><td>0.25</td><td>0.33</td><td>0.24</td><td>0.22</td><td>0.22</td><td>0.46</td><td>0.24</td></tr><tr><td>Income Q1</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.15</td><td>0.25</td><td>0.16</td><td>0.25</td><td>0.19</td></tr><tr><td>Income Q2</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.24</td><td>0.25</td><td>0.24</td></tr><tr><td>Income Q3</td><td>0.25</td><td>0.24</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.32</td><td>0.25</td><td>0.24</td></tr><tr><td>Income Q4</td><td>0.25</td><td>0.21</td><td>0.25</td><td>0.28</td><td>0.25</td><td>0.27</td><td>0.25</td><td>0.34</td></tr><tr><td>Region 1</td><td>0.33</td><td>0.39</td><td>0.25</td><td>0.28</td><td>0.12</td><td>0.09</td><td>0.31</td><td>0.37</td></tr><tr><td>Region 2</td><td>0.22</td><td>0.18</td><td>0.18</td><td>0.12</td><td>0.24</td><td>0.29</td><td>0.21</td><td>0.17</td></tr><tr><td>Region 3</td><td>0.10</td><td>0.10</td><td>0.30</td><td>0.34</td><td>0.18</td><td>0.17</td><td>0.22</td><td>0.26</td></tr><tr><td>Region 4</td><td>0.13</td><td>0.11</td><td>0.26</td><td>0.26</td><td>0.33</td><td>0.26</td><td>0.25</td><td>0.20</td></tr><tr><td>Region 5</td><td>0.23</td><td>0.22</td><td>NA</td><td>NA</td><td>0.13</td><td>0.18</td><td>NA</td><td>NA</td></tr><tr><td>Urban</td><td>0.64</td><td>0.81</td><td>0.87</td><td>0.96</td><td>0.49</td><td>0.63</td><td>0.70</td><td>0.88</td></tr><tr><td>College education (25-64)</td><td>0.19</td><td>0.67</td><td>0.16</td><td>0.65</td><td>0.16</td><td>0.49</td><td>NA</td><td>0.67</td></tr><tr><td>Inactivity rate (15-64)</td><td>0.35</td><td>0.13</td><td>0.45</td><td>0.21</td><td>0.45</td><td>0.16</td><td>0.38</td><td>0.15</td></tr><tr><td>Unemployment rate (15-64)</td><td>0.04</td><td>0.07</td><td>0.13</td><td>0.12</td><td>0.29</td><td>0.16</td><td>0.10</td><td>0.10</td></tr><tr><td>Employment rate (15-64)</td><td>0.59</td><td>0.81</td><td>0.48</td><td>0.69</td><td>0.38</td><td>0.71</td><td>0.56</td><td>0.76</td></tr></tbody></table>

*Note*: This table displays summary statistics of the samples alongside nationally representative statistics. See notes to Table 1. Detailed sources for each variable and country, as well as the definitions of regions, college education, and urban categories are available in Appendix A-8.
<table><caption>Figure 5: Knowledge about climate change (share of correct answers)</caption><thead><tr><th></th><th>High-income</th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>Spain</th><th>United Kingdom</th><th>United States</th><th>Middle-income</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South</th><th>Africa</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><td><strong>CC is real, human-made, & its dynamics</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CC exists, is anthropogenic</td><td>70</td><td>63</td><td>69</td><td>63</td><td>57</td><td>71</td><td>85</td><td>65</td><td>74</td><td>80</td><td>80</td><td>67</td><td>61</td><td>82</td><td>84</td><td>80</td><td>81</td><td>82</td><td>87</td><td>81</td><td>83</td><td>76</td><td></td></tr><tr><td>Cutting emissions by half insufficient to stop global warming</td><td>52</td><td>52</td><td>53</td><td>63</td><td>54</td><td>69</td><td>51</td><td>59</td><td>40</td><td>34</td><td>56</td><td>53</td><td>44</td><td>28</td><td>28</td><td>15</td><td>13</td><td>13</td><td>39</td><td>33</td><td>38</td><td>44</td><td></td></tr><tr><td><strong>GHG emission ranking</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GHG footprint of beef/meat is higher than chicken or pasta</td><td>80</td><td>82</td><td>82</td><td>86</td><td>72</td><td>86</td><td>82</td><td>73</td><td>77</td><td>85</td><td>74</td><td>84</td><td>74</td><td>59</td><td>65</td><td>54</td><td>55</td><td>50</td><td>57</td><td>74</td><td>60</td><td>58</td><td></td></tr><tr><td>GHG footprint of nuclear is lower than gas or coal</td><td>64</td><td>67</td><td>62</td><td>73</td><td>50</td><td>56</td><td>65</td><td>73</td><td>71</td><td>71</td><td>50</td><td>70</td><td>57</td><td>48</td><td>43</td><td>56</td><td>46</td><td>53</td><td>44</td><td>55</td><td>32</td><td>58</td><td></td></tr><tr><td>GHG footprint of plane is higher than car or train/bus</td><td>55</td><td>56</td><td>56</td><td>70</td><td>62</td><td>73</td><td>51</td><td>37</td><td>55</td><td>31</td><td>62</td><td>67</td><td>41</td><td>29</td><td>25</td><td>31</td><td>24</td><td>18</td><td>36</td><td>38</td><td>32</td><td>29</td><td></td></tr><tr><td>Total emissions of China are higher than other regions</td><td>71</td><td>71</td><td>68</td><td>66</td><td>61</td><td>70</td><td>81</td><td>82</td><td>65</td><td>86</td><td>73</td><td>69</td><td>60</td><td>58</td><td>64</td><td>33</td><td>52</td><td>45</td><td>70</td><td>62</td><td>71</td><td>62</td><td></td></tr><tr><td>Per capita emissions of the US are higher than other regions</td><td>49</td><td>36</td><td>48</td><td>64</td><td>50</td><td>58</td><td>60</td><td>36</td><td>54</td><td>26</td><td>52</td><td>44</td><td>54</td><td>44</td><td>53</td><td>39</td><td>32</td><td>35</td><td>49</td><td>44</td><td>55</td><td>45</td><td></td></tr><tr><td><strong>CC gases</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CO<sub>2</sub> is a greenhouse gas</td><td>83</td><td>69</td><td>78</td><td>93</td><td>78</td><td>86</td><td>87</td><td>94</td><td>88</td><td>77</td><td>87</td><td>84</td><td>75</td><td>75</td><td>78</td><td>89</td><td>88</td><td>81</td><td>72</td><td>70</td><td>50</td><td>77</td><td></td></tr><tr><td>Methane is a greenhouse gas</td><td>59</td><td>76</td><td>71</td><td>61</td><td>45</td><td>62</td><td>35</td><td>42</td><td>49</td><td>68</td><td>68</td><td>74</td><td>63</td><td>52</td><td>58</td><td>44</td><td>32</td><td>36</td><td>61</td><td>61</td><td>71</td><td>49</td><td></td></tr><tr><td><strong>CC impacts if CC goes unabated</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Severe droughts and heatwaves are likely</td><td>86</td><td>84</td><td>90</td><td>86</td><td>84</td><td>89</td><td>90</td><td>89</td><td>89</td><td>90</td><td>87</td><td>85</td><td>75</td><td>86</td><td>81</td><td>89</td><td>83</td><td>93</td><td>79</td><td>89</td><td>91</td><td>86</td><td></td></tr><tr><td>Sea-level rise is likely</td><td>86</td><td>83</td><td>85</td><td>92</td><td>82</td><td>87</td><td>89</td><td>92</td><td>86</td><td>90</td><td>85</td><td>89</td><td>75</td><td>84</td><td>78</td><td>88</td><td>87</td><td>93</td><td>83</td><td>85</td><td>82</td><td>78</td><td></td></tr><tr><td>More frequent volcanic eruptions are unlikely</td><td>44</td><td>41</td><td>37</td><td>62</td><td>37</td><td>60</td><td>49</td><td>52</td><td>31</td><td>31</td><td>41</td><td>41</td><td>43</td><td>26</td><td>33</td><td>22</td><td>18</td><td>17</td><td>34</td><td>26</td><td>21</td><td>36</td><td></td></tr></tbody></table>

*Note*: Share of respondents who agree with the statements listed on the left. The statements are oriented so that they represent the correct answer, according to the current scientific literature (see the sources in Appendix A-8). For the formulation of each question, see Appendix A-5.
<figure><img src="image_6.png" /><figcaption>Figure 6: Who has better knowledge about climate change?</figcaption></figure>

