<figure><img src="image_15.png" /><figcaption>Figure A10: Heterogeneous country-patterns for car-dependency.</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for the main policies on car dependency (also an indicator) in each country. Socio-economic and energy usage indicators are included as controls, i.e., age, gender, income, education, economic leaning, living with children, treatment, size of agglomeration, availability of public transport, car dependency, high gas expenses, high heating expenses, frequent flights, working in a polluting sector, frequent beef consumption, home owner. 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*.
<figure><img src="image_16.png" /><figcaption>Figure A11: Determinants of support for cattle-related policies</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for policies aiming at reducing beef consumption on socio-economic indicators (left panel) and on socio-economic and energy usage indicators (right panel). Country fixed effects and treatments 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*.
<figure><img src="image_17.png" /><figcaption>Figure A12: Reasoning underlying support, views on fairness, and willingness to change behavior</figcaption></figure>

*Note:* The figure shows the results of regressions of standardized attitudinal indices on standardized indices of reasonings about climate policies as well as undisplayed controls: country fixed effects, treatment, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions. Covariates and explained variables are all standardized indices.
<figure><img src="image_18.png" /><figcaption>Figure A13: Reasoning underlying support for other climate policies</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for climate policies on standardized indices of reasonings about climate policies as well as undisplayed controls: country fixed effects, treatment, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions. Covariates and explained variables are all standardized indices.
<figure><img src="image_19.png" /><figcaption>Figure A14: How different groups reason about perceived efficiency and distributional effects</figcaption></figure>

*Note:* The figure shows the coefficients from a regression of the indices listed in the legend, on indicator variables for socio-economic characteristics (left panel), and on socio-economic and energy usage (right panel). Country fixed effects and indicators for each treatment are included but not displayed, likewise for socio-economic indicators in the right panel. The indices are standardized averages of the z-scores of the support for the three main policies (see Appendix A-1 for their definition). Notes to Figure 9 list the omitted variables.
<figure><img src="image_20.png" /><figcaption>Figure A15: Treatment effects on reasonings</figcaption></figure>

*Note:* The figure shows the results of regressions of standardized indices of beliefs about climate policies on the treatments and undisplayed controls: country fixed effects and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<figure><img src="image_21.png" /><figcaption>Figure A16: Treatment effects on beliefs about policies' properties</figcaption></figure>

*Note:* The figure shows the results of regressions of beliefs about the main policies (indicator variables) on the treatments and undisplayed controls: country fixed effects and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<figure><img src="image_22.png" /><figcaption>Figure A17: Support of the carbon tax depending on revenue-use: heterogeneous patterns for Income, Education, & Economic Leaning</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for carbon taxes (by revenue use) on country fixed effects, treatments, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. Only coefficients of income, education and economic leaning are displayed. See Appendix A-1 for variable definitions.
<figure><img src="image_23.png" /><figcaption>Figure A18: Reasoning underlying support for a carbon tax with different variants of cash transfers.</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for carbon taxes (with different revenue use) on standardized indices on reasoning about climate policies and undisplayed controls: country fixed effects, treatments, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<figure><img src="image_24.png" /><figcaption>Figure A19: Reasoning underlying support for a carbon tax with different variants of tax reduction.</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for carbon taxes (with different revenue use) on standardized indices on reasoning about climate policies and undisplayed controls: country fixed effects, treatments, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<figure><img src="image_25.png" /><figcaption>Figure A20: Reasoning underlying support for other use of the revenues from a carbon tax.</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for carbon taxes (with different revenue use) on standardized indices on reasoning about climate policies and undisplayed controls: country fixed effects, treatments, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<figure><img src="image_26.png" /><figcaption>Figure A21: Experimental Evidence – Use of revenues from a carbon tax</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators of support for carbon taxes (with different revenue use) on treatment and undisplayed controls: country fixed effects, and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. Control group mean support is given in the legend. See Appendix A-1 for covariate variable definitions.
<figure><img src="image_27.png" /><figcaption>Figure A22: What sources of funding do you find appropriate for public investments in green infrastructure? (Multiple answers are possible)</figcaption></figure>

*Note*: Percentage share of selected choices are shown.
<figure><img src="image_28.png" /><figcaption>Figure A23: Preferences for funding of green infrastructures across groups.</figcaption></figure>

*Note:* The figure shows the results of regressions of indicators that a funding option is selected as appropriate for a green infrastructure program on socio-economic indicators (left panel) and on socio-economic and energy usage indicators (right panel). Country fixed effects and treatments are included but not displayed, likewise for socio-economic indicators in the right panel. See Appendix A-1 for variable definitions.
A-4 Regression tables
<table><caption>Table A1: Support for the main policies regressed on individual characteristics.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Support</th></tr><tr><th>Main climate policies index<br>(1)</th><th>Ban on combustion-engine cars<br>(2)</th><th>Green infrastructure program<br>(3)</th><th>Carbon tax with cash transfers<br>(4)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.097</td><td>0.652</td><td>0.514</td><td>0.456</td></tr><tr><td colspan="5"><strong>Panel A: Socio-economic indicators</strong></td></tr><tr><td>Gender: Female</td><td>0.054***<br>(0.013)</td><td>0.009<br>(0.006)</td><td>0.004<br>(0.006)</td><td>-0.015**<br>(0.006)</td></tr><tr><td>Lives with child(ren) under 14</td><td>0.129***<br>(0.015)</td><td>0.031***<br>(0.007)</td><td>0.049***<br>(0.007)</td><td>0.052***<br>(0.007)</td></tr><tr><td>Age: 25 - 34</td><td>0.067***<br>(0.023)</td><td>0.008<br>(0.011)</td><td>0.021*<br>(0.012)</td><td>0.022*<br>(0.011)</td></tr><tr><td>Age: 35 - 49</td><td>0.078***<br>(0.022)</td><td>0.019*<br>(0.010)</td><td>0.037***<br>(0.011)</td><td>0.032***<br>(0.011)</td></tr><tr><td>Age: 50 or older</td><td>0.175***<br>(0.020)</td><td>0.065***<br>(0.010)</td><td>0.087***<br>(0.010)</td><td>0.079***<br>(0.010)</td></tr><tr><td>Household income: Q2</td><td>0.050***<br>(0.018)</td><td>0.029***<br>(0.008)</td><td>0.030***<br>(0.009)</td><td>0.009<br>(0.008)</td></tr><tr><td>Household income: Q3</td><td>0.088***<br>(0.019)</td><td>0.043***<br>(0.009)</td><td>0.046***<br>(0.009)</td><td>0.022**<br>(0.009)</td></tr><tr><td>Household income: Q4</td><td>0.080***<br>(0.020)</td><td>0.047***<br>(0.009)</td><td>0.047***<br>(0.010)</td><td>0.022**<br>(0.010)</td></tr><tr><td>Highest diploma: College</td><td>0.119***<br>(0.026)</td><td>0.089***<br>(0.012)</td><td>0.079***<br>(0.012)</td><td>0.059***<br>(0.012)</td></tr><tr><td>Highest diploma: High school</td><td>0.073***<br>(0.025)</td><td>0.055***<br>(0.011)</td><td>0.048***<br>(0.012)</td><td>0.039***<br>(0.012)</td></tr><tr><td>Economic Leaning: Very Left</td><td>0.133***<br>(0.029)</td><td>0.002<br>(0.012)</td><td>0.036***<br>(0.014)</td><td>0.033**<br>(0.014)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.222***<br>(0.018)</td><td>-0.111***<br>(0.008)</td><td>-0.101***<br>(0.009)</td><td>-0.101***<br>(0.009)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.331***<br>(0.021)</td><td>-0.125***<br>(0.009)</td><td>-0.102***<br>(0.010)</td><td>-0.086***<br>(0.010)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.262***<br>(0.028)</td><td>-0.140***<br>(0.011)</td><td>-0.092***<br>(0.012)</td><td>-0.090***<br>(0.012)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.064***<br>(0.017)</td><td>0.023***<br>(0.008)</td><td>0.022***<br>(0.008)</td><td>0.031***<br>(0.008)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.135***<br>(0.017)</td><td>0.027***<br>(0.008)</td><td>0.045***<br>(0.008)</td><td>0.102***<br>(0.008)</td></tr><tr><td colspan="5"><strong>Panel B: Energy usage indicators</strong></td></tr><tr><td>Treatment: Both</td><td>0.213***<br>(0.017)</td><td>0.049***<br>(0.008)</td><td>0.075***<br>(0.008)</td><td>0.130***<br>(0.008)</td></tr><tr><td>Agglomeration size: Small</td><td>0.056***<br>(0.021)</td><td>0.018*<br>(0.009)</td><td>0.012<br>(0.010)</td><td>0.0005<br>(0.010)</td></tr><tr><td>Agglomeration size: Medium</td><td>0.057**<br>(0.023)</td><td>0.029***<br>(0.010)</td><td>0.020*<br>(0.011)</td><td>0.010<br>(0.011)</td></tr><tr><td>Agglomeration size: Large</td><td>0.097***<br>(0.022)</td><td>0.032***<br>(0.010)</td><td>0.036***<br>(0.010)</td><td>0.018*<br>(0.010)</td></tr><tr><td>Public transport available</td><td>0.268***<br>(0.014)</td><td>0.086***<br>(0.006)</td><td>0.089***<br>(0.007)</td><td>0.106***<br>(0.007)</td></tr><tr><td>Uses car</td><td>-0.131***<br>(0.017)</td><td>-0.023***<br>(0.007)</td><td>-0.055***<br>(0.008)</td><td>-0.038***<br>(0.008)</td></tr><tr><td>High gas expenses</td><td>-0.068***<br>(0.014)</td><td>-0.019***<br>(0.006)</td><td>-0.023***<br>(0.007)</td><td>-0.016**<br>(0.007)</td></tr><tr><td>High heating expenses</td><td>0.036**<br>(0.014)</td><td>0.029***<br>(0.007)</td><td>0.025***<br>(0.007)</td><td>0.026***<br>(0.007)</td></tr><tr><td>Flies more than once a year</td><td>0.125***<br>(0.015)</td><td>0.044***<br>(0.006)</td><td>0.054***<br>(0.007)</td><td>0.060***<br>(0.007)</td></tr><tr><td>Polluting sector</td><td>0.002<br>(0.018)</td><td>-0.004<br>(0.008)</td><td>-0.006<br>(0.009)</td><td>0.011<br>(0.009)</td></tr><tr><td>Eats beef weekly or more</td><td>-0.074***<br>(0.013)</td><td>-0.037***<br>(0.006)</td><td>-0.034***<br>(0.007)</td><td>-0.012*<br>(0.007)</td></tr><tr><td>Owner or landlord</td><td>0.016<br>(0.015)</td><td>0.006<br>(0.007)</td><td>0.008<br>(0.007)</td><td>0.014*<br>(0.007)</td></tr><tr><td>Observations</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td></tr></tbody></table>

Note: The table shows the results of regressions of the variables listed in the columns on socio-economic characteristics (Panel A) and on energy usage characteristics (Panel B), controlling for country fixed effects. Panel B also controls for socio-economic characteristics, but the coefficients are not displayed. The dependent variable in column 1 is the standardized Support for main policies index, while the remaining columns are indicator variables equal to 1 if the respondent (somewhat or strongly) supports each of the main climate policies. See Appendix A-1 for variable definitions.
<table><caption>Table A2: Reasonings underlying knowledge and support for the main policies.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Knowledge or Support</th></tr><tr><th>Knowledge index<br>(1)</th><th>Main climate policies index<br>(2)</th><th>Ban on combustion-engine cars<br>(3)</th><th>Green infrastructure program<br>(4)</th><th>Carbon tax with cash transfers<br>(5)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.064</td><td>-0.097</td><td>0.652</td><td>0.514</td><td>0.456</td></tr><tr><td>Trusts the government</td><td>0.002**<br>(0.001)</td><td>0.039***<br>(0.005)</td><td>0.008***<br>(0.002)</td><td>0.007**<br>(0.003)</td><td>0.023***<br>(0.003)</td></tr><tr><td>Believes inequality is an important problem</td><td>0.001<br>(0.001)</td><td>0.041***<br>(0.005)</td><td>0.013***<br>(0.003)</td><td>0.012***<br>(0.003)</td><td>0.025***<br>(0.003)</td></tr><tr><td>Worries about the consequences of CC</td><td>-0.003***<br>(0.001)</td><td>0.040***<br>(0.005)</td><td>0.019***<br>(0.003)</td><td>0.012***<br>(0.003)</td><td>0.007***<br>(0.003)</td></tr><tr><td>Believes net-zero is technically feasible</td><td>-0.003***<br>(0.001)</td><td>0.022***<br>(0.005)</td><td>0.011***<br>(0.003)</td><td>0.012***<br>(0.003)</td><td>0.002<br>(0.003)</td></tr><tr><td>Believes will suffer from climate change</td><td>0.0001<br>(0.001)</td><td>0.056***<br>(0.005)</td><td>0.020***<br>(0.003)</td><td>0.028***<br>(0.003)</td><td>0.010***<br>(0.003)</td></tr><tr><td>Understands emission across activities/regions</td><td>0.522***<br>(0.001)</td><td>0.013***<br>(0.004)</td><td>0.012***<br>(0.002)</td><td>0.007***<br>(0.003)</td><td>0.007***<br>(0.003)</td></tr><tr><td>Knows CC is real & caused by human</td><td>0.393***<br>(0.005)</td><td>0.064***<br>(0.005)</td><td>0.022***<br>(0.003)</td><td>0.020***<br>(0.003)</td><td>0.008***<br>(0.003)</td></tr><tr><td>Knows which gases cause CC</td><td>0.381***<br>(0.001)</td><td>0.014***<br>(0.004)</td><td>0.009***<br>(0.002)</td><td>0.009***<br>(0.003)</td><td>0.010***<br>(0.003)</td></tr><tr><td>Understands impacts of CC</td><td>0.347***<br>(0.001)</td><td>0.004<br>(0.004)</td><td>0.006**<br>(0.003)</td><td>-0.004<br>(0.003)</td><td>-0.008***<br>(0.003)</td></tr><tr><td>Believes policies would have positive economic effects</td><td>-0.001*<br>(0.001)</td><td>0.075***<br>(0.005)</td><td>0.025***<br>(0.002)</td><td>0.018***<br>(0.003)</td><td>0.019***<br>(0.003)</td></tr><tr><td>Believes policies would reduce pollution</td><td>-0.001<br>(0.001)</td><td>0.109***<br>(0.008)</td><td>0.078***<br>(0.005)</td><td>0.048***<br>(0.005)</td><td>0.021***<br>(0.005)</td></tr><tr><td>Believes policies would reduce emissions</td><td>0.001<br>(0.001)</td><td>0.290***<br>(0.009)</td><td>0.082***<br>(0.005)</td><td>0.088***<br>(0.005)</td><td>0.119***<br>(0.005)</td></tr><tr><td>Believes will personally lose</td><td>0.001<br>(0.001)</td><td>-0.336***<br>(0.007)</td><td>-0.082***<br>(0.004)</td><td>-0.114***<br>(0.004)</td><td>-0.109***<br>(0.004)</td></tr><tr><td>Believes poor people will lose</td><td>-0.001<br>(0.001)</td><td>-0.062***<br>(0.007)</td><td>-0.002<br>(0.004)</td><td>-0.010***<br>(0.004)</td><td>-0.033***<br>(0.004)</td></tr><tr><td>Believes rich people will lose</td><td>0.001*<br>(0.001)</td><td>0.012***<br>(0.004)</td><td>0.007***<br>(0.002)</td><td>0.007***<br>(0.003)</td><td>0.009***<br>(0.003)</td></tr><tr><td>Observations</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td></tr></tbody></table>

Note: The table shows the results of OLS regressions. Dependent variables are standardized indices (columns 1, 2), or indicator variables equal to 1 if the respondent (somewhat or strongly) supports each of the main climate policies (3, 4, 5). Displayed covariates are reasoning about policies; undisplayed ones include country fixed effects, treatments and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.

<table><caption>Table A3: Treatment effects on support for climate action.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Support or Agreement</th></tr><tr><th>Ban on combustion-engine cars<br>(1)</th><th>Green infrastructure program<br>(2)</th><th>Carbon tax with cash transfers<br>(3)</th><th>Main policies are fair<br>(4)</th><th>Adopt climate-friendly behaviors<br>(5)</th></tr></thead><tbody><tr><td>Control group mean</td><td>0.652</td><td>0.514</td><td>0.456</td><td>-0.096</td><td>-0.044</td></tr><tr><td>Treatment: Climate impacts</td><td>0.020**<br>(0.008)</td><td>0.019**<br>(0.009)</td><td>0.028***<br>(0.009)</td><td>0.051***<br>(0.018)</td><td>0.065***<br>(0.018)</td></tr><tr><td>Treatment: Climate policy</td><td>0.022***<br>(0.008)</td><td>0.039***<br>(0.009)</td><td>0.096***<br>(0.009)</td><td>0.146***<br>(0.018)</td><td>0.033*<br>(0.018)</td></tr><tr><td>Treatment: Both</td><td>0.046***<br>(0.008)</td><td>0.072***<br>(0.009)</td><td>0.127***<br>(0.009)</td><td>0.206***<br>(0.017)</td><td>0.087***<br>(0.018)</td></tr><tr><td>Observations</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td></tr></tbody></table>

Note: The table shows the results of OLS regressions. Dependent variables are indicator variables equal to 1 if the respondent (somewhat or strongly) supports each of the main climate policies (columns 1, 2, 3), or standardized indices (4, 5). Covariates are reasoning about policies, undisplayed ones include country fixed effects, treatments and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<table><caption>Table A4: Knowledge regressed on individual characteristics.</caption><thead><tr><th rowspan="3"></th><th colspan="5">Knowledge of climate change</th></tr><tr><th>Knowledge index</th><th>Footprint</th><th>Fundamentals</th><th>Greenhouse gases</th><th>Impacts</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.064</td><td>-0.025</td><td>-0.026</td><td>-0.112</td><td>0.002</td></tr><tr><td colspan="6"><strong>Panel A: Socio-economic indicators</strong></td></tr><tr><td>Gender: Female</td><td>-0.165***<br>(0.013)</td><td>-0.102***<br>(0.013)</td><td>-0.012<br>(0.013)</td><td>-0.154***<br>(0.013)</td><td>-0.139***<br>(0.013)</td></tr><tr><td>Lives with child(ren) under 14</td><td>-0.105***<br>(0.015)</td><td>-0.073***<br>(0.015)</td><td>-0.009<br>(0.016)</td><td>-0.100***<br>(0.015)</td><td>-0.081***<br>(0.015)</td></tr><tr><td>Age: 25 - 34</td><td>-0.081***<br>(0.024)</td><td>0.001<br>(0.024)</td><td>-0.124***<br>(0.023)</td><td>-0.061**<br>(0.026)</td><td>-0.015<br>(0.024)</td></tr><tr><td>Age: 35 - 49</td><td>-0.069***<br>(0.023)</td><td>0.016<br>(0.022)</td><td>-0.118***<br>(0.022)</td><td>-0.082***<br>(0.024)</td><td>0.018<br>(0.023)</td></tr><tr><td>Age: 50 or older</td><td>0.099***<br>(0.021)</td><td>0.173***<br>(0.021)</td><td>-0.099***<br>(0.021)</td><td>0.018<br>(0.023)</td><td>0.123***<br>(0.022)</td></tr><tr><td>Household income: Q2</td><td>0.088***<br>(0.018)</td><td>0.037**<br>(0.018)</td><td>0.033*<br>(0.018)</td><td>0.094***<br>(0.018)</td><td>0.046**<br>(0.018)</td></tr><tr><td>Household income: Q3</td><td>0.099***<br>(0.019)</td><td>0.069***<br>(0.019)</td><td>0.028<br>(0.020)</td><td>0.085***<br>(0.020)</td><td>0.061***<br>(0.020)</td></tr><tr><td>Household income: Q4</td><td>0.196***<br>(0.021)</td><td>0.144***<br>(0.021)</td><td>0.029<br>(0.021)</td><td>0.160***<br>(0.021)</td><td>0.122***<br>(0.021)</td></tr><tr><td>Highest diploma: College</td><td>0.341***<br>(0.026)</td><td>0.206***<br>(0.026)</td><td>0.171***<br>(0.026)</td><td>0.235***<br>(0.027)</td><td>0.239***<br>(0.026)</td></tr><tr><td>Highest diploma: High school</td><td>0.187***<br>(0.025)</td><td>0.083***<br>(0.025)</td><td>0.098***<br>(0.025)</td><td>0.149***<br>(0.026)</td><td>0.148***<br>(0.025)</td></tr><tr><td>Economic Leaning: Very Left</td><td>-0.039<br>(0.029)</td><td>-0.062**<br>(0.029)</td><td>0.073**<br>(0.029)</td><td>-0.028<br>(0.029)</td><td>-0.078***<br>(0.029)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.218***<br>(0.018)</td><td>-0.169***<br>(0.019)</td><td>-0.167***<br>(0.019)</td><td>-0.094***<br>(0.019)</td><td>-0.095***<br>(0.018)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.296***<br>(0.022)</td><td>-0.171***<br>(0.022)</td><td>-0.303***<br>(0.022)</td><td>-0.126***<br>(0.022)</td><td>-0.148***<br>(0.021)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.419***<br>(0.025)</td><td>-0.278***<br>(0.025)</td><td>-0.285***<br>(0.026)</td><td>-0.172***<br>(0.025)</td><td>-0.304***<br>(0.026)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.146***<br>(0.017)</td><td>0.069***<br>(0.017)</td><td>0.093***<br>(0.018)</td><td>0.168***<br>(0.018)</td><td>0.032*<br>(0.017)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.037**<br>(0.017)</td><td>0.018<br>(0.018)</td><td>-0.015<br>(0.017)</td><td>0.126***<br>(0.018)</td><td>-0.041**<br>(0.018)</td></tr><tr><td colspan="6"><strong>Panel B: Energy usage indicators</strong></td></tr><tr><td>Treatment: Both</td><td>0.089***<br>(0.017)</td><td>0.022<br>(0.017)</td><td>0.028<br>(0.018)</td><td>0.176***<br>(0.018)</td><td>0.003<br>(0.017)</td></tr><tr><td>Agglomeration size: Small</td><td>-0.003<br>(0.021)</td><td>0.016<br>(0.021)</td><td>-0.013<br>(0.021)</td><td>-0.026<br>(0.021)</td><td>0.025<br>(0.021)</td></tr><tr><td>Agglomeration size: Medium</td><td>0.043*<br>(0.023)</td><td>0.034<br>(0.023)</td><td>0.037<br>(0.024)</td><td>0.005<br>(0.023)</td><td>0.043*<br>(0.023)</td></tr><tr><td>Agglomeration size: Large</td><td>0.050**<br>(0.022)</td><td>0.031<br>(0.022)</td><td>0.062***<br>(0.022)</td><td>-0.010<br>(0.022)</td><td>0.051**<br>(0.022)</td></tr><tr><td>Public transport available</td><td>0.029**<br>(0.014)</td><td>-0.019<br>(0.014)</td><td>0.033**<br>(0.014)</td><td>0.017<br>(0.014)</td><td>0.050***<br>(0.014)</td></tr><tr><td>Uses car</td><td>0.057***<br>(0.017)</td><td>-0.0004<br>(0.017)</td><td>0.054***<br>(0.018)</td><td>0.037**<br>(0.017)</td><td>0.060***<br>(0.017)</td></tr><tr><td>High gas expenses</td><td>-0.076***<br>(0.014)</td><td>-0.059***<br>(0.014)</td><td>-0.023*<br>(0.014)</td><td>-0.058***<br>(0.014)</td><td>-0.051***<br>(0.014)</td></tr><tr><td>High heating expenses</td><td>-0.024*<br>(0.014)</td><td>-0.039***<br>(0.014)</td><td>-0.003<br>(0.014)</td><td>0.006<br>(0.014)</td><td>-0.014<br>(0.014)</td></tr><tr><td>Flies more than once a year</td><td>0.026*<br>(0.014)</td><td>0.020<br>(0.014)</td><td>0.043***<br>(0.015)</td><td>-0.024<br>(0.015)</td><td>0.030**<br>(0.014)</td></tr><tr><td>Polluting sector</td><td>-0.178***<br>(0.019)</td><td>-0.116***<br>(0.019)</td><td>-0.081***<br>(0.018)</td><td>-0.101***<br>(0.019)</td><td>-0.138***<br>(0.019)</td></tr><tr><td>Eats beef weekly or more</td><td>-0.054***<br>(0.014)</td><td>-0.064***<br>(0.014)</td><td>-0.067***<br>(0.014)</td><td>0.027*<br>(0.014)</td><td>-0.017<br>(0.014)</td></tr><tr><td>Owner or landlord</td><td>0.001<br>(0.015)</td><td>-0.020<br>(0.015)</td><td>-0.012<br>(0.016)</td><td>0.024<br>(0.016)</td><td>0.021<br>(0.015)</td></tr><tr><td>Observations</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td><td>39,309</td></tr></tbody></table>

Note: The table shows the results of regressions of knowledge indices on socio-economic indicators (Panel A) and on energy usage indicators (Panel B), controlling for country fixed effects. Panel B also controls for socio-economic indicators, but the coefficients are not displayed. All indices are standardized. The dependent variable in column 1 is the *Knowledge index*, whose components are the indices in the remaining columns. See Appendix A-1 for variable definitions.
<table><caption>Table A5: *Knowledge index* regressed on individual characteristics in high-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="11">Knowledge Index</th></tr><tr><th>AUS<br>(1)</th><th>CAN<br>(2)</th><th>DEU<br>(3)</th><th>DNK<br>(4)</th><th>ESP<br>(5)</th><th>FRA<br>(6)</th><th>GBR<br>(7)</th><th>ITA<br>(8)</th><th>JPN<br>(9)</th><th>KOR<br>(10)</th><th>POL<br>(11)</th><th>USA<br>(12)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.044</td><td>-0.07</td><td>-0.02</td><td>0.004</td><td>-0.065</td><td>-0.163</td><td>-0.019</td><td>-0.031</td><td>0.014</td><td>-0.058</td><td>-0.035</td><td>-0.022</td></tr><tr><td colspan="13"><strong>Panel A: Socio-demographics</strong></td></tr><tr><td>Gender: Female</td><td>-0.054<br>(0.044)</td><td>-0.201***<br>(0.043)</td><td>-0.136***<br>(0.044)</td><td>-0.128***<br>(0.045)</td><td>-0.239***<br>(0.041)</td><td>-0.316***<br>(0.045)</td><td>-0.004<br>(0.045)</td><td>-0.152***<br>(0.043)</td><td>-0.262***<br>(0.047)</td><td>-0.074<br>(0.047)</td><td>-0.176***<br>(0.045)</td><td>-0.102**<br>(0.042)</td></tr><tr><td>Lives with child(ren) under 14</td><td>-0.202***<br>(0.055)</td><td>-0.216***<br>(0.048)</td><td>-0.247***<br>(0.061)</td><td>-0.090*<br>(0.052)</td><td>-0.109**<br>(0.048)</td><td>-0.210***<br>(0.057)</td><td>-0.263***<br>(0.057)</td><td>-0.193***<br>(0.055)</td><td>-0.074<br>(0.068)</td><td>-0.121**<br>(0.062)</td><td>-0.057<br>(0.051)</td><td>-0.255***<br>(0.045)</td></tr><tr><td>Age: 25 - 34</td><td>-0.218***<br>(0.083)</td><td>-0.068<br>(0.085)</td><td>-0.325***<br>(0.090)</td><td>0.025<br>(0.086)</td><td>-0.189**<br>(0.091)</td><td>-0.055<br>(0.086)</td><td>0.041<br>(0.085)</td><td>-0.177*<br>(0.097)</td><td>0.264***<br>(0.102)</td><td>-0.325***<br>(0.094)</td><td>-0.234***<br>(0.090)</td><td>0.013<br>(0.077)</td></tr><tr><td>Age: 35 - 49</td><td>-0.223***<br>(0.080)</td><td>-0.019<br>(0.081)</td><td>-0.168**<br>(0.084)</td><td>-0.005<br>(0.088)</td><td>-0.086<br>(0.083)</td><td>-0.023<br>(0.078)</td><td>-0.159*<br>(0.081)</td><td>-0.147*<br>(0.088)</td><td>0.146<br>(0.093)</td><td>-0.410***<br>(0.087)</td><td>-0.064<br>(0.083)</td><td>-0.062<br>(0.076)</td></tr><tr><td>Age: 50 or older</td><td>-0.023<br>(0.076)</td><td>0.129*<br>(0.075)</td><td>0.017<br>(0.077)</td><td>0.300***<br>(0.083)</td><td>0.106<br>(0.078)</td><td>0.025<br>(0.074)</td><td>0.295***<br>(0.075)</td><td>-0.110<br>(0.082)</td><td>0.176**<br>(0.087)</td><td>-0.443***<br>(0.081)</td><td>0.052<br>(0.079)</td><td>0.292***<br>(0.071)</td></tr><tr><td>Household income: Q2</td><td>0.091<br>(0.061)</td><td>0.151**<br>(0.062)</td><td>0.016<br>(0.060)</td><td>-0.075<br>(0.063)</td><td>0.157***<br>(0.057)</td><td>-0.034<br>(0.060)</td><td>0.088<br>(0.060)</td><td>0.205***<br>(0.061)</td><td>-0.017<br>(0.063)</td><td>0.110*<br>(0.064)</td><td>0.216***<br>(0.061)</td><td>-0.038<br>(0.062)</td></tr><tr><td>Household income: Q3</td><td>0.086<br>(0.063)</td><td>0.237***<br>(0.064)</td><td>0.064<br>(0.061)</td><td>0.056<br>(0.064)</td><td>0.223***<br>(0.059)</td><td>0.051<br>(0.063)</td><td>0.249***<br>(0.061)</td><td>0.279***<br>(0.062)</td><td>-0.040<br>(0.064)</td><td>0.065<br>(0.065)</td><td>0.267***<br>(0.062)</td><td>0.014<br>(0.065)</td></tr><tr><td>Household income: Q4</td><td>0.291***<br>(0.066)</td><td>0.436***<br>(0.066)</td><td>0.153**<br>(0.063)</td><td>0.151**<br>(0.072)</td><td>0.199***<br>(0.061)</td><td>-0.079<br>(0.068)</td><td>0.248***<br>(0.064)</td><td>0.320***<br>(0.065)</td><td>0.074<br>(0.064)</td><td>0.118*<br>(0.067)</td><td>0.346***<br>(0.064)</td><td>0.099<br>(0.066)</td></tr><tr><td>Highest diploma: College</td><td>0.306***<br>(0.084)</td><td>0.436***<br>(0.066)</td><td>0.701***<br>(0.075)</td><td>0.579***<br>(0.083)</td><td>0.348***<br>(0.065)</td><td>0.400***<br>(0.064)</td><td>0.259***<br>(0.065)</td><td>0.387***<br>(0.073)</td><td>0.712***<br>(0.166)</td><td>0.629***<br>(0.137)</td><td>0.486***<br>(0.153)</td><td>0.385***<br>(0.097)</td></tr><tr><td>Highest diploma: High school</td><td>0.095<br>(0.082)</td><td>0.032<br>(0.081)</td><td>0.467***<br>(0.068)</td><td>0.331***<br>(0.078)</td><td>0.234***<br>(0.061)</td><td>0.112**<br>(0.054)</td><td>0.185***<br>(0.061)</td><td>0.146**<br>(0.060)</td><td>0.559***<br>(0.165)</td><td>0.344**<br>(0.139)</td><td>0.313**<br>(0.149)</td><td>0.286***<br>(0.093)</td></tr><tr><td>Economic Leaning: Very Left</td><td>-0.010<br>(0.112)</td><td>-0.079<br>(0.097)</td><td>-0.114<br>(0.117)</td><td>0.343***<br>(0.120)</td><td>0.130*<br>(0.070)</td><td>-0.611***<br>(0.146)</td><td>-0.055<br>(0.111)</td><td>0.112<br>(0.081)</td><td>-0.193<br>(0.155)</td><td>-0.232*<br>(0.133)</td><td>-0.205**<br>(0.098)</td><td>-0.121<br>(0.083)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.323***<br>(0.063)</td><td>-0.378***<br>(0.062)</td><td>-0.376***<br>(0.059)</td><td>-0.103*<br>(0.058)</td><td>-0.213***<br>(0.052)</td><td>0.073<br>(0.065)</td><td>-0.470***<br>(0.063)</td><td>-0.205***<br>(0.058)</td><td>-0.299***<br>(0.069)</td><td>-0.268***<br>(0.067)</td><td>-0.200***<br>(0.060)</td><td>-0.232***<br>(0.060)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.638***<br>(0.073)</td><td>-0.570***<br>(0.073)</td><td>-0.566***<br>(0.074)</td><td>-0.298***<br>(0.062)</td><td>-0.428***<br>(0.063)</td><td>-0.183***<br>(0.065)</td><td>-0.492***<br>(0.071)</td><td>-0.158***<br>(0.063)</td><td>-0.261***<br>(0.081)</td><td>-0.242***<br>(0.076)</td><td>-0.265***<br>(0.074)</td><td>-0.546***<br>(0.069)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.681***<br>(0.095)</td><td>-0.926***<br>(0.091)</td><td>-0.600***<br>(0.115)</td><td>-0.600***<br>(0.133)</td><td>-0.542***<br>(0.075)</td><td>-0.407***<br>(0.087)</td><td>-0.955***<br>(0.100)</td><td>-0.332***<br>(0.080)</td><td>-0.418***<br>(0.113)</td><td>-0.374***<br>(0.115)</td><td>-0.491***<br>(0.080)</td><td>-0.760***<br>(0.073)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.126**<br>(0.060)</td><td>0.097*<br>(0.059)</td><td>0.139**<br>(0.057)</td><td>0.052<br>(0.058)</td><td>0.071<br>(0.056)</td><td>0.243***<br>(0.060)</td><td>0.118**<br>(0.059)</td><td>0.130**<br>(0.059)</td><td>0.078<br>(0.061)</td><td>0.169***<br>(0.063)</td><td>0.125**<br>(0.059)</td><td>0.116**<br>(0.056)</td></tr><tr><td>Treatment: Climate Policies</td><td>-0.005<br>(0.059)</td><td>0.101*<br>(0.059)</td><td>-0.068<br>(0.059)</td><td>-0.040<br>(0.058)</td><td>0.116**<br>(0.056)</td><td>0.042<br>(0.058)</td><td>0.047<br>(0.059)</td><td>0.004<br>(0.060)</td><td>-0.047<br>(0.062)</td><td>-0.005<br>(0.062)</td><td>0.056<br>(0.060)</td><td>-0.017<br>(0.055)</td></tr><tr><td>Treatment: Both</td><td>0.059<br>(0.060)</td><td>0.088<br>(0.057)</td><td>-0.0002<br>(0.060)</td><td>0.028<br>(0.059)</td><td>0.118**<br>(0.055)</td><td>0.194***<br>(0.059)</td><td>0.002<br>(0.058)</td><td>0.119**<br>(0.059)</td><td>-0.044<br>(0.061)</td><td>0.034<br>(0.063)</td><td>0.093<br>(0.059)</td><td>0.058<br>(0.056)</td></tr><tr><td colspan="13"><strong>Panel B: Energy characteristics</strong></td></tr><tr><td>Agglomeration size: Medium</td><td>0.100<br>(0.090)</td><td>0.190**<br>(0.075)</td><td>0.110<br>(0.073)</td><td>-0.065<br>(0.064)</td><td>0.052<br>(0.079)</td><td>-0.055<br>(0.073)</td><td>0.135*<br>(0.077)</td><td>0.029<br>(0.074)</td><td>0.099<br>(0.139)</td><td>0.030<br>(0.146)</td><td>0.137**<br>(0.066)</td><td>0.126*<br>(0.071)</td></tr><tr><td>Agglomeration size: Large</td><td>0.229***<br>(0.080)</td><td>0.091<br>(0.071)</td><td>0.150**<br>(0.073)</td><td>0.043<br>(0.070)</td><td>0.046<br>(0.077)</td><td>-0.112<br>(0.078)</td><td>0.043<br>(0.073)</td><td>-0.021<br>(0.078)</td><td>0.025<br>(0.137)</td><td>-0.010<br>(0.137)</td><td>0.122*<br>(0.069)</td><td>0.094<br>(0.064)</td></tr><tr><td>Public transport available</td><td>0.024<br>(0.046)</td><td>-0.045<br>(0.046)</td><td>0.061<br>(0.045)</td><td>0.072<br>(0.049)</td><td>-0.038<br>(0.044)</td><td>0.110**<br>(0.047)</td><td>0.008<br>(0.045)</td><td>-0.053<br>(0.055)</td><td>0.067<br>(0.050)</td><td>0.097**<br>(0.047)</td><td>0.014<br>(0.047)</td><td>-0.152***<br>(0.043)</td></tr><tr><td>Uses car</td><td>0.222***<br>(0.069)</td><td>0.010<br>(0.066)</td><td>0.176***<br>(0.056)</td><td>-0.063<br>(0.054)</td><td>0.002<br>(0.052)</td><td>0.035<br>(0.069)</td><td>0.047<br>(0.059)</td><td>0.194***<br>(0.067)</td><td>-0.110*<br>(0.061)</td><td>0.256***<br>(0.056)</td><td>-0.099*<br>(0.057)</td><td>-0.246***<br>(0.071)</td></tr><tr><td>High gas expenses</td><td>-0.078<br>(0.048)</td><td>-0.127***<br>(0.048)</td><td>-0.203***<br>(0.048)</td><td>-0.103**<br>(0.047)</td><td>0.041<br>(0.044)</td><td>-0.157***<br>(0.047)</td><td>-0.086*<br>(0.051)</td><td>0.039<br>(0.044)</td><td>-0.065<br>(0.056)</td><td>-0.084*<br>(0.051)</td><td>-0.045<br>(0.048)</td><td>-0.151***<br>(0.044)</td></tr><tr><td>High heating expenses</td><td>-0.067<br>(0.045)</td><td>0.080*<br>(0.046)</td><td>-0.007<br>(0.046)</td><td>-0.001<br>(0.046)</td><td>0.008<br>(0.043)</td><td>-0.024<br>(0.045)</td><td>-0.112**<br>(0.045)</td><td>0.018<br>(0.046)</td><td>0.030<br>(0.048)</td><td>-0.020<br>(0.049)</td><td>0.109**<br>(0.047)</td><td>-0.221***<br>(0.042)</td></tr><tr><td>Flies more than once a year</td><td>0.153***<br>(0.046)</td><td>0.035<br>(0.049)</td><td>0.001<br>(0.049)</td><td>0.121***<br>(0.046)</td><td>0.050<br>(0.044)</td><td>-0.025<br>(0.054)</td><td>-0.093**<br>(0.047)</td><td>0.073<br>(0.047)</td><td>-0.025<br>(0.053)</td><td>0.063<br>(0.050)</td><td>0.080<br>(0.054)</td><td>0.095**<br>(0.046)</td></tr><tr><td>Polluting sector</td><td>-0.104<br>(0.067)</td><td>-0.286***<br>(0.062)</td><td>-0.187***<br>(0.061)</td><td>-0.377***<br>(0.067)</td><td>-0.143***<br>(0.064)</td><td>0.048<br>(0.065)</td><td>-0.237***<br>(0.072)</td><td>-0.084<br>(0.070)</td><td>0.003<br>(0.068)</td><td>-0.294***<br>(0.060)</td><td>-0.128***<br>(0.057)</td><td>-0.186***<br>(0.059)</td></tr><tr><td>Eats beef weekly or more</td><td>-0.072<br>(0.044)</td><td>-0.083*<br>(0.043)</td><td>0.056<br>(0.049)</td><td>-0.187***<br>(0.046)</td><td>-0.148***<br>(0.041)</td><td>-0.077*<br>(0.044)</td><td>-0.133***<br>(0.045)</td><td>-0.140***<br>(0.044)</td><td>0.043<br>(0.048)</td><td>0.050<br>(0.056)</td><td>-0.124**<br>(0.061)</td><td>0.180***<br>(0.042)</td></tr><tr><td>Owner or landlord</td><td>-0.0001<br>(0.053)</td><td>-0.008<br>(0.052)</td><td>0.034<br>(0.049)</td><td>-0.011<br>(0.049)</td><td>-0.014<br>(0.049)</td><td>-0.064<br>(0.052)</td><td>0.172***<br>(0.051)</td><td>-0.093*<br>(0.055)</td><td>0.121**<br>(0.052)</td><td>-0.046<br>(0.051)</td><td>0.024<br>(0.054)</td><td>-0.179***<br>(0.049)</td></tr><tr><td>Observations</td><td>1.978</td><td>2.022</td><td>2.006</td><td>2.013</td><td>2.268</td><td>2.006</td><td>2.008</td><td>2.088</td><td>1.990</td><td>1.874</td><td>2.053</td><td>2.218</td></tr></tbody></table>

Note: The table shows the results of regressions of the standardized *Knowledge index* on socio-economic indicators (Panel A) and on energy usage indicators (Panel B). Panel B also controls for socio-economic indicators, but the coefficients are not displayed. See Appendix A-1 for variable definitions.
<table><caption>Table A6: *Knowledge index* regressed on individual characteristics in middle-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Knowledge Index</th></tr><tr><th>BRA<br>(1)</th><th>CHN<br>(2)</th><th>IDN<br>(3)</th><th>IND<br>(4)</th><th>MEX<br>(5)</th><th>TUR<br>(6)</th><th>UKR<br>(7)</th><th>ZAF<br>(8)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.161</td><td>-0.052</td><td>-0.103</td><td>-0.047</td><td>-0.097</td><td>-0.051</td><td>-0.192</td><td>-0.098</td></tr><tr><td colspan="9"><strong>Panel A: Socio-demographics</strong></td></tr><tr><td>Gender: Female</td><td>-0.179***<br>(0.046)</td><td>-0.077<br>(0.051)</td><td>-0.070<br>(0.047)</td><td>-0.297***<br>(0.047)</td><td>-0.178***<br>(0.047)</td><td>-0.137***<br>(0.046)</td><td>-0.095*<br>(0.050)</td><td>-0.180***<br>(0.044)</td></tr><tr><td>Lives with child(ren) under 14</td><td>-0.135***<br>(0.048)</td><td>-0.023<br>(0.058)</td><td>-0.022<br>(0.059)</td><td>-0.140**<br>(0.055)</td><td>-0.121**<br>(0.049)</td><td>0.096**<br>(0.049)</td><td>-0.093*<br>(0.054)</td><td>-0.235***<br>(0.047)</td></tr><tr><td>Age: 25 - 34</td><td>-0.226***<br>(0.076)</td><td>0.107<br>(0.098)</td><td>0.014<br>(0.074)</td><td>-0.040<br>(0.074)</td><td>0.139**<br>(0.069)</td><td>-0.228***<br>(0.076)</td><td>0.222**<br>(0.101)</td><td>-0.343***<br>(0.062)</td></tr><tr><td>Age: 35 - 49</td><td>-0.032<br>(0.072)</td><td>0.046<br>(0.094)</td><td>-0.044<br>(0.072)</td><td>-0.226***<br>(0.078)</td><td>-0.060<br>(0.066)</td><td>-0.287***<br>(0.071)</td><td>0.329***<br>(0.094)</td><td>-0.427***<br>(0.063)</td></tr><tr><td>Age: 50 or older</td><td>-0.062<br>(0.073)</td><td>0.214**<br>(0.093)</td><td>0.069<br>(0.081)</td><td>0.244***<br>(0.068)</td><td>0.052<br>(0.073)</td><td>0.143*<br>(0.074)</td><td>0.384***<br>(0.091)</td><td>-0.328***<br>(0.069)</td></tr><tr><td>Household income: Q2</td><td>0.261***<br>(0.062)</td><td>0.136*<br>(0.072)</td><td>0.153**<br>(0.065)</td><td>0.392***<br>(0.070)</td><td>-0.026<br>(0.062)</td><td>0.098<br>(0.065)</td><td>0.118*<br>(0.071)</td><td>0.053<br>(0.062)</td></tr><tr><td>Household income: Q3</td><td>0.347***<br>(0.066)</td><td>-0.141*<br>(0.077)</td><td>0.062<br>(0.074)</td><td>0.426***<br>(0.076)</td><td>-0.054<br>(0.066)</td><td>0.015<br>(0.069)</td><td>0.119*<br>(0.072)</td><td>0.100<br>(0.063)</td></tr><tr><td>Household income: Q4</td><td>0.438***<br>(0.070)</td><td>0.034<br>(0.087)</td><td>0.109<br>(0.075)</td><td>0.539***<br>(0.066)</td><td>0.114*<br>(0.069)</td><td>0.067<br>(0.072)</td><td>0.300***<br>(0.073)</td><td>0.241***<br>(0.065)</td></tr><tr><td>Highest diploma: College</td><td>0.614***<br>(0.113)</td><td>0.412***<br>(0.092)</td><td>0.418***<br>(0.124)</td><td>0.044<br>(0.108)</td><td>0.406***<br>(0.079)</td><td>0.208***<br>(0.080)</td><td>0.460***<br>(0.133)</td><td>0.451***<br>(0.105)</td></tr><tr><td>Highest diploma: High school</td><td>0.433***<br>(0.104)</td><td>0.160**<br>(0.069)</td><td>0.232**<br>(0.116)</td><td>0.121<br>(0.109)</td><td>0.345***<br>(0.069)</td><td>0.052<br>(0.069)</td><td>0.145<br>(0.133)</td><td>0.370***<br>(0.094)</td></tr><tr><td>Economic Leaning: Very Left</td><td>0.075<br>(0.101)</td><td>0.128<br>(0.113)</td><td>0.131<br>(0.165)</td><td>0.371**<br>(0.185)</td><td>-0.305***<br>(0.107)</td><td>-0.063<br>(0.090)</td><td>0.062<br>(0.120)</td><td>0.216**<br>(0.101)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.081<br>(0.075)</td><td>-0.451***<br>(0.073)</td><td>-0.139<br>(0.093)</td><td>-0.148<br>(0.094)</td><td>-0.244***<br>(0.073)</td><td>-0.089<br>(0.068)</td><td>0.119<br>(0.079)</td><td>-0.098<br>(0.071)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.138<br>(0.086)</td><td>-0.500***<br>(0.078)</td><td>-0.269***<br>(0.102)</td><td>-0.150<br>(0.100)</td><td>-0.249***<br>(0.085)</td><td>-0.041<br>(0.084)</td><td>0.197**<br>(0.094)</td><td>0.024<br>(0.081)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.141*<br>(0.083)</td><td>-0.552***<br>(0.094)</td><td>-0.003<br>(0.101)</td><td>-0.282***<br>(0.105)</td><td>-0.487***<br>(0.093)</td><td>-0.330***<br>(0.088)</td><td>0.072<br>(0.093)</td><td>-0.107<br>(0.087)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.238***<br>(0.062)</td><td>0.145**<br>(0.069)</td><td>0.244***<br>(0.060)</td><td>0.060<br>(0.060)</td><td>0.157***<br>(0.060)</td><td>0.052<br>(0.060)</td><td>0.295***<br>(0.067)</td><td>0.257***<br>(0.059)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.232***<br>(0.061)</td><td>0.041<br>(0.067)</td><td>0.012<br>(0.062)</td><td>0.002<br>(0.061)</td><td>0.087<br>(0.061)</td><td>0.044<br>(0.063)</td><td>0.088<br>(0.069)</td><td>0.020<br>(0.059)</td></tr><tr><td>Treatment: Both</td><td>0.189***<br>(0.062)</td><td>-0.052<br>(0.067)</td><td>0.149**<br>(0.061)</td><td>0.033<br>(0.061)</td><td>0.130**<br>(0.062)</td><td>0.091<br>(0.063)</td><td>0.269***<br>(0.068)</td><td>0.153**<br>(0.061)</td></tr><tr><td colspan="9"><strong>Panel B: Energy characteristics</strong></td></tr><tr><td>Agglomeration size: Medium</td><td>0.061<br>(0.111)</td><td>-0.156*<br>(0.094)</td><td>0.182**<br>(0.080)</td><td>0.290**<br>(0.116)</td><td>-0.050<br>(0.097)</td><td>-0.368**<br>(0.171)</td><td>0.054<br>(0.084)</td><td>-0.058<br>(0.080)</td></tr><tr><td>Agglomeration size: Large</td><td>0.040<br>(0.106)</td><td>0.071<br>(0.097)</td><td>0.244***<br>(0.070)</td><td>0.081<br>(0.077)</td><td>-0.037<br>(0.085)</td><td>-0.365**<br>(0.166)</td><td>0.228***<br>(0.075)</td><td>-0.046<br>(0.068)</td></tr><tr><td>Public transport available</td><td>0.034<br>(0.050)</td><td>0.003<br>(0.057)</td><td>0.129**<br>(0.053)</td><td>0.302***<br>(0.055)</td><td>0.071<br>(0.052)</td><td>0.129***<br>(0.047)</td><td>-0.022<br>(0.053)</td><td>-0.105**<br>(0.046)</td></tr><tr><td>Uses car</td><td>0.016<br>(0.059)</td><td>0.089<br>(0.056)</td><td>0.599***<br>(0.099)</td><td>0.147***<br>(0.056)</td><td>0.049<br>(0.058)</td><td>0.062<br>(0.052)</td><td>-0.004<br>(0.056)</td><td>0.157***<br>(0.055)</td></tr><tr><td>High gas expenses</td><td>0.011<br>(0.049)</td><td>-0.118**<br>(0.055)</td><td>-0.044<br>(0.051)</td><td></td><td>0.042<br>(0.049)</td><td>-0.029<br>(0.051)</td><td>-0.108*<br>(0.057)</td><td>-0.026<br>(0.047)</td></tr><tr><td>High heating expenses</td><td></td><td>-0.040<br>(0.056)</td><td></td><td></td><td></td><td>0.039<br>(0.052)</td><td>0.013<br>(0.051)</td><td>0.045<br>(0.046)</td></tr><tr><td>Flies more than once a year</td><td>0.037<br>(0.059)</td><td>0.226***<br>(0.066)</td><td>0.129**<br>(0.053)</td><td>-0.192***<br>(0.065)</td><td>-0.094*<br>(0.055)</td><td>-0.019<br>(0.057)</td><td>-0.123**<br>(0.060)</td><td>-0.156***<br>(0.059)</td></tr><tr><td>Polluting sector</td><td>-0.313***<br>(0.067)</td><td>-0.044<br>(0.054)</td><td>-0.261***<br>(0.060)</td><td>-0.179***<br>(0.065)</td><td>-0.280***<br>(0.058)</td><td>0.064<br>(0.058)</td><td>-0.286***<br>(0.056)</td><td>0.035<br>(0.057)</td></tr><tr><td>Eats beef weekly or more</td><td>0.122**<br>(0.055)</td><td>-0.072<br>(0.059)</td><td>-0.162***<br>(0.051)</td><td>-0.286***<br>(0.059)</td><td>-0.003<br>(0.047)</td><td>0.017<br>(0.050)</td><td>-0.050<br>(0.064)</td><td>0.013<br>(0.044)</td></tr><tr><td>Owner or landlord</td><td>0.035<br>(0.051)</td><td>0.073<br>(0.070)</td><td>0.181**<br>(0.077)</td><td>-0.070<br>(0.072)</td><td>-0.097*<br>(0.052)</td><td>0.012<br>(0.052)</td><td>0.129**<br>(0.057)</td><td>-0.023<br>(0.047)</td></tr><tr><td>Observations</td><td>1.860</td><td>1.533</td><td>1.998</td><td>1.925</td><td>1.970</td><td>1.932</td><td>1.564</td><td>2.003</td></tr></tbody></table>

Note: The table shows the results of regressions of the standardized *Knowledge index* on socio-economic indicators (Panel A) and on energy usage indicators (Panel B). Panel B also controls for socio-economic indicators, but the coefficients are not displayed. See Appendix A-1 for variable definitions.
<table><caption>Table A7: *Support for main climate policies index* regressed on individual characteristics in high-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="11">Support for main climate policies index</th></tr><tr><th>AUS<br>(1)</th><th>CAN<br>(2)</th><th>DEU<br>(3)</th><th>DNK<br>(4)</th><th>ESP<br>(5)</th><th>FRA<br>(6)</th><th>GBR<br>(7)</th><th>ITA<br>(8)</th><th>JPN<br>(9)</th><th>KOR<br>(10)</th><th>POL<br>(11)</th><th>USA<br>(12)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.206</td><td>-0.099</td><td>-0.095</td><td>-0.138</td><td>-0.102</td><td>-0.088</td><td>-0.107</td><td>-0.19</td><td>-0.102</td><td>-0.054</td><td>-0.048</td><td>0.03</td></tr><tr><td colspan="13" style="text-align: left; font-weight: bold;">Panel A: Socio-demographics</td></tr><tr><td>Gender: Female</td><td>-0.008<br>(0.044)</td><td>-0.105**<br>(0.045)</td><td>-0.061<br>(0.046)</td><td>0.162***<br>(0.045)</td><td>0.059<br>(0.041)</td><td>0.077*<br>(0.045)</td><td>0.047<br>(0.046)</td><td>0.033<br>(0.044)</td><td>0.195***<br>(0.047)</td><td>-0.046<br>(0.047)</td><td>0.059<br>(0.046)</td><td>0.045<br>(0.040)</td></tr><tr><td>Lives with child(ren) under 14</td><td>0.169***<br>(0.055)</td><td>0.167***<br>(0.049)</td><td>0.056<br>(0.063)</td><td>-0.055<br>(0.052)</td><td>0.111**<br>(0.049)</td><td>0.201***<br>(0.058)</td><td>0.121**<br>(0.058)</td><td>0.153***<br>(0.055)</td><td>0.077<br>(0.067)</td><td>-0.001<br>(0.061)</td><td>0.176***<br>(0.052)</td><td>0.033<br>(0.043)</td></tr><tr><td>Age: 25 - 34</td><td>-0.080<br>(0.083)</td><td>-0.024<br>(0.088)</td><td>-0.156*<br>(0.093)</td><td>-0.093<br>(0.086)</td><td>0.012<br>(0.092)</td><td>-0.133<br>(0.087)</td><td>-0.045<br>(0.087)</td><td>-0.202**<br>(0.097)</td><td>0.134<br>(0.101)</td><td>0.104<br>(0.093)</td><td>-0.120<br>(0.092)</td><td>0.102<br>(0.074)</td></tr><tr><td>Age: 35 - 49</td><td>-0.099<br>(0.080)</td><td>-0.214**<br>(0.083)</td><td>-0.093<br>(0.087)</td><td>-0.075<br>(0.087)</td><td>-0.073<br>(0.083)</td><td>-0.319***<br>(0.079)</td><td>0.126<br>(0.083)</td><td>-0.122<br>(0.089)</td><td>0.228**<br>(0.093)</td><td>0.204**<br>(0.085)</td><td>-0.026<br>(0.085)</td><td>0.089<br>(0.073)</td></tr><tr><td>Age: 50 or older</td><td>-0.223***<br>(0.076)</td><td>-0.092<br>(0.077)</td><td>-0.134*<br>(0.079)</td><td>-0.033<br>(0.083)</td><td>0.041<br>(0.079)</td><td>-0.397***<br>(0.075)</td><td>-0.052<br>(0.076)</td><td>-0.126<br>(0.082)</td><td>0.421***<br>(0.087)</td><td>0.428***<br>(0.080)</td><td>0.227***<br>(0.081)</td><td>-0.199***<br>(0.068)</td></tr><tr><td>Household income: Q2</td><td>0.072<br>(0.061)</td><td>0.042<br>(0.064)</td><td>-0.066<br>(0.062)</td><td>-0.062<br>(0.063)</td><td>0.087<br>(0.058)</td><td>-0.078<br>(0.061)</td><td>-0.046<br>(0.062)</td><td>0.081<br>(0.061)</td><td>0.157**<br>(0.062)</td><td>0.085<br>(0.063)</td><td>0.158**<br>(0.062)</td><td>0.013<br>(0.060)</td></tr><tr><td>Household income: Q3</td><td>0.150**<br>(0.063)</td><td>0.026<br>(0.066)</td><td>0.018<br>(0.063)</td><td>-0.005<br>(0.064)</td><td>0.097<br>(0.060)</td><td>-0.034<br>(0.064)</td><td>0.004<br>(0.063)</td><td>0.130**<br>(0.063)</td><td>0.173***<br>(0.064)</td><td>0.164**<br>(0.064)</td><td>0.115*<br>(0.063)</td><td>-0.029<br>(0.063)</td></tr><tr><td>Household income: Q4</td><td>0.018<br>(0.066)</td><td>0.030<br>(0.068)</td><td>-0.106<br>(0.065)</td><td>-0.078<br>(0.072)</td><td>0.052<br>(0.062)</td><td>-0.089<br>(0.069)</td><td>0.029<br>(0.066)</td><td>0.226***<br>(0.065)</td><td>0.143**<br>(0.064)</td><td>0.226***<br>(0.066)</td><td>0.155**<br>(0.065)</td><td>0.080<br>(0.064)</td></tr><tr><td>Highest diploma: College</td><td>0.263***<br>(0.084)</td><td>-0.020<br>(0.086)</td><td>0.021<br>(0.077)</td><td>0.223***<br>(0.083)</td><td>0.164**<br>(0.065)</td><td>0.029<br>(0.064)</td><td>0.306***<br>(0.066)</td><td>0.160**<br>(0.073)</td><td>0.299*<br>(0.165)</td><td>-0.948***<br>(0.135)</td><td>-0.160<br>(0.156)</td><td>0.275***<br>(0.094)</td></tr><tr><td>Highest diploma: High school</td><td>0.035<br>(0.082)</td><td>-0.139*<br>(0.083)</td><td>-0.122*<br>(0.070)</td><td>0.164**<br>(0.078)</td><td>0.131**<br>(0.061)</td><td>-0.082<br>(0.055)</td><td>0.129**<br>(0.063)</td><td>0.093<br>(0.061)</td><td>0.168<br>(0.164)</td><td>-0.979***<br>(0.137)</td><td>-0.164<br>(0.152)</td><td>0.137<br>(0.090)</td></tr><tr><td>Economic Leaning: Very Left</td><td>0.023<br>(0.111)</td><td>0.088<br>(0.100)</td><td>0.097<br>(0.120)</td><td>0.491***<br>(0.120)</td><td>0.101<br>(0.070)</td><td>-0.444***<br>(0.149)</td><td>0.036<br>(0.114)</td><td>0.024<br>(0.081)</td><td>0.262*<br>(0.154)</td><td>-0.034<br>(0.131)</td><td>-0.093<br>(0.101)</td><td>0.284***<br>(0.081)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.502***<br>(0.063)</td><td>-0.366***<br>(0.064)</td><td>-0.398***<br>(0.060)</td><td>-0.254***<br>(0.058)</td><td>-0.278***<br>(0.053)</td><td>-0.094<br>(0.066)</td><td>-0.451***<br>(0.065)</td><td>-0.286***<br>(0.058)</td><td>-0.211***<br>(0.068)</td><td>-0.439***<br>(0.066)</td><td>-0.107*<br>(0.062)</td><td>-0.331***<br>(0.058)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.697***<br>(0.073)</td><td>-0.585***<br>(0.075)</td><td>-0.746***<br>(0.076)</td><td>-0.661***<br>(0.062)</td><td>-0.594***<br>(0.064)</td><td>-0.274***<br>(0.066)</td><td>-0.442***<br>(0.073)</td><td>-0.285***<br>(0.064)</td><td>-0.314***<br>(0.081)</td><td>-0.511***<br>(0.075)</td><td>-0.332***<br>(0.076)</td><td>-0.757***<br>(0.067)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.731***<br>(0.095)</td><td>-0.695***<br>(0.093)</td><td>-0.776***<br>(0.119)</td><td>-0.682***<br>(0.133)</td><td>-0.744***<br>(0.076)</td><td>-0.581***<br>(0.089)</td><td>-0.401***<br>(0.102)</td><td>-0.551***<br>(0.080)</td><td>-0.704***<br>(0.113)</td><td>-0.490***<br>(0.114)</td><td>-0.428***<br>(0.082)</td><td>-0.824***<br>(0.071)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.221***<br>(0.060)</td><td>0.003<br>(0.060)</td><td>0.022<br>(0.059)</td><td>0.151***<br>(0.058)</td><td>0.016<br>(0.057)</td><td>0.058<br>(0.061)</td><td>0.053<br>(0.061)</td><td>0.145**<br>(0.060)</td><td>0.045<br>(0.061)</td><td>-0.010<br>(0.062)</td><td>0.042<br>(0.060)</td><td>-0.097*<br>(0.054)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.272***<br>(0.059)</td><td>0.222***<br>(0.061)</td><td>0.189***<br>(0.061)</td><td>0.128**<br>(0.058)</td><td>0.110*<br>(0.057)</td><td>0.058<br>(0.059)</td><td>0.123**<br>(0.060)</td><td>0.309***<br>(0.060)</td><td>0.176***<br>(0.062)</td><td>0.076<br>(0.061)</td><td>0.102*<br>(0.061)</td><td>-0.031<br>(0.053)</td></tr><tr><td>Treatment: Both</td><td>0.334***<br>(0.060)</td><td>0.193***<br>(0.059)</td><td>0.183***<br>(0.061)</td><td>0.288***<br>(0.059)</td><td>0.282***<br>(0.055)</td><td>0.207***<br>(0.060)</td><td>0.305***<br>(0.059)</td><td>0.357***<br>(0.059)</td><td>0.188***<br>(0.061)</td><td>0.184***<br>(0.062)</td><td>0.124**<br>(0.061)</td><td>0.061<br>(0.054)</td></tr></tbody></table>

<table><caption>Panel B: Energy characteristics</caption><tbody><tr><td>Agglomeration size: Medium</td><td>0.130<br>(0.087)</td><td>0.123<br>(0.075)</td><td>0.003<br>(0.062)</td><td>0.278***<br>(0.078)</td><td>0.088<br>(0.072)</td><td>0.119*<br>(0.074)</td><td>0.151*<br>(0.078)</td><td>0.162**<br>(0.074)</td><td>0.090<br>(0.138)</td><td>0.020<br>(0.145)</td><td>-0.016<br>(0.067)</td><td>-0.004<br>(0.068)</td></tr><tr><td>Agglomeration size: Large</td><td>0.085<br>(0.078)</td><td>0.090<br>(0.071)</td><td>0.012<br>(0.067)</td><td>0.273***<br>(0.077)</td><td>0.082<br>(0.077)</td><td>0.186**<br>(0.077)</td><td>0.242***<br>(0.073)</td><td>0.032<br>(0.078)</td><td>0.070<br>(0.135)</td><td>-0.078<br>(0.136)</td><td>-0.006<br>(0.070)</td><td>0.198***<br>(0.062)</td></tr><tr><td>Public transport available</td><td>0.335***<br>(0.044)</td><td>0.276***<br>(0.046)</td><td>0.251***<br>(0.045)</td><td>0.270***<br>(0.047)</td><td>0.238***<br>(0.044)</td><td>0.232***<br>(0.046)</td><td>0.233***<br>(0.045)</td><td>0.192***<br>(0.055)</td><td>0.030<br>(0.050)</td><td>0.168***<br>(0.047)</td><td>0.187***<br>(0.047)</td><td>0.327***<br>(0.041)</td></tr><tr><td>Uses car</td><td>-0.325***<br>(0.067)</td><td>-0.232***<br>(0.067)</td><td>-0.295***<br>(0.056)</td><td>-0.140***<br>(0.051)</td><td>-0.223***<br>(0.051)</td><td>-0.440***<br>(0.068)</td><td>-0.353***<br>(0.060)</td><td>-0.187***<br>(0.066)</td><td>-0.220***<br>(0.060)</td><td>-0.154***<br>(0.055)</td><td>-0.298***<br>(0.058)</td><td>-0.007<br>(0.069)</td></tr><tr><td>High gas expenses</td><td>-0.028<br>(0.046)</td><td>-0.157***<br>(0.048)</td><td>-0.230***<br>(0.048)</td><td>-0.198***<br>(0.045)</td><td>0.043<br>(0.043)</td><td>-0.026<br>(0.046)</td><td>-0.015<br>(0.052)</td><td>0.136***<br>(0.044)</td><td>-0.088<br>(0.056)</td><td>-0.045<br>(0.050)</td><td>-0.050<br>(0.049)</td><td>-0.033<br>(0.042)</td></tr><tr><td>High heating expenses</td><td>0.095**<br>(0.044)</td><td>0.076<br>(0.046)</td><td>0.104**<br>(0.046)</td><td>0.033<br>(0.044)</td><td>0.010<br>(0.042)</td><td>0.010<br>(0.044)</td><td>-0.004<br>(0.045)</td><td>-0.060<br>(0.046)</td><td>0.082*<br>(0.047)</td><td>0.092*<br>(0.049)</td><td>0.120**<br>(0.048)</td><td>0.087**<br>(0.040)</td></tr><tr><td>Flies more than once a year</td><td>0.174***<br>(0.045)</td><td>0.112**<br>(0.050)</td><td>0.132***<br>(0.049)</td><td>0.071<br>(0.044)</td><td>0.157***<br>(0.044)</td><td>0.096*<br>(0.053)</td><td>-0.118**<br>(0.047)</td><td>0.184***<br>(0.047)</td><td>0.150***<br>(0.052)</td><td>0.132***<br>(0.050)</td><td>0.126**<br>(0.055)</td><td>0.101**<br>(0.044)</td></tr><tr><td>Polluting sector</td><td>-0.089<br>(0.066)</td><td>-0.121*<br>(0.063)</td><td>0.123**<br>(0.062)</td><td>-0.035<br>(0.065)</td><td>0.061<br>(0.063)</td><td>0.182***<br>(0.064)</td><td>0.009<br>(0.073)</td><td>0.066<br>(0.070)</td><td>-0.060<br>(0.067)</td><td>0.062<br>(0.059)</td><td>0.059<br>(0.058)</td><td>0.058<br>(0.056)</td></tr><tr><td>Eats beef weekly or more</td><td>-0.135***<br>(0.043)</td><td>-0.118***<br>(0.043)</td><td>-0.163***<br>(0.049)</td><td>-0.297***<br>(0.045)</td><td>-0.237***<br>(0.041)</td><td>-0.225***<br>(0.043)</td><td>-0.064<br>(0.045)</td><td>-0.048<br>(0.044)</td><td>0.048<br>(0.047)</td><td>0.008<br>(0.055)</td><td>-0.050<br>(0.062)</td><td>-0.097**<br>(0.041)</td></tr><tr><td>Owner or landlord</td><td>0.099*<br>(0.051)</td><td>0.082<br>(0.053)</td><td>0.007<br>(0.049)</td><td>-0.060<br>(0.047)</td><td>-0.031<br>(0.048)</td><td>0.069<br>(0.051)</td><td>0.089*<br>(0.052)</td><td>-0.017<br>(0.055)</td><td>0.159***<br>(0.052)</td><td>0.015<br>(0.051)</td><td>-0.007<br>(0.055)</td><td>-0.109**<br>(0.047)</td></tr><tr><td>Observations</td><td>1.978</td><td>2.022</td><td>2.006</td><td>2.013</td><td>2.268</td><td>2.006</td><td>2.008</td><td>2.088</td><td>1.990</td><td>1.874</td><td>2.053</td><td>2.218</td></tr></tbody></table>

Note: The table shows the results of regressions of the standardized *Support for main policies index* on socio-economic indicators (Panel A) and on energy usage indicators (Panel B). Panel B also controls for socio-economic indicators, but the coefficients are not displayed. See Appendix A-1 for variable definitions.
<table><caption>Table A8: *Support for main climate policies index* regressed on individual characteristics in middle-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Support for main climate policies index</th></tr><tr><th>BRA<br>(1)</th><th>CHN<br>(2)</th><th>IDN<br>(3)</th><th>IND<br>(4)</th><th>MEX<br>(5)</th><th>TUR<br>(6)</th><th>UKR<br>(7)</th><th>ZAF<br>(8)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.161</td><td>-0.161</td><td>-0.066</td><td>-0.066</td><td>-0.057</td><td>-0.04</td><td>-0.121</td><td>-0.113</td></tr><tr><th colspan="9">Panel A: Socio-demographics</th></tr><tr><td>Gender: Female</td><td>0.100**<br>(0.046)</td><td>0.092*<br>(0.050)</td><td>0.108***<br>(0.040)</td><td>-0.034<br>(0.044)</td><td>-0.126***<br>(0.047)</td><td>-0.018<br>(0.044)</td><td>0.042<br>(0.051)</td><td>-0.143***<br>(0.046)</td></tr><tr><td>Lives with child(ren) under 14</td><td>0.147***<br>(0.049)</td><td>-0.105*<br>(0.057)</td><td>0.207***<br>(0.050)</td><td>0.019<br>(0.052)</td><td>0.149***<br>(0.050)</td><td>0.363***<br>(0.047)</td><td>-0.035<br>(0.055)</td><td>0.098**<br>(0.048)</td></tr><tr><td>Age: 25 - 34</td><td>-0.006<br>(0.077)</td><td>0.381***<br>(0.097)</td><td>0.187***<br>(0.063)</td><td>0.327***<br>(0.069)</td><td>0.075<br>(0.069)</td><td>0.068<br>(0.073)</td><td>0.023<br>(0.102)</td><td>-0.066<br>(0.064)</td></tr><tr><td>Age: 35 - 49</td><td>0.287***<br>(0.073)</td><td>0.426***<br>(0.093)</td><td>0.296***<br>(0.062)</td><td>-0.00003<br>(0.072)</td><td>0.068<br>(0.066)</td><td>0.032<br>(0.068)</td><td>0.154<br>(0.096)</td><td>-0.099<br>(0.065)</td></tr><tr><td>Age: 50 or older</td><td>0.242***<br>(0.074)</td><td>0.738***<br>(0.091)</td><td>0.480***<br>(0.069)</td><td>0.721***<br>(0.063)</td><td>0.314***<br>(0.074)</td><td>0.517***<br>(0.071)</td><td>0.179*<br>(0.093)</td><td>0.060<br>(0.071)</td></tr><tr><td>Household income: Q2</td><td>0.045<br>(0.063)</td><td>0.158**<br>(0.071)</td><td>0.309***<br>(0.055)</td><td>0.169***<br>(0.065)</td><td>0.024<br>(0.062)</td><td>0.116*<br>(0.062)</td><td>0.261***<br>(0.073)</td><td>0.033<br>(0.064)</td></tr><tr><td>Household income: Q3</td><td>0.250***<br>(0.067)</td><td>0.211***<br>(0.076)</td><td>0.453***<br>(0.063)</td><td>0.591***<br>(0.071)</td><td>0.061<br>(0.066)</td><td>-0.051<br>(0.066)</td><td>0.213***<br>(0.073)</td><td>-0.057<br>(0.064)</td></tr><tr><td>Household income: Q4</td><td>0.168**<br>(0.071)</td><td>0.358***<br>(0.086)</td><td>0.636***<br>(0.064)</td><td>0.184***<br>(0.061)</td><td>0.003<br>(0.069)</td><td>0.214***<br>(0.070)</td><td>0.324***<br>(0.074)</td><td>-0.173***<br>(0.067)</td></tr><tr><td>Highest diploma: College</td><td>0.312***<br>(0.114)</td><td>0.461***<br>(0.090)</td><td>0.368***<br>(0.106)</td><td>0.648***<br>(0.101)</td><td>0.244***<br>(0.079)</td><td>0.182**<br>(0.077)</td><td>0.115<br>(0.136)</td><td>0.070<br>(0.108)</td></tr><tr><td>Highest diploma: High school</td><td>0.250**<br>(0.106)</td><td>0.495***<br>(0.068)</td><td>0.398***<br>(0.099)</td><td>0.437***<br>(0.102)</td><td>0.205***<br>(0.070)</td><td>-0.063<br>(0.067)</td><td>0.271**<br>(0.135)</td><td>0.031<br>(0.096)</td></tr><tr><td>Economic Leaning: Very Left</td><td>0.155<br>(0.102)</td><td>0.529**<br>(0.111)</td><td>0.415***<br>(0.140)</td><td>0.714***<br>(0.172)</td><td>0.121<br>(0.107)</td><td>0.344***<br>(0.086)</td><td>0.084<br>(0.122)</td><td>0.475***<br>(0.103)</td></tr><tr><td>Economic Leaning: Center</td><td>-0.224***<br>(0.076)</td><td>0.209***<br>(0.072)</td><td>0.051<br>(0.079)</td><td>0.208**<br>(0.087)</td><td>-0.136*<br>(0.073)</td><td>0.035<br>(0.065)</td><td>0.140*<br>(0.080)</td><td>-0.009<br>(0.073)</td></tr><tr><td>Economic Leaning: Right</td><td>-0.225***<br>(0.087)</td><td>0.176**<br>(0.076)</td><td>0.107<br>(0.087)</td><td>0.167*<br>(0.093)</td><td>0.187**<br>(0.085)</td><td>0.043<br>(0.081)</td><td>0.415***<br>(0.095)</td><td>0.100<br>(0.083)</td></tr><tr><td>Economic Leaning: Very Right</td><td>-0.265***<br>(0.084)</td><td>0.505***<br>(0.093)</td><td>0.553***<br>(0.085)</td><td>0.346***<br>(0.097)</td><td>-0.063<br>(0.094)</td><td>-0.143*<br>(0.085)</td><td>0.513***<br>(0.095)</td><td>0.157*<br>(0.089)</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.142**<br>(0.063)</td><td>0.239***<br>(0.068)</td><td>0.056<br>(0.051)</td><td>0.063<br>(0.056)</td><td>0.070<br>(0.060)</td><td>-0.119**<br>(0.058)</td><td>0.034<br>(0.068)</td><td>0.110*<br>(0.061)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.187***<br>(0.062)</td><td>0.105<br>(0.066)</td><td>0.048<br>(0.053)</td><td>0.049<br>(0.057)</td><td>0.030<br>(0.061)</td><td>0.140**<br>(0.061)</td><td>0.175**<br>(0.070)</td><td>0.186***<br>(0.060)</td></tr><tr><td>Treatment: Both</td><td>0.348***<br>(0.063)</td><td>0.343***<br>(0.066)</td><td>0.116**<br>(0.052)</td><td>0.074<br>(0.057)</td><td>0.166***<br>(0.062)</td><td>0.114*<br>(0.061)</td><td>0.227***<br>(0.069)</td><td>0.253***<br>(0.062)</td></tr><tr><th colspan="9">Panel B: Energy characteristics</th></tr><tr><td>Agglomeration size: Medium</td><td>0.210*<br>(0.111)</td><td>0.031<br>(0.091)</td><td>0.123*<br>(0.066)</td><td>-0.204*<br>(0.106)</td><td>0.162*<br>(0.098)</td><td>0.221<br>(0.161)</td><td>-0.096<br>(0.087)</td><td>-0.098<br>(0.081)</td></tr><tr><td>Agglomeration size: Large</td><td>0.228**<br>(0.107)</td><td>0.301***<br>(0.095)</td><td>0.067<br>(0.058)</td><td>0.001<br>(0.070)</td><td>0.110<br>(0.086)</td><td>0.427***<br>(0.157)</td><td>-0.047<br>(0.078)</td><td>-0.014<br>(0.069)</td></tr><tr><td>Public transport available</td><td>0.193***<br>(0.051)</td><td>0.183***<br>(0.055)</td><td>0.430***<br>(0.044)</td><td>0.336***<br>(0.050)</td><td>0.010<br>(0.053)</td><td>0.165***<br>(0.045)</td><td>0.109**<br>(0.055)</td><td>0.257***<br>(0.046)</td></tr><tr><td>Uses car</td><td>-0.029<br>(0.059)</td><td>0.251***<br>(0.054)</td><td>0.281***<br>(0.082)</td><td>0.433***<br>(0.051)</td><td>-0.073<br>(0.059)</td><td>-0.005<br>(0.049)</td><td>-0.031<br>(0.058)</td><td>-0.099*<br>(0.055)</td></tr><tr><td>High gas expenses</td><td>0.020<br>(0.050)</td><td>-0.119**<br>(0.054)</td><td>-0.049<br>(0.042)</td><td></td><td>-0.142***<br>(0.049)</td><td>-0.018<br>(0.048)</td><td>-0.127**<br>(0.059)</td><td>-0.029<br>(0.048)</td></tr><tr><td>High heating expenses</td><td></td><td>0.031<br>(0.055)</td><td></td><td></td><td></td><td>-0.287***<br>(0.049)</td><td>-0.012<br>(0.052)</td><td>0.135***<br>(0.047)</td></tr><tr><td>Flies more than once a year</td><td>0.093<br>(0.059)</td><td>0.002<br>(0.064)</td><td>0.203***<br>(0.044)</td><td>-0.014<br>(0.059)</td><td>0.237***<br>(0.055)</td><td>0.236***<br>(0.054)</td><td>-0.208***<br>(0.062)</td><td>0.182***<br>(0.060)</td></tr><tr><td>Polluting sector</td><td>-0.315***<br>(0.067)</td><td>0.240***<br>(0.053)</td><td>-0.239***<br>(0.050)</td><td>-0.323***<br>(0.060)</td><td>0.036<br>(0.059)</td><td>0.120**<br>(0.054)</td><td>0.052<br>(0.058)</td><td>0.016<br>(0.058)</td></tr><tr><td>Eats beef weekly or more</td><td>-0.002<br>(0.055)</td><td>-0.051<br>(0.057)</td><td>0.033<br>(0.042)</td><td>-0.062<br>(0.054)</td><td>0.040<br>(0.048)</td><td>0.120**<br>(0.047)</td><td>0.031<br>(0.066)</td><td>-0.076*<br>(0.045)</td></tr><tr><td>Owner or landlord</td><td>-0.010<br>(0.051)</td><td>0.048<br>(0.069)</td><td>0.197***<br>(0.064)</td><td>0.106<br>(0.065)</td><td>0.096*<br>(0.052)</td><td>0.074<br>(0.048)</td><td>0.071<br>(0.059)</td><td>0.062<br>(0.048)</td></tr><tr><td>Observations</td><td>1.860</td><td>1.533</td><td>1.998</td><td>1.925</td><td>1.970</td><td>1.932</td><td>1.564</td><td>2.003</td></tr></tbody></table>

Note: The table shows the results of regressions of the standardized *Support for main policies index* on socio-economic indicators (Panel A) and on energy usage indicators (Panel B). Panel B also controls for socio-economic indicators, but the coefficients are not displayed. See Appendix A-1 for variable definitions.
<table><caption>Table A9: Reasonings underlying *support for main climate policies index* in high-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="11">Support for main climate policies index</th></tr><tr><th>AUS<br>(1)</th><th>CAN<br>(2)</th><th>DEU<br>(3)</th><th>DNK<br>(4)</th><th>ESP<br>(5)</th><th>FRA<br>(6)</th><th>GBR<br>(7)</th><th>ITA<br>(8)</th><th>JPN<br>(9)</th><th>KOR<br>(10)</th><th>POL<br>(11)</th><th>USA<br>(12)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.206</td><td>-0.099</td><td>-0.095</td><td>-0.138</td><td>-0.102</td><td>-0.088</td><td>-0.107</td><td>-0.19</td><td>-0.102</td><td>-0.054</td><td>-0.048</td><td>0.03</td></tr><tr><td>Trusts the government</td><td>-0.003<br>(0.012)</td><td>0.039***<br>(0.012)</td><td>0.032**<br>(0.014)</td><td>0.028**<br>(0.014)</td><td>0.039***<br>(0.013)</td><td>0.066***<br>(0.015)</td><td>0.021<br>(0.013)</td><td>0.038**<br>(0.015)</td><td>0.020<br>(0.016)</td><td>0.067***<br>(0.017)</td><td>0.060***<br>(0.014)</td><td>0.025**<br>(0.012)</td></tr><tr><td>Believes inequality is an important problem</td><td>-0.001<br>(0.013)</td><td>0.035***<br>(0.013)</td><td>0.030**<br>(0.013)</td><td>0.090***<br>(0.015)</td><td>0.010<br>(0.013)</td><td>-0.006<br>(0.015)</td><td>0.032**<br>(0.014)</td><td>0.022<br>(0.014)</td><td>0.016<br>(0.016)</td><td>0.059***<br>(0.016)</td><td>0.024*<br>(0.013)</td><td>0.064***<br>(0.014)</td></tr><tr><td>Worries about the consequences of CC</td><td>0.071***<br>(0.017)</td><td>0.037**<br>(0.015)</td><td>0.035**<br>(0.015)</td><td>0.060***<br>(0.017)</td><td>0.009<br>(0.016)</td><td>0.026<br>(0.017)</td><td>0.070***<br>(0.017)</td><td>0.033**<br>(0.017)</td><td>0.024<br>(0.018)</td><td>0.025<br>(0.017)</td><td>0.044***<br>(0.016)</td><td>0.094***<br>(0.016)</td></tr><tr><td>Believes net-zero is technically feasible</td><td>0.052***<br>(0.014)</td><td>0.022<br>(0.014)</td><td>0.009<br>(0.014)</td><td>0.041***<br>(0.014)</td><td>0.033**<br>(0.013)</td><td>-0.006<br>(0.016)</td><td>0.059***<br>(0.015)</td><td>0.001<br>(0.015)</td><td>0.026<br>(0.016)</td><td>-0.008<br>(0.016)</td><td>-0.003<br>(0.014)</td><td>0.016<br>(0.015)</td></tr><tr><td>Believes will suffer from climate change</td><td>0.048***<br>(0.016)</td><td>0.046***<br>(0.014)</td><td>0.046***<br>(0.014)</td><td>0.060***<br>(0.015)</td><td>0.014<br>(0.014)</td><td>0.006<br>(0.016)</td><td>0.0001<br>(0.015)</td><td>0.014<br>(0.016)</td><td>0.063***<br>(0.018)</td><td>0.085***<br>(0.017)</td><td>0.062***<br>(0.016)</td><td>0.061***<br>(0.015)</td></tr><tr><td>Understands emission across activities/regions</td><td>-0.015<br>(0.012)</td><td>0.050***<br>(0.012)</td><td>0.016<br>(0.014)</td><td>0.016<br>(0.014)</td><td>0.019<br>(0.012)</td><td>0.018<br>(0.015)</td><td>0.012<br>(0.013)</td><td>0.025*<br>(0.014)</td><td>0.026*<br>(0.015)</td><td>-0.009<br>(0.016)</td><td>0.015<br>(0.013)</td><td>0.002<br>(0.011)</td></tr><tr><td>Knows CC is real &amp; caused by human</td><td>0.081***<br>(0.013)</td><td>0.087***<br>(0.013)</td><td>0.066***<br>(0.014)</td><td>0.040***<br>(0.015)</td><td>0.094***<br>(0.014)</td><td>0.094***<br>(0.016)</td><td>0.090***<br>(0.014)</td><td>0.077***<br>(0.015)</td><td>0.016<br>(0.016)</td><td>0.042***<br>(0.016)</td><td>0.067***<br>(0.014)</td><td>0.059***<br>(0.012)</td></tr><tr><td>Knows which gases cause CC</td><td>-0.003<br>(0.012)</td><td>0.012<br>(0.011)</td><td>0.013<br>(0.013)</td><td>0.012<br>(0.014)</td><td>0.012<br>(0.012)</td><td>0.020<br>(0.015)</td><td>0.010<br>(0.012)</td><td>0.025*<br>(0.014)</td><td>-0.006<br>(0.015)</td><td>0.007<br>(0.016)</td><td>0.010<br>(0.013)</td><td>-0.012<br>(0.011)</td></tr><tr><td>Understands impacts of CC</td><td>0.018<br>(0.013)</td><td>-0.003<br>(0.013)</td><td>-0.036**<br>(0.014)</td><td>-0.006<br>(0.015)</td><td>0.018<br>(0.013)</td><td>0.028*<br>(0.016)</td><td>-0.002<br>(0.014)</td><td>-0.012<br>(0.015)</td><td>0.020<br>(0.016)</td><td>-0.054***<br>(0.016)</td><td>-0.027*<br>(0.014)</td><td>-0.022*<br>(0.012)</td></tr><tr><td>Believes policies would have positive economic effects</td><td>0.141***<br>(0.015)</td><td>0.131***<br>(0.014)</td><td>0.107***<br>(0.016)</td><td>0.087***<br>(0.015)</td><td>0.108***<br>(0.015)</td><td>0.054***<br>(0.015)</td><td>0.158***<br>(0.015)</td><td>0.119***<br>(0.017)</td><td>0.068***<br>(0.016)</td><td>0.077***<br>(0.016)</td><td>0.102***<br>(0.015)</td><td>0.070***<br>(0.013)</td></tr><tr><td>Believes policies would reduce pollution</td><td>0.147***<br>(0.021)</td><td>0.121***<br>(0.022)</td><td>0.056**<br>(0.024)</td><td>0.147***<br>(0.024)</td><td>0.115***<br>(0.023)</td><td>0.125***<br>(0.026)</td><td>0.114***<br>(0.023)</td><td>0.198***<br>(0.025)</td><td>-0.014<br>(0.026)</td><td>0.155***<br>(0.026)</td><td>0.074***<br>(0.024)</td><td>0.049**<br>(0.022)</td></tr><tr><td>Believes policies would reduce emissions</td><td>0.144***<br>(0.022)</td><td>0.196***<br>(0.023)</td><td>0.261***<br>(0.026)</td><td>0.241***<br>(0.025)</td><td>0.266***<br>(0.025)</td><td>0.345***<br>(0.028)</td><td>0.235***<br>(0.025)</td><td>0.333***<br>(0.027)</td><td>0.485***<br>(0.027)</td><td>0.346***<br>(0.027)</td><td>0.311***<br>(0.025)</td><td>0.187***<br>(0.024)</td></tr><tr><td>Believes will personally lose</td><td>-0.329***<br>(0.019)</td><td>-0.388***<br>(0.018)</td><td>-0.373***<br>(0.020)</td><td>-0.294***<br>(0.018)</td><td>-0.338***<br>(0.019)</td><td>-0.248***<br>(0.021)</td><td>-0.346***<br>(0.019)</td><td>-0.210***<br>(0.021)</td><td>-0.300***<br>(0.021)</td><td>-0.275***<br>(0.022)</td><td>-0.374***<br>(0.020)</td><td>-0.342***<br>(0.018)</td></tr><tr><td>Believes poor people will lose</td><td>-0.085***<br>(0.018)</td><td>-0.061***<br>(0.018)</td><td>-0.119***<br>(0.019)</td><td>-0.102***<br>(0.018)</td><td>-0.080***<br>(0.018)</td><td>-0.120***<br>(0.020)</td><td>-0.041**<br>(0.019)</td><td>-0.015<br>(0.022)</td><td>-0.089***<br>(0.021)</td><td>-0.030<br>(0.022)</td><td>-0.070***<br>(0.019)</td><td>-0.147***<br>(0.017)</td></tr><tr><td>Believes rich people will lose</td><td>-0.035***<br>(0.012)</td><td>0.020<br>(0.012)</td><td>0.012<br>(0.013)</td><td>-0.029**<br>(0.015)</td><td>0.028**<br>(0.012)</td><td>0.038**<br>(0.015)</td><td>0.016<br>(0.013)</td><td>0.013<br>(0.014)</td><td>0.031**<br>(0.015)</td><td>0.030*<br>(0.016)</td><td>0.016<br>(0.013)</td><td>-0.017<br>(0.012)</td></tr><tr><td>Observations</td><td>1,978</td><td>2,022</td><td>2,006</td><td>2,013</td><td>2,268</td><td>2,006</td><td>2,008</td><td>2,088</td><td>1,990</td><td>1,874</td><td>2,053</td><td>2,218</td></tr></tbody></table>

Note: The table shows the results of regressions of the standardized *Support for main policies index* on reasoning about policies, controlling for treatments and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<table><caption>Table A10: Reasonings underlying *support for main climate policies index* in middle-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Support for main climate policies index</th></tr><tr><th>BRA<br>(1)</th><th>CHN<br>(2)</th><th>IDN<br>(3)</th><th>IND<br>(4)</th><th>MEX<br>(5)</th><th>TUR<br>(6)</th><th>UKR<br>(7)</th><th>ZAF<br>(8)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.161</td><td>-0.161</td><td>-0.066</td><td>-0.066</td><td>-0.057</td><td>-0.04</td><td>-0.121</td><td>-0.113</td></tr><tr><td>Trusts the government</td><td>-0.012<br>(0.015)</td><td>0.083***<br>(0.019)</td><td>0.146***<br>(0.018)</td><td>-0.006<br>(0.017)</td><td>0.048***<br>(0.016)</td><td>0.039**<br>(0.016)</td><td>0.083***<br>(0.017)</td><td>0.062***<br>(0.017)</td></tr><tr><td>Believes inequality is an important problem</td><td>0.064***<br>(0.017)</td><td>0.073***<br>(0.018)</td><td>0.059***<br>(0.015)</td><td>0.082***<br>(0.019)</td><td>0.051***<br>(0.015)</td><td>0.007<br>(0.015)</td><td>0.037**<br>(0.017)</td><td>0.026<br>(0.016)</td></tr><tr><td>Worries about the consequences of CC</td><td>0.044**<br>(0.017)</td><td>0.075***<br>(0.020)</td><td>0.005<br>(0.017)</td><td>-0.031<br>(0.019)</td><td>0.042**<br>(0.017)</td><td>0.056***<br>(0.016)</td><td>0.022<br>(0.018)</td><td>0.058***<br>(0.018)</td></tr><tr><td>Believes net-zero is technically feasible</td><td>0.017<br>(0.015)</td><td>-0.004<br>(0.020)</td><td>0.035*<br>(0.019)</td><td>0.041*<br>(0.021)</td><td>0.015<br>(0.016)</td><td>0.048***<br>(0.016)</td><td>0.036**<br>(0.018)</td><td>0.017<br>(0.017)</td></tr><tr><td>Believes will suffer from climate change</td><td>0.050***<br>(0.017)</td><td>0.031<br>(0.019)</td><td>0.053***<br>(0.016)</td><td>0.043**<br>(0.019)</td><td>0.087***<br>(0.017)</td><td>0.079***<br>(0.017)</td><td>0.066***<br>(0.018)</td><td>0.015<br>(0.018)</td></tr><tr><td>Understands emission across activities/regions</td><td>0.044***<br>(0.015)</td><td>0.023<br>(0.017)</td><td>0.014<br>(0.012)</td><td>0.053***<br>(0.015)</td><td>0.016<br>(0.014)</td><td>-0.014<br>(0.014)</td><td>-0.012<br>(0.016)</td><td>-0.011<br>(0.015)</td></tr><tr><td>Knows CC is real &amp; caused by human</td><td>0.026*<br>(0.015)</td><td>-0.021<br>(0.018)</td><td>0.021*<br>(0.013)</td><td>0.088***<br>(0.015)</td><td>0.060***<br>(0.016)</td><td>0.067***<br>(0.015)</td><td>0.060***<br>(0.017)</td><td>0.053***<br>(0.016)</td></tr><tr><td>Knows which gases cause CC</td><td>0.018<br>(0.015)</td><td>-0.025<br>(0.018)</td><td>-0.001<br>(0.012)</td><td>0.068***<br>(0.015)</td><td>0.049***<br>(0.015)</td><td>0.043***<br>(0.014)</td><td>-0.013<br>(0.016)</td><td>0.050***<br>(0.016)</td></tr><tr><td>Understands impacts of CC</td><td>0.024<br>(0.016)</td><td>0.040**<br>(0.018)</td><td>0.016<br>(0.013)</td><td>0.031**<br>(0.015)</td><td>0.004<br>(0.016)</td><td>0.014<br>(0.015)</td><td>0.030*<br>(0.017)</td><td>0.021<br>(0.016)</td></tr><tr><td>Believes policies would have positive economic effects</td><td>0.052***<br>(0.016)</td><td>0.001<br>(0.017)</td><td>0.014<br>(0.012)</td><td>-0.004<br>(0.015)</td><td>0.068***<br>(0.017)</td><td>0.010<br>(0.014)</td><td>0.116***<br>(0.018)</td><td>0.079***<br>(0.017)</td></tr><tr><td>Believes policies would reduce pollution</td><td>0.161***<br>(0.023)</td><td>-0.018<br>(0.024)</td><td>0.063***<br>(0.019)</td><td>0.147***<br>(0.025)</td><td>0.095***<br>(0.025)</td><td>0.234***<br>(0.025)</td><td>0.156***<br>(0.029)</td><td>0.122***<br>(0.023)</td></tr><tr><td>Believes policies would reduce emissions</td><td>0.293***<br>(0.025)</td><td>0.308***<br>(0.026)</td><td>0.335***<br>(0.025)</td><td>0.247***<br>(0.030)</td><td>0.267***<br>(0.026)</td><td>0.236***<br>(0.027)</td><td>0.241***<br>(0.030)</td><td>0.279***<br>(0.024)</td></tr><tr><td>Believes will personally lose</td><td>-0.307***<br>(0.022)</td><td>-0.348***<br>(0.027)</td><td>-0.315***<br>(0.025)</td><td>-0.448***<br>(0.026)</td><td>-0.370***<br>(0.022)</td><td>-0.269***<br>(0.021)</td><td>-0.350***<br>(0.024)</td><td>-0.366***<br>(0.022)</td></tr><tr><td>Believes poor people will lose</td><td>-0.035*<br>(0.020)</td><td>-0.116***<br>(0.023)</td><td>-0.053**<br>(0.024)</td><td>0.121***<br>(0.025)</td><td>-0.043**<br>(0.020)</td><td>-0.124***<br>(0.020)</td><td>-0.016<br>(0.024)</td><td>-0.015<br>(0.022)</td></tr><tr><td>Believes rich people will lose</td><td>-0.002<br>(0.015)</td><td>0.007<br>(0.020)</td><td>0.029**<br>(0.015)</td><td>0.033*<br>(0.019)</td><td>0.040***<br>(0.015)</td><td>0.035**<br>(0.014)</td><td>0.043***<br>(0.016)</td><td>-0.025<br>(0.017)</td></tr><tr><td>Observations</td><td>1,860</td><td>1,533</td><td>1,998</td><td>1,925</td><td>1,970</td><td>1,932</td><td>1,564</td><td>2,003</td></tr></tbody></table>

*Note:* The table shows the results of regressions of the standardized *Support for main policies index* on reasoning about policies, controlling for treatments and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.

<table><caption>Table A11: Treatment effects on the *Fairness of main climate policies index* in high-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Fairness of main policies index</th></tr><tr><th>AUS<br>(1)</th><th>CAN<br>(2)</th><th>DEU<br>(3)</th><th>DNK<br>(4)</th><th>ESP<br>(5)</th><th>FRA<br>(6)</th><th>GBR<br>(7)</th><th>ITA<br>(8)</th><th>JPN<br>(9)</th><th>KOR<br>(10)</th><th>POL<br>(11)</th><th>USA<br>(12)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.186</td><td>-0.091</td><td>-0.093</td><td>-0.154</td><td>-0.065</td><td>-0.05</td><td>-0.099</td><td>-0.185</td><td>-0.121</td><td>-0.081</td><td>-0.038</td><td>0.026</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.202***<br>(0.060)</td><td>-0.014<br>(0.060)</td><td>0.050<br>(0.060)</td><td>0.177***<br>(0.058)</td><td>-0.019<br>(0.057)</td><td>-0.003<br>(0.061)</td><td>0.035<br>(0.061)</td><td>0.103*<br>(0.060)</td><td>0.078<br>(0.062)</td><td>0.020<br>(0.063)</td><td>0.040<br>(0.060)</td><td>-0.084<br>(0.054)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.246***<br>(0.059)</td><td>0.221***<br>(0.060)</td><td>0.147**<br>(0.061)</td><td>0.163***<br>(0.059)</td><td>0.062<br>(0.057)</td><td>0.030<br>(0.059)</td><td>0.113*<br>(0.060)</td><td>0.300***<br>(0.060)</td><td>0.172***<br>(0.062)</td><td>0.113*<br>(0.062)</td><td>0.073<br>(0.061)</td><td>-0.019<br>(0.054)</td></tr><tr><td>Treatment: Both</td><td>0.283***<br>(0.060)</td><td>0.196***<br>(0.059)</td><td>0.195***<br>(0.062)</td><td>0.281***<br>(0.060)</td><td>0.219***<br>(0.055)</td><td>0.131**<br>(0.060)</td><td>0.304***<br>(0.059)</td><td>0.369***<br>(0.059)</td><td>0.219***<br>(0.061)</td><td>0.223***<br>(0.063)</td><td>0.095<br>(0.061)</td><td>0.048<br>(0.055)</td></tr><tr><td>Observations</td><td>1,978</td><td>2,022</td><td>2,006</td><td>2,013</td><td>2,268</td><td>2,006</td><td>2,008</td><td>2,088</td><td>1,990</td><td>1,874</td><td>2,053</td><td>2,218</td></tr></tbody></table>

*Note:* The table shows the results of regressions of the standardized *Fairness of main policies index* on indicators for each treatment, controlling for socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
<table><caption>Table A12: Treatment effects on the *Fairness of main climate policies index* in middle-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Fairness of main policies index</th></tr><tr><th>BRA<br>(1)</th><th>CHN<br>(2)</th><th>IDN<br>(3)</th><th>IND<br>(4)</th><th>MEX<br>(5)</th><th>TUR<br>(6)</th><th>UKR<br>(7)</th><th>ZAF<br>(8)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.136</td><td>-0.152</td><td>-0.097</td><td>-0.014</td><td>-0.075</td><td>-0.081</td><td>-0.155</td><td>-0.112</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.100<br>(0.063)</td><td>0.185***<br>(0.067)</td><td>0.062<br>(0.052)</td><td>-0.067<br>(0.058)</td><td>0.104*<br>(0.060)</td><td>-0.068<br>(0.058)</td><td>0.056<br>(0.068)</td><td>0.037<br>(0.061)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.199***<br>(0.062)</td><td>0.128*<br>(0.065)</td><td>0.116**<br>(0.054)</td><td>0.055<br>(0.058)</td><td>0.081<br>(0.061)</td><td>0.257***<br>(0.061)</td><td>0.237***<br>(0.070)</td><td>0.230***<br>(0.060)</td></tr><tr><td>Treatment: Both</td><td>0.261***<br>(0.063)</td><td>0.299***<br>(0.065)</td><td>0.209***<br>(0.052)</td><td>0.004<br>(0.058)</td><td>0.153**<br>(0.062)</td><td>0.137**<br>(0.061)</td><td>0.279***<br>(0.069)</td><td>0.262***<br>(0.062)</td></tr><tr><td>Observations</td><td>1,860</td><td>1,533</td><td>1,998</td><td>1,925</td><td>1,970</td><td>1,932</td><td>1,564</td><td>2,003</td></tr></tbody></table>

*Note:* The table shows the results of regressions of the standardized *Fairness of main policies index* on indicators for each treatment, controlling for socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.

<table><caption>Table A13: Treatment effects on the *Willingness to adopt climate friendly behavior index* in high-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Willingness to adopt climate friendly behavior index</th></tr><tr><th>AUS<br>(1)</th><th>CAN<br>(2)</th><th>DEU<br>(3)</th><th>DNK<br>(4)</th><th>ESP<br>(5)</th><th>FRA<br>(6)</th><th>GBR<br>(7)</th><th>ITA<br>(8)</th><th>JPN<br>(9)</th><th>KOR<br>(10)</th><th>POL<br>(11)</th><th>USA<br>(12)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.112</td><td>-0.008</td><td>-0.037</td><td>0.041</td><td>-0.048</td><td>-0.045</td><td>-0.068</td><td>-0.024</td><td>-0.081</td><td>0.021</td><td>-0.061</td><td>0.019</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.157**<br>(0.061)</td><td>-0.026<br>(0.061)</td><td>0.068<br>(0.060)</td><td>0.005<br>(0.058)</td><td>0.054<br>(0.057)</td><td>0.082<br>(0.061)</td><td>0.044<br>(0.061)</td><td>-0.00001<br>(0.061)</td><td>0.155**<br>(0.062)</td><td>0.036<br>(0.063)</td><td>0.121**<br>(0.060)</td><td>-0.055<br>(0.056)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.197***<br>(0.060)</td><td>0.040<br>(0.062)</td><td>0.016<br>(0.061)</td><td>-0.140**<br>(0.058)</td><td>-0.004<br>(0.057)</td><td>-0.031<br>(0.059)</td><td>0.065<br>(0.060)</td><td>-0.012<br>(0.061)</td><td>0.045<br>(0.062)</td><td>-0.092<br>(0.062)</td><td>0.097<br>(0.061)</td><td>-0.002<br>(0.055)</td></tr><tr><td>Treatment: Both</td><td>0.096<br>(0.061)</td><td>0.048<br>(0.060)</td><td>0.082<br>(0.062)</td><td>-0.073<br>(0.059)</td><td>0.129**<br>(0.056)</td><td>0.121**<br>(0.060)</td><td>0.179***<br>(0.059)</td><td>0.092<br>(0.061)</td><td>0.153**<br>(0.061)</td><td>0.020<br>(0.060)</td><td>0.113*<br>(0.060)</td><td>0.014<br>(0.056)</td></tr><tr><td>Observations</td><td>1,978</td><td>2,022</td><td>2,006</td><td>2,013</td><td>2,268</td><td>2,006</td><td>2,008</td><td>2,088</td><td>1,990</td><td>1,874</td><td>2,053</td><td>2,218</td></tr></tbody></table>

*Note:* The table shows the results of regressions of the standardized *Willingness to adopt climate friendly behavior index* on indicators for each treatment, controlling for socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.

<table><caption>Table A14: Treatment effects on the *Willingness to adopt climate friendly behavior index* in middle-income countries.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Willingness to adopt climate friendly behavior index</th></tr><tr><th>BRA<br>(1)</th><th>CHN<br>(2)</th><th>IDN<br>(3)</th><th>IND<br>(4)</th><th>MEX<br>(5)</th><th>TUR<br>(6)</th><th>UKR<br>(7)</th><th>ZAF<br>(8)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.055</td><td>-0.007</td><td>-0.068</td><td>-0.04</td><td>-0.078</td><td>-0.073</td><td>-0.069</td><td>-0.09</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.077<br>(0.063)</td><td>0.067<br>(0.069)</td><td>0.105**<br>(0.049)</td><td>-0.002<br>(0.055)</td><td>0.168***<br>(0.061)</td><td>-0.022<br>(0.058)</td><td>0.064<br>(0.069)</td><td>0.171***<br>(0.061)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.045<br>(0.062)</td><td>-0.033<br>(0.067)</td><td>0.026<br>(0.051)</td><td>0.036<br>(0.056)</td><td>0.091<br>(0.062)</td><td>0.156***<br>(0.060)</td><td>0.046<br>(0.072)</td><td>0.091<br>(0.061)</td></tr><tr><td>Treatment: Both</td><td>0.086<br>(0.063)</td><td>0.012<br>(0.067)</td><td>0.117**<br>(0.049)</td><td>0.042<br>(0.056)</td><td>0.126**<br>(0.062)</td><td>0.146**<br>(0.060)</td><td>0.129*<br>(0.071)</td><td>0.151**<br>(0.063)</td></tr><tr><td>Observations</td><td>1,860</td><td>1,533</td><td>1,998</td><td>1,925</td><td>1,970</td><td>1,932</td><td>1,564</td><td>2,003</td></tr></tbody></table>

*Note:* The table shows the results of regressions of the standardized *Willingness to adopt climate friendly behavior index* on indicators for each treatment, controlling for socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.
# A-5 Questionnaire

## Survey links

Here are links to the questionnaires of each country:

* **Australia**: https://lse.eu.qualtrics.com/jfe/form/SV_0HrxQpnzN85dR2K?Q_Language=EN-GB

* **Brazil**: https://lse.eu.qualtrics.com/jfe/form/SV_bjhZJbHP1U820tE?Q_Language=PT-BR

* **Canada (English)**: https://lse.eu.qualtrics.com/jfe/form/SV_9FveryHcJFsYfoq?Q_Language=EN

* **Canada (French)**: https://lse.eu.qualtrics.com/jfe/form/SV_9FveryHcJFsYfoq?Q_Language=FR-CA

* **China**: https://lse.eu.qualtrics.com/jfe/form/SV_3ad13wqkW9bBvfw?Q_Language=ZN

* **Denmark**: https://lse.eu.qualtrics.com/jfe/form/SV_1MiPDLoaL1xf9X0?Q_Language=DA

* **France**: https://lse.eu.qualtrics.com/jfe/form/SV_8CfmrUXhHRZJT14?Q_Language=FR

* **Germany**: https://lse.eu.qualtrics.com/jfe/form/SV_OcWAJE2W8bdBPkG?Q_Language=DE

* **India (English)**: https://lse.eu.qualtrics.com/jfe/form/SV_07HaTFCaGAklSrI?Q_Language=EN

* **India (Hindi)**: https://lse.eu.qualtrics.com/jfe/form/SV_07HaTFCaGAklSrI?Q_Language=HI

* **Indonesia**: https://lse.eu.qualtrics.com/jfe/form/SV_3mV8QUArjqZOhtc?Q_Language=ID

* **Italy**: https://lse.eu.qualtrics.com/jfe/form/SV_bpiASf7NzB8u0wS?Q_Language=IT

* **Japan**: https://lse.eu.qualtrics.com/jfe/form/SV_6FE480tnfRWabRQ?Q_Language=JA

* **Mexico**: https://lse.eu.qualtrics.com/jfe/form/SV_8csgJ7Uuymp7irY?Q_Language=ES
* Poland: https://lse.eu.qualtrics.com/jfe/form/SV_7Qc5KCPcIVv5qFE?Q_Language=PL

* South Africa (English): https://lse.eu.qualtrics.com/jfe/form/SV_bvC37FRXIyGewKi?Q_Language=EN-US

* South Africa (Zulu): https://lse.eu.qualtrics.com/jfe/form/SV_bvC37FRXIyGewKi?Q_Language=ZU

* South Korea: https://lse.eu.qualtrics.com/jfe/form/SV_bwNjSPYjPojkuk6?Q_Language=KO

* Spain: https://lse.eu.qualtrics.com/jfe/form/SV_Od0TZD6KT4L2SOi?Q_Language=ES-ES

* Turkey: https://lse.eu.qualtrics.com/jfe/form/SV_3krmyMYslsDFBI2?Q_Language=TR

* Ukraine (Ukrainian): https://lse.eu.qualtrics.com/jfe/form/SV_3gdsY6iHVO6IKNg?Q_Language=UK

* Ukraine (Russian): https://lse.eu.qualtrics.com/jfe/form/SV_3gdsY6iHVO6IKNg?Q_Language=RU

* United Kingdom: https://lse.eu.qualtrics.com/jfe/form/SV_40Dm4ZTOR8mlzaS?Q_Language=EN-GB

* United States: https://lse.eu.qualtrics.com/jfe/form/SV_1ST7y8mz1Eib9iu

Below is the benchmark questionnaire, with country-specific variations indicated in square brackets.

## Consent

1. This is a survey conducted for academic research purposes by researchers from Harvard University and the OECD. It will take approximately 25 minutes to complete. The survey data is used for research purposes only, and the research is non-partisan. You will be compensated for this survey if you complete the survey and your responses pass our survey quality checks. These checks use statistical control methods to detect incoherent and rushed responses. It is very important for the validity of our research that you answer honestly and read the questions carefully before answering.

The survey collects personal data, including socio-demographic characteristics and political views. All of the answers you provide will remain anonymous and be treated with absolute confidentiality. The personal data we collect will be transferred and stored on secure servers. Only researchers working on the project will have access to the anonymized data. Your participation in this survey is completely
voluntary. You are entitled to choose not to take part. If at first you agree to take part, you can later
change your mind. Your decision will not be held against you in any way. Your refusal to participate
will not result in any consequences or any loss of benefits that you are otherwise entitled to receive.
You can ask any questions before you decide whether to participate.

If you have questions, concerns, or complaints, or think the research has offended you, you can
contact the research team at social.economics.research2020@gmail.com or call the Harvard Univer-
sity Area Institutional Review Board ("IRB") at +1 (617) 496-2847. The OECD is committed to
protecting the personal data it processes, in accordance with its Personal Data Protection Rules
(https://www.oecd.org/general/data-protection.htm). If you have further queries or complaints re-
lated to the processing of your personal data, please contact the Data Protection Officer (DPO@oecd.org).
If you need further assistance in resolving claims related to personal data protection you can contact
the Data Protection Commissioner (DPC@oecd.org).

**Do you agree to participate in the survey?**

Yes; No

**Background questions**

2. What is your gender?

Male; Female; Other

3. How old are you?

Below 18; 18 to 24; 25 to 24; 35 to 49; 50 to 64; 65 and above

4. What is your zipcode?

5. What type of agglomeration do you live in?

A rural area; A small town (5,000 - 20,000 inhabitants); A large town (20,000 - 50,000 inhabitants); A small city or its suburbs (50,000 - 250,000 inhabitants); A large city or its suburbs (250,000 - 3,000,000 inhabitants); A very large city or its suburbs (more than 3 million inhabitants)

6. What is the nationality of your parents? (Multiple answers allowed) [For the U.S. and South Africa, we asked the ethnicity instead; and for India, the religion.]

[Country]; [Continent except Country]; Other; Prefer not to say

7. Do you live with your partner (if you have one)?

Yes; No or I don't have a partner

8. What is your marital status?

Single; Married; Divorced or legally separated; Widowed

9. How many people are in your household? The household includes: you, the members of your family who live with you (including children), and your dependents. This excludes flatmates.

1; 2; 3; 4; 5 or more
10. How many children below 14 live with you?
0; 1; 2; 3; 4 or more

11. What is the highest level of education you have completed?
No schooling completed; Primary school; Lower secondary school; Vocational degree;
High school; College degree; Master's degree or above

12. What is your employment status?
Full-time employed; Part-time employed; Self-employed; Student; Retired; Unemployed
(searching for a job); Inactive (not searching for a job)

13. (If “Full-time employed”, “Part-time employed”, or “Self-employed” to 12) If you work in any of the following industries, please select one describing your industry best.
Oil, gas or coal; Other energy industries; Cement production; Construction; Automo-
bile manufacturing; Iron and steel manufacturing; Chemical manufacturing; Plastics
production; Pulp and paper production; Farming (crop or livestock); Air transport (e.g.
airlines); No, none of the above

14. (If “Retired”, “Unemployed (searching for a job)”, “Inactive (not searching for a job)” to 12) If in your last job you worked in any of the following industries, please select one describing your industry best

Oil, gas or coal; Other energy industries; Cement production; Construction; Automo-
bile manufacturing; Iron and steel manufacturing; Chemical manufacturing; Plastics
production; Pulp and paper production; Farming (crop or livestock); Air transport (e.g.
airlines); No, none of the above

15. (If “Full-time employed”, “Part-time employed”, or “Self-employed” to 12) What is the main activity of the company or organization where you work?

Agriculture, forestry, fishing, hunting; Mining, quarrying, oil, gas, extraction; Utili-
ties; Construction; Manufacturing; Wholesale trade; Retail trade; Transportation and
warehousing; Information technology (IT); Finance and insurance; Real estate and
rental and leasing; Professional, scientific and technical; Management of companies
and enterprises; Administrative and support activities; Waste management and reme-
diation; Educational services; Healthcare and social assistance; Arts, entertainment
and recreation; Accommodation and food services; Other services; Public administra-
tion; Homemaker; None of the above / Other

16. (If “Retired”, “Unemployed (searching for a job)”, “Inactive (not searching for a job)” to 12) What was the main activity of the company or organization at which you last worked?

Agriculture, forestry, fishing, hunting; Mining, quarrying, oil, gas, extraction; Utili-
ties; Construction; Manufacturing; Wholesale trade; Retail trade; Transportation and
warehousing; Information technology (IT); Finance and insurance; Real estate and
rental and leasing; Professional, scientific and technical; Management of companies
and enterprises; Administrative and support activities; Waste management and reme-
diation; Educational services; Healthcare and social assistance; Arts, entertainment
and recreation; Accommodation and food services; Other services; Public administra-
tion; Homemaker; None of the above / Other

17. What was the annual income of your household in 2019 (before withholding tax)?
[Depending on the country, we ask this question in monthly or yearly terms. Except
in the U.S., we adjust the quartile thresholds by multiplying them by the number of
consumption units in the households.]
[quartiles thresholds are given for the U.S. ] Less than [$35,000] ; between [$35,000] -
[$70,000]; between [$70,000] - [$120,000]; More than [$120,000]

18. Have you or a member of your household been laid off or had to take a cut in your salary or wages due to the COVID-19 pandemic?
Yes; No

19. Are you a homeowner or a tenant? (Multiple answers are possible)
Tenant; Owner; Landlord renting out property

20. What is the estimated value of your assets, or the assets of your household if you are married (in [currency])? Include here all your possessions (home, car, savings, etc.) net of debt. For example, if you own a house worth [$300,000] and you have [$100,000] left to repay on your mortgage, your assets are [$200,000]. I estimate my assets net of debt to be:

[Quintiles thresholds are given for the U.S. ] Less than [$0]; Between [$0] - [$4,000]; Between [$4,000] - [$120,000]; Between [$120,000] - [$380,000]; More than [$380,000]

Political views

21. To what extent are you interested in politics?
Not at all; A little; Moderately; A lot; A great deal

22. Are you a member of an environmental organization?
Yes; No

23. Do you have any relatives who are environmentalists?
Yes; No

24. (In China, the next three questions were not asked, and the other questions from this block were asked at the end of the survey.) Did you vote in the [last] election?
Yes; No: I don't have the right to vote in [Country]; Prefer not to say

25. (If “Yes” to 24) Which candidate did you vote for in the [last] election?
[Main candidates or parties]; Other; Prefer not to say
26. (If not "Yes" to 24) Even if you did NOT vote in the [last] election, please indicate the candidate that you were most likely to have voted for or who represents your views more closely.
[Main candidates or parties]; Other; Prefer not to say

27. On economic policy matters, where do you see yourself on a scale from 1 to 5, where 1 is Left and 5 is Right? [in the U.S., Denmark and France, the formulation was different: "On economic policy matters, where do you see yourself on the liberal/conservative spectrum?" and the answers were Very liberal; Liberal; Moderate; Conservative; Very conservative; Prefer not to say]
1; 2; 3; 4; 5

28. [In the U.S. only] What do you consider to be your political affiliation, as of today?
*Republican*; *Democrat*; *Independent*; *Other*; *Non-Affiliated*

Household composition and energy characteristics

(In Brazil, Mexico, India, and Indonesia, the next two questions on heating were not asked.)

29. What is the main way you heat your home? Electricity; Gas; Heating oil; Coal; Wood, solar, geothermal, or heat pump; District heating; Don't know, or prefer not to say

30. In a typical month [or year, depending on countries], how much do you spend on heating for your accommodation?
[Numbers are given for the U.S. ] I don't know; Less than [$20]; [$20]-[$75]; [$75]-[$125]; [$125]-[$200]; [$200]-[$250]; [$250]-[$300]; More than [$300]

31. Good insulation can keep a building warm in the winter and cool in the summer. How do you rate the insulation of your accommodation?
*Very poor*; *Poor*; *Fair*; *Good*; *Excellent*

32. In a typical month, how much do you spend on gas for driving?
[Numbers are given for the U.S. ] Less than [$5]; [$5]-[$25]; [$25]-[$75]; [$75]-[$125]; [$125]-[$175]; [$175]-[$225]; More than [$225]

33. How many round-trip flights did you take between 2017 and 2019?
0; 1; 2; 3 or 4; 5 to 7; 8 to 14; 15 or more

34. How often do you eat [beef / India: meat]?
*Never*; *Less than once a week*; *One to four times per week*; *Almost or at least daily*

35. Which mode of transport did you mainly use for each of the following trips in 2019?

* Commute to work or place of study

* Grocery shopping
* Recreational and leisure activities (excluding holiday travel)

Car or Motorbike; Public Transport; Walking or Cycling; Other; Not Applicable

36. How do you rate the availability (ease of access and frequency) of public transportation where you live?

Very poor; Poor; Fair; Good; Excellent

## Open-ended question

37. When thinking about climate change, what are your main considerations? What should [country] government do regarding climate change? Please write as much as you would like, your response will be very useful.

## Video Treatments

Randomized groups of respondents see one of two videos, both videos, or neither.

### Climate Impacts video

Recent academic studies have assessed the effects of climate change in [country]. We will now show you a 3 minute video (with sound) that summarizes the results of these studies. Please pay attention to the information provided as you will be asked questions about it later. Do not skip forward or close the page while the video is running. Please proceed to the next page when you are ready.

[Here are the links to the video of each country:]

* Australia: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_6zC4wlmsEXrDnYq

* Brazil: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_571ND31Sz5SL4oK

* Canada (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9zxyasw9TTVFqx8

* Canada (French): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1QSWUKIYiJDNxfE

* China: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9vHesDcevMYMffU

* Denmark: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_dgnXQoN84vq2YXs
* France: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9YacIn03B7TVcGy

* Germany: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_3NNS6u7MbEm738y

* India (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_b91U7goEX1i0FvM

* India (Hindi): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bvLcTKdd7WG8SZ8

* Indonesia: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9QQCwEicwdwYp94

* Italy: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1GpaU9A0p0uA246

* Japan: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_e3BFKqjnqsSOwaW

* Mexico: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_cSdiidvle1QaekS

* Poland: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_6SahJCEqAUd5bdc

* South Africa (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_8iAWsyQlvy07iJg

* South Africa (Zulu): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_4NHM2UHj6XttP70

* South Korea: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_2071FHigxMNs2rk

* Spain: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_4NsVOyDmpposo3I

* Turkey: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_8AKIwJiwMxyQnyu

* Ukraine (Ukrainian): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1Bz6VaDS6IzAMGq

* Ukraine (Russian): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bemd3trrg7wgFym
* United Kingdom: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bj8yT5eiDpZCR82

* United States: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_cT8837yWYLScqLs

[Below is the script used for the U.S.]

Over the past decades, humans have been burning more and more fossil fuels like coal, gas or oil. Burning fossil fuels releases CO₂ into the atmosphere. Today, the concentration of CO₂ in the atmosphere is higher than at any point in time over the last 800,000 years. And it's the concentration of greenhouse gases like CO₂ that drives global temperature. Climate scientists agree: the build-up of greenhouse gases released by human activity in the atmosphere causes climate change. A rapid transition away from fossil fuels is possible and could contain global warming below +[2°C / 3.6°F], meaning 3.6°F. But if greenhouse gas emissions continue on their current trend, the average global warming will be +[4°C / 8°F] in 2100 and +[7°C / 13°F] in 2200. This may seem far away, but climate change is already affecting us right now in the places where we live.

* Because of climate change, in the U.S. hurricanes have become increasingly intense and cause much more harm and damages. Hurricane Katrina caused more than 1,800 deaths and more than 100 billion dollars in damages.

* The amount of air pollution generated by burning fossil fuels is already responsible for 200,000 deaths in the US each year.

* Heatwaves are becoming longer, more frequent and more severe. In the absence of ambitious action against climate change, the US will experience 70 days of extreme heat per year (that is six times more than in the past) and up to 135 days a year in a State like Texas.

* In the South and in the Midwest, agricultural yields will decrease because of the heat.

* With the mix of more hurricanes, rising sea levels, more heatwaves, and lower agricultural output, the average income in Southern states will be 10 to 20% lower than it could be.

* In the North-East, the risk of heavy rain has already increased by 55%. More severe storms and rising sea levels will lead to more flooding.

* In the West, hotter and drier conditions are causing more wildfires. Since the mid 80s, the area burned by wildfires across the Western US is estimated to have been twice what it would have been without climate change. This was even before accounting for the California wildfires last summer, which were by far the largest on record.

To tackle climate change, we need to bring greenhouse gas emissions close to zero. This is possible, but it requires a deep transformation in the sectors most responsible for emissions: energy, transport, and industry.
38. Were you able to watch and listen to the video until the end?
*Yes; No, there was a technical problem; No, I skipped part of the video*

39. From what was said in the video, if greenhouse gas emissions continue on their current trend, what will be the rise in global average temperature in 2100?
[1°C / 2°F]; [2°C / 3.6°F]; [4°C / 8°F]; [7°C / 15°F]; Don't know

40. [This question depends on the country, U.S. one is given] From what was said in the video, in the absence of ambitious action against climate change, how frequent will extreme temperatures (that is, temperature above 95°F) occur on average across the U.S. by the end of the century?
*70 days per year; 80 days per year; 90 days per year; 100 days per year; Don't know*

Climate Policy video

We will now show you a 5 minute video (with sound) that summarizes the features of some policies proposed to fight climate change. Please pay attention to the information provided as you will be asked questions about it later. Do not skip forward or close the page while the video is running. Please proceed to the next page when you are ready.

* Australia: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_3gagRLUpgyAicVE

* Brazil: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_eCZzzoblKYpWKh0

* Canada (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9Lekk0zTPurlzkG

* Canada (French): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9twKmQCtMuJpfp4

* China: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1ZhXvFBoUtvq7qK

* Denmark: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_390XHJ3gT6p4U74

* France: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_6F2lryw2eo1eQNU

* Germany: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9SvqNOCSY8ywnHw

* India (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_2mj1MdvMpAYJAuG
* India (Hindi): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_00696ZTnBDTFQ10

* Indonesia: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1RqbYYeT2cOnOPc

* Italy: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_6mMBZqNPLgvUKZo

* Japan: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_0rCWm2QnbEfaR1k

* Mexico: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_3UbhIz7hb99f0wu

* Poland: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_etkOtRoDmoSXkSq

* South Africa (English): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9FDOxYLGIwdrYh0

* South Africa (Zulu): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_1zij8ULej3rYsXs

* South Korea: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_402BSbDDYVUUhb8

* Spain: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9ZCXWK6BphbFQWy

* Turkey: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_9RF3ckVwWR9MH1Y

* Ukraine (Ukrainian): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bDbSZHrj0tU9b7w

* Ukraine (Russian): https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_3wr99GUKuUVgK3k

* United Kingdom: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bg5w9RRYbGtMrwa

* United States: https://lse.eu.qualtrics.com/WRQualtricsControlPanel/File.php?F=F_bj5mFN15bJnlUbk
Below is the script used for the U.S.]

To fight climate change and avoid an ever-warming climate, we need an array of policies. Climate policies are needed to transform the way we produce energy, to make buildings greener, to put greener cars on the roads and reduce our fuel consumption. But these policies also need to protect people's jobs and incomes. Let's have a closer look on three possible climate policies.

Let's start with a policy that forces car producers to produce greener cars – a ban on combustion-engine cars. With a ban on combustion-engine cars, car producers are first required by law to produce cars that emit less CO2 per [kilometre/mile]. The emission limit is lowered every year, so that only electric or hydrogen vehicles can be sold after 2030. Note that electric vehicles currently cannot travel as far and can be more expensive than cars that run on petrol. Together with a plan to produce electricity from clean sources, a ban on combustion-engine cars would accomplish the transition needed in the car industry.

Now, let's turn to a policy that combines a tax on carbon emissions to reduce emissions and cash transfers to protect people's purchasing power. With a carbon tax, all products that emit greenhouse gases would be taxed. For example, the price of gasoline would increase by [40 cents per gallon]. With a carbon tax, companies and people pay for the greenhouse gases they emit. This pushes them to reduce their emissions. To compensate people for the price increases, the revenues of the carbon tax would be redistributed to all households, regardless of their income. Each adult would thus receive [600 dollar] per year. On average, poorer people own smaller cars, live in smaller houses and fly less, so they use less fossil fuels than average. [The previous sentence is adapted in middle-income countries.] As they would receive the same cash transfer as everyone else, poorer people will generally gain from a carbon tax with cash transfers. Conversely, rich people will tend to lose. Does this policy work? Yes! The Canadian province of British Columbia has a carbon tax with cash transfers since 2008. Research has shown that this policy has decreased carbon emissions, increased employment, and made a majority of people richer. The last policy is a large program of public investment in green infrastructure, which would be financed by additional debt taken up by the government. A green infrastructure program would bring about the transition in energy infrastructure needed to halt climate change but it could come at the expense of other possible projects funded by the government. In [the U.S.], such a programme could create [4 million] jobs in green sectors, such as public transportation, renewable power plants, buildings' insulation, or sustainable agriculture, but [2 million] of people could lose their job in the fossil fuel industry. In general, all climate policies have the potential to transform the economy into a greener, safer, less polluted world. This green transformation has some downsides: people will have to change their habits, and some people will even have to change job. For example, there will be less demand for polluting sectors such as coal mining. But re-training options would be offered to workers in these sectors to ensure that they could find a new job elsewhere. And the green transition also comes with benefits: a safer world for future generations of course, but also less pollution. And climate policies can be designed to protect poor and middle-class households, as they can have more income with the carbon tax
with cash transfers, and more jobs with a green infrastructure program. We have focused on three important policies, but many others would be useful to fight climate change, including funding research into green technologies, subsidising the insulation of buildings, or stopping deforestation. To stop climate change, we probably need all of them together.

41. Were you able to watch and listen to the video until the end?
Yes; No, there was a technical problem; No, I skipped part of the video

42. The video presented three climate policies. What was the first policy about?
A ban on combustion-engine cars; A ban on short-haul flights; A ban on coal power plants; A ban on single-use plastic bags; Don't know

43. The green infrastructure program described in the video would be financed by:
Additional government debt; Taxes on the wealthiest; Increase in the VAT (value-added tax); Reduction in social spending; Don't know

## Climate knowledge

44. How often do you think or talk with people about climate change?
Almost never; Several times a year; Several times a month

45. In your opinion, is climate change real?
Yes; No

46. (If "Yes" to 45) What part of climate change do you think is due to human activity?
None; A little; Some; A lot; Most

47. Do you agree or disagree with the following statement: "Climate change is an important problem."
Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree

48. How knowledgeable do you consider yourself about climate change?
Not at all; A little; Moderately; A lot; A great deal

49. Greenhouse gases are gases that trap heat in the atmosphere and make the Earth warmer, causing climate change. In particular, the burning of fossil fuels and agricultural production emit greenhouse gases. Which of the following elements contribute to climate change? (Multiple answers are possible)
CO₂; Hydrogen; Methane; Particulate matter

50. Do you think that cutting global greenhouse gas emissions by half would be sufficient to eventually stop temperatures from rising?
Yes; No

For the next three questions we would like you to rank the items according to the greenhouse gas emissions they emit, to the best of your knowledge (where 1 is the item
that emits the most and 3 the item that emits the least). The greenhouse gas emissions of a product are those emitted at all steps involved in its production and distribution.

51. If a [family of 4 or couple or person, depending on the country] travels [500 km from New York City to Toronto (for the U.S.)], with which mode of transportation do they emit the most greenhouse gases? Please rank the items from 1 (most) to 3 (least) (by clicking and dragging the items).

*Car (running on diesel or gasoline); [Coach or Train, depending on the country]; Plane*

52. Which dish emits the most greenhouse gases? We consider that each dish weighs half a pound. Please rank the items from 1 (most) to 3 (least) (by clicking and dragging the items).

*A [beef] steak; One serving of [pasta]; Chicken wings*

53. Which source of electric energy emits the most greenhouse gases to provide power for a house? Please rank the items from 1 (most) to 3 (least) (by clicking and dragging the items).

*Gas-fired power plant; Nuclear power plant; Coal-fired power station*

54. Which region contributes most to global greenhouse gas emissions? Please rank the regions from 1 (most) to 4 (least) and note that multiple regions may have the same rank.

* The U.S.

* The European Union

* China

* India

1; 2; 3; 4

55. Consider now per capita emissions: in which region does the consumption of an average person contribute most to greenhouse gas emissions? Please rank the regions from 1 (most) to [4 / 5] (least).

* The U.S.

* The European Union

* China

* India

* [Country, if not above or not in the E.U.]

1; 2; 3; 4; [5]

56. If nothing is done to limit climate change, how likely do you think it is that climate change will lead to the following events?
* Severe droughts and heatwaves

* More frequent volcanic eruptions

* Rising sea levels

* Lower agricultural production

* Drop in standards of living

* Larger migration flows

* More armed conflicts

* Extinction of humankind

Very unlikely; Somewhat unlikely; Somewhat likely; Very likely

## Attitudes and risks

57. To what extent are the following groups responsible for climate change in [country]?

* Each of us

* The high income earners

* [country] government

* Companies

* Previous generations

Not at all; A little; Moderately; A lot; A great deal

58. To what extent do you think that it is technically feasible to stop greenhouse gas emissions by the end of the century while [maintaining / sustaining] satisfactory standards of living in [country]?

Not at all; A little; Moderately; A lot; A great deal

59. To what extent do you think climate change already affects or will affect your personal life negatively?

Not at all; A little; Moderately; A lot; A great deal

60. How likely is it that human kind halts climate change by the end of the century?

Very unlikely; Somewhat unlikely; Somewhat likely; Very likely

61. If we decide to halt climate change through ambitious policies, what would be the effects on [country] economy and employment?

Very negative effects; Somewhat negative effects; No noticeable effects; Somewhat positive effects; Very positive effects

62. If we decide to halt climate change through ambitious policies, to what extent do you think it would negatively affect your lifestyle?

Not at all; A little; Moderately; A lot; A great deal
63. Here are possible behaviors that experts say would help reduce greenhouse gas emissions. To what extent would you be willing to adopt the following behaviors?

* Limit flying

* Limit driving

* Have an electric vehicle

* Limit [beef] consumption

* Limit heating or cooling your home

*Not at all; A little; Moderately; A lot; A great deal*

64. 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.)?

* Ambitious climate policies

* Having enough financial support

* People around you also changing their behavior

* The most well-off also changing their behavior

*Not at all; A little; Moderately; A lot; A great deal*

## Policy 1: Ban on the sale of combustion-engine cars

To fight climate change, car producers can be required by law to produce cars that emit less CO2 per [kilometer / mile] of the cars they sell. The emission limit is lowered every year so that only electric or hydrogen vehicles can be sold after 2030. This policy is called a ban on combustion-engine cars. We will now ask you a few questions regarding this specific policy.

65. Do you agree or disagree with the following statements? A ban on combustion engine cars would...

* reduce CO₂ emissions from cars

* reduce air pollution

* have a

*negative/positive(randomized)*

effect on [country] economy and employment

* have a large effect on [country] economy and employment

* be a

*costly/costless(randomized)*

way to fight climate change
* Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree;
* Strongly agree

66. In your view, would the following groups win or lose if a ban on combustion-engine cars was implemented in [country]?

* Low-income earners

* The middle class

* High-income earners

* Those living in rural areas

* Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot

67. Do you think that your household would win or lose financially from a ban on combustion-engine cars?

* Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot

68. Do you agree or disagree with the following statement: "A ban on combustion-engine cars is fair"?

* Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree;
* Strongly agree

69. Do you support or oppose a ban on combustion-engine cars?

* Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support;
* Strongly support

70. Do you support or oppose a ban on combustion-engine cars where alternatives such as public transports are made available to people?

* Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support;
* Strongly support

**Policy 2: Green infrastructure program**

A green infrastructure program is a large public investment program, which would be financed by additional public debt, to accomplish the transition needed to cut greenhouse gas emissions. Investments would concern renewable power plants, public transport, thermal renovation of buildings, and sustainable agriculture. We will now ask you a few questions regarding this specific policy.

71. Do you agree or disagree with the following statements? A green infrastructure program would...

* make electricity production greener

* increase the use of public transport
* reduce air pollution

* have a negative effect on [country] economy and employment

* have a large effect on [country] economy and employment

* be a costly way to fight climate change

Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree;
Strongly agree

72. In your view, would the following groups win or lose with a green infrastructure program?

* Low-income earners

* The middle class

* High-income earners

* Those living in rural areas

Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot

73. Do you think that your household would win or lose financially from a green infrastructure program?

Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot

74. Do you agree or disagree with the following statement: "A green infrastructure program is fair"?

Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree;
Strongly agree

75. Do you support or oppose a green infrastructure program?

Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support;
Strongly support

76. Until now, we have considered that a green infrastructure program would be financed by public debt, but other sources of funding are possible.

What sources of funding do you find appropriate for public investments in green in-
frastructure? (Multiple answers are possible)

Additional public debt; Increase in the [sales tax / VAT (value-added tax)]; Increase in taxes on the wealthiest; Reduction in social spending; Reduction in military spending

**Policy 3: Carbon tax with cash transfers**

To fight climate change, [country] government can make greenhouse gas emissions costly, to make people and firms change their equipment and reduce their emissions. The government could do this through a policy called a carbon tax with cash transfers. Under such a
policy, the government would tax all products that emit greenhouse gas. For example, the price of gasoline would increase by [40 cents per gallon]. To compensate households for the price increases, the revenues from the carbon tax would be redistributed to all households, regardless of their income. Each adult would thus receive [600 dollar] per year.<fnref n="8" /> We will now ask you a few questions regarding this specific policy.

77. Do you agree or disagree with the following statements? A carbon tax with cash transfers would...

* encourage people to drive less

* encourage people and companies to insulate buildings

* reduce the use of fossil fuels and greenhouse gas emissions

* reduce air pollution

* have a negative effect on [country] economy and employment

* have a large effect on [country] economy and employment

* be a costly way to fight climate change

*Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree*

78. In your view, would the following groups win or lose under a carbon tax with cash transfers?

* Low-income earners

* The middle class

* High-income earners

* Those living in rural areas

*Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot*

79. Do you think that your household would win or lose financially under a carbon tax with cash transfers?

*Lose a lot; Mostly lose; Neither win nor lose; Mostly win; Win a lot*

80. Do you agree or disagree with the following statement: "A carbon tax with cash transfers is fair"?

*Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree*

<footnote n="8">⁸The tax considered is (implicitly) set at $45 per ton of CO₂ (except for Denmark where it is three times higher) and its revenues are redistributed equally among each adult. To compute the cash transfers, we assumed that the tax covers territorial CO₂ emissions from fossil fuels, that consumers bear 80% of the incidence of the carbon tax, and a price elasticity of -0.2.</footnote>
81. Do you support or oppose a carbon tax with cash transfers?
*Strongly oppose*; *Somewhat oppose*; *Neither support nor oppose*; *Somewhat support*; *Strongly support*

82. Now, we consider a variant of the policy where the cash transfers are higher for low-income people compared to high-income people. Do you agree or disagree that such a policy would be fair?
*Strongly disagree*; *Somewhat disagree*; *Neither agree nor disagree*; *Somewhat agree*; *Strongly agree*

83. Do you support or oppose a carbon tax with cash transfers with higher transfers for low-income people compared to high-income people?
*Strongly oppose*; *Somewhat oppose*; *Neither support nor oppose*; *Somewhat support*; *Strongly support*

## Preferences on climate policies

84. To show that you are attentive, please select “a little” in the following list: Not at all; A little; Moderately; A lot; A great deal

85. Do you support or oppose the following climate policies?

* A tax on flying (that increases ticket prices by 20%)

* A national tax on fossil fuels (increasing gasoline prices by [40 cents per gallon])

* A ban of polluting vehicles in dense areas, like city centers

* Subsidies for low-carbon technologies (renewable energy, capture and storage of carbon...)

* A contribution to a global climate fund to finance clean energy in low-income countries

*Strongly oppose*; *Somewhat oppose*; *Neither support nor oppose*; *Somewhat support*; *Strongly support*

86. Governments can use the revenues from carbon taxes in different ways. Would you support or oppose introducing a carbon tax that would raise gasoline prices by [40 cents per gallon], if the government used this revenue to finance...

* Cash transfers to households with no alternative to using fossil fuels

* Cash transfers to the poorest households

* Equal cash transfers to all households

* A reduction in personal income taxes

* A reduction in corporate income taxes
* Tax rebates for the most affected firms

* Funding environmental infrastructure projects (public transport, cycling ways, etc.)

* Subsidizing low-carbon technologies, including renewable energy

* A reduction in the public deficit

Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support

## Willingness to Pay and real stake questions

87. To fight global warming, [country] government could implement a policy package to reduce emissions, for example by investing in clean technologies (renewable energy, electric vehicles, public transport, more efficient insulation, etc.). The funding for these investments could be collected annually through an additional individual contribution for the foreseeable future. Assume that everyone in [country] as well as citizens of other countries would be required to contribute according to their means. Are you willing to pay ([$10 / $30 / $50 / $100 / $300 / $500 / $1,000]) annually through an additional individual contribution to limit global warming to safe levels (less than 2 degrees Celsius)?
Yes; No

88. By taking this survey, you are automatically entered into a lottery to win [$100]. In a few days you will know whether you have been selected in the lottery. The payment will be made to you in the same way as your compensation for this survey, so no further action is required on your part. You can also donate a part of this additional compensation (should you be selected in the lottery) to a reforestation project through the charity The Gold Standard. This charity has already proven effective to reduce 151 million tons of CO₂ to fight climate change and has been carefully selected by our team. The Gold Standard is highly transparent and ensures that its projects feature the highest levels of environmental integrity and contribute to sustainable development. Should you win the lottery, please enter your donation amount using the slider below:
Slider going from 0 to [100]

## International burden-sharing

89. At which level(s) do you think public policies to tackle climate change need to be put in place? (Multiple answers are possible)
Global; [Federal / European / ...]; [State / National]; Local

90. Do you agree or disagree with the following statement: "[country] should take measures to fight climate change."
Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree;
Strongly agree

91. How should [country] climate policies depend on what other countries do?

* If other countries do more, [country] should do...

* If other countries do less, [country] should do...

Much less; Less; About the same; More; Much more

92. [In all countries but the U.S., Denmark and France] All countries have signed the Paris agreement that aims to contain global warming "well below +2 °C'. To limit global warming to this level, there is a maximum amount of greenhouse gases we can emit globally, called the carbon budget. Each country could aim to emit less than a share of the carbon budget. To respect the global carbon budget, countries that emit more than their national share would pay a fee to countries that emit less than their share. Do you support such a policy?

Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support;
Strongly support

93. [In all countries but the U.S., Denmark and France] Suppose the above policy is in place. How should the carbon budget be divided among countries?

The emission share of a country should be proportional to its population, so that each human has an equal right to emit.; The emission share of a country should be proportional to its current emissions, so that those who already emit more have more rights to emit.; Countries that have emitted more over the past decades (from 1990 onwards) should receive a lower emission share, because they have already used some of their fair share.; Countries that will be hurt more by climate change should receive a higher emission share, to compensate them for the damages.

94. [In the U.S., Denmark, and France only] To achieve a given reduction of greenhouse gas emissions globally, costly investments are needed. Ideally, how should countries bear the costs of fighting climate change?

* Countries should pay in proportion to their income

* Countries should pay in proportion to their current emissions

* Countries should pay in proportion to their past emissions (from 1990 onwards)

* The richest countries should pay it all, so that the poorest countries do not have to pay anything

* The richest countries should pay even more, to help vulnerable countries face adverse consequences: vulnerable countries would then receive money instead of paying
*Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree*

95. Do you support or oppose establishing a global democratic assembly whose role would be to draft international treaties against climate change? Each adult across the world would have one vote to elect members of the assembly.

*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

96. Imagine the following policy: a global tax on greenhouse gas emissions funding a global basic income. Such a policy would progressively raise the price of fossil fuels (for example, the price of gasoline would increase by [40 cents per gallon] in the first years). Higher prices would encourage people and companies to use less fossil fuels, reducing greenhouse gas emissions. Revenues from the tax would be used to finance a basic income of [$30] per month to each human adult, thereby lifting the 700 million people who earn less than $2/day out of extreme poverty. The average British person would lose a bit from this policy as they would face [$130] per month in price increases, which is higher than the [$30] they would receive.

Do you support or oppose such a policy?

*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

97. Do you support or oppose a tax on all millionaires around the world to finance low-income countries that comply with international standards regarding climate action? This would finance infrastructure and public services such as access to drinking water, healthcare, and education.

*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

## Housing and cattle products

(In Brazil, Mexico, India, and Indonesia, these 5 questions on heating were not asked. In Australia, they were asked with *cooling* instead of *heating*.)

98. (If “Owner” or “Landlord renting out” at 19) How likely is it that you will improve the insulation or replace the heating system of your accommodation over the next 5 years?

*Very unlikely; Somewhat unlikely; Somewhat likely; Very likely*

99. (If “Owner” or “Landlord renting out” at 19) What are the main hurdles preventing you from improving the insulation or replace the heating system of your accommodation? (Multiple answers are possible)

*The choice to insulate or replace the heating system is not mine; The upfront costs are too high; It is too much effort; It won't improve its energy efficiency; My insulation and heating systems are already satisfactory*
100. GROUP 1. Imagine that [country] government makes it mandatory for all residential buildings to have insulation that meets a certain energy efficiency standard before 2040. The government would subsidise half of the insulation costs to help households with the transition. Do you support or oppose such policy?

101. GROUP 2. Imagine that [country] government makes it mandatory for all residential buildings to have insulation that meets a certain energy efficiency standard before 2040. The government would subsidise half of the insulation costs to help households with the transition. Insulating your home can take long, may cause disruptions to your daily life during the renovation works, and may even require you to leave your home until the renovation is completed. Do you support or oppose such policy?
*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

102. Imagine that [country] government makes it mandatory for all residential buildings to have insulation that meets a certain energy efficiency standard before 2040. The government would subsidise half of the insulation costs to help households with the transition. Insulating your home can take long, may cause disruptions to your daily life during the renovation works, and may even require you to leave your home until the renovation is completed. Do you support or oppose such policy?
*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

103. (In India, this question was skipped.) Imagine that, in order to fight climate change, [country] government decides to limit the consumption of cattle products like beef and dairy. Do you support or oppose the following options?

* A high tax on cattle products, so that the price of beef doubles
* Subsidies on organic and local vegetables, fruits, and nuts
* The removal of subsidies for cattle farming
* The ban of intensive cattle farming

*Strongly oppose; Somewhat oppose; Neither support nor oppose; Somewhat support; Strongly support*

## Trust, perceptions of institutions, inequality, and the future

104. Do you agree or disagree with the following statement: "Most people can be trusted." *Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree*

105. Do you agree or disagree with the following statement: "Over the last decade, [country] government could generally be trusted to do what is right."
*Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat agree; Strongly agree*

106. Some people think the government is trying to do too many things that should be left to individuals and businesses. Others think that the government should do more to solve our country's problems. Which come closer to your own view?

*Government is doing too much; Government is doing just the right amount; Government should do more*

107. How big of an issue do you think income inequality is in [country]?

*Not an issue at all; A small issue; An issue; A serious issue; A very serious issue*

108. Do you think that overall people in the world will be richer or poorer in 100 years from now?

*Much poorer; Poorer; As rich as now; Richer; Much richer*

Feedback

109. Do you feel that this survey was politically biased?

*Yes, left-wing biased; Yes, right-wing biased; No, I do not feel it was biased*

110. The survey is nearing completion. You can now enter any comments, thoughts or suggestions in the field below.

Petition

111. Finally, are you willing to sign a petition to "stand up for real climate action"? As soon as the survey is complete, we will send the results to the [head of state's] office, informing him what share of people who took this survey were willing to support the following petition. "I agree that immediate action on climate change is critical. Now is the time to dedicate ourselves to a low-carbon future and prevent lasting damage to all living things. Science shows us we cannot afford to wait to cut harmful carbon emissions. I'm adding my voice to the call to world leaders in [country] and beyond – to act so we do not lose ground in combating climate change." Do you support this petition (you will NOT be asked to sign, only your answer here is required and remains anonymous)?

*Yes; No*
## A-6 Robustness checks

<table><caption>Table A15: Treatment effects on support for climate action, among respondents who do not have both answers incorrect at the video attention questions.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Support or Agreement</th></tr><tr><th>Ban on combustion-engine cars<br>(1)</th><th>Green infrastructure program<br>(2)</th><th>Carbon tax with cash transfers<br>(3)</th><th>Main policies are fair<br>(4)</th><th>Adopt climate-friendly behaviors<br>(5)</th></tr></thead><tbody><tr><td>Control group mean</td><td>0.652</td><td>0.514</td><td>0.456</td><td>-0.096</td><td>-0.044</td></tr><tr><td>Treatment: Climate impacts</td><td>0.050***<br>(0.009)</td><td>0.044***<br>(0.009)</td><td>0.053***<br>(0.009)</td><td>0.087***<br>(0.019)</td><td>0.118***<br>(0.020)</td></tr><tr><td>Treatment: Climate policy</td><td>0.047***<br>(0.008)</td><td>0.062***<br>(0.009)</td><td>0.121***<br>(0.009)</td><td>0.177***<br>(0.019)</td><td>0.048**<br>(0.019)</td></tr><tr><td>Treatment: Both</td><td>0.081***<br>(0.009)</td><td>0.106***<br>(0.009)</td><td>0.170***<br>(0.009)</td><td>0.261***<br>(0.020)</td><td>0.123***<br>(0.020)</td></tr><tr><td>Observations</td><td>30,704</td><td>30,704</td><td>30,704</td><td>30,704</td><td>30,704</td></tr></tbody></table>

*Note:* The table shows the results of OLS regressions. Dependent variables (1, 2, 3) are indicator variables equal to 1 if the respondent (somewhat or strongly) supports each of the main climate policies while (4, 5) are standardized indices. Independent variables are indicators for each treatment as well as undisplayed covariates: country fixed effects and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. See Appendix A-1 for variable definitions.

## A-7 Acquiescence in China, India, and Indonesia

To assess whether people expect positive or negative effects from the main climate policies, 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. 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 economic effects and disagreement with negative effects, is 35% in high-income countries and 70% in China, India and Indonesia, three countries particularly prone to acquiescence (Table A16).

Acquiescence bias is not particularly related to sociodemographic characteristics, but it is clearly related to the care with which a respondent answers. Indeed, respondents who take more time to answer are systematically less prone to acquiescence bias. For example, acquiescence bias reduces to 58% in China, India and Indonesia for those who answer is more than 28 minutes (the global median duration). Figure A24 shows the support for national climate policies among respondents who answer in more than 28 minutes. Despite a lower acquiescence bias, the support is similar to the whole sample (see Figure 7). This shows that either acquiescence remains too high in this subsample to perceive a significant difference, either support for climate policies is genuinely higher in China, India, and Indonesia. In any case, it is difficult to draw conclusions from such a small subsample (e.g. in China, there are 154 respondents in the control group who answer in more than 28 min).
Respondents who rush through the survey particularly do so at the video treatments: they often skip part of the videos. This may explain why treatment effects are lower (and generally non-significant) in India and Indonesia. Table A17 shows that among respondents whose time spent on the videos is no less than the video duration, treatment effects are significant and comparable to other countries'.

<table><caption>Table A16: Acquiescence bias across countries.</caption><thead><tr><th></th><th>High-income</th><th>CHN</th><th>IND</th><th>IDN</th><th>AUS</th><th>CAN</th><th>DEU</th><th>ITA</th><th>JPN</th><th>POL</th><th>KOR</th><th>ESP</th><th>GBR</th><th>BRA</th><th>CHN</th><th>IND</th><th>IDN</th><th>MEX</th><th>ZAF</th><th>TUR</th><th>UKR</th></tr></thead><tbody><tr><th>All</th><td>35</td><td></td><td></td><td></td><td>70</td><td>27</td><td>28</td><td>29</td><td>40</td><td>45</td><td>33</td><td>52</td><td>38</td><td>26</td><td>51</td><td>54</td><td>79</td><td>72</td><td>57</td><td>48</td><td>51</td><td>34</td></tr><tr><th>Duration > 28 min</th><td>32</td><td></td><td></td><td></td><td>58</td><td>22</td><td>24</td><td>28</td><td>37</td><td>44</td><td>29</td><td>53</td><td>31</td><td>22</td><td>49</td><td>42</td><td>67</td><td>62</td><td>53</td><td>45</td><td>48</td><td>38</td></tr><tr><th>Duration < 28 min</th><td>39</td><td></td><td></td><td></td><td>78</td><td>32</td><td>32</td><td>31</td><td>43</td><td>46</td><td>39</td><td>51</td><td>46</td><td>28</td><td>54</td><td>61</td><td>84</td><td>82</td><td>63</td><td>58</td><td>54</td><td>25</td></tr><tr><th>Share > 28 min</th><td>39</td><td></td><td></td><td></td><td>43</td><td>53</td><td>48</td><td>51</td><td>45</td><td>59</td><td>39</td><td>53</td><td>42</td><td>70</td><td>36</td><td>32</td><td>50</td><td>65</td><td>78</td><td>44</td><td>74</td></tr></tbody></table>

<figure><img src="image_29.png" /><figcaption>Figure A24: Share of respondents who somewhat to strongly support climate change policies, among those answering in more than 28 min.</figcaption></figure>
<table><caption>Table A17: Heterogeneous treatment effects on support for Main Policies in China, India & Indonesia</caption><thead><tr><th></th><th colspan="3">Support for Main Policies</th></tr><tr><th></th><th>CHN</th><th>IDN</th><th>IND</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.161</td><td>-0.066</td><td>-0.066</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.262***<br>(0.067)</td><td>0.058<br>(0.049)</td><td>0.069<br>(0.054)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.064<br>(0.065)</td><td>0.059<br>(0.051)</td><td>0.048<br>(0.055)</td></tr><tr><td>Treatment: Both</td><td>0.324***<br>(0.065)</td><td>0.112**<br>(0.049)</td><td>0.080<br>(0.055)</td></tr><tr><td>Observations</td><td>1,533</td><td>1,998</td><td>1,925</td></tr></tbody></table><table><thead><tr><th></th><th colspan="3">Support for Main Policies</th></tr><tr><th></th><th>CHN</th><th>IDN</th><th>IND</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Control group mean</td><td>-0.161</td><td>-0.066</td><td>-0.066</td></tr><tr><td>Treatment: Climate Impacts</td><td>0.443***<br>(0.103)</td><td>0.020<br>(0.064)</td><td>0.200***<br>(0.060)</td></tr><tr><td>Treatment: Climate Policies</td><td>0.211*<br>(0.113)</td><td>0.172**<br>(0.069)</td><td>0.206***<br>(0.063)</td></tr><tr><td>Treatment: Both</td><td>0.408***<br>(0.124)</td><td>0.268***<br>(0.072)</td><td>0.209***<br>(0.076)</td></tr><tr><td>Observations</td><td>754</td><td>1,285</td><td>1,307</td></tr></tbody></table>

Note: The table shows the results of OLS regressions of the standardized index support for Main Policies on indicators for each treatment as well as undisplayed covariates: country fixed effects and socio-economic indicators, i.e., age, gender, income, education, economic leaning, living with children. Table (a) includes all respondents of the specified country, while Table (b) includes only respondents who did not rush through the videos, i.e. who spend at least as much time on each video seen as its duration. See Appendix A-1 for variable definitions.
# A-8 Data sources

## Australia:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* LIS (2020) 2014 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region:* Australian Bureau of Statistics (2021)

- *Urban:* sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## Canada:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* WID (2020) 2019 pre-tax income equal split

- *Region:* Canada Post

- *Urban:* sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## Denmark:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Statistics Denmark (2020b) 2019 individual pre-tax income

- *Region:* Statistics Denmark (2020a)

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## France:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* INSEE (2020)

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)
• **Germany:**

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* Statistisches Bundesamt (2020)

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

• **Italy:**

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* IStat.it

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

• **Japan:**

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* LIS (2020) 2013 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region:* citypopulaiton.de

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

• **Mexico:**

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* LIS (2020) 2018 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region:* INEGI (2020)

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

• **Poland:**
- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* Statistics Poland (2020)

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## • South Korea:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* LIS (2020) 2016 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region:* KOSIS

- *Urban:* KOSIS

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## • Spain:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* INE

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## • Turkey:

- *Age and gender:* United Nations and Social Affairs (2019)

- *Income:* Eurostat (2020) 2019 equivalised disposable income

- *Region:* ICISLERI

- *Urban:* see below sub-section A-8.2

- *College:* OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate:* OECD (2021b)

## • U.K.:

- *Age and gender:* United Nations and Social Affairs (2019)
- *Income*: Eurostat (2020) 2018 equivalised disposable income

- *Region*: Doogal.co.uk

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

• U.S.:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: U.S. Census Bureau (2020) 2019 total money income

- *Region*: U.S. Census Bureau (2020)

- *Urban*: see below sub-section A-8.2

- *College*: ?

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

• Brazil:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: LIS (2020) 2016 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region*: IBGE

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

• China:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: LIS (2020) 2013 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region*: stats.gov.cn

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

• India:

- *Age and gender*: United Nations and Social Affairs (2019)
- *Income*: LIS (2020) 2011 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region*: statisticstimes.com

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

## Indonesia:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: WID (2020) 2019 individual pre-tax income

- *Region*: BPS

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

## South Africa:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: LIS (2020) 2017 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region*: Statistics South Africa

- *Urban*: see below sub-section A-8.2

- *College*: OECD (2021a)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)

## Ukraine:

- *Age and gender*: United Nations and Social Affairs (2019)

- *Income*: LIS (2020) 2018 equivalised cash disposable income inflated to 2019 using nominal GDP growth from The World Bank (2020).

- *Region*: ukrstat.org

- *Urban*: see below sub-section A-8.2

- *College*: State Statistics Service of Ukraine (2020)

- *Inactivity rate / Unemployment rate / Employment rate*: OECD (2021b)
## A-8.1 Detailed Regional Brackets

* **Australia:**

- Region 1: *Broad New South Wales* (Australian Capital Territory; New South Wales)

- Region 2: *Queensland*

- Region 3: *South Australia*

- Region 4: *Victoria-Tasmania* (Tasmania; Victoria; Other territories)

- Region 5: *West Australia* (Northern Territory; Western Australia)

* **Canada:**

- Region 1: *Central* (Manitoba; Saskatchewan)

- Region 2: *East* (New Brunswick; Newfoundland and Labrador; Nova Scotia; Prince Edward Island)

- Region 3: *North West* (Alberta; British Columbia; Northwest Territories; Nunavut; Yukon)

- Region 4: *Ontario*

- Region 5: *Quebec*

* **Denmark:**

- Region 1: *Hovedstaden*

- Region 2: *Midtjylland*

- Region 3: *Nordjylland*

- Region 4: *Sjælland*

- Region 5: *Syddanmark*

* **France:**

- Region 1: *Île de France*

- Region 2: *Nord-Est* (Bourgogne-Franche-Comté; Grand Est ; Hauts-de-France)

- Region 3: *Nord-Ouest* (Bretagne; Centre-Val de Loire; Normandie; Pays de la Loire ; Poitou-Charentes)

- Region 4: *Sud-Est* (Auvergne-Rhône-Alpes; PACA)

- Region 5: *Sud-Ouest* (Aquitaine; Languedoc-Roussillon; Limousin; Midi-Pyrénées)

* **Germany:**

- Region 1: *Central* (Hesse; Thuringia)
- Region 2: *Eastern* (Berlin; Brandenburg; Saxony; Saxony-Anhalt)

- Region 3: *Northern* (Bremen; Hamburg; Lower Saxony; Mecklenburg-Western Pomerania; Schleswig-Holstein)

- Region 4: *Southern* (Baden-Württemberg; Bavaria)

- Region 5: *Western* (North Rhine-Westphalia; Rhineland-Palatinate; Saarland)

## Italy:

- Region 1: *Centre*

- Region 2: *Islands*

- Region 3: *North-East*

- Region 4: *North-West*

- Region 5: (South)

## Japan:

- Region 1: *Chubu* (Aichi; Fukui; Gifu; Ishikawa; Nagano; Niigata; Shizuoka; Toyama; Yamanashi)

- Region 2: *Kansai* (Hyōgo; Kyōto; Mie; Nara; Ōsaka; Shiga; Wakayama)

- Region 3: *Kanto* (Chiba; Gunma; Ibaraki; Kanagawa; Saitama; Tochigi; Tōkyō)

- Region 4: *North* (Akita; Aomori; Fukushima; Hokkaido; Iwate; Miyagi; Yamagata)

- Region 5: *South* (Ehime; Fukuoka; Hiroshima; Kagawa; Kagoshima; Kōchi; Kumamoto; Miyazaki; Nagasaki; Ōita; Okayama; Okinawa; Saga; Shimane; Tokushima; Tottori; Yamaguchi)

## Mexico:

- Region 1: *Central-Eastern* (Federal District; Hidalgo; Mexico; Morelos; Puebla; Queretaro; Tlaxcala)

- Region 2: *Central-Western* (Aguascalientes; Colima; Jalisco; Guanajuato; Michoacan; Nayarit; San Luis Potosi; Zacatecas)

- Region 3: *North-East* (Coahuila; Nuevo Leon; Tamaulipas)

- Region 4: *North-West* (Baja California; Baja California Sur; Chihuahua; Durango; Sinaloa; Sonora)

- Region 5: *South* (Campeche; Chiapas; Guerrero; Oaxaca; Quintana Roo; Tabasco; Varacruz; Yucatan)

## Poland:

- Region 1: *Central* (Lubusz; Greater Poland)
- Region 2: *Central-East* (Lesser Poland; Subcarpathian)

- Region 3: *North* (Podlaskie; Pomeranian; Kuyavian-Pomeranian; Warman-Masurian; West Pomeranian)

- Region 4: *South-East* (Holy Cross; Lodz; Lubin; Masovian)

- Region 5: *South-West* (Lower Silesian; Opole; Silesia)

## South Korea:

- Region 1: *East* (Busan; Daegu; North Gyeongsang; South Gyeongsang; Ulsan)

- Region 2: *North* (Gangwon; Gyeonggi; Incheon)

- Region 3: *Seoul*

- Region 4: *West* (Daejeon; Gwangju; Jeju; North Chungcheong; North Jeolla; Sejong; South Chungcheong; South Jeolla)

## Spain:

- Region 1: *Center* (Castilla-La Mancha; Comunidad de Madrid)

- Region 2: *East* (Cataluña; Comunidad Valenciana; Islas Baleares)

- Region 3: *North* (Aragón; Cantabria; La Rioja; Navarra; País Vasco)

- Region 4: *North-West* (Castilla y León; Galicia; Principado de Asturias)

- Region 5: *South* (Andalucía; Canarias; Ceuta (Ciudad Autónoma); Extremadura; Melilla (Ciudad Autónoma); Región de Murcia)

## Turkey:

- Region 1: *Central* (Black Sea; Central Anatolia)

- Region 2: *East* (Eastern Anatolia; Southeastern Anatolia)

- Region 3: *Marmara*

- Region 4: *West* (Aegean; Mediterranean)

## U.K.:

- Region 1: *Central UK* (East Midlands; Wales; West Midlands)

- Region 2: *London*

- Region 3: *Northern England* (North East; North West; Yorkshire and The Humber)

- Region 4: *Northern UK* (Northern Ireland; Scotland)

- Region 5: *Southern England* (East of England; South East; South West)

## U.S.:
- Region 1: Midwest (Ohio; Illinois; Indiana; Iowa; Kansas; Michigan; Minnesota; Missouri; Nebraska; North Dakota; South Dakota; Wisconsin)

- Region 2: Northeast (Connecticut; Maine; Massachusetts; New Hampshire; New Jersey; New York; Pennsylvania; Rhode Islands; Vermont)

- Region 3: South (Alabama; Arkansas; Delaware; District of Columbia; Florida; Georgia; Kentucky; Louisiana; Maryland; Mississippi; North Carolina; South Carolina; Oklahoma; Tennessee; Texas; Virginia; West Virginia)

- Region 4: West (Alaska; Arizona; California; Colorado; Hawaii; Idaho; Montana; Nevada; New Mexico; Oregon; Utah; Washington; Wyoming)

## Brazil:

- Region 1: Central-West

- Region 2: North

- Region 3: North-East

- Region 4: South

- Region 5: South-East

## China:

- Region 1: East

- Region 2: North

- Region 3: Northeast

- Region 4: South Central

- Region 5: West (Northwest China; Southwest China)

## India:

- Region 1: Central Zonal Council

- Region 2: Eastern Zonal Council; Andaman and Nicobar Islands; North Eastern

- Region 3: Northern Zonal Council

- Region 4: Southern Zonal Council; Lakshadweep

- Region 5: Western Zonal Council

## Indonesia:

- Region 1: Eastern Islands (Bali; East Nusa Tenggara; Maluku; North Maluku; Papua; West Nusa Tenggara; West Papua)

- Region 2: Eastern Java (Central Java; East Java; Yogyakarta)
- Region 3: *Northern Islands* (Central Kalimantan; Central Sulawesi; East Kalimantan; Gorontalo; North Kalimantan; North Sulawesi; Southeast Sulawesi; South Kalimantan; South Sulawesi; West Kalimantan; West Sulawesi)

