Global Environmental Change 74 (2022) 102528 Contents lists available at ScienceDirect Global Environmental Change journal homepage: www.elsevier.com/locate/gloenvcha Co-dynamics of climate policy stringency and public support Théo Konc a, b, c, *, Stefan Drews a, Ivan Savin a, d, Jeroen C.J.M. van den Bergh a, e, f a Institute of Environmental Science and Technology, UAB, Edifici Z, UAB Campus, 08193 Cerdanyola del Vallès, Barcelona, Spain b Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Telegraphenberg A 31, 14473 Potsdam, Germany c Technical University of Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany d Graduate School of Economics and Management, Ural Federal University, Ulitsa Mira 19, 620002 Yekaterinburg, Russian Federation e ICREA, Passeig de Lluís Companys 23, 08010 Barcelona, Spain f School of Business and Economics & Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands A R T I C L E I N F O A B S T R A C T Keywords: Public support for stringent climate policies is currently weak. We develop a model to study the dynamics of Carbon taxation public support for climate policies. It comprises three interconnected modules: one calculates policy impacts; a Performance standards second translates these into policy support mediated by social influence; and a third represents the regulator Income inequality adapting policy stringency depending on public support. The model combines general-equilibrium and agent- Opinion dynamics based elements and is empirically grounded in a household survey, which allows quantifying policy support Social network as a function of effectiveness, personal wellbeing and distributional effects. We apply our approach to compare two policy instruments, namely carbon taxation and performance standards, and identify intertemporal trajec­ tories that meet the climate target and count on sufficient public support. Our results highlight the importance of social influence, opinion stability and income inequality for public support of climate policies. Our model pre­ dicts that carbon taxation consistently generates more public support than standards. Finally, we show that under moderate social influence and income inequality, an increasing carbon tax trajectory combined with progressive revenue redistribution receives the highest average public support over time. 1. Introduction support for the current design exceeds a critical threshold. Our approach deviates from the conventional economic approach to identify optimal To mitigate climate change, countries need to implement stringent policy trajectories in that welfare impact is not the dominant criterion policies. But public support for such policies is weak (Anderson et al., (Hänsel et al., 2020) but only one among several factors that influences 2017; Klenert et al., 2018; Levi et al., 2020). It moreover tends to policy design. decrease with stringency (Lachapelle and Paterson, 2013; Carattini Given the extent of emissions reduction required, it is questionable to et al., 2017; Beiser-McGrath and Bernauer, 2019). Public opinion affects focus on a theoretically ideal carbon tax that has little chance of being the feasibility of effective climate policy in various ways: through gen­ implemented under actual political circumstances (Goulder, 2020). eral elections, illustrated by a repeal of carbon pricing in Australia Once policymakers have committed to a climate mitigation goal, their (Crowley, 2017); through direct referenda, illustrated by rejections of objective is no longer to maximize welfare but to implement effective carbon taxes in Washington State (USA) (Reed et al., 2019); and through policies under acceptability constraints. In view of these considerations, social movements, illustrated by the 2018 Yellow Vest protests in France we offer a method to identify a politically more realistic perspective on against a fuel tax with a carbon component (Douenne and Fabre, 2020). climate policy design. It consists of a model, called GE-ABM, with three On the other hand, experiences such as with the carbon tax in British modules to describe the interconnection between public opinion and Columbia, Canada show that favourable public opinion and increasing climate policy stringency, and study the political feasibility of climate policy ambition can also go hand in hand (Murray and Rivers, 2015). policy, focusing on carbon taxation with several revenue use options and To achieve sufficiently strong policies that can count on critical performance standards. The first module is a simple general equilibrium public support, we propose a new approach to study climate policy. It model (GE) that is derived from the climate economics literature (Kle­ treats climate policy design as dynamic and endogenous on policy nert et al., 2018; Jacobs and van der Ploeg, 2019). The second module is support. The idea is that a policy can be implemented only if public an agent-based model (ABM) to capture the social interactions that * Corresponding author. https://doi.org/10.1016/j.gloenvcha.2022.102528 Received 14 July 2021; Received in revised form 14 April 2022; Accepted 28 April 2022 Available online 7 May 2022 0959-3780/© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). T. Konc et al. Global Environmental Change 74 (2022) 102528 underlie opinion dynamics regarding policy support. In each period, the methods (Carattini et al., 2018; Maestre-Andrés et al., 2019; Bechtel (change in) stringency of the policy depends on the prevailing public et al., 2020). Conceptually, our approach shares some characteristics opinion, giving rise to interactive dynamics of policy support and policy with so-called ”robust decision-making” (Hall et al., 2012), which sug­ design in a third module. gests to sacrifice some economic efficiency to reduce the probability of We model public opinion as depending on policy performance in adverse climatic events. Our model instead suggests to sacrifice eco­ terms of economic, environmental and equity impacts. This choice is nomic efficiency to increase the likelihood of public support for climate motivated by earlier empirical studies showing that public acceptability policy. of carbon taxes is mediated by