*Note:* Panel A shows the coefficients from an OLS regression of the *Knowledge index* on indicators for individual socio-economic characteristics and treatment effects. Country fixed effects, treatment indicators, and age are included, the latter is displayed separately in Panel B, for each country to highlight the heterogeneity. The omitted categories for Panel A are “male” for *gender* (*gender*: “other” is not displayed), lowest income quartile for *income*, “no schooling, or highest level achieved is primary or lower secondary education” for *education*; “left leaning” for *economic leaning*. For Panel B, the omitted category is “18-34 years old” for *age*. See Appendix A-1 for variable definitions.
<table><caption>Figure 7: Share of respondents who somewhat to strongly support climate change policies.</caption><thead><tr><th></th><th colspan="12">High-income</th><th colspan="12">Middle-income</th></tr><tr><th></th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>Spain</th><th>United Kingdom</th><th>United States</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><td colspan="21"><strong>Support for Main Climate Policies</strong></td></tr><tr><td>Green infrastructure program</td><td>57</td><td>49</td><td>56</td><td>53</td><td>57</td><td>42</td><td>78</td><td>48</td><td>58</td><td>70</td><td>71</td><td>55</td><td>50</td><td>78</td><td>77</td><td>83</td><td>79</td><td>82</td><td>84</td><td>73</td><td>76</td><td>69</td></tr><tr><td>Ban on combustion-engine cars</td><td>43</td><td>35</td><td>47</td><td>41</td><td>28</td><td>32</td><td>54</td><td>41</td><td>44</td><td>53</td><td>54</td><td>45</td><td>39</td><td>65</td><td>60</td><td>72</td><td>81</td><td>67</td><td>67</td><td>53</td><td>62</td><td>58</td></tr><tr><td>Carbon tax with cash transfers</td><td>37</td><td>34</td><td>41</td><td>30</td><td>29</td><td>28</td><td>47</td><td>35</td><td>36</td><td>53</td><td>44</td><td>34</td><td>33</td><td>59</td><td>47</td><td>79</td><td>76</td><td>69</td><td>55</td><td>52</td><td>56</td><td>40</td></tr><tr><td colspan="21"><strong>Support for Other Climate Policies</strong></td></tr><tr><td>Subsidies to low-carbon technologies</td><td>67</td><td>62</td><td>65</td><td>67</td><td>56</td><td>64</td><td>79</td><td>69</td><td>75</td><td>71</td><td>73</td><td>65</td><td>57</td><td>74</td><td>77</td><td>75</td><td>72</td><td>81</td><td>68</td><td>75</td><td>75</td><td>69</td></tr><tr><td>Mandatory and subsidized insulation of buildings</td><td>66</td><td>70</td><td>64</td><td>70</td><td>64</td><td>60</td><td>73</td><td>59</td><td>72</td><td>72</td><td>71</td><td>70</td><td>53</td><td>77</td><td>88</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ban on polluting cars in city centers</td><td>60</td><td>53</td><td>60</td><td>66</td><td>57</td><td>50</td><td>76</td><td>64</td><td>61</td><td>52</td><td>64</td><td>65</td><td>49</td><td>71</td><td>65</td><td>75</td><td>74</td><td>85</td><td>72</td><td>66</td><td>60</td><td>67</td></tr><tr><td>Funding clean energy in less developed countries</td><td>54</td><td>49</td><td>50</td><td>53</td><td>48</td><td>48</td><td>76</td><td>53</td><td>55</td><td>57</td><td>65</td><td>51</td><td>50</td><td>73</td><td>63</td><td>71</td><td>75</td><td>82</td><td>74</td><td>76</td><td>66</td><td>78</td></tr><tr><td>Ban on combustion-engine cars w. alternatives available</td><td>48</td><td>38</td><td>47</td><td>42</td><td>42</td><td>41</td><td>58</td><td>51</td><td>48</td><td>59</td><td>57</td><td>52</td><td>44</td><td>68</td><td>60</td><td>80</td><td>78</td><td>72</td><td>66</td><td>62</td><td>64</td><td>63</td></tr><tr><td>Tax on flying (+20%)</td><td>45</td><td>35</td><td>44</td><td>60</td><td>46</td><td>53</td><td>42</td><td>47</td><td>44</td><td>44</td><td>44</td><td>46</td><td>33</td><td>52</td><td>39</td><td>64</td><td>67</td><td>71</td><td>49</td><td>43</td><td>46</td><td>36</td></tr><tr><td>Tax on fossil fuels ($45/tCO2)</td><td>36</td><td>36</td><td>40</td><td>43</td><td>31</td><td>31</td><td>38</td><td>35</td><td>27</td><td>43</td><td>39</td><td>38</td><td>34</td><td>48</td><td>35</td><td>60</td><td>70</td><td>61</td><td>40</td><td>38</td><td>52</td><td>27</td></tr><tr><td colspan="21"><strong>Support for Carbon Tax With:</strong></td></tr><tr><td>Funding environmental infrastructures</td><td>63</td><td>60</td><td>48</td><td>60</td><td>65</td><td>60</td><td>76</td><td>56</td><td>68</td><td>80</td><td>69</td><td>63</td><td>56</td><td>76</td><td>78</td><td>77</td><td>73</td><td>81</td><td>74</td><td>79</td><td>73</td><td>70</td></tr><tr><td>Subsidies to low-carbon tech.</td><td>63</td><td>58</td><td>49</td><td>52</td><td>57</td><td>66</td><td>76</td><td>68</td><td>71</td><td>81</td><td>69</td><td>59</td><td>53</td><td>74</td><td>74</td><td>82</td><td>73</td><td>80</td><td>71</td><td>78</td><td>66</td><td>66</td></tr><tr><td>Reduction in personal income taxes</td><td>57</td><td>52</td><td>48</td><td>38</td><td>62</td><td>54</td><td>72</td><td>64</td><td>69</td><td>63</td><td>67</td><td>52</td><td>49</td><td>69</td><td>69</td><td>71</td><td>70</td><td>73</td><td>69</td><td>68</td><td>66</td><td>64</td></tr><tr><td>Cash transfers to the poorest households</td><td>53</td><td>51</td><td>48</td><td>41</td><td>55</td><td>47</td><td>68</td><td>54</td><td>50</td><td>59</td><td>63</td><td>57</td><td>46</td><td>73</td><td>67</td><td>81</td><td>73</td><td>87</td><td>66</td><td>65</td><td>82</td><td>61</td></tr><tr><td>Cash transfers to constrained households</td><td>50</td><td>50</td><td>42</td><td>36</td><td>55</td><td>47</td><td>62</td><td>47</td><td>39</td><td>62</td><td>61</td><td>52</td><td>44</td><td>64</td><td>59</td><td>68</td><td>66</td><td>75</td><td>59</td><td>60</td><td>65</td><td>60</td></tr><tr><td>Tax rebates for the most affected firms</td><td>48</td><td>41</td><td>41</td><td>38</td><td>52</td><td>34</td><td>66</td><td>49</td><td>61</td><td>60</td><td>55</td><td>41</td><td>43</td><td>63</td><td>59</td><td>73</td><td>71</td><td>72</td><td>54</td><td>63</td><td>55</td><td>56</td></tr><tr><td>Reduction in the public deficit</td><td>48</td><td>40</td><td>39</td><td>34</td><td>49</td><td>39</td><td>65</td><td>50</td><td>56</td><td>48</td><td>62</td><td>44</td><td>48</td><td>64</td><td>62</td><td>72</td><td>71</td><td>73</td><td>60</td><td>62</td><td>58</td><td>52</td></tr><tr><td>Equal cash transfers to all households</td><td>38</td><td>37</td><td>38</td><td>27</td><td>45</td><td>31</td><td>42</td><td>43</td><td>37</td><td>43</td><td>44</td><td>33</td><td>38</td><td>62</td><td>45</td><td>70</td><td>69</td><td>77</td><td>60</td><td>57</td><td>59</td><td>53</td></tr><tr><td>Reduction in corporate income taxes</td><td>37</td><td>29</td><td>32</td><td>24</td><td>37</td><td>25</td><td>55</td><td>38</td><td>48</td><td>49</td><td>50</td><td>26</td><td>29</td><td>59</td><td>54</td><td>67</td><td>64</td><td>69</td><td>60</td><td>50</td><td>60</td><td>42</td></tr><tr><td colspan="21"><strong>Support for Cattle-Related Policies</strong></td></tr><tr><td>Subsidies on organic and local vegetables</td><td>56</td><td>42</td><td>50</td><td>59</td><td>52</td><td>56</td><td>71</td><td>46</td><td>73</td><td>63</td><td>65</td><td>49</td><td>43</td><td>68</td><td>62</td><td>81</td><td></td><td>78</td><td>58</td><td>59</td><td>80</td><td>57</td></tr><tr><td>Ban of intensive cattle farming</td><td>42</td><td>32</td><td>41</td><td>31</td><td>55</td><td>49</td><td>64</td><td>17</td><td>44</td><td>44</td><td>43</td><td>50</td><td>36</td><td>39</td><td>38</td><td>55</td><td></td><td>48</td><td>44</td><td>28</td><td>32</td><td>25</td></tr><tr><td>Removal of subsidies for cattle farming</td><td>34</td><td>31</td><td>33</td><td>32</td><td>28</td><td>38</td><td>42</td><td>16</td><td>34</td><td>31</td><td>41</td><td>37</td><td>38</td><td>39</td><td>43</td><td>52</td><td></td><td>54</td><td>45</td><td>27</td><td>30</td><td>22</td></tr><tr><td>A high tax on cattle products, doubling beef prices</td><td>30</td><td>24</td><td>27</td><td>31</td><td>29</td><td>40</td><td>37</td><td>19</td><td>30</td><td>27</td><td>31</td><td>31</td><td>31</td><td>36</td><td>33</td><td>52</td><td></td><td>50</td><td>36</td><td>30</td><td>26</td><td>24</td></tr></tbody></table>

Note: Opposition or support is asked on a 5-point scale with “Indifferent” as the middle option. For support shares excluding “Indifferent” answers, see Figure A3. For the formulation of each question, see Appendix A-5.
<figure><img src="image_7.png" /><figcaption>Figure 8: Share of people answering “A lot” or “A great deal” to the willingness to adopt different behaviors, and to the importance of different conditions for such adoption, as well as real-stakes questions.</figcaption></figure>

Note: *Behaviors* relate to the question “To what extent would you be willing to adopt the following behaviors?” and *Factors* to “How important are the factors below in order for you to adopt a sustainable lifestyle (i.e. limit driving, flying, and consumption, cycle more, etc.)?”. Both questions use a 5-point scale: “Not at all”, “A little”, “Moderately”, “A lot”, “A great deal”. Real-stake questions include the signature of a petition to “stand up for real climate action” and an indicator that the respondent chose to donate more than 20% of the prize of about $100 should they win the survey’s lottery.
<figure><img src="image_8.png" /><figcaption>Figure 9: Which respondents support climate action?</figcaption></figure>

*Note*: Panel A shows the coefficients from a regression of the *Support for main climate policies index* on socio-economic indicators (left panel) and on socio-economic and energy usage indicator (right panel). All covariates are indicators. Country fixed effects, indicators for age, gender, income, and each treatment are included but not displayed. The omitted category for *Place characteristics* is “Rural or very small agglomeration.” See Appendix A-1 for variable definitions. Panel B reports the coefficients on being 50 years and older (relative to the 18-34 years old), being female (relative to being a male), and being in the higher half of the income distribution (relative to being in the 1st quartile).
<table><caption>Figure 10: Properties of the main policies. [Share of agreement]</caption><thead><tr><th rowspan="2"></th><th colspan="3">Green Infrastructure</th><th colspan="3">Carbon Tax w. Transfers</th><th colspan="3">Ban on Combustion-Engine Cars</th></tr><tr><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th></tr></thead><tbody><tr><td colspan="10"><strong>Effects of Main Climate Policies</strong></td></tr><tr><td>Reduce air pollution</td><td>76</td><td>83</td><td>82</td><td>68</td><td>84</td><td>78</td><td>79</td><td>85</td><td>84</td></tr><tr><td>Make electricity production greener</td><td>71</td><td>82</td><td>77</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Increase the use of public transport/Encourage less driving</td><td>60</td><td>78</td><td>67</td><td>51</td><td>77</td><td>64</td><td></td><td></td><td></td></tr><tr><td>Positive effect on economy and employment</td><td>37</td><td>44</td><td>45</td><td>31</td><td>43</td><td>42</td><td>35</td><td>42</td><td>39</td></tr><tr><td>Costless way to fight climate change</td><td>30</td><td>40</td><td>39</td><td>27</td><td>39</td><td>34</td><td>39</td><td>39</td><td>37</td></tr><tr><td>Encourage insulation of buildings</td><td></td><td></td><td></td><td>64</td><td>75</td><td>67</td><td></td><td></td><td></td></tr><tr><td>Reduce GHG emissions/Reduce CO<sub>2</sub> emissions from cars</td><td></td><td></td><td></td><td>64</td><td>79</td><td>71</td><td>73</td><td>81</td><td>77</td></tr><tr><td colspan="10"><strong>Distributional Impacts of Main Climate Policies</strong></td></tr><tr><td colspan="10">Believes the following groups would gain</td></tr><tr><td>High-income earners</td><td>39</td><td>57</td><td>50</td><td>33</td><td>50</td><td>37</td><td>40</td><td>54</td><td>47</td></tr><tr><td>Those living in rural areas</td><td>25</td><td>66</td><td>41</td><td>21</td><td>61</td><td>32</td><td>16</td><td>56</td><td>24</td></tr><tr><td>Own household</td><td>23</td><td>66</td><td>40</td><td>20</td><td>63</td><td>28</td><td>15</td><td>57</td><td>24</td></tr><tr><td>The middle class</td><td>22</td><td>59</td><td>43</td><td>21</td><td>56</td><td>31</td><td>15</td><td>53</td><td>26</td></tr><tr><td>Low-income earners</td><td>21</td><td>62</td><td>40</td><td>22</td><td>63</td><td>31</td><td>12</td><td>55</td><td>24</td></tr><tr><td colspan="10"><strong>Perceived Fairness and Support</strong></td></tr><tr><td>Support</td><td>57</td><td>81</td><td>76</td><td>37</td><td>74</td><td>50</td><td>43</td><td>73</td><td>60</td></tr><tr><td>Is fair</td><td>51</td><td>79</td><td>67</td><td>35</td><td>69</td><td>47</td><td>40</td><td>70</td><td>53</td></tr></tbody></table>

Note: Questions use 5-point scales: *Disagree/Agree* for Effects and Is fair, *Lose/Win* for Distributional impacts, and *Oppose/Support* for Support. For results excluding the middle answers “Neither agree or disagree”, “Neither win nor lose” or “Indifferent”, see Figure A5. For the formulation of each question, see Appendix A-5.
<figure><img src="image_9.png" /><figcaption>Figure 11: Reasoning underlying support for main climate policies.</figcaption></figure>

*Note:* The figure shows the coefficients from a regression of support for each policy (indicator variable) on variables measuring respondents’ beliefs and perceptions, all standardized. Country fixed effects, treatment and individual socio-demographic characteristics are included but not displayed. See Appendix A-1 for variable definitions. A coefficient corresponds to the percentage point increase in Support following a one standard deviation increase in the covariate.
<figure><img src="image_10.png" /><figcaption>Figure 12: Effects of the Treatment on Support for Climate Action</figcaption></figure>

*Note:* The figure shows the coefficients from a regression of the indicator variables listed on the left, capturing support for various policies and willingness to change behaviors, on indicators for each treatment, controlling for country fixed effects, socio-economic characteristics, i.e., age, gender, income, education, economic leaning, living with a child (controls are not displayed). See Appendix A-1 for variable definitions.
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# Appendix

<table><tr><td>List of Figures</td><td>44</td></tr><tr><td>List of Tables</td><td>45</td></tr><tr><td>A-1 Variable Definition</td><td>47</td></tr><tr><td>A-2 Data collection and survey information</td><td>60</td></tr><tr><td>A-2.1 Data collection</td><td>60</td></tr><tr><td>A-2.2 Data quality</td><td>61</td></tr><tr><td>A-3 Additional figures</td><td>63</td></tr><tr><td>A-4 Regression tables</td><td>85</td></tr><tr><td>A-5 Questionnaire</td><td>96</td></tr><tr><td>A-6 Robustness checks</td><td>121</td></tr><tr><td>A-7 Acquiescence in China, India, and Indonesia</td><td>121</td></tr><tr><td>A-8 Data sources</td><td>124</td></tr><tr><td>A-8.1 Detailed Regional Brackets</td><td>129</td></tr><tr><td>A-8.2 Urban-Rural Categories</td><td>133</td></tr><tr><td>A-8.3 Detailed Education Brackets</td><td>139</td></tr><tr><td>A-9 Correct Answers to Knowledge Questions</td><td>143</td></tr><tr><td>A-9.1 Video sources</td><td>144</td></tr><tr><td>A-10 Appendix References</td><td>146</td></tr></table>
List of Figures

<table><tr><td>1</td><td>Share of respondents who somewhat to strongly agree that “Climate change is an important problem” and that their country “should take measures to fight climate change.”</td><td>4</td></tr><tr><td>2</td><td>The 20 countries covered in the survey.</td><td>5</td></tr><tr><td>3</td><td>Survey outline.</td><td>9</td></tr><tr><td>4</td><td>Climate policies video</td><td>10</td></tr><tr><td>5</td><td>Knowledge about climate change (share of correct answers)</td><td>30</td></tr><tr><td>6</td><td>Who has better knowledge about climate change?</td><td>31</td></tr><tr><td>7</td><td>Share of respondents who somewhat to strongly support climate change policies.</td><td>32</td></tr><tr><td>8</td><td>Share of people answering “A lot” or “A great deal” to the willingness to adopt different behaviors, and to the importance of different conditions for such adoption, as well as real-stakes questions.</td><td>33</td></tr><tr><td>9</td><td>Which respondents support climate action?</td><td>34</td></tr><tr><td>10</td><td>Properties of the main policies. [Share of agreement]</td><td>35</td></tr><tr><td>11</td><td>Reasoning underlying support for main climate policies.</td><td>36</td></tr><tr><td>12</td><td>Effects of the Treatment on Support for Climate Action</td><td>37</td></tr><tr><td>A1</td><td>Distribution of durations of responses</td><td>61</td></tr><tr><td>A2</td><td>Expectations about climate change Share of respondents who somewhat or strongly agree with the following views on climate change and climate policies.</td><td>63</td></tr><tr><td>A3</td><td>Share of non-indifferent respondents who somewhat or strongly support policies.</td><td>64</td></tr><tr><td>A4</td><td>Support for other climate policies.</td><td>65</td></tr><tr><td>A5</td><td>Properties of the main policies. [Share of agreement among non-indifferent]</td><td>66</td></tr><tr><td>A6</td><td>Properties of a ban on combustion-engine cars. [Share of agreement]</td><td>67</td></tr><tr><td>A7</td><td>Properties of a carbon tax with cash transfers. [Share of agreement or support]</td><td>68</td></tr><tr><td>A8</td><td>Properties of a green infrastructure program. [Share of agreement or support]</td><td>69</td></tr><tr><td>A9</td><td>Zooming-in on individual main climate policies.</td><td>70</td></tr><tr><td>A10</td><td>Heterogeneous country-patterns for car-dependency.</td><td>71</td></tr><tr><td>A11</td><td>Determinants of support for cattle-related policies</td><td>72</td></tr><tr><td>A12</td><td>Reasoning underlying support, views on fairness, and willingness to change behavior</td><td>73</td></tr><tr><td>A13</td><td>Reasoning underlying support for other climate policies</td><td>74</td></tr><tr><td>A14</td><td>How different groups reason about perceived efficiency and distributional effects</td><td>75</td></tr><tr><td>A15</td><td>Treatment effects on reasonings</td><td>76</td></tr><tr><td>A16</td><td>Treatment effects on beliefs about policies' properties</td><td>77</td></tr><tr><td>A17</td><td>Support of the carbon tax depending on revenue-use: heterogeneous patterns for Income, Education, &amp; Economic Leaning</td><td>78</td></tr><tr><td>A18</td><td>Reasoning underlying support for a carbon tax with different variants of cash transfers.</td><td>79</td></tr><tr><td>A19</td><td>Reasoning underlying support for a carbon tax with different variants of tax reduction.</td><td>80</td></tr><tr><td>A20</td><td>Reasoning underlying support for other use of the revenues from a carbon tax.</td><td>81</td></tr></table>
<table><tr><td>A21</td><td>Experimental Evidence – Use of revenues from a carbon tax</td><td>82</td></tr><tr><td>A22</td><td>What sources of funding do you find appropriate for public investments in green infrastructure? (Multiple answers are possible)</td><td>83</td></tr><tr><td>A23</td><td>Preferences for funding of green infrastructures across groups.</td><td>84</td></tr><tr><td>A24</td><td>Share of respondents who somewhat to strongly support climate change policies, among those answering in more than 28 min.</td><td>122</td></tr></table>

List of Tables

<table><tr><td>1</td><td>Summary Statistics – High-income countries 1</td><td>26</td></tr><tr><td>2</td><td>Summary Statistics – High-income countries 2</td><td>27</td></tr><tr><td>3</td><td>Summary Statistics – High-income countries 3</td><td>28</td></tr><tr><td>4</td><td>Summary Statistics – Middle-income countries 1</td><td>29</td></tr><tr><td>5</td><td>Summary Statistics – Middle-income countries 2</td><td>29</td></tr><tr><td>A1</td><td>Support for the main policies regressed on individual characteristics.</td><td>86</td></tr><tr><td>A2</td><td>Reasonings underlying knowledge and support for the main policies.</td><td>87</td></tr><tr><td>A3</td><td>Treatment effects on support for climate action.</td><td>87</td></tr><tr><td>A4</td><td>Knowledge regressed on individual characteristics.</td><td>88</td></tr><tr><td>A5</td><td>Knowledge index regressed on individual characteristics in high-income countries.</td><td>89</td></tr><tr><td>A6</td><td>Knowledge index regressed on individual characteristics in middle-income countries.</td><td>90</td></tr><tr><td>A7</td><td>Support for main climate policies index regressed on individual characteristics in high-income countries.</td><td>91</td></tr><tr><td>A8</td><td>Support for main climate policies index regressed on individual characteristics in middle-income countries.</td><td>92</td></tr><tr><td>A9</td><td>Reasonings underlying support for main climate policies index in high-income countries.</td><td>93</td></tr><tr><td>A10</td><td>Reasonings underlying support for main climate policies index in middle-income countries.</td><td>94</td></tr><tr><td>A11</td><td>Treatment effects on the Fairness of main climate policies index in high-income countries.</td><td>94</td></tr><tr><td>A12</td><td>Treatment effects on the Fairness of main climate policies index in middle-income countries.</td><td>95</td></tr><tr><td>A13</td><td>Treatment effects on the Willingness to adopt climate friendly behavior index in high-income countries.</td><td>95</td></tr><tr><td>A14</td><td>Treatment effects on the Willingness to adopt climate friendly behavior index in middle-income countries.</td><td>95</td></tr><tr><td>A15</td><td>Treatment effects on support for climate action, among respondents who do not have both answers incorrect at the video attention questions.</td><td>121</td></tr><tr><td>A16</td><td>Acquiescence bias across countries.</td><td>122</td></tr></table>
<table><tr><td>A17 Heterogeneous treatment effects on support for Main Policies in China, India & Indonesia</td><td>123</td></tr></table>
# A-1 Variable Definition

## Indices

The summary indices that aggregate information over the same domain are constructed following the methodology in Kling, Liebman and Katz (2007). Each index consists of an equally weighted average of the z-scores of its components with signs oriented consistently within domain (e.g., the higher the *Knowledge index*, the higher the belief of the climate knowledge of the respondent). Variables are transformed into z-scores by subtracting the control group mean and dividing by the control group standard deviation, so that each z-score has mean 0 and standard deviation 1 for the control group. To further ease interpretation, the resulting index is itself standardized by subtracting the mean and dividing by the standard deviation, so that each index has mean zero and standard deviation one.

## Set A: Socio-economic indicators

*Female:* respondent is a female.

*Other:* respondent's gender is neither female nor male.

*Lives with child(ren) under 14:* respondent lives with at least one child below 14 (or has at least one child, for the U.S.).

*Age 18-24:* respondent's age is between 18 and 24 years (usually omitted category in the regressions).

*Age 25-34:* respondent's age is between 25 and 34 years.

*Age 35-49:* respondent's age is between 35 and 49 years.

*Age 50+:* respondent's age is more than 50 years old.

*Income Q1:* respondent's household income (before withholding tax) is in the first quartile of her country distribution (usually omitted category in the regressions).

*Income Q2:* respondent's household income (before withholding tax) is between the first and second quartiles of her country distribution.

*Income Q3:* respondent's household income (before withholding tax) is between the second and third quartiles of her country distribution.

*Income Q4:* respondent's household income (before withholding tax) is above the third quartile of her country distribution.

*Has little to no schooling:* respondent received no schooling or highest level achieved is primary or lower secondary education (usually omitted category for the regressions). *Has vocational or high-school degree:* respondent's highest degree is either a vocational or a high-school degree and has at least achieved primary or lower secondary education.

*Has a college degree:* respondent has at least a college degree.

*Very Left leaning:* respondent's economic policy leaning is very left.

*Left leaning:* respondent's economic policy leaning is either left (usually omitted category in the regressions).

*Center leaning:* respondent's economic policy leaning is center.

*Right leaning:* respondent's economic policy leaning is right.
*Very Right leaning:* respondent's economic policy leaning is very right.

*Treatment: None:* respondent was randomized to see no information treatment (usually omitted category in the regressions).

*Treatment: Climate impacts:* respondent was randomized to see the information treatment focused on the effects of climate change.

*Treatment: Climate policies:* respondent was randomized to see the information treatment focused on the climate policies.

*Treatment: Both:* respondent was randomized to see the information treatment focused on both climate policies and the effects of climate change.

## Set B: Energy usage indicators

*Rural area:* respondent indicates living in a rural area, i.e., a town of less than 5,000 inhabitants (for China in a town of less than 10,000 inhabitants, for Denmark in a town of less than 1,000 inhabitants).

*Small agglomeration:* respondent indicates living in a town between 5,000 and 10,000 inhabitants (for China in a town between 10,000 and 100,000 inhabitants, for Denmark in a town between 1,000 and 20,000 inhabitants).

*Medium agglomeration:* respondent indicates living in an agglomeration between 50,000 and 250,000 inhabitants (for China in an agglomeration between 100,000 and 1,000,000 inhabitants, for Denmark in an agglomeration between 20,000 and 100,000 inhabitants).

*Large agglomeration:* respondent lives in an agglomeration of more than 500,000 inhabitants (for China more than 1,000,000 inhabitants, for Denmark in an agglomeration of more than 100,000 inhabitants).

*Public transport available:* respondent indicates that the availability of public transport are “very poor” or “poor” whe she lives.

*Uses car:* respondent indicates shes uses a car or a motorbike for at least one activity (work, leisure, or shopping).

*High gas expenses:* respondent's monthly gas expenses are above the median expenses of the respondent's income quartile in her country.

*High heating expenses:* respondent's yearly heating or cooling expenses are above the median expenses of the respondent's income quartile in her country.

*Flies more than once a year:* respondent takes on average more than one round-trip flight per year.

*Polluting Sector:* respondent's economic works in a polluting sector.

*Eats beef/meat weekly ore more:* respondent indicates eating beef (meat in India) weekly or daily.

*Owner or landlord:* respondent is an homeowner or a landlord renting out property.

## Set C: Underlying reasoning indices

*Trusts the government:* index based on the following variable

* Trust govt: respondent's answer to the question: "Do you agree or disagree with the following statement: 'Over the last decade the [Country] government could generally be
trusted to do what is right.," coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

*Believes inequality is an important problem:* index based on the following variable

*   *Ineq. problem:* respondent's answer to the question: "How big of an issue do you think income inequality is in [Country]?" coded on a -2 to 2 scale, where -2 is "Not an issue at all," 0 is "An issue," and +2 is "A very serious issue."

*Worries about the consequences of CC:* index based on the following variables

*   *More migration:* respondent's answer to the question: "If nothing is done to limit climate change, how likely do you think it is that climate change will lead to larger immigration flows" coded on a -2 to 2 scale, where -2 is "Very unlikely," there is no 0, and +2 is "Very likely."

*   *More wars:* respondent's answer to the question: "If nothing is done to limit climate change, how likely do you think it is that climate change will lead to more armed conflicts" coded on a -2 to 2 scale, where -2 is "Very unlikely," there is no 0, and +2 is "Very likely."

*   *Extinction of humankind:* respondent's answer to the question: "If nothing is done to limit climate change, how likely do you think it is that climate change will lead to the extinction of humankind" coded on a -2 to 2 scale, where -2 is "Very unlikely," there is no 0, and +2 is "Very likely."

*   *Drop in standard of living:* respondent's answer to the question: "If nothing is done to limit climate change, how likely do you think it is that climate change will lead to drop in standards of living" coded on a -2 to 2 scale, where -2 is "Very unlikely," there is no 0, and +2 is "Very likely."

*   *Climate change problem:* respondent's answer to the question: "Do you agree or disagree with the following statement: 'Climate change is an important problem.'" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

*   *Climate change end:* respondent's answer to the question: "How likely is it that human kind halts climate change by the end of the century?" coded on a -2 to 2 scale, where -2 is "Very unlikely," there is no 0, and +2 is "Very likely."

*   *Environmentalist:* respondent is a member of an environmental organization.

*Believe will suffer from climate change:* index based on the following variable

*   *Suffers from CC:* respondent's answer to the question: "To what extent do you think climate change already affects or will affect your personal life negatively?" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."
*Understands emissions across activities/regions:* index based on the following variables

* *Score footprint transport:* respondent's Kendall distance with true ranking on knowledge questions about transport emissions.

* *Score footprint electricity:* respondent's Kendall distance with true ranking on knowledge questions about electricity production emissions.

* *Score footprint food:* respondent's Kendall distance with true ranking on knowledge questions about food emissions.

* *Score footprint countries per capita:* respondent's Kendall distance with true ranking on knowledge questions about countries' emissions per capita.

* *Score footprint countries per region:* respondent's Kendall distance with true ranking on knowledge questions about total regions' emissions.

*Knows climate change real:* index based on the following variables

* *Climate change real:* respondent indicates that climate change is real.

* *Dynamic of Climate change:* respondent indicates that halving global emissions would not be sufficient to stop temperatures from rising.

* *Climate change anthropogenic:* respondent indicates that “A lot” or “Most” of climate change is due to human activity.

*Knows which gases cause CC:* index based on the following variables

* *Knows methane:* respondent indicates that methane is a GHG.

* *Knows CO₂:* respondent indicates that CO₂ is a GHG.

* *Knows H₂:* respondent indicates that H₂ is not a GHG.

* *Knows particulates:* respondent indicates that particulates are not a GHG.

*Understands impacts of CC:* index based on the following variables

* *Droughts:* respondent indicates that it is “Somewhat likely” or “Very likely” that climate change will lead to severe droughts and heatwaves.

* *Sea levels:* respondent indicates that it is “Somewhat likely” or “Very likely” that climate change will lead to rising sea levels.

* *Volcanos:* respondent indicates that it is “Somewhat unlikely” or “Very unlikely” that climate change will lead to more frequent volcanic eruptions.

*Believes policies would have positive econ. effects:* index based on the following variable and on the variables of the three following indices
* *Econ. effects halting CC:* respondent's answer to the question: "If we decide to halt climate change through ambitious policies, what would be the effects on the [Country] economy and employment?" coded on a -2 to 2 scale, where -2 is "Very negative effects," 0 is "No noticeable effects," and +2 is "Very positive effects."

* *Believes the policy would have positive econ. effect – Ban on combustion-engine cars:* index based on the following variable

- respondent's answer to the question: "Do you agree or disagree with the following statements? A ban on combustion-engine cars would have a positive effect on the [Country] economy and employment" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes the policy would have positive econ. effect – Green infrastructure program:* index based on the following variable

- respondent's answer to the question: "Do you agree or disagree with the following statements? A green infrastructure program would have a positive effect on the [Country] economy and employment" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes the policy would have positive econ. effect – Carbon tax with cash transfers:* index based on the following variable

- respondent's answer to the question: "Do you agree or disagree with the following statements? A carbon tax with cash transfers would have a positive effect on the [Country] economy and employment" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes policies would reduce pollution:* index based on the variables of the three following indices

* *Believes the policy would reduce pollution – Ban on combustion-engine cars:* index based on the following variable

- respondent's answer to the question: "Do you agree or disagree with the following statements? A ban on combustion-engine cars would reduce air pollution" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes the policy would reduce pollution – Green infrastructure program:* index based on the following variable

- respondent's answer to the question: "Do you agree or disagree with the following statements? A green infrastructure program would reduce air pollution" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."
* *Believes the policy would reduce pollution – Carbon tax with cash transfers:* index based on the following variable

– respondent's answer to the question: "Do you agree or disagree with the following statements? A carbon tax with cash transfers would reduce air pollution" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

*Believes policies would reduce emissions:* index based on the variables of the three following indices

* *Believes the policy would reduce pollution – Ban on combustion-engine cars:* index based on the following variable

– respondent's answer to the question: "Do you agree or disagree with the following statements? A ban on combustion-engine cars would reduce CO2 emissions from cars" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes the policy would reduce pollution – Green infrastructure program:* index based on the following variable

– respondent's answer to the question: "Do you agree or disagree with the following statements? A green infrastructure program would make electricity production greener" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

– respondent's answer to the question: "Do you agree or disagree with the following statements? A green infrastructure program would increase the use of public transport" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

* *Believes the policy would reduce pollution – Carbon tax with cash transfers:* index based on the following variable

– respondent's answer to the question: "Do you agree or disagree with the following statements? A carbon tax with cash transfers would reduce the use of fossil fuels and GHG emissions" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

– respondent's answer to the question: "Do you agree or disagree with the following statements? A carbon tax with cash transfers would encourage people to drive less" coded on a -2 to 2 scale, where -2 is "Strongly disagree," 0 is "Neither agree nor disagree," and +2 is "Strongly agree."

– respondent's answer to the question: "Do you agree or disagree with the following statements? A carbon tax with cash transfers would reduceencourage people and
*companies to insulate buildings*” coded on a -2 to 2 scale, where -2 is “Strongly disagree,” 0 is “Neither agree nor disagree,” and +2 is “Strongly agree.”

*Believes will personally lose:* index based on the variables of the three following indices

* *Believes will personally lose* – *Ban on combustion-engine cars:* index based on the following variable

– respondent's answer to the question: “Do you think that your household would win or lose financially from a ban on combustion-engine cars?” coded on a -2 to 2 scale, where -2 is “Lose a lot,” 0 is “Neither win nor lose,” and +2 is “Win a lot.”

* *Believes will personally lose* – *Green infrastructure program:* index based on the following variable

– respondent's answer to the question: “Do you think that your household would win or lose financially from a green infrastructure program?” coded on a -2 to 2 scale, where -2 is “Lose a lot,” 0 is “Neither win nor lose,” and +2 is “Win a lot.”

* *Believes will personally lose* – *Carbon tax with cash transfers:* index based on the following variable

– respondent's answer to the question: “Do you think that your household would win or lose financially from a carbon tax with cash transfers?” coded on a -2 to 2 scale, where -2 is “Lose a lot,” 0 is “Neither win nor lose,” and +2 is “Win a lot.”

*Believes poor people will lose:* index based on the variables of the three following indices

* *Believes poor people will lose* – *Ban on combustion-engine cars:* index based on the following variable

– respondent's answer to the question: “In your view, would the low-income earners win or lose if a ban on combustion-engine cars was implemented in [Country]?” coded on a -2 to 2 scale, where -2 is “Lose a lot,” 0 is “Neither win nor lose,” and +2 is “Win a lot.”

* *Believes poor people will lose* – *Green infrastructure program:* index based on the following variable

– respondent's answer to the question: “In your view, would the low-income earners win or lose if a green infrastructure program was implemented in [Country]?” coded on a -2 to 2 scale, where -2 is “Lose a lot,” 0 is “Neither win nor lose,” and +2 is “Win a lot.”
* *Believes poor people will lose - Carbon tax with cash transfers:* index based on the following variable

– respondent's answer to the question: "In your view, would the low-income earners win or lose if a carbon tax with cash transfers was implemented in [Country]?" coded on a -2 to 2 scale, where -2 is "Lose a lot," 0 is "Neither win nor lose," and +2 is "Win a lot."

*Believes rich people will lose:* index based on the variables of the three following indices

* *Believes rich people will lose - Ban on combustion-engine cars:* index based on the following variable

– respondent's answer to the question: "In your view, would the high-income earners win or lose if a ban on combustion-engine cars was implemented in [Country]?" coded on a -2 to 2 scale, where -2 is "Lose a lot," 0 is "Neither win nor lose," and +2 is "Win a lot."

* *Believes rich people will lose - Green infrastructure program:* index based on the following variable

– respondent's answer to the question: "In your view, would the high-income earners win or lose if a green infrastructure program was implemented in [Country]?" coded on a -2 to 2 scale, where -2 is "Lose a lot," 0 is "Neither win nor lose," and +2 is "Win a lot."

* *Believes rich people will lose - Carbon tax with cash transfers:* index based on the following variable

– respondent's answer to the question: "In your view, would the high-income earners win or lose if a carbon tax with cash transfers was implemented in [Country]?" coded on a -2 to 2 scale, where -2 is "Lose a lot," 0 is "Neither win nor lose," and +2 is "Win a lot."

## Set D: Outcomes

*CC exists, is anthropogenic:* respondent thinks that "a lot" or "most" of climate change is due to human activity.

*CO₂ is a greenhouse gas:* indicator variable equal to one if the respondent considers that CO₂ contributes to climate change.

*Cutting emissions by half insufficient to stop global warming:* indicator variable equal to one if the respondent thinks that cutting global greenhouse gas emissions by half would not be sufficient to eventually stop temperatures from rising.

*Behavior - Have a fuel-efficient or electric vehicle:* indicator variable equal to one if the respondent would be "a lot" or "a great deal" willing to have an electric vehicle.

*Behavior - Limit beef/meat consumption:* indicator variable equal to one if the respondent would be "a lot" or "a great deal" willing to limit beef (meat in India) consumption.
*Behavior – Limit driving:* indicator variable equal to one if the respondent would be “*a lot*” or “*a great deal*” willing to limit driving.

*Behavior – Limit flying:* indicator variable equal to one if the respondent would be “*a lot*” or “*a great deal*” willing to limit flying.

*Behavior – Limit heating or cooling your home:* indicator variable equal to one if the respondent would be “*a lot*” or “*a great deal*” willing to limit heating or cooling their home.

*Distributional Impacts – High-income earners (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent considers that high-income people would “*mostly win*” or “*win a lot*” from a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

*Distributional Impacts – Low-income earners (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent considers that low-income earners would “*mostly win*” or “*win a lot*” from a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

*Distributional Impacts – Own household (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent considers that their own household would “*mostly win*” or “*win a lot*” from a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

*Distributional Impacts – The middle class (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent considers that the middle class would “*mostly win*” or “*win a lot*” from a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

*Distributional Impacts – Those living in rural areas (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent considers that those living in rural areas would “*mostly win*” or “*win a lot*” from a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

*Donation to reforest (>20% of lottery prize):* indicator variable equal to one if the respondent is willing to give more than 20% of the lottery prize.

*Effects – Costless way to fight climate change (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent “*somewhat agrees*” or “*strongly agrees*” that a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars would be a costless way to fight climate change.

*Effects – Encourage insulation of buildings (Carbon tax w. transfers):* indicator variable equal to one if the respondent “*somewhat agrees*” or “*strongly agrees*” that a carbon tax with cash transfers would encourage people and companies to insulate buildings.

*Effects – Increase the use of public transport/Encourage less driving (Green infrastructure/Ban on combustion-engine cars):* indicator variable equal to one if the respondent “*somewhat agrees*” or “*strongly agrees*” that a green infrastructure program would increase the use of public transport or that a carbon tax with cash transfers would encourage people to drive less.

*Effects – Make electricity production greener (Green infrastructure):* indicator variable equal to one if the respondent considers that green infrastructure would increase the use of public transport or that a carbon tax with cash transfers/a ban on combustion-engine cars.
to one if the respondent “somewhat agrees” or “strongly agrees” that a green infrastructure program would make electricity production greener.

*Effects – Positive effect on economy and employment (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent “somewhat agrees” or “strongly agrees” that a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars would have a positive effect on the respondent’s country economy and employment.

*Effects – Reduce air pollution (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent “somewhat agrees” or “strongly agrees” that a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars would reduce air pollution.

*Effects – Reduce GHG emissions/Reduce CO₂ emissions from cars (Carbon tax w. transfers/Ban on combustion-engine cars):* indicator variable equal to one if the respondent “somewhat agrees” or “strongly agrees” that a carbon tax with cash transfers would reduce the use of fossil fuels and greenhouse gas emissions or that a ban on combustion-engine cars would reduce CO₂ emissions from cars.

*Factors – Ambitious climate policies:* indicator variable equal to one if the respondent indicates that it is “*a lot*” or “*a great deal*” important for them to adopt a sustainable life (i.e. limit driving, flying, and consumption, cycle more, etc.) to have ambitious climate policies.

*Factors – Having enough financial support:* indicator variable equal to one if the respondent indicates that it is “*a lot*” or “*a great deal*” important for them to adopt a sustainable life (i.e. limit driving, flying, and consumption, cycle more, etc.) that they have enough financial support.

*Factors – People around you also changing their behavior:* indicator variable equal to one if the respondent indicates that it is “*a lot*” or “*a great deal*” important for them to adopt a sustainable life (i.e. limit driving, flying, and consumption, cycle more, etc.) that the people around them also change their behavior.

*Factors – The most well off also changing their behavior:* indicator variable equal to one if the respondent indicates that it is “*a lot*” or “*a great deal*” important for them to adopt a sustainable life (i.e. limit driving, flying, and consumption, cycle more, etc.) that the most well-off also change their behavior.

*Fairness of main climate policies:* indicator variable equal to one if the numerical mean of the three variables used for the *Fairness of main climate policies index* is greater than or equal to one and where missing values are replaced with 0 when all the variables are not missing.

*Fairness of main climate policies index:* index based on the following variables

* *Ban on combustion-engine cars fairness:* respondent’s answer to the question: “Do you agree or disagree with the following statement: ‘A ban on combustion-engine cars is fair.’” coded on a -2 to 2 scale, where -2 is “Strongly disagree,” 0 is “Neither agree nor disagree,” and +2 is “Strongly agree.”

* *Carbon tax with cash transfers fairness:* respondent’s answer to the question: “Do you agree or disagree with the following statement: ‘A carbon tax with cash transfers is
fair.’” coded on a -2 to 2 scale, where -2 is “Strongly disagree,” 0 is “Neither agree nor disagree,” and +2 is “Strongly agree.”

*   **Green infrastructure program fairness:** respondent's answer to the question: “Do you agree or disagree with the following statement: ‘A green infrastructure program is fair.’” coded on a -2 to 2 scale, where -2 is “Strongly disagree,” 0 is “Neither agree nor disagree,” and +2 is “Strongly agree.”

**GHG footprint of beef/meat is higher than chicken or pasta:** indicator variable equal to one if the respondent considers that a beef steak (or lamb chop in India) of 200gr emits more greenhouse gases than 200gr of a serving of pasta or chicken wings.

**GHG footprint of nuclear is lower than gas or coal:** indicator variable equal to one if the respondent considers that a nuclear power plant emits less greenhouse gases to provide electricity for a house than a gas-fired power plant or a coal-fired power station.

**GHG footprint of plane is higher than car or train/bus:** indicator variable equal to one if the respondent considers that for a trip of 700 km family of four emits more greenhouse gases travelling by plane, than by travelling by car or a train/bus.

**Knowledge index:** index based on the variables used for the *Understands emissions across activities/regions, Knows climate change real, Knows which gases cause CC, and Understands impacts of CC* indices listed above.

**Methane is a greenhouse gas:** indicator variable equal to one if the respondent considers that methane contributes to climate change.

**More frequent volcanic eruptions are unlikely:** indicator variable equal to one if the respondent considers that it is “somewhat unlikely” or “very unlikely” that climate change will lead to more frequent volcanic eruptions.

**Per capita emissions of the US are higher than other regions:** indicator variable equal to one if the respondent considers that the consumption of an average person in the U.S. contributes more to global greenhouse gas emissions than the consumption of an average person in the European Union, China, or India.

**Perceived Fairness and Support – Support (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):** indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars.

**Perceived Fairness and Support – Is fair (Green infrastructure/Carbon tax w. transfers/Ban on combustion-engine cars):** indicator variable equal to one if the respondent “somewhat agrees” or “strongly agrees” that a green infrastructure program/a carbon tax with cash transfers/a ban on combustion-engine cars is fair.

**Sea-level rise is likely:** indicator variable equal to one if the respondent considers that it is “somewhat likely” or “very likely” that climate change will lead to rising sea levels.

**Severe droughts and heatwaves are likely:** indicator variable equal to one if the respondent considers that it is “somewhat likely” or “very likely” that climate change will lead to severe droughts and heatwaves.

**Support – A high tax on cattle products, doubling beef prices:** indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a high tax on cattle products,
so that the price of beef doubles.

*Support* – *Ban of intensive cattle farming:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” the ban of intensive cattle farming.

*Support* – *Ban of polluting vehicles in dense areas:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a ban of polluting vehicles in dense areas, like city centers.

*Support* – *Ban on combustion-engine cars:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a ban on combustion-engine cars.

*Support* – *Ban on combustion-engine cars w. alternatives available:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a ban on combustion-engine cars where alternatives such as public transports are made available to people.

*Support* – *Carbon tax with cash transfers:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax with cash transfers.

*Support* – *Cash transfers to the constrained households:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance cash transfers to households with no alternative to using fossil fuels.

*Support* – *Cash transfers to the poorest households:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance cash transfers to the poorest households.

*Support* – *Equal cash transfers to all households:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance equal cash transfers to all households.

*Support* – *Funding environmental infrastructures:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to fund environmental infrastructure projects (public transport, cycling ways, etc.).

*Support* – *Green infrastructure program:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a green infrastructure program.

*Support* – *Mandatory and subsidized insulation of buildings:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a policy where the governments makes it mandatory for all residential buildings to have insulation that meets a certain energy efficiency standard before 2040 and where it would subsidize half of the insulation costs.

*Support* – *Reduction in corporate income taxes:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance a reduction in corporate income taxes.

*Support* – *Reduction in personal income taxes:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance a reduction in
personal income taxes.

*Support* – *Reduction in the public deficit:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance a reduction in the public deficit.

*Support* – *Removal of subsidies for cattle farming:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” the removal of subsidies for cattle farming.

*Support* – *Subsidies for low-carbon technologies:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” subsidies for low-carbon technologies (renewable energy, capture and storage of carbon. . . ).

*Support* – *Subsidies on organic and local vegetables:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” subsidies on organic and local vegetables, fruits, and nuts.

*Support* – *Subsidies to low-carbon tech.:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to subsidize low-carbon technologies, including renewable energy.

*Support* – *Tax on flying (+20%):* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a tax on flying (that increases ticket prices by 20%).

*Support* – *Tax on fossil fuels ($45/tCO₂):* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a national tax on fossil fuels (increasing gasoline prices by the equivalent of 8 cents per liter).

*Support* – *Tax rebates for the most affected firms:* indicator variable equal to one if the respondent “somewhat supports” or “strongly supports” a carbon tax that would raise gasoline prices by 8 cents per liter, if the government used this revenue to finance tax rebates for the most affected firms.

*Support main climate policies index:* index based on the following variables

* *Ban on combustion-engine cars support:* respondent's answer to the question: "Do you support or oppose a ban on combustion-engine cars?" coded on a -2 to 2 scale, where -2 is "Strongly oppose," 0 is "Neither support nor oppose," and +2 is "Strongly support."

* *Carbon tax with cash transfers support:* respondent's answer to the question: "Do you support or oppose a carbon tax with cash transfers?" coded on a -2 to 2 scale, where -2 is "Strongly oppose," 0 is "Neither support nor oppose," and +2 is "Strongly support."

* *Green infrastructure program support:* respondent's answer to the question: "Do you support or oppose a green infrastructure program?" coded on a -2 to 2 scale, where -2 is "Strongly oppose," 0 is "Neither support nor oppose," and +2 is "Strongly support."

*Total emissions of China are higher than other regions:* indicator variable equal to one if the respondent considers that the total emissions of China are higher than those of the U.S., the European Union, or India.
*Willingness to adopt climate friendly behavior*: indicator variable equal to one if the numerical mean of the three variables used for the *Willingness to adopt climate friendly behavior index* is greater than or equal to one and where missing values are replaced with 0 when all the variables are not missing.

*Willingness to adopt climate friendly behavior index*: index based on the following variables

*   *Limit flying:* respondent's answer to the question: "Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to limit flying" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."

*   *Limit driving:* respondent's answer to the question: "Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to limit driving" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."

*   *Have an electric vehicle:* respondent's answer to the question: "Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to have an electric vehicle" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."

*   *Limit beef consumption:* respondent's answer to the question: "Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to limit beef consumption" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."

*   *Limit heating or cooling your home:* respondent's answer to the question: "Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to limit heating or cooling your home" coded on a -2 to 2 scale, where -2 is "Not at all," 0 is "Moderately," and +2 is "A great deal."

*Willing to sign petition:* indicator variable equal to one if the respondent supports the petition.

# A-2 Data collection and survey information

## A-2.1 Data collection

**Sociodemographic composition** The respondents who choose to respond are first channeled through screening questions that ensure that the final sample is representative along the dimensions of gender, age, income (by quartile), region, and urban versus rural place of residence.<fnref n="7" />

<footnote n="7">⁷An additional quota variable was used in two countries: ethnicity in the U.S. and education in France. Whenever possible, we recover region and rural/urban category from the zipcode. The income variable used</footnote>
**Duration** We launched the survey at different dates for each country, starting with the U.S. in March, Denmark and France in May, Germany in August, and the other countries in the Fall. Although the duration of data collection varied from country to country, on average we collected 87% of our data less than one month after the launch.

Median duration of responses is 28 minutes (excluding responses below 11 minutes), with some heterogeneity within and between countries. Figure A1 shows the distribution of durations on the whole sample as well as on some specific countries, including those with the lowest and the highest median durations (India and South Africa).

<figure><img src="image_11.png" /><figcaption>Figure A1: Distribution of durations of responses</figcaption></figure>

## A-2.2 Data quality

**Careless responses** Ex post, we checked that there were few careless response patterns (such as choosing the same answer to all items of a matrix).

**Acquiescence bias** To assess whether people expect positive or negative effects, and whether they believe the policy is cost-effective or not, we randomize the displayed items so that each respondent face either the positive or the negative formulation (see Questions 65 and 65 in Appendix A-5). This allows us to measure the acquiescence bias: the tendency to answer positively when one does not have a strong view or does not know the answer. The acquiescence bias, measured as the difference between the share of agreement with positive

<footnote>is the standard of living (or equivalised disposable income as defined per Eurostat). We ask for the household income and adjust the categories displayed to the respondent to the number of consumption units in their household (e.g., we multiply the income thresholds by 1.5 for a childless couple). See Appendix A-8 for details on the data sources.</footnote>
economic effects and disagreement with negative effects, is 30% in high-income countries and 51% in middle-income countries. Measured using the question that policies are costly or costless, the acquiescence bias is 42% in high-income countries and 61% in middle-income countries. This reminds us that many respondents do not have fixed views on these issues as they rarely talk or think about them. In the main text, we present the combined results of both variants, expressed under the positive formulation: the responses to the positive formulation are counted positively, while negative formulation are counted negatively. For example, “Somewhat agree” to a negative effect is counted as “Somewhat disagree” to a positive effect.
A-3 Additional figures

Figure A2: Expectations about climate change

Share of respondents who somewhat or strongly agree with the following views on climate change and climate policies.

<figure><img src="image_12.png" /></figure>

*Note*: Likelihood of CC impacts is asked on a 4-point scale, other questions are asked on 5-point scales. For the formulation of each question, see Appendix A-5.
<table><caption>Figure A3: Share of non-indifferent respondents who somewhat or strongly support policies.</caption><thead><tr><th></th><th colspan="12">High-income</th><th colspan="12">Middle-income</th></tr><tr><th></th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>United Kingdom</th><th>United States</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South Africa</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><td colspan="20"><strong>Support for Main Climate Policies</strong></td></tr><tr><td>Green infrastructure program</td><td>79</td><td>68</td><td>77</td><td>76</td><td>77</td><td>58</td><td>94</td><td>78</td><td>82</td><td>95</td><td>86</td><td>78</td><td>71</td><td>92</td><td>90</td><td>98</td><td>88</td><td>97</td><td>94</td><td>89</td><td>92</td><td>87</td></tr><tr><td>Ban on combustion-engine cars</td><td>57</td><td>50</td><td>61</td><td>49</td><td>39</td><td>40</td><td>74</td><td>60</td><td>56</td><td>74</td><td>62</td><td>60</td><td>55</td><td>81</td><td>77</td><td>94</td><td>85</td><td>89</td><td>76</td><td>70</td><td>83</td><td>70</td></tr><tr><td>Carbon tax with cash transfers</td><td>56</td><td>50</td><td>60</td><td>45</td><td>45</td><td>39</td><td>72</td><td>60</td><td>55</td><td>78</td><td>59</td><td>56</td><td>52</td><td>79</td><td>70</td><td>96</td><td>85</td><td>92</td><td>70</td><td>73</td><td>74</td><td>64</td></tr><tr><td colspan="20"><strong>Support for Other Climate Policies</strong></td></tr><tr><td>Subsidies to low-carbon technologies</td><td>87</td><td>82</td><td>86</td><td>89</td><td>76</td><td>84</td><td>96</td><td>91</td><td>91</td><td>93</td><td>87</td><td>90</td><td>78</td><td>90</td><td>86</td><td>93</td><td>85</td><td>95</td><td>88</td><td>93</td><td>90</td><td>90</td></tr><tr><td>Mandatory and subsidized insulation of buildings</td><td>84</td><td>86</td><td>83</td><td>84</td><td>81</td><td>77</td><td>90</td><td>83</td><td>88</td><td>95</td><td>86</td><td>88</td><td>71</td><td>90</td><td>98</td><td></td><td></td><td></td><td></td><td>91</td><td>87</td><td>83</td></tr><tr><td>Ban on polluting cars in city centers</td><td>75</td><td>70</td><td>76</td><td>78</td><td>69</td><td>67</td><td>89</td><td>85</td><td>78</td><td>70</td><td>73</td><td>80</td><td>65</td><td>84</td><td>78</td><td>94</td><td>87</td><td>96</td><td>85</td><td>82</td><td>72</td><td>78</td></tr><tr><td>Funding clean energy in less developed countries</td><td>75</td><td>68</td><td>71</td><td>75</td><td>67</td><td>69</td><td>91</td><td>80</td><td>80</td><td>79</td><td>80</td><td>71</td><td>66</td><td>88</td><td>77</td><td>92</td><td>86</td><td>94</td><td>92</td><td>90</td><td>83</td><td>91</td></tr><tr><td>Ban on combustion-engine cars w. alternatives available</td><td>64</td><td>52</td><td>63</td><td>53</td><td>59</td><td>53</td><td>81</td><td>72</td><td>61</td><td>80</td><td>65</td><td>66</td><td>62</td><td>82</td><td>73</td><td>95</td><td>83</td><td>87</td><td>77</td><td>72</td><td>87</td><td>77</td></tr><tr><td>Tax on flying (+20%)</td><td>57</td><td>45</td><td>58</td><td>70</td><td>56</td><td>64</td><td>57</td><td>65</td><td>59</td><td>58</td><td>53</td><td>59</td><td>44</td><td>65</td><td>48</td><td>85</td><td>80</td><td>85</td><td>61</td><td>52</td><td>57</td><td>49</td></tr><tr><td>Tax on fossil fuels ($45/TCO2)</td><td>46</td><td>45</td><td>50</td><td>50</td><td>38</td><td>40</td><td>49</td><td>47</td><td>37</td><td>59</td><td>46</td><td>52</td><td>42</td><td>61</td><td>43</td><td>82</td><td>81</td><td>74</td><td>49</td><td>48</td><td>72</td><td>37</td></tr><tr><td colspan="20"><strong>Support for Carbon Tax With:</strong></td></tr><tr><td>Subsidies to low-carbon tech.</td><td>85</td><td>80</td><td>67</td><td>84</td><td>83</td><td>88</td><td>94</td><td>92</td><td>89</td><td>98</td><td>86</td><td>87</td><td>75</td><td>92</td><td>93</td><td>97</td><td>88</td><td>98</td><td>91</td><td>92</td><td>88</td><td>89</td></tr><tr><td>Funding environmental infrastructures</td><td>85</td><td>80</td><td>68</td><td>83</td><td>88</td><td>83</td><td>92</td><td>90</td><td>87</td><td>95</td><td>88</td><td>84</td><td>77</td><td>92</td><td>92</td><td>95</td><td>89</td><td>96</td><td>92</td><td>94</td><td>89</td><td>90</td></tr><tr><td>Reduction in personal income taxes</td><td>79</td><td>73</td><td>66</td><td>60</td><td>80</td><td>80</td><td>92</td><td>88</td><td>87</td><td>88</td><td>83</td><td>76</td><td>69</td><td>86</td><td>86</td><td>93</td><td>83</td><td>90</td><td>85</td><td>85</td><td>85</td><td>88</td></tr><tr><td>Reduction in the public deficit</td><td>78</td><td>74</td><td>61</td><td>67</td><td>79</td><td>73</td><td>90</td><td>80</td><td>82</td><td>82</td><td>86</td><td>78</td><td>74</td><td>88</td><td>84</td><td>94</td><td>90</td><td>96</td><td>87</td><td>90</td><td>81</td><td>84</td></tr><tr><td>Cash transfers to the poorest households</td><td>71</td><td>70</td><td>64</td><td>60</td><td>71</td><td>69</td><td>86</td><td>75</td><td>65</td><td>78</td><td>75</td><td>77</td><td>62</td><td>85</td><td>81</td><td>96</td><td>84</td><td>95</td><td>75</td><td>77</td><td>93</td><td>79</td></tr><tr><td>Tax rebates for the most affected firms</td><td>71</td><td>64</td><td>61</td><td>59</td><td>71</td><td>53</td><td>89</td><td>75</td><td>80</td><td>86</td><td>74</td><td>68</td><td>63</td><td>82</td><td>77</td><td>94</td><td>87</td><td>93</td><td>69</td><td>83</td><td>73</td><td>80</td></tr><tr><td>Cash transfers to constrained households</td><td>70</td><td>70</td><td>59</td><td>58</td><td>73</td><td>66</td><td>84</td><td>71</td><td>62</td><td>81</td><td>73</td><td>77</td><td>62</td><td>82</td><td>77</td><td>92</td><td>84</td><td>93</td><td>71</td><td>76</td><td>86</td><td>81</td></tr><tr><td>Reduction in corporate income taxes</td><td>55</td><td>43</td><td>46</td><td>40</td><td>56</td><td>41</td><td>82</td><td>57</td><td>73</td><td>74</td><td>67</td><td>40</td><td>42</td><td>79</td><td>74</td><td>87</td><td>82</td><td>89</td><td>82</td><td>72</td><td>77</td><td>64</td></tr><tr><td>Equal cash transfers to all households</td><td>54</td><td>52</td><td>52</td><td>40</td><td>60</td><td>46</td><td>63</td><td>62</td><td>55</td><td>62</td><td>56</td><td>50</td><td>55</td><td>76</td><td>58</td><td>90</td><td>83</td><td>91</td><td>71</td><td>70</td><td>76</td><td>67</td></tr><tr><td colspan="20"><strong>Support for Cattle-Related Policies</strong></td></tr><tr><td>Subsidies on organic and local vegetables</td><td>75</td><td>59</td><td>69</td><td>72</td><td>73</td><td>74</td><td>91</td><td>72</td><td>89</td><td>84</td><td>78</td><td>70</td><td>64</td><td>81</td><td>77</td><td>96</td><td></td><td>91</td><td>71</td><td>72</td><td>91</td><td>69</td></tr><tr><td>Ban of intensive cattle farming</td><td>57</td><td>44</td><td>56</td><td>40</td><td>69</td><td>66</td><td>81</td><td>31</td><td>60</td><td>65</td><td>54</td><td>66</td><td>51</td><td>51</td><td>49</td><td>81</td><td></td><td>63</td><td>56</td><td>36</td><td>42</td><td>31</td></tr><tr><td>Removal of subsidies for cattle farming</td><td>49</td><td>44</td><td>51</td><td>43</td><td>41</td><td>61</td><td>65</td><td>26</td><td>50</td><td>51</td><td>51</td><td>53</td><td>55</td><td>54</td><td>60</td><td>80</td><td></td><td>74</td><td>63</td><td>38</td><td>36</td><td>30</td></tr><tr><td>A high tax on cattle products, doubling beef prices</td><td>39</td><td>31</td><td>36</td><td>37</td><td>37</td><td>53</td><td>52</td><td>26</td><td>43</td><td>39</td><td>37</td><td>42</td><td>39</td><td>45</td><td>41</td><td>68</td><td></td><td>63</td><td>43</td><td>38</td><td>32</td><td>29</td></tr></tbody></table>

Note: Opposition or support is asked on a 5-point scale with “Indifferent” as the middle option. Relative support is shown, i.e. after excluding “Indifferent” answers. For absolute support shares, see Figure 7. For the formulation of each question, see Appendix A-5.
<figure><img src="image_13.png" /><figcaption>Figure A4: Support for other climate policies.</figcaption></figure>

*Note:* The figure shows the results of regressions of support for climate policies (indicators) on socio-economic indicators (left panel) and on socio-economic and energy usage indicators (right panel). Country fixed effects are included but not displayed, likewise for socio-economic indicators in the right panel. See Appendix A-1 for variable definitions. Control group means are .57 for *Ban on combustion-engine cars w. alternatives available*, .65 for *Ban of polluting vehicles in dense areas*, .42 for *Tax on fossil fuels*, .48 for *Tax on flying (raising price by 20%)*, .71 for *Subsidies for low-carbon technologies*, and .62 for *Support of mandatory and subsidized insulation of buildings*.
<table><caption>Figure A5: Properties of the main policies. [Share of agreement among non-indifferent]</caption><thead><tr><th rowspan="2"></th><th colspan="3">Green Infrastructure</th><th colspan="3">Carbon Tax w. Transfers</th><th colspan="3">Ban on Combustion-Engine Cars</th></tr><tr><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th><th>High Income</th><th>Indonesia India China</th><th>Ohter Middle Income</th></tr></thead><tbody><tr><td colspan="10"><strong>Effects of Main Climate Policies</strong></td></tr><tr><td>Reduce air pollution</td><td>92</td><td>94</td><td>93</td><td>86</td><td>95</td><td>91</td><td>91</td><td>95</td><td>93</td></tr><tr><td>Make electricity production greener</td><td>89</td><td>94</td><td>91</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Increase the use of public transport/Encourage less driving</td><td>82</td><td>92</td><td>85</td><td>68</td><td>90</td><td>79</td><td></td><td></td><td></td></tr><tr><td>Positive effect on economy and employment</td><td>58</td><td>60</td><td>64</td><td>51</td><td>58</td><td>59</td><td>54</td><td>56</td><td>54</td></tr><tr><td>Costless way to fight climate change</td><td>43</td><td>50</td><td>51</td><td>39</td><td>50</td><td>47</td><td>53</td><td>49</td><td>49</td></tr><tr><td>Reduce GHG emissions/Reduce CO<sub>2</sub> emissions from cars</td><td></td><td></td><td></td><td>83</td><td>93</td><td>87</td><td>88</td><td>92</td><td>90</td></tr><tr><td>Encourage insulation of buildings</td><td></td><td></td><td></td><td>84</td><td>91</td><td>85</td><td></td><td></td><td></td></tr><tr><td colspan="10"><strong>Distributional Impacts of Main Climate Policies</strong></td></tr><tr><td colspan="10">Believes the following groups would gain</td></tr><tr><td>High-income earners</td><td>73</td><td>75</td><td>74</td><td>63</td><td>74</td><td>61</td><td>73</td><td>72</td><td>67</td></tr><tr><td>Own household</td><td>44</td><td>90</td><td>67</td><td>37</td><td>85</td><td>45</td><td>27</td><td>79</td><td>40</td></tr><tr><td>The middle class</td><td>39</td><td>83</td><td>64</td><td>35</td><td>78</td><td>46</td><td>24</td><td>73</td><td>38</td></tr><tr><td>Those living in rural areas</td><td>39</td><td>89</td><td>58</td><td>33</td><td>84</td><td>46</td><td>23</td><td>78</td><td>33</td></tr><tr><td>Low-income earners</td><td>32</td><td>86</td><td>56</td><td>32</td><td>81</td><td>43</td><td>17</td><td>72</td><td>32</td></tr><tr><td colspan="10"><strong>Perceived Fairness and Support</strong></td></tr><tr><td>Support</td><td>79</td><td>94</td><td>91</td><td>56</td><td>91</td><td>70</td><td>57</td><td>89</td><td>75</td></tr><tr><td>Is fair</td><td>75</td><td>93</td><td>88</td><td>54</td><td>88</td><td>68</td><td>55</td><td>87</td><td>71</td></tr></tbody></table>

Note: Questions use 5-point scales: *Disagree/Agree* for Effects and Is fair, *Lose/Win* for Distributional impacts, and *Oppose/Support* for Support. Relative agreement is shown, i.e. after excluding “Indifferent” answers. For absolute support shares, see Figure 10. For the formulation of each question, see Appendix A-5.
<table><caption>Figure A6: Properties of a ban on combustion-engine cars. [Share of agreement]</caption><thead><tr><th></th><th colspan="12">High-income</th><th colspan="12">Middle-income</th></tr><tr><th></th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>Spain</th><th>United Kingdom</th><th>United States</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South Africa</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><th colspan="15" style="text-align: left;">Effects of Main Climate Policies</th></tr><tr><td>Reduce air pollution</td><td>79</td><td>77</td><td>83</td><td>75</td><td>65</td><td>73</td><td>86</td><td>87</td><td>79</td><td>86</td><td>82</td><td>81</td><td>75</td><td>84</td><td>87</td><td>83</td><td>84</td><td>89</td><td>82</td><td>84</td><td>83</td><td>83</td></tr><tr><td>Reduce CO<sub>2</sub> emissions from cars</td><td>73</td><td>70</td><td>77</td><td>68</td><td>59</td><td>69</td><td>82</td><td>80</td><td>72</td><td>81</td><td>76</td><td>77</td><td>70</td><td>79</td><td>83</td><td>76</td><td>85</td><td>82</td><td>72</td><td>78</td><td>78</td><td>76</td></tr><tr><td>Large effect on economy and employment</td><td>52</td><td>55</td><td>54</td><td>36</td><td>48</td><td>59</td><td>55</td><td>61</td><td>54</td><td>50</td><td>51</td><td>54</td><td>54</td><td>64</td><td>67</td><td>57</td><td>69</td><td>73</td><td>57</td><td>68</td><td>61</td><td>54</td></tr><tr><td>Costless way to fight climate change</td><td>39</td><td>25</td><td>33</td><td>49</td><td>58</td><td>25</td><td>36</td><td>25</td><td>59</td><td>41</td><td>37</td><td>23</td><td>51</td><td>38</td><td>41</td><td>34</td><td>41</td><td>43</td><td>39</td><td>29</td><td>39</td><td>37</td></tr><tr><td>Positive effect on economy and employment</td><td>35</td><td>33</td><td>36</td><td>32</td><td>38</td><td>28</td><td>41</td><td>32</td><td>39</td><td>29</td><td>36</td><td>33</td><td>44</td><td>40</td><td>48</td><td>35</td><td>45</td><td>45</td><td>37</td><td>34</td><td>40</td><td>36</td></tr><tr><th colspan="15" style="text-align: left;">Distributional Impacts of Main Climate Policies</th></tr><tr><td colspan="15" style="text-align: left;">Believes the following groups would gain</td></tr><tr><td>High-income earners</td><td>40</td><td>44</td><td>48</td><td>47</td><td>40</td><td>39</td><td>44</td><td>23</td><td>33</td><td>30</td><td>38</td><td>47</td><td>43</td><td>50</td><td>54</td><td>48</td><td>57</td><td>56</td><td>46</td><td>56</td><td>45</td><td>37</td></tr><tr><td>Those living in rural areas</td><td>16</td><td>13</td><td>19</td><td>8</td><td>21</td><td>10</td><td>25</td><td>14</td><td>17</td><td>15</td><td>15</td><td>16</td><td>19</td><td>36</td><td>30</td><td>48</td><td>66</td><td>52</td><td>27</td><td>18</td><td>34</td><td>13</td></tr><tr><td>Own household</td><td>15</td><td>15</td><td>23</td><td>11</td><td>25</td><td>10</td><td>15</td><td>10</td><td>14</td><td>10</td><td>16</td><td>14</td><td>19</td><td>37</td><td>33</td><td>56</td><td>67</td><td>48</td><td>31</td><td>16</td><td>28</td><td>12</td></tr><tr><td>The middle class</td><td>15</td><td>15</td><td>20</td><td>8</td><td>11</td><td>10</td><td>20</td><td>11</td><td>14</td><td>14</td><td>14</td><td>16</td><td>23</td><td>37</td><td>36</td><td>47</td><td>65</td><td>48</td><td>28</td><td>20</td><td>31</td><td>14</td></tr><tr><td>Low-income earners</td><td>12</td><td>10</td><td>17</td><td>6</td><td>15</td><td>9</td><td>17</td><td>10</td><td>13</td><td>9</td><td>12</td><td>11</td><td>19</td><td>35</td><td>29</td><td>51</td><td>68</td><td>46</td><td>25</td><td>17</td><td>32</td><td>15</td></tr><tr><th colspan="15" style="text-align: left;">Perceived Fairness and Support</th></tr><tr><td>Support</td><td>43</td><td>35</td><td>47</td><td>41</td><td>28</td><td>32</td><td>54</td><td>41</td><td>44</td><td>53</td><td>54</td><td>45</td><td>39</td><td>65</td><td>60</td><td>72</td><td>81</td><td>67</td><td>67</td><td>53</td><td>62</td><td>58</td></tr><tr><td>Is fair</td><td>40</td><td>35</td><td>43</td><td>35</td><td>27</td><td>31</td><td>59</td><td>31</td><td>38</td><td>51</td><td>43</td><td>43</td><td>37</td><td>59</td><td>62</td><td>65</td><td>82</td><td>63</td><td>50</td><td>43</td><td>56</td><td>51</td></tr></tbody></table>

