## Public Perceptions of Climate Mitigation Policies: Evidence from Cross Country Surveys ## Source details **Canonical URL:** [Public Perceptions of Climate Mitigation Policies: Evidence from Cross Country Surveys](https://www.imf.org/-/media/files/oap/oap-home/2023/public-perceptions-survey-oap-seminar.pdf) ## Other formats - [Markdown version](/-/media/files/oap/oap-home/2023/public-perceptions-survey-oap-seminar.pdf.md) - [Structured JSON version](/-/media/files/oap/oap-home/2023/public-perceptions-survey-oap-seminar.pdf.json) --- ### The challenge of climate mitigation - Urgent need to narrow gaps in climate mitigation ambitions and policy, and growing public awareness of climate threat that does not always translate into actions (IMF Asia and Pacific Department). - Study objective: understand drivers of public perceptions of climate change and support for climate policies. ### Survey design and sample - Representative standardized surveys run by YouGov across 28 advanced and emerging market economies. - Sample size: more than 28,000 respondents (>1000 per country). - Fieldwork dates: run July 5 - Aug 11, 2022. - Survey modules: respondent socioeconomic background; climate risk perceptions and knowledge; randomized control and treatment information on policy effectiveness; views on carbon tax, emissions trading, subsidies and regulations; international dimension and willingness to change behavior; open-ended question on climate policy aims. ### Climate risk perceptions - Majority agree climate change is a serious problem (share of respondents answering “a very serious problem” or “a fairly serious problem”). - Perceptions of imminence are higher in emerging markets and correlate with country climate change exposure as measured by the IMF’s INFORM Climate-driven Hazard and Exposure component. - Determinants of higher climate risk perception include gender, education, energy usage, news consumption, and political views, with notable cross-country heterogeneity (e.g., females in Japan but not India; more educated in Australia and Indonesia but not Korea). - Regression analysis: OLS on z-scores of seriousness of climate change with country fixed effects; coefficients differ across demographic and attitudinal covariates. ### Support for emission-reducing policies - Baseline awareness: public more informed about subsidies for green technologies/renewables and regulations than about carbon tax or cap-and-trade; awareness varies by country. - Relative support (control group, no additional information): - Subsidies to low-carbon technologies/renewables are the most favored policy (higher share of favorable responses across countries). - Carbon pricing and regulations have lower and more variable support across countries and regions. - Drivers of support for carbon pricing: - Key positive predictors: perception that climate change affects you, perception climate change is serious, belief carbon pricing is effective. - Key concerns reducing support: perceptions of job losses, more expensive energy, increased fuel costs, and higher prices. - Policy benefits cited include new low-carbon jobs, better public transport, more investment in renewables, more money for social goods and services, better public health, and better air quality. - Explained variation in support for carbon pricing (OLS on z-scores): contributions from knowledge of climate policies, demographic and socioeconomic factors, perceptions of policy costs and benefits, equity/distributional concerns, perception of climate risk, and perception of policy effectiveness (relative shares shown in analysis). ### Revenue recycling and distributional concerns - Revenue recycling increases support for carbon pricing; people prefer allocations that address progressivity and distributional impacts. - Top revenue uses that increase support (share of responses): Helping low-income households — 54 (percentage points). - Other preferred revenue uses include climate projects (renewables/green technology) and social services (health care/education). - Preferences underscore the need for complementary policies (e.g., strengthened social safety nets, green investment efficiency) to enhance public acceptability. ### Information treatments and behavioral effects - Large information gaps: sizable shares in many countries have no clear opinion about carbon pricing (support/oppose/neutral distributions vary by country and region). - Information interventions: - Policy efficacy treatment (informing respondents that carbon pricing provides correct incentives to decarbonize, can encourage innovation, and revenues can be recycled) yields a statistically significant shift toward greater support. - Cost-of-living priming (emphasizing that carbon pricing increases cost of living) shifts responses toward greater opposition or neutrality. - Similar priming on tax increases reduces support for subsidies for renewable energy. - Heterogeneity: treatment effects vary by respondents’ prior knowledge of climate policies. ### International burden sharing and collective action - Broad public support for collective action: many respondents agree that climate policy will only be effective if most countries adopt measures to reduce emissions. - Views on who should pay differ: preferences include paying based on current emissions, past emissions, only rich countries, or all countries — with differences between Emerging Markets and Advanced Economies statistically significant at the 1 percent level. ### Reasons for not supporting carbon pricing policies - Policy costs, ineffectiveness, and harm to economy/job losses are the most important concerns reported. - Categories shown (axis fragments and example category magnitudes across countries/groups): - Better air quality: 50 63 54 55 29 66 55 65 57 65 39 57 47 53 44 44 42 49 - Better public health: 38 36 51 45 13 52 52 46 17 48 52 33 45 34 53 43 93 36 34 40 44 - More investment in renewables: 39 44 35 28 29 45 45 39 45 29 42 94 30 44 34 39 37 27 30 31 39 23 39 25 25 32 34 48 30 - Higher prices: 56 49 47 47 42 61 56 65 52 42 55 39 41 62 47 50 50 41 39 45 53 47 60 51 53 48 41 58 - More expensive energy: 57 57 43 30 42 54 46 57 45 37 42 39 38 56 32 34 48 39 48 47 57 42 53 45 57 48 44 56 - Job losses (examples across countries): 35 18 29 19 11 27 29 24 17 23 25 22 22 29 24 23 29 20 22 22 23 18 17 21 23 25 24 23 ### Revenue recycling and demographic characteristics - Linear probability models with country fixed effects estimate coefficients and 95% confidence intervals for responses to: “Which, if any, of the following would increase your support for the policy? Please select up to three”. Only the three most popular choices are displayed. - Group differences in preferences for how carbon pricing revenues should be used: - High-income, older, and educated respondents prefer earmarking revenues to clean technologies and renewables. - Belief that government should play a role in regulating the economy is associated with using revenues to support low-income households. - Estimated coefficient axis shown from -0.15 to 0.2 with covariates (examples shown): Age (35-54); Age (55+); Female; Children in household; Education (vocational or high-school); Education (college); Employed; Income (medium); Income (high); Car(s) in household; Use public transport; News from traditional sources; News from modern sources; Trust people; Supports govt. regulating economy. ### Information treatment heterogeneity - Finding: Higher impact of information treatment in countries with lower pre-existing knowledge of carbon tax. - Country-level relation (example label): Carbon pricing efficacy treatment and country-level heterogeneity. ### International burden sharing: who should pay? - Responses (percentage points) to questions on who should pay and on whether payments should be based on current or historic emissions (examples across regions/countries): - Only rich countries: 16 21 28 16 19 25 22 14 16 23 23 24 23 16 21 23 14 28 28 27 13 14 13 19 21 24 27 14 - All countries: 69 68 59 73 48 66 69 73 74 61 70 57 65 65 68 71 63 51 51 58 69 73 70 57 56 64 55 70 - Don't know: 15 12 12 11 33 88 13 10 16 72 01 21 91 06 23 22 21 51 81 31 18 24 24 12 18 16 - Current emissions: 51 40 50 47 35 50 48 50 47 47 52 41 43 50 48 54 44 37 36 49 48 49 53 45 55 48 41 51 - Past emissions: 28 44 35 35 26 38 41 32 38 35 37 33 41 27 37 38 26 35 37 35 20 31 16 18 16 33 37 26 - Don't know (second series): 22 16 15 18 38 12 11 18 15 18 10 26 16 23 22 62 61 32 28 26 16 32 19 31 36 29 19 22 23 ### Policy implications and conclusions (IMF Asia and Pacific Department) - Devil is in the policy design: pre-existing beliefs about policy efficacy, costs, and progressivity are key drivers of support for carbon pricing. - Scope to improve support with targeted information on policy efficacy and co-benefits. - Address distributional concerns to increase public acceptability; revenue recycling preferences favor household support and green investment. - Complementary measures needed: strengthened social safety nets and green investment efficiency. - Raising awareness is key: continue communication on climate risks, costs of inaction, and concrete policy impacts. - Securing international cooperation may foster political support for climate action. *IMF | Asia and Pacific Department* ### Section 1 ### Public Perceptions of Climate Mitigation Policies: Evidence from Cross Country Surveys ### The challenge of climate mitigation - Urgent need to narrow gaps in climate mitigation ambitions and policy, and growing public awareness of climate threat that does not always translate into actions (IMF Asia and Pacific Department). - Study objective: understand drivers of public perceptions of climate change and support for climate policies. ### Survey design and sample - Representative standardized surveys run by YouGov across 28 advanced