- Region 4: *Sumatra* (Aceh; Bangka Belitung Islands; Bengkulu; Jambi; Lampung; North Sumatra; Riau; Riau Islands; South Sumatra; West Sumatra)

- Region 5: *Western Java* (Banten; Jakarta; West Java)

## South Africa:

- Region 1: *Center* (Free State; North West)

- Region 2: *Gauteng*

- Region 3: *North-East* (Limpopo; Mpumalanga)

- Region 4: *South-East* (Eastern Cape; KwaZulu-Natal)

- Region 5: *West* (Northern Cape; Western Cape)

## Ukraine:

- Region 1: *Center* (Cherkasy; Chernihiv; Kirovohrad; Kyiv; Poltava; Sumy; Vinnytsya; Zhytomyr)

- Region 2: *East* (Donetsk; Kharkiv; Luhansk)

- Region 3: *South* (Dnipropetrovsk; Kherson; Mykolayiv; Odesa; Zaporizhzhya)

- Region 4: *West* (Chernivtsi; Ivano-Frankivsk; Khmelnytski; Lviv; Rivne; Ternopil; Volyn; Zakarpattya)

## A-8.2 Urban-Rural Categories

### Australia

- Rural: Inner Regional Australia; Outer Regional Australia; Remote Australia; Very Remote Australia

- Urban: Major Cities of Australia

**Description:** From source 1, we obtain the Remoteness Area of each mesh blocks, from source 2 we obtain the Postal Area, and from source 3 the population. We then merge the three sources with the mesh blocks information. We sum the population of each mesh block contained in a Postal Area. If a Postal Area is linked to several urban categories or several region, we attribute the option tied to the largest share of population. Finally, we add additional postal codes from source 4. Those postal codes already have a region and a urban category. determined with: zip code

source 1: Remoteness Area (RA)ASGS Ed 2016 in .csv format)

source 2: POAs (Postal Areas ASGS Edition 2016 in .csv Format)

source 3: Census

source 4: GitHub
• Canada

– Rural: Forward Sortation Area second character is 0

– Urban: Forward Sortation Area second character is different from 0

Description: From the source, we compute share of population based on second char-
acter of the Forward Sortation Area (if 0 it corresponds to a rural area according to
Canada Post)

source: Stat Canada

• Denmark

– Rural: Live in town with less than 20,000 inhabitants

– Urban: Live in town with more than 20,000 inhabitants

source: statbank.dk

• France

– Rural

* Rural category 1: Couronnes de Grand-Pôle

* Rual category 2: Autre

– Urban: Grand-Pôle

Description: Match postal codes with statistical identifier and retrieve urban category associated with the latter.

source 1: [Opendatasoft.com](https://Opendatasoft.com)

source 2: Insee

• Germany

– Rural: Rural areas

– Urban:

* Urban category 1: Cities

* Urban category 2: Towns and Suburbs

Description: From source 1, we obtain the list of zipcodes with the area type (defined according to source 3) as well as the population of each city. However, source 1 does not list all the zipcodes for each city. Source 2 contains the additional zipcodes. We first merge the existing zipcodes in source 1 with the data from source 2. We then uniquely identify a city with the city's name and Vorwahl, and attribute to a zipcode the most rural category (thus by order of priority Rual area; Towns and Suburbs; Cities) of its city. We finally remove zipcodes duplicates by keeping the most rural category for each zipcode.
source 1: Destatis.de

source 2: GitHub

source 3: EuroStat

• Italy

– Rural: Rural areas

– Urban:

* Urban category 1: Cities

* Urban category 2: Towns and Suburbs

Description: From source 1, we obtain the population for each municipality, its area type (defined according to https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-008), as well as a statistical identifier. From source 2 we obtain the zip codes and statistical identifier. For some zip codes we obtain different area types, we address this issue by determining the percentage of population living in each area types for a given zip code and attribute the area type with the greatest share of population. There also are a few zipcodes for which we do not have an area type, we assign them to rural if their population is under the biggest rural area (23,000 inhabitants) and to towns and suburbs otherwise. determined with: zip code

source 1: IStat

source 2: Comuni-italiani.it

• Japan

– Rural: Living in a town of less than 100,000 inhabitants.

– Urban: Living in a town of more than 100,000 inhabitants.

Description: From source 1 we obtain postal information on municipalities and town
in Japan, and obtain population from source 2. We then merge the two datasets using
the locality name and prefecture (cleaning for some discrepancies). In a second step,
we regroup municipalities by zipcodes prefix (the first five numbers of the zipcodes).
If the prefix is linked to both towns with less than 100,000 inhabitants and with more
than 100,000 habitants, we link it the latter category.

source 1: Japanpost

source 2: e-stat.go.jp

• Mexico

– Rural

* Rural category 1: Rural

* Rural category 2: Semiurbano

– Urban: Urbano
Description: From source 1 we obtain the postal code and the different categories of locality (among Rural; Semiurbano; Urbano) associated with it. For postal codes associated with several categories, we assign the most urban category. From source 2 (slide 19), we retrieve the population distribution by categories (where we apply the definition from CS02a.2-1 in source 3).

source 1: [Correosdemexico.gob.mx](https://correosdemexico.gob.mx)

source 2: [issea.gob.mx](https://issea.gob.mx)

source 3: [inee.edu.mx](https://eei.inee.edu.mx)

### Poland

- Rural: Living in a town of less than 20,000 inhabitants.

- Urban: Living in a town of more than 20,000 inhabitants.

Description: From the official statistics we calculate the distribution of population by towns' sizes. (NB: we were not able to link it with a zip code as zip code and municipalities each overlap several elements of the other category for an important share of the population).

source: [stat.gov.pl](https://stat.gov.pl)

### South Korea

- Rural: Live in a District (i.e., “Gum”)

- Urban:

* Urban category 1: Live in a Town (i.e., “Si”)

* Urban category 2: Live in a City (i.e., “Gu”)

Description: From source 1, we collect the share of population living in each Municipal administrative level. From source 2, we collect the Municipal administrative level assigned to each zip code.

source 1: [index.go.kr](https://index.go.kr)

source 2: [epost.go.kr](https://epost.go.kr)

### Spain

- Rural: Living in a town of less than 20,000 inhabitants.

- Urban: Living in a town of more than 20,000 inhabitants.

Description: From the official statistics we calculate the distribution of population by towns' sizes.

Determined with: Declarative question: What is the size of the agglomeration you currently live in? Less than 20,000 inhabitants; More than 20,000 inhabitants.

source: [ine.es](https://ine.es)

### Turkey
- Rural: Living in a district with a share of rural population greater than the national average for districts.

- Urban: Living in a district with a share of rural population smaller than the national average for districts.

**Description:** From source 1 (which is official data but with a better formatting for our matching) we obtain the District associated to each zip code. From source 2, we obtain the share of rural population and the total population (defined by national statistics) of each district. We then assign districts with a share of rural population greater than the national average (around 22%) as rural. We finally merge the two datasets.

**Determined with:** zip code

source 1: GitHub

source 2: biruni.tuik.gov.tr

• U.K.

- Rural: Rural village; Rural hamlet and isolated dwellings; Rural town and fringe; Rural town and fringe in a sparse setting; Rural hamlet and isolated dwellings in a sparse setting; Rural village in a sparse setting; Accessible rural area; Remote rural area; Very remote rural area; Very remote small town; Accessible small town; Remote small town

- Urban:

* Urban category 1: Urban city and town; Urban city and town in a sparse setting

* Urban category 2: Urban major conurbation; Urban minor conurbation; Large urban area; Other urban area

**Description:** From the source we obtain the area type (defined according to the categories in source 3) for each zip code. We then compute the area type for each outcode (i.e. the first characters of a U.K. zip code) by attributing the area type with the greatest share of population within the outcode area. For outcode with no population in the data we assign them to the rural category, except for three outcodes that do not include any zip code of a rural category. For the latter, we assign them the City or Town category.

**Determined with:** zip code / outcode

source: doogal.co.uk

• U.S.

- Rural: RUCA code different from 1 (core metropolitan)

- Urban: RUCA code 1 (core metropolitan)

**Description:** Obtain RUCA code associated with each zipcode.

**Determined with:** zipcode
source 1: ers.usda.gov

source 2: Arcgis

source 3: ons.gov.uk

• Brazil

– Rural: Live in a municipality with less than 50,000 inhabitants

– Urban: Live in a municipality with more than 50,000 inhabitants

Determined: Declarative questions. “What is the size of the municipality you live in?”

source: caurj.gov.br

• China

– Rural: Live in an agglomeration of less than 10,000 inhabitants

– Urban:

* Urban category 1: Live in an agglomeration of more than 10,000 inhabitants and less than 500,000 inhabitants

* Urban category 2: Live in an agglomeration of more than 500,000 inhabitants

Description: We use the population figures from Table 1 in the source and compute
ratio using the total population of China according to the 2010 Census.

source: Elfie Swerts, « A data base on Chinese urbanization: ChinaCities », Cybergeo: European Journal of Geography

• India

– Rural: Live in an agglomeration of more than 20,000 inhabitants

– Urban: Live in an agglomeration of more than 20,000 inhabitants

Description: We use data from the source and remove duplicates by name.

Determined with: Declarative question: What is the size of the agglomeration you live in? Less than 5,000 inhabitants; 5,000-20,000 inhabitants; 20,000-50,000 inhabitants; 50,000-250,000 inhabitants; 250,000-3 millions inhabitants; More than 3 millions inhabitants

source: censusindia.gov.in

• Indonesia

– Rural: In a Kabupaten outside of the Capital town

– Urban: Kota; Capital town of a Kabupaten

Determined with: declarative question: In which of the following areas are you currently living? Kota; Capital town of a Kabupaten; In a Kabupaten outside of the Capital town (TODO: check with translator if those categories make sense)

source: unstats.un.org
• South Africa

– Rural: Live in a District municipality other than the District capital.

– Urban: Live in a metropolitan municipality or in a capital of a District municipality

Description: We compute the share of the population living in a metropolitan municipality or in the capital of a district municipality (as a proxy for living in a big urban area).

Determined with: declarative question "In which type of municipality do you currently live in? A metropolitan municipality; In a capital of a District municipality; In a District municipality other than the District capital." source: statssa.gov.za

• Ukraine

– Rural: Living in a Village or a settlement

– Urban: Living in a City or an Urban settlement

Description: From source 1, we obtain the share of population living in rural areas (defined as our urban category 1) and in urban areas (defined as our urban category 2). From source 2, we obtain the postal code of all localities in Ukraine. The first element in the name of the locality indicates the type of locality (among the four types mentioned above). There is one postal code that covers both urban and rural areas, we categorize it as urban since those localities are in the suburbs of Odessa.

Determined with: zip code

source 1: ukrstat [page 5]

source 2: data.gov.ua

### A-8.3 Detailed Education Brackets

• Australia:

– Official categories used (OECD): Tertiary education

– Corresponding questionnaire categories: College degree; Master's degree or above

• Canada:

– Official categories used (OECD): Tertiary education

– Corresponding questionnaire categories: College degree; Master's degree or above

• Denmark:

– Official categories used (OECD): Bachelor's or equivalent education; Master's or equivalent education; Doctoral or equivalent education
- Corresponding questionnaire categories: Professional bachelor's education; Bachelor's degree; Master's degree or higher

• **France:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Bac + 2 or Bac + 3 (license, BTS, DUT, DEUG, etc.) ; Bac + 5 or more (master's degree, engineering or business school, doctorate, medicine, master's degree, DEA, DESS ...)

• **Germany:**

- Official categories used (OECD): Bachelor's or equivalent education; Master's or equivalent education; Doctoral or equivalent education

- Corresponding questionnaire categories: University degree (e.g. Bachelor) ; Master's degree or higher

• **Italy:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Professional degree ; Bachelor's degree ; Master's degree or higher

• **Japan:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Vocational school; University; Graduate school and above

• **Mexico:**

- Official categories used (OECD): Bachelor's or equivalent education; Master's or equivalent education; Doctoral or equivalent education

- Corresponding questionnaire categories: Technical or intermediate education ; University degree or higher vocational training ; Master's degree/doctorate

• **Poland:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Bachelor's degree ; Master's degree or higher

• **South Korea:**

- Official categories used (OECD): Tertiary education
- Corresponding questionnaire categories: Bachelor's degree; Master's degree or higher

* **Spain:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: University degree or higher vocational training; Master's degree/doctoral degree

* **Turkey:**

- Official categories used (OECD): Bachelor's or equivalent education; Master's or equivalent education; Doctoral or equivalent education

- Corresponding questionnaire categories: Graduated from a University; Master's degree or higher

* **U.K.:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Vocational degree; College degree; Master's degree or above

* **U.S.:**

- Official categories used (U.S. Census): Some college, no degree; Associate's degree; Bachelor's degree; Graduate or professional degree

- Corresponding questionnaire categories: College degree; Master's degree or above

* **Brazil:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: University education; Graduate or higher

* **China:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: Undergraduate; Master and above

* **India:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: College degree; Master's degree or above

* **Indonesia:**

- Official categories used (OECD): Tertiary education
- Corresponding questionnaire categories: Bachelor; Master or higher

* **South Africa:**

- Official categories used (OECD): Tertiary education

- Corresponding questionnaire categories: College degree; Master's degree or above

* **Ukraine:**

- Official categories used (State Statistics Service of Ukraine): Primary level (short cycle) of higher education; The first (bachelor's) level of higher education; The second (master's) level of higher education; The third (educational-scientific / educational-creative) level of higher education; Scientific level of higher education

- Corresponding questionnaire categories: Specialist or bachelor's degree; Master's or higher degree
A-9 Correct Answers to Knowledge Questions

<table><thead><tr><td>Question</td><td>Correct Answer</td><td>Source</td></tr></thead><tbody><tr><td>In your opinion, is climate change real?</td><td>Yes</td><td>IPCC (2021)</td></tr><tr><td>What part of climate change do you think is due to human activity?</td><td>Most (if not all)</td><td>IPCC (2021), Figure SPM.1</td></tr><tr><td>Which of the following elements contribute to climate change? (Multiple answers are possible)</td><td>CO<sub>2</sub>; Methane</td><td>IPCC (2021), Figure SPM.1</td></tr><tr><td>Do you think that cutting global greenhouse gas emissions by half would be sufficient to eventually stop temperatures from rising?</td><td>No (net zero CO<sub>2</sub> emissions is required)</td><td>IPCC (2021), D.1</td></tr><tr><td>If a family of 4 travels 700 km from A to B, with which mode of transportation do they emit the most greenhouse gases? Please rank the items from 1 (most) to 3 (least)</td><td>Plane (1)<br/>Car (running on diesel or gasoline) (2)<br/>Train / Coach (3)</td><td>Ecopassenger,<br/>US: National Geographic<br/>Other: China (1), China (2),<br/>India, Indonesia</td></tr><tr><td>Which dish emits the most greenhouse gases? We consider that each dish weighs half a pound. Please rank the items from 1 (most) to 3 (least)</td><td>Beef [India: Lamb] (1)<br/>Chicken wings (2)<br/>Serving of Pasta [Asia: rice] (3)</td><td>Poore and Nemecek (2018)</td></tr><tr><td>Which source of electric energy emits the most greenhouse gases to provide power for a house? Please rank the items from 1 (most) to 3 (least)</td><td>Coal-fired power station (1)<br/>Gas-fired power plant (2)<br/>Nuclear power plant (3)</td><td>Pehl et al. (2017)</td></tr><tr><td>Which region contributes most to global greenhouse gas emissions? Please rank the regions from 1 (most) to 4 (least)</td><td>China (1); U.S. (2)<br/>E.U. (3); India (4)</td><td>JRC (2018)</td></tr><tr><td>In which region does the consumption of an average person contribute most to greenhouse gas emissions? Please rank the regions from 1 (most) to 5 (least).</td><td>U.S. (1); E.U. (2)<br/>China (3); India (4)</td><td>Global Carbon Project (2019)</td></tr><tr><td>If nothing is done to limit climate change, how likely do you think it is that climate change will lead to the following events?</td><td>Severe droughts and heatwaves (Likely)<br/>Rising sea levels (Likely)<br/>More frequent volcanic eruptions (Unlikely)</td><td>IPCC (2014)</td></tr></tbody></table>
A-9.1 Video sources

* **Australia:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6, Source 7, Source 8

* **Canada:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6, Source 7

* **Denmark:**

– Sources: Hinkel et al. (2010), Source 2, Source 3, Source 4

* **France:**

– Sources: Source 1, Santé Publique (2016), Source 3, Source 4, Source 5, Source 6

* **Germany:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6, Source 7, Source 8, Source 9,

* **Italy:**

– Sources: Source 1, Source 2, Lelieveld et al. (2019), Source 4, Source 5, Source 6

* **Japan:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5

* **Mexico:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6

* **Poland:**

– Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6

* **South Korea:**

– Sources: Source 1, Source 2, Source 3, Source 4

* **Spain:**

– Sources: Source 1, Source 2, de Rigo et al. (2017), Source 5, Source 6, Source 7, Source 8, Source 9

* **Turkey:**
- Sources: Source 1, Source 2, Source 3, Source 4, Source 5

* U.K.:

- Sources: Source 1, Source 2, C40 (2018), Sayers (2017); Source 5, Source 6

* U.S.:

- Sources: Lelieveld et al. (2019), Source 2, Source 3, Source 4

* Brazil:

- Sources: Source 1, Source 2, Source 3, Source 4, Salazar, Nobre and Oyama (2007)

* China:

- Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6, Source 7, Source 8

* India:

- Sources: Source 1, Source 2, Im, Pal and Eltahir (2017), Source 4, Kulp and Strauss (2019), Source 6, Lelieveld et al. (2019), Source 8, Source 9, Source 10

* Indonesia:

- Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6, Source 7

* South Africa:

- Sources: Source 1, Source 2, Source 3, Source 4, Source 5

* Ukraine:

- Sources: Source 1, Source 2, Source 3, Source 4, Source 5, Source 6
A-10 Appendix References

**Australian Bureau of Statistics (2021).** National, state, and territory population. https://www abs.gov.au/statistics/people/population/national-state-and-territory population/latest-release.

C40, Cities (2018). The Future We Don't Want.

de Rigo, Daniele, Giorgio Libertà, Tracy Houston Durrant, Tomàs Artés Vivancos, Jesús San-Miguel-Ayanz, and Publications Office of the European Union (2017). Forest fire danger extremes in Europe under climate change: variability and uncertainty. Publications Office of the European Union Research Report.

Eurostat (2020). https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?file=data/ilcdi01.tsv.gz.

**Global Carbon Project (2019).** Supplemental data of Global Carbon Project 2019. Version Number: 1.0 Type: dataset.

Hinkel, Jochen, Robert J. Nicholls, Athanasios T. Vafeidis, Richard S.J. Tol, and Thaleia Avagianou (2010). Assessing risk of and adaptation to sea-level rise in the European Union: an application of DIVA. *Mitigation and Adaptation Strategies for Global Change*, 15(7): 703–719.

Im, Eun-Soon, Jeremy S. Pal, and Elfatih A. B. Eltahir (2017). Deadly heat waves projected in the densely populated agricultural regions of South Asia. *Science Advances*, 3(8): e1603322.

INEGI (2020). Principales resultados por localidad (ITER). https://www.inegi.org.mx/app/scitel/Default?ev=9.

INSEE (2020). Régions – Départements. https://www.insee.fr/fr/statistiques/4277596?sommaire=43182

IPCC, AR5, ed (2014). *Climate change 2014: impacts, adaptation, and vulnerability: Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change*. New York, NY:Cambridge University Press.

IPCC, AR6 (2021). Summary for Policymakers. In *Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*.

JRC, European Commission (2018). *Fossil CO2 emissions of all world countries: 2018 report*. LU:Publications Office.

Kling, Jeffrey R, Jeffrey B Liebman, and Lawrence F Katz (2007). Experimental Analysis of Neighborhood Effects. *Econometrica*, 75(1): 83–119.
**Kulp, Scott A., and Benjamin H. Strauss (2019).** New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. *Nature Communications*, 10(1): 1–12.

**Lelieveld, J., K. Klingmüller, A. Pozzer, R. T. Burnett, A. Haines, and V. Ramanathan (2019).** Effects of fossil fuel and total anthropogenic emission removal on public health and climate. *Proceedings of the National Academy of Sciences of the United States of America*, 116(15): 7192–7197.

**LIS (2020).** Luxembourg Income Study (LIS) Database. https://www.lisdatacenter.org/.

**OECD (2021a).** Educational attainment and labour-force status. https://stats.oecd.org/viewhtml.aspx?datasetcode=EAGNAClang = en.

**OECD (2021b).** LFS by sex and age - indicators. https://stats.oecd.org/Index.aspx?DatasetCode=LFSSEXAGEIR.

**Pehl, Michaja, Anders Arvesen, Florian Humpenöder, Alexander Popp, Edgar G. Hertwich, and Gunnar Luderer (2017).** Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling. *Nature Energy*, 2(12): 939–945.

**Poore, J., and T. Nemecek (2018).** Reducing food's environmental impacts through producers and consumers. *Science*, 360(6392): 987–992.

**Salazar, Luis F., Carlos A. Nobre, and Marcos D. Oyama (2007).** Climate change consequences on the biome distribution in tropical South America. *Geophysical Research Letters*, 34(9).

**Santé Publique, France (2016).** Impacts de l'exposition chronique aux particules fines sur la mortalité en France continentale et analyse des gains en santé de plusieurs scénarios de réduction de la pollution atmosphérique / 2016 / Environnement et santé / Rapports et synthèses / Publications et outils / Accueil.

**Sayers, P. B. (2017).** Projections of future flood risk in the UK.

**State Statistics Service of Ukraine (2020).** Labour Force of Ukraine. http://www.ukrstat.gov.ua/druk/publicat/kat_u/2021/zb/08/rabsula_e.pdf.

**Statistics Denmark (2020a).** Population 1. January by municipality, size of the city, age and sex. https://www.statbank.dk/BY2.

**Statistics Denmark (2020b).** Pre-tax income for people (14 years +) by region, unit, sex, age and income interval. https://www.statbank.dk/statbank5a/SelectVarVal/Define.asp?MainTable=INDKP105PLanguage=1P
**Statistics Poland (2020).** Population. Size and structure and vi-
tal statistics in Poland by territorial divison. As of June 30, 2020.
https://stat.gov.pl/en/topics/population/population/population-size-and-structure-
and-vital-statistics-in-poland-by-territorial-divison-as-of-june-30-2020,3,28.html.

**Statistisches Bundesamt (2020).** Population as of 31.12.2020 by nationality and federal states. https://www destatis de/EN/Themes/Society-Environment/Population/Current-Population/Tables/population-by-laender.html.

**The World Bank (2020).** GDP: linked series (current LCU). https://data worldbank org/indicator/NY.GDP.MKTP.CN.AD.

**United Nations, Department of Economic, and Population Division Social Affairs (2019).** World Population Prospects 2019, Online Edition. Rev. 1.

**U.S. Census Bureau (2020).** Current Population Survey, 2020 Annual Social and Economic Supplement (CPS ASEC). https://www2 census gov/programs-surveys/cps/tables/hinc-01/2020/hinc01.xlsx.

**WID (2020).** World Inequality Database. https://wid.world/.