*Note*: Questions use 5-point scales: *Disagree/Agree* for Effects and Is fair, *Lose/Win* for Distributional impacts, and *Oppose/Support* for Support. For the formulation of each question, see Appendix A-5.
<table><caption>Figure A7: Properties of a carbon tax with cash transfers. [Share of agreement or support]</caption><thead><tr><th></th><th colspan="12">High-income</th><th colspan="12">Middle-income</th></tr><tr><th></th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>Spain</th><th>United Kingdom</th><th>United States</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South Africa</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><th colspan="15" style="text-align: left;">Effects of Main Climate Policies</th></tr><tr><td>Reduce air pollution</td><td>68</td><td>64</td><td>67</td><td>64</td><td>61</td><td>58</td><td>74</td><td>82</td><td>74</td><td>79</td><td>69</td><td>66</td><td>65</td><td>80</td><td>76</td><td>84</td><td>81</td><td>87</td><td>75</td><td>83</td><td>80</td><td>74</td></tr><tr><td>Encourage insulation of buildings</td><td>64</td><td>59</td><td>68</td><td>62</td><td>65</td><td>49</td><td>67</td><td>76</td><td>71</td><td>71</td><td>59</td><td>62</td><td>61</td><td>70</td><td>51</td><td>73</td><td>77</td><td>74</td><td>63</td><td>74</td><td>75</td><td>73</td></tr><tr><td>Reduce GHG emissions</td><td>64</td><td>60</td><td>64</td><td>62</td><td>56</td><td>52</td><td>72</td><td>75</td><td>66</td><td>75</td><td>63</td><td>63</td><td>58</td><td>74</td><td>70</td><td>77</td><td>78</td><td>82</td><td>70</td><td>79</td><td>73</td><td>65</td></tr><tr><td>Encourage people to drive less</td><td>51</td><td>46</td><td>51</td><td>50</td><td>43</td><td>40</td><td>49</td><td>58</td><td>57</td><td>68</td><td>55</td><td>47</td><td>51</td><td>69</td><td>70</td><td>77</td><td>81</td><td>72</td><td>62</td><td>67</td><td>71</td><td>52</td></tr><tr><td>Large effect on economy and employment</td><td>47</td><td>52</td><td>47</td><td>34</td><td>41</td><td>48</td><td>46</td><td>53</td><td>55</td><td>40</td><td>46</td><td>44</td><td>53</td><td>61</td><td>62</td><td>61</td><td>70</td><td>72</td><td>49</td><td>65</td><td>60</td><td>48</td></tr><tr><td>Positive effect on economy and employment</td><td>31</td><td>34</td><td>34</td><td>18</td><td>25</td><td>28</td><td>37</td><td>31</td><td>41</td><td>34</td><td>38</td><td>30</td><td>21</td><td>42</td><td>42</td><td>37</td><td>45</td><td>46</td><td>41</td><td>42</td><td>44</td><td>40</td></tr><tr><td>Costless way to fight climate change</td><td>27</td><td>24</td><td>28</td><td>17</td><td>9</td><td>24</td><td>33</td><td>24</td><td>56</td><td>36</td><td>38</td><td>20</td><td>13</td><td>36</td><td>37</td><td>31</td><td>45</td><td>40</td><td>33</td><td>33</td><td>38</td><td>31</td></tr><tr><th colspan="15" style="text-align: left;">Distributional Impacts of Main Climate Policies</th></tr><tr><td colspan="15" style="text-align: left;">Believes the following groups would gain</td></tr><tr><td>High-income earners</td><td>33</td><td>38</td><td>37</td><td>31</td><td>31</td><td>36</td><td>37</td><td>26</td><td>27</td><td>32</td><td>31</td><td>37</td><td>31</td><td>42</td><td>41</td><td>46</td><td>48</td><td>54</td><td>34</td><td>38</td><td>38</td><td>32</td></tr><tr><td>Low-income earners</td><td>22</td><td>19</td><td>26</td><td>16</td><td>20</td><td>17</td><td>26</td><td>26</td><td>24</td><td>27</td><td>22</td><td>19</td><td>23</td><td>43</td><td>36</td><td>66</td><td>70</td><td>51</td><td>33</td><td>28</td><td>39</td><td>18</td></tr><tr><td>The middle class</td><td>21</td><td>22</td><td>23</td><td>12</td><td>19</td><td>14</td><td>24</td><td>25</td><td>21</td><td>23</td><td>20</td><td>20</td><td>26</td><td>40</td><td>35</td><td>54</td><td>63</td><td>50</td><td>31</td><td>29</td><td>40</td><td>18</td></tr><tr><td>Those living in rural areas</td><td>21</td><td>20</td><td>21</td><td>9</td><td>23</td><td>15</td><td>27</td><td>24</td><td>23</td><td>26</td><td>22</td><td>15</td><td>22</td><td>43</td><td>34</td><td>63</td><td>68</td><td>53</td><td>37</td><td>31</td><td>40</td><td>17</td></tr><tr><td>Own household</td><td>20</td><td>22</td><td>26</td><td>14</td><td>18</td><td>16</td><td>20</td><td>18</td><td>25</td><td>23</td><td>22</td><td>18</td><td>23</td><td>41</td><td>31</td><td>67</td><td>68</td><td>54</td><td>32</td><td>28</td><td>36</td><td>14</td></tr><tr><th colspan="15" style="text-align: left;">Perceived Fairness and Support</th></tr><tr><td>Support</td><td>37</td><td>34</td><td>41</td><td>30</td><td>29</td><td>28</td><td>47</td><td>35</td><td>36</td><td>53</td><td>44</td><td>34</td><td>33</td><td>59</td><td>47</td><td>79</td><td>76</td><td>69</td><td>55</td><td>52</td><td>56</td><td>40</td></tr><tr><td>Is fair</td><td>35</td><td>33</td><td>40</td><td>28</td><td>32</td><td>27</td><td>45</td><td>29</td><td>33</td><td>50</td><td>34</td><td>33</td><td>34</td><td>55</td><td>48</td><td>72</td><td>78</td><td>57</td><td>45</td><td>53</td><td>50</td><td>41</td></tr></tbody></table>

*Note*: Questions use 5-point scales: *Disagree/Agree* for Effects and Is fair, *Lose/Win* for Distributional impacts, and *Oppose/Support* for Support. For the formulation of each question, see Appendix A-5.
<table><caption>Figure A8: Properties of a green infrastructure program. [Share of agreement or support]</caption><thead><tr><th></th><th colspan="12">High-income</th><th colspan="12">Middle-income</th></tr><tr><th></th><th>Australia</th><th>Canada</th><th>Denmark</th><th>France</th><th>Germany</th><th>Italy</th><th>Japan</th><th>Poland</th><th>South Korea</th><th>Spain</th><th>United Kingdom</th><th>United States</th><th>Brazil</th><th>China</th><th>India</th><th>Indonesia</th><th>Mexico</th><th>South Africa</th><th>Turkey</th><th>Ukraine</th></tr></thead><tbody><tr><th colspan="15" style="text-align: left;">Effects of Main Climate Policies</th></tr><tr><th scope="row">Reduce air pollution</th><td>76</td><td>71</td><td>76</td><td>72</td><td>67</td><td>67</td><td>85</td><td>86</td><td>81</td><td>87</td><td>79</td><td>77</td><td>70</td><td>83</td><td>88</td><td>78</td><td>80</td><td>91</td><td>80</td><td>85</td><td>81</td><td>77</td></tr><tr><th scope="row">Make electricity production greener</th><td>71</td><td>68</td><td>69</td><td>70</td><td>58</td><td>62</td><td>80</td><td>78</td><td>78</td><td>80</td><td>70</td><td>72</td><td>64</td><td>79</td><td>83</td><td>79</td><td>85</td><td>83</td><td>73</td><td>73</td><td>82</td><td>74</td></tr><tr><th scope="row">Increase the use of public transport</th><td>60</td><td>57</td><td>59</td><td>46</td><td>54</td><td>56</td><td>68</td><td>71</td><td>69</td><td>75</td><td>68</td><td>53</td><td>53</td><td>71</td><td>73</td><td>73</td><td>81</td><td>80</td><td>58</td><td>65</td><td>77</td><td>62</td></tr><tr><th scope="row">Large effect on economy and employment</th><td>50</td><td>56</td><td>52</td><td>37</td><td>42</td><td>56</td><td>51</td><td>59</td><td>60</td><td>38</td><td>51</td><td>49</td><td>55</td><td>63</td><td>66</td><td>62</td><td>72</td><td>73</td><td>58</td><td>63</td><td>57</td><td>52</td></tr><tr><th scope="row">Positive effect on economy and employment</th><td>37</td><td>41</td><td>40</td><td>23</td><td>31</td><td>34</td><td>47</td><td>35</td><td>50</td><td>31</td><td>46</td><td>33</td><td>28</td><td>45</td><td>51</td><td>38</td><td>44</td><td>50</td><td>46</td><td>44</td><td>44</td><td>40</td></tr><tr><th scope="row">Costless way to fight climate change</th><td>30</td><td>30</td><td>27</td><td>16</td><td>15</td><td>24</td><td>38</td><td>26</td><td>66</td><td>40</td><td>44</td><td>21</td><td>15</td><td>39</td><td>45</td><td>29</td><td>43</td><td>47</td><td>42</td><td>33</td><td>39</td><td>34</td></tr><tr><th colspan="15" style="text-align: left;">Distributional Impacts of Main Climate Policies</th></tr><tr><th colspan="15" style="text-align: left;">Believes the following groups would gain</th></tr><tr><th scope="row">High-income earners</th><td>39</td><td>42</td><td>45</td><td>39</td><td>40</td><td>36</td><td>46</td><td>28</td><td>35</td><td>40</td><td>38</td><td>42</td><td>37</td><td>53</td><td>56</td><td>52</td><td>55</td><td>63</td><td>48</td><td>56</td><td>48</td><td>42</td></tr><tr><th scope="row">Those living in rural areas</th><td>25</td><td>23</td><td>25</td><td>10</td><td>30</td><td>15</td><td>32</td><td>24</td><td>28</td><td>28</td><td>31</td><td>21</td><td>26</td><td>51</td><td>48</td><td>64</td><td>71</td><td>63</td><td>49</td><td>32</td><td>52</td><td>22</td></tr><tr><th scope="row">Own household</th><td>23</td><td>23</td><td>31</td><td>16</td><td>26</td><td>14</td><td>23</td><td>12</td><td>24</td><td>21</td><td>31</td><td>21</td><td>28</td><td>51</td><td>48</td><td>64</td><td>74</td><td>61</td><td>55</td><td>34</td><td>47</td><td>17</td></tr><tr><th scope="row">The middle class</th><td>22</td><td>25</td><td>25</td><td>14</td><td>21</td><td>15</td><td>29</td><td>15</td><td>21</td><td>23</td><td>26</td><td>20</td><td>27</td><td>50</td><td>57</td><td>51</td><td>68</td><td>58</td><td>45</td><td>37</td><td>52</td><td>26</td></tr><tr><th scope="row">Low-income earners</th><td>21</td><td>20</td><td>25</td><td>14</td><td>25</td><td>13</td><td>27</td><td>15</td><td>23</td><td>18</td><td>26</td><td>18</td><td>27</td><td>49</td><td>54</td><td>59</td><td>70</td><td>57</td><td>46</td><td>31</td><td>50</td><td>18</td></tr><tr><th colspan="15" style="text-align: left;">Perceived Fairness and Support</th></tr><tr><th scope="row">Support</th><td>57</td><td>49</td><td>56</td><td>53</td><td>57</td><td>42</td><td>78</td><td>48</td><td>58</td><td>70</td><td>71</td><td>55</td><td>50</td><td>78</td><td>77</td><td>83</td><td>79</td><td>82</td><td>84</td><td>73</td><td>76</td><td>69</td></tr><tr><th scope="row">Is fair</th><td>51</td><td>45</td><td>51</td><td>44</td><td>49</td><td>36</td><td>77</td><td>36</td><td>48</td><td>65</td><td>63</td><td>48</td><td>49</td><td>72</td><td>76</td><td>80</td><td>83</td><td>73</td><td>70</td><td>68</td><td>67</td><td>56</td></tr></tbody></table>

*Note*: Questions use 5-point scales: *Disagree/Agree* for Effects and Is fair, *Lose/Win* for Distributional impacts, and *Oppose/Support* for Support. For the formulation of each question, see Appendix A-5.
<figure><img src="image_14.png" /><figcaption>Figure A9: Zooming-in on individual main climate policies.</figcaption></figure>

*Note:* The figure shows the results of regressions of support for climate policies (indicators) on socio-economic indicators (left panel) and on socio-economic and energy usage indicators (right panel). Country fixed effects are included but not displayed, likewise for socio-economic indicators in the right panel. See Appendix A-1 for variable definitions. Control group means are .52 for *Ban on combustion-engine cars*, .66 for *Green infrastructure program*, and .46 for *Carbon tax with cash transfers*.