perceptions of effectiveness and fairness Several studies have shown that environmental and climate policies (Maestre-Andrés et al., 2019), where the latter is further distinguished have potential inequitable impacts, undermining their acceptability. into individual fairness, i.e. focused on personal wellbeing, and distri­ Our model assumes that households require a minimal consumption butional fairness, comprising distribution of costs and benefits among all level of the high-carbon good, reflecting the empirical regularity that agents (Dreyer and Walker, 2013; Kim et al., 2013; Clayton, 2018). low-income households spend a larger share of their income on carbon- A major challenge for public support of carbon taxation is the intensive subsistence goods (Klenert and Mattauch, 2016; Oswald et al., misperception of the economic effects of taxation. People consistently 2020). As a result, carbon taxes absent of revenue recycling, as well as overestimate its costs and underestimate its effectiveness (Douenne and performance standards, have regressive distributional impacts, meaning Fabre, 2022). The main assumption of the model is that when a policy is that they place a relatively high burden on low-income households implemented, people learn and gradually adjust their perceptions until (Levinson, 2018; Pizer and Sexton, 2019). However, the use of carbon- they match the actual effects. The model then predicts which policy tax revenues can compensate for regressive effects (Grainger and Kol­ trajectories and revenue uses allow achieving a predetermined mitiga­ stad, 2010; Klenert and Mattauch, 2016; Goulder et al., 2019; Aubert tion target while ensuring sufficient public support over time. In addi­ and Chiroleu-Assouline, 2019), which has been shown to critically affect tion, we investigate the impact of opinion stability and social influence public opinion (Beiser-McGrath and Bernauer, 2019; Savin et al., 2020). dynamics on the public support of distinct policy trajectories. Finally, we For this reason, we devote attention to different revenue uses in our study the role of underlying income inequality in obtaining critical analysis, and show that the impact of regressive distributional effects on public support for climate policies. policy acceptability is closely linked to the features of the social network Our study contributes to the literature on design and public support in which agents interact. of climate policies. While several papers recognize that public support Our paper further adds insights to the literature studying the inter­ for climate policy is dependent on dynamic factors (Drews and van den action between climate policy and behavioural economics. Following Bergh, 2016; Beiser-McGrath and Bernauer, 2019; Maestre-Andrés et al., research showing that the social environment affects agents’ decisions 2019; Douenne and Fabre, 2022; Bergquist et al., 2020; Sommer et al., (Elster, 1989; Bowles, 1998; Mailath and Postlewaite, 2010; Hoff and 2020; Douenne and Fabre, 2020; Carlsson et al., 2021), this has not Stiglitz, 2016), many authors have argued that social interactions should translated into studies that systematically investigate co-dynamics of be taken into account when designing policy instruments addressing policy design and support. This is understandable as it requires an labour market and saving decisions (Lindbeck, 1997), public good integration of policy design, economic impact assessment and public- contribution (Bénabou and Tirole, 2006; Meunier and Schumacher, support analysis – which tend to be studied in separate disciplines. An 2020), energy conservation (Allcott, 2011), or environmental external­ exception is a game-theoretic study that examined how firms form ities (Nyborg, 2018; Ulph and Ulph, 2018; Konc et al., 2021). These lobbies that influence the stringency of government’s emission reduc­ contributions analyze how the introduction of social interactions tion policies (Isley et al., 2015). Another study developed a simple changes what constitutes an optimal policy. We add to this literature by framework to describe feedback from public opinion to environmental studying the role of social influence for the acceptability and imple­ problems through environmental policy and then back to public opinion mentation of desirable policy instruments. Our model reflects that in­ (van den Bergh et al., 2019). Here we provide a richer and arguably teractions with peers and behavioural biases play a significant role to more realistic and flexible approach that can identify policy trajectories shape political opinion and voting decisions (Bond et al., 2012; Muchnik meeting emissions reduction targets and maximizing public support, by et al., 2013; Levine and Mattozzi, 2020). In particular, Braha and Aguiar accounting for its components, namely wellbeing and equity. The GE- (2017) show that social influence has a growing importance to explain ABM allows to represent the labour and goods market in a tractable the results of U.S. presidential elections, which could be linked to the way while identifying economic impacts on heterogeneous households emergence of large digital social networks. We integrate these insights in and providing detailed information about individual support for the our study and model the acceptability of climate policies as dependent policy (Castro et al., 2020; Niamir et al., 2020). Using GE-ABM in this on the political opinion of agents who influence each other. way represents a novel approach that enriches the literature on policy Our results indicate that carbon taxation is more likely to achieve a acceptability, which traditionally relies on survey and experimental wider public support than performance standards, assuming that policy Fig. 1. A schematic representation of the three modules, which can be seen as a timeline of events in our model, repeated for a pre-specified number of periods (years). The impacts of a specific policy design are calculated with a general-equilibrium model. These impacts determine the support for climate policy, assessed with an agent-based model, which in turn affects the policy design for the subsequent period. 2 T. Konc et al. