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Adrian Rinscheid*, Silvia Pianta‡ & Elke Weber§ Prepared for ICPP, June 26-28 2019, Montreal

* University of St.Gallen & Swiss Competence Center for Energy Research (SCCER-CREST) ‡ Bocconi University & RFF-CMCC European Institute on Economics and the Environment, Italy § Woodrow Wilson School for Public and International Affairs & Andlinger Center for Energy and the Environment, Princeton University

Climate Change Action – it’s all about the speed of it

• Urgency of bold climate measures stressed with

increasing vigor (IPCC 2018; US Global Change Research 2018).

• Crossing of climate tipping points could lead to

uncontrollable and irreversible climate change (e.g., Lenton 2011; Lontzek et al. 2015).

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• Voters matter in democracies.
• Recent concern about voters becoming an increasing barrier to ambitious climate policies

(Batel and Devine-Wright 2018; Fraune and Knodt 2018; Lockwood 2018).

• Our focus: Do citizens support the (necessary) implementation of decarbonization policies

as early as possible or are they in favor of postponing them to later dates?

Why investigating public preferences?

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Phasing out internal combustion engines

Table 1. Jurisdictions with a political commitment to ban new gasoline and diesel vehicle sales, and planned year of policy enactment (as of early 2019)

Own compilation, based on: IEA (2018b) | Center for Climate Protection (Burch and Gilchrist 2018) | https://www.reuters.com/article/us-denmark-autos/denmark-embraces-electric-car-revolution-with- petrol-and-diesel-ban-plan-idUSKCN1MC121 | https://www.regeringen.se/tal/20192/01/regeringsforklaringen-den-21-januari-2019/

Contribution of the transportation sector to greenhouse gas emissions:

• Globally: 14 %
• USA: 29 % (no. 1 contributor to GHG emissions)

Fossil fuel consumption is still rising globally (IEA 2018a). Decarbonizing transportation is a key element to mitigate CC (Creutzig et al. 2015; Fuglestvedt et al. 2008). To achieve the 2°-target, Internal combustion engines for personal transportation need to disappear no later than 2040 (Rockström et al. 2017).

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Exploring Temporal Preferences: Expectations

• Behavioral science: present bias; orientation towards immediate benefits (e.g., Frederick et al.

2002; Weber and Johnson 2016)

• Political science: citizens tend to reject policies that impose short-term costs, despite potentially

large benefits in the future (Jacobs and Matthews 2012)

• Environmental Psychology: CC often perceived as a problem with consequences distant in time

(Brügger et al. 2015) Hypothesis 1: Citizens, when faced with the choice of enacting climate policies now or later, will favor later over immediate action.

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Exploring Temporal Preferences: Expectations

• Environmental Psychology: temporal perception of CC differs (Brügger et al. 2015), also

depending on own experiences (Spence et al. 2012). Hypothesis 2a: Citizens who perceive CC to be proximal will be in favor of earlier policy implementation.

• Political Science: in the US, perception of CC is stongly tied to different party ideologies (McCright

and Dunlap 2011) Hypothesis 2b: Self-identified Democrats will be in favor of earlier policy implementation, while self-identified Republicans will be in favor of later policy implementation.

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Our study: Research Design

• representative sample of 1,520 American residents
• Conjoint experiment

ØRespondents rank & rate several (8 x 2) hypothetical policy proposals Øpolicy proposals randomly vary on different (5) policy attributes

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Our study: Research Design

• representative sample of 1,520 American residents
• Conjoint experiment

ØRespondents rank & rate several (8 x 2) hypothetical policy proposals Øpolicy proposals randomly vary on different (5) policy attributes

Policy attributes Attribute levels Beginning of policy implementation 2020 2030 2040 2050 Policy costs (per household, per month) $ 2 $ 6 $ 10 $ 14 Policy instrument Ban on new fossil fuel car sales Increase in fossil fuel taxes

• Gov. subsidies for low-

emission transportation alternatives

• Policy endorsement by

Democratic Party Republican Party U.S. Alliance of Auto- mobile Manufacturers Greenpeace Pollution reduction 10% 20% 30%

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Our study: Research Design

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• Fully randomized conjoint design

Scenario 1 Scenario 2 Policy instruments Ban on new fossil fuel car sales Government subsidies for low- emission transportation alternatives Beginning of policy implementation 2020 2030 Policy costs (per household, per month) $6 $10 Immediate pollution reduction 10% immediate reduction of air pollution 10% immediate reduction of air pollution Policy endorsement by Democratic Party U.S. Alliance of Automobile Manufacturers Select one

Our study: Research Design

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• Fully randomized conjoint design

Scenario 1 Scenario 2 Policy instruments Ban on new fossil fuel car sales Government subsidies for low- emission transportation alternatives Beginning of policy implementation 2020 2030 Policy costs (per household, per month) $6 $10 Immediate pollution reduction 10% immediate reduction of air pollution 10% immediate reduction of air pollution Policy endorsement by Democratic Party U.S. Alliance of Automobile Manufacturers Select one

• Scenario 1

1 2 3 4 5 6 7 8 9 10 Scenario 2 1 2 3 4 5 6 7 8 9 10 If you had the possibility to vote for Scenario 1 in a direct democratic vote, how likely would you vote for it? (0 is “would definitely NOT vote for” and 10 is “would definitely vote for”) If you had the possibility to vote for Scenario 2 in a direct democratic vote, how likely would you vote for it? (0 is “would definitely NOT vote for” and 10 is “would definitely vote for”)

Results

• Republican Party

Democratic Party Greenpeace Automobile Manufacturers Endorsement by: 30 % less pollution 20 % less pollution 10 % less pollution Pollution reduction: $14 $10 $6 $2 Costs: Taxes Subsidies Ban Policy instrument: 2050 2040 2030 2020 Timing: −.1 .1 Change in Pr(Vote for Phase−Out)

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DV: dichotomized rating outcome Marginal effects indicate change in probability to vote for a policy when comparing an attribute level with the baseline category, all else equal. E.g., the probability that voters support policies implemented in 2030 is 2.6 percentage points higher than the probability to support policies implemented in 2020

Figure 1. Average effects of policy attributes on respondents’ policy preference to phase out fossil fuel cars.

Results

• Republican Party

Democratic Party Greenpeace Automobile Manufacturers Endorsement by: 30 % less pollution 20 % less pollution 10 % less pollution Pollution reduction: $14 $10 $6 $2 Costs: Taxes Subsidies Ban Policy instrument: 2050 2040 2030 2020 Timing: −.1 .1 Change in Pr(Vote for Phase−Out)

Hypothesis 1: citizens, when faced with the choice

• f enacting climate policies now or later, will favor

later over immediate action. (✓) => only when the choice is 2020 vs. 2030

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DV: dichotomized rating outcome Marginal effects indicate change in probability to vote for a policy when comparing an attribute level with the baseline category, all else equal. E.g., the probability that voters support policies implemented in 2030 is 2.6 percentage points higher than the probability to support policies implemented in 2020

Figure 1. Average effects of policy attributes on respondents’ policy preference to phase out fossil fuel cars.

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Results

Figure 2. Average effects of timing attribute on respondents’ policy preference by (a) psychological distance of climate change and (b) party identification. (a) (b)

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Results

Figure 2. Average effects of timing attribute on respondents’ policy preference by (a) psychological distance of climate change and (b) party identification. (a) (b)

Hypothesis 2a: Citizens who perceive CC to be proximal will be in favor of earlier policy implementation. (✓) => indifference between 2020 vs. 2030

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Results

Figure 2. Average effects of timing attribute on respondents’ policy preference by (a) psychological distance of climate change and (b) party identification. (a) (b)

Hypothesis 2b: Self-identified Democrats will be in favor of earlier policy implementation, while self-identified Republicans will be in favor of later policy implementation. (✓) => hypothesized patterns confirmed for after 2030 Hypothesis 2a: Citizens who perceive CC to be proximal will be in favor of earlier policy implementation. (✓) => indifference between 2020 vs. 2030

Exploring Arguments about Policy Sequencing

• Do timing preferences and preferences for policy instruments interact?
• Idea: smart sequencing of climate policies as an effective way to avoid political dead-ends in

the decarbonization of energy systems

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Source: Meckling et al. (2017).

Exploring Arguments about Policy Sequencing

• Do timing preferences and preferences for policy instruments interact?
• Idea: smart sequencing of climate policies as an effective way to avoid political dead-ends in

the decarbonization of energy systems

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Source: Meckling et al. (2017).

e.g., subsidies for low- emission alternatives e.g., increase in fossil fuel taxes e.g., ban on new fossil fuel car sales

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Results

Figure 3. Bandwidths of predicted phase-out policy support

Predicting policy support for all possible instrument x timing combinations. Policy design matters => considerable variation in predicted policy support, depending

• n other policy design features.

Political viability for subsidies already in 2020. About half of policy scenarios that include mandates attain majority support if implemented in 2030.

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Conclusion

• Americans’ support for policies to phase-out fossil fuel cars is maximized if these are

implemented in 2030.

• On average, later implementation dates significantly decrease policy support.
• The preferences of some subgroups (Republicans; people perceiving the distance of CC to be

high) are less affected by implementation timing. ØStatus quo bias as a transient and malleable phenomenon (Weber 2015).

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Conclusion

• Americans’ support for policies to phase-out fossil fuel cars is maximized if these are

implemented in 2030.

• On average, later implementation dates significantly decrease policy support.
• The preferences of some subgroups (Republicans; people perceiving the distance of CC to be

high) are less affected by implementation timing. ØStatus quo bias as a transient and malleable phenomenon (Weber 2015).

• Other policy design elements matter, too.

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Conclusion

• Americans’ support for policies to phase-out fossil fuel cars is maximized if these are

implemented in 2030.

• On average, later implementation dates significantly decrease policy support.
• The preferences of some subgroups (Republicans; people perceiving the distance of CC to be

high) are less affected by implementation timing. ØStatus quo bias as a transient and malleable phenomenon (Weber 2015).

• Other policy design elements matter, too.
• The coming decade might provide a window of opportunity for adopting effective phase-out

policies that find public support

• Policy sequencing might obtain wider public acceptance.

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THANK YOU J

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References (I/II)

• Batel, S., & Devine-Wright, P. (2018). Populism, identities and responses to energy infrastructures at different scales in the United Kingdom: A post-Brexit reflection. Energy

Research and Social Science, 43(May), 41–47.

• Brügger, A. et al. (2015) ‘Psychological responses to the proximity of climate change’, Nature Climate Change. Nature Publishing Group, 5(12), pp. 1031–1037. doi:

10.1038/nclimate2760.

• Burch, I., & Gilchrist, J. (2018). Survey of Global Activity to Phase Out Internal Combustion Engine Vehicles. Santa Rosa, CA: Center for Climate Protection. Retrieved from

https://climateprotection.org/wp-content/uploads/2018/10/Survey-on-Global-Activities-to-Phase-Out-ICE-Vehicles-FINAL-Oct-3-2018.pdf

• Creutzig, F., Jochem, P., Edelenbosch, O. Y., Mattauch, L., van Vuuren, D. P., McCollum, D., & Minx, J. (2015). Energy and environment. Transport: A roadblock to climate

change mitigation? Science, 350(6263), 911–912.

• Fraune, C. and Knodt, M. (2018) ‘Sustainable energy transformations in an age of populism, post-truth politics, and local resistance’, Energy Research and Social Science.

Elsevier, 43, pp. 1–7. doi: 10.1016/j.erss.2018.05.029.

• Frederick, S., Loewenstein, G., & O’donoghue, T. (2002). Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature 40(2), 351–401.
• Fuglestvedt, J., Berntsen, T., Myhre, G., Rypdal, K., & Skeie, R. B. (2008). Climate forcing from the transport sectors. Proceedings of the National Academy of Sciences,

105(2), 454–458.

• International Energy Agency (2018a). Global Energy & CO2 Status Report. Paris: International Energy Agency. Retrieved from URL: https://www.iea.org/geco/ (accessed

21.05.2018)

• International Energy Agency (2018b). Global EV Outlook 2018 – Towards cross-modal electrification. Paris: OECD/IEA. Retrieved from

https://www.connaissancedesenergies.org/sites/default/files/pdf-actualites/globalevoutlook2018.pdf

• IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and

related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, Maycock, M. Tignor, and T. Waterfield (eds.)]. World Meteorological Organization, Geneva, Switzerland.

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References (II/II)

• Jacobs, A. M. and Matthews, J. S. (2012) Why Do Citizens Discount the Future? Public Opinion and the Timing of Policy Consequences, British Journal of Political Science.

doi: 10.1017/S0007123412000117.

• Lenton, T. M. (2011). Early warning of climate tipping points. Nature Climate Change, 1(4), 201–209.
• Lockwood, M. (2018). Right-wing populism and the climate change agenda: exploring the linkages. Environmental Politics, 27(4), 712–732.
• Lontzek, T. S., Cai, Y., Judd, K. L., & Lenton, T. M. (2015). Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy. Nature

Climate Change 5, 441–444.

• McCright, A. M. and Dunlap, R. E. (2011) ‘The Politicization of Climate Change and Polarization in the American Public’s Views of Global Warming, 2001 – 2010’,

Sociological Quarterly, 52(2), pp. 155–194. doi: 10.1111/j.1533-8525.2011.01198.x.

• Rockström, J. et al. (2017) ‘A Roadmap for Rapid Decarbonization’, Science, 355(6331), pp. 1269–1271. doi: 10.1126/science.aah3443.
• Spence, A., Poortinga, W., Butler, C., & Pidgeon, N. F. (2011). Perceptions of climate change and willingness to save energy related to flood experience. Nature Climate

Change, 1(1), 46–49.

• U.S. Global Change Research (2018). Fourth National Climate Assessment.
• Weber, E. U. (2015). Climate change demands behavioral change: What are the challenges? Social Research: An International Quarterly, 82, 561-581.
• Weber, E.U. & Johnson, E.J. (2016). Can we think of the future? Cognitive barriers to future-oriented thinking. In: Messner, D., & Weinlich, S (Eds.), Global cooperation and

the human factor (pp.139-154), New York, NY: Routledge.

Backup

Information on socio-demographic characteristics of the US population was obtained from the U.S. Census Bureau (for age and sex composition (2016) see https://www.census.gov/data/tables/2016/demo/age-and-sex/2016-age-sex-composition.html; for regions (2016) see https://www.census.gov/popclock/data_tables.php?component=growth, for income (2017) see https://www2.census.gov/programs-surveys/cps/tables/hinc- 06/2017/hinc06.xls). Information on party affiliation is based on Pew Research Center surveys conducted in 2017 (http://www.people-press.org/wp- content/uploads/sites/4/2018/03/03-20-18-Party-Identification.pdf). The total percentage for Pew data does not add up to 100 as the remaining share belongs to the category “other.”

Sample distribution of socio-demographic variables and comparison with US population.

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Results: Interacting the attributes timing and policy instrument

Figure 4. Interaction of policy instrument and timing attribute