and emerging market economies. - Sample size: more than 28,000 respondents (>1000 per country). - Fieldwork dates: run July 5 - Aug 11, 2022. - Survey modules: respondent socioeconomic background; climate risk perceptions and knowledge; randomized control and treatment information on policy effectiveness; views on carbon tax, emissions trading, subsidies and regulations; international dimension and willingness to change behavior; open-ended question on climate policy aims. ### Climate risk perceptions - Majority agree climate change is a serious problem (share of respondents answering “a very serious problem” or “a fairly serious problem”). - Perceptions of imminence are higher in emerging markets and correlate with country climate change exposure as measured by the IMF’s INFORM Climate-driven Hazard and Exposure component. - Determinants of higher climate risk perception include gender, education, energy usage, news consumption, and political views, with notable cross-country heterogeneity (e.g., females in Japan but not India; more educated in Australia and Indonesia but not Korea). - Regression analysis: OLS on z-scores of seriousness of climate change with country fixed effects; coefficients differ across demographic and attitudinal covariates. ### Support for emission-reducing policies - Baseline awareness: public more informed about subsidies for green technologies/renewables and regulations than about carbon tax or cap-and-trade; awareness varies by country. - Relative support (control group, no additional information): - Subsidies to low-carbon technologies/renewables are the most favored policy (higher share of favorable responses across countries). - Carbon pricing and regulations have lower and more variable support across countries and regions. - Drivers of support for carbon pricing: - Key positive predictors: perception that climate change affects you, perception climate change is serious, belief carbon pricing is effective. - Key concerns reducing support: perceptions of job losses, more expensive energy, increased fuel costs, and higher prices. - Policy benefits cited include new low-carbon jobs, better public transport, more investment in renewables, more money for social goods and services, better public health, and better air quality. - Explained variation in support for carbon pricing (OLS on z-scores): contributions from knowledge of climate policies, demographic and socioeconomic factors, perceptions of policy costs and benefits, equity/distributional concerns, perception of climate risk, and perception of policy effectiveness (relative shares shown in analysis). ### Revenue recycling and distributional concerns - Revenue recycling increases support for carbon pricing; people prefer allocations that address progressivity and distributional impacts. - Top revenue uses that increase support (share of responses): Helping low-income households — 54 (percentage points). - Other preferred revenue uses include climate projects (renewables/green technology) and social services (health care/education). - Preferences underscore the need for complementary policies (e.g., strengthened social safety nets, green investment efficiency) to enhance public acceptability. ### Information treatments and behavioral effects - Large information gaps: sizable shares in many countries have no clear opinion about carbon pricing (support/oppose/neutral distributions vary by country and region). - Information interventions: - Policy efficacy treatment (informing respondents that carbon pricing provides correct incentives to decarbonize, can encourage innovation, and revenues can be recycled) yields a statistically significant shift toward greater support. - Cost-of-living priming (emphasizing that carbon pricing increases cost of living) shifts responses toward greater opposition or neutrality. - Similar priming on tax increases reduces support for subsidies for renewable energy. - Heterogeneity: treatment effects vary by respondents’ prior knowledge of climate policies. ### International burden sharing and collective action - Broad public support for collective action: many respondents agree that climate policy will only be effective if most countries adopt measures to reduce emissions. - Views on who should pay differ: preferences include paying based on current emissions, past emissions, only rich countries, or all countries — with differences between Emerging Markets and Advanced Economies statistically significant at the 1 percent level. ### Policy implications and conclusions (IMF Asia and Pacific Department) - Devil is in the policy design: pre-existing beliefs about policy efficacy, costs, and progressivity are key drivers of support for carbon pricing. - Scope to improve support with targeted information on policy efficacy and co-benefits. - Address distributional concerns to increase public acceptability; revenue recycling preferences favor household support and green investment. - Complementary measures needed: strengthened social safety nets and green investment efficiency. - Raising awareness is key: continue communication on climate risks, costs of inaction, and concrete policy impacts. - Securing international cooperation may foster political support for climate action. *IMF Asia and Pacific Regional Seminar, February 15, 2023 (IMF | Asia and Pacific Department).* ### Section 2 ### public-perceptions-survey-oap-seminar - Section 2 ### Reasons for not supporting carbon pricing policies - Policy costs, ineffectiveness, and harm to economy/job losses are the most important concerns reported. - Distribution of responses (percentage points) to the question: “A carbon pricing policy that charges companies for their emissions would also raise the amount of money the government is able to collect and spend. Which, if any, of the following would increase your support for the policy? Please select up to three” (differences between AEs and EMs are statistically significant at the 1 percent level for all reasons reported): - My country should not pay to reduce climate change: 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 (values shown on axis; distribution by Advanced Economies and Emerging Markets plotted) - No need to reduce carbon/tackle climate change (axis shown) - Not politically feasible (axis shown) - Increases inequality (axis shown) - Harms economy/job losses (axis shown) - Costs me money (axis shown) - Ineffective at reducing climate change (axis shown) - Increases energy costs (axis shown) - Specific numerical labels shown on chart fragments (interpreted as example category magnitudes across countries/groups): - Better air quality: 50 63 54 55 29 66 55 65 57 65 39 57 47 53 44 44 42 49 - Better public health: 38 36 51 45 13 52 52 46 17 48 52 33 45 34 53 43 93 36 34 40 44 - More investment in renewables: 39 44 35 28 29 45 45 39 45 29 42 94 30 44 34 39 37 27 30 31 39 23 39 25 25 32 34 48 30 - Higher prices: 56 49 47 47 42 61 56 65 52 42 55 39 41 62 47 50 50 41 39 45 53 47 60 51 53 48 41 58 - More expensive energy: 57 57 43 30 42 54 46 57 45 37 42 39 38 56 32 34 48 39 48 47 57 42 53 45 57 48 44 56 - Job losses (examples across countries): 35 18 29 19 11 27 29 24 17 23 25 22 22 29 24 23 29 20 22 22 23 18 17 21 23 25 24 23 ### Revenue recycling and demographic characteristics - Linear probability models with country fixed effects estimate coefficients and 95% confidence intervals for responses to: “Which, if any, of the following would increase your support for the policy? Please select up to three”. Only the three most popular choices are displayed. - Group differences in preferences for how carbon pricing revenues should be used: - High-income, older, and educated respondents prefer earmarking revenues to clean technologies and renewables. - Belief that government should play a role in regulating the economy is associated with using revenues to support low-income households. - Estimated coefficient axis shown from -0.15 to 0.2 with covariates (examples shown): - Age (35-54) - Age (55+) - Female - Children in household - Education (vocational or high-school) - Education (college) - Employed - Income (medium) - Income (high) - Car(s) in household - Use public transport - News from traditional sources - News from modern sources - Trust people - Supports govt. regulating economy ### Information treatment heterogeneity - Finding: Higher impact of information treatment in countries with lower pre-existing knowledge of carbon tax. - Country-level plot shows respondents’ prior knowledge of carbon pricing (x-axis) and the size of the treatment effect from a regression analysis including information provision about how effective carbon pricing policies are in reducing greenhouse gas emissions. - Label: Carbon pricing efficacy treatment and country-level heterogeneity. ### International burden sharing: who should pay? - Responses (percentage points) to: “Should countries be paying to reduce carbon emissions based on their current or accumulated historic levels of emissions?” and “Which countries do you think should be paying to reduce carbon emissions?”: - Only rich countries (examples across regions/countries): 16 21 28 16 19 25 22 14 16 23 23 24 23 16 21 23 14 28 28 27 13 14 13 19 21 24 27 14 - All countries: 69 68 59 73 48 66 69 73 74 61 70 57 65 65 68 71 63 51 51 58 69 73 70 57 56 64 55 70 - Don't know: 15 12 12 11 33 88 13 10 16 72 01 21 91 06 23 22 21 51 81 31 18 24 24 12 18 16 - Current emissions: 51 40 50 47 35 50 48 50 47 47 52 41 43 50 48 54 44 37 36 49 48 49 53 45 55 48 41 51 - Past emissions: 28 44 35 35 26 38 41 32 38 35 37 33 41 27 37 38 26 35 37 35 20 31 16 18 16 33 37 26 - Don't know (second series): 22 16 15 18 38 12 11 18 15 18 10 26 16 23 22 62 61 32 28 26 16 32 19 31 36 29 19 22 23 --- _Source: https://www.imf.org/-/media/files/oap/oap-home/2023/public-perceptions-survey-oap-seminar.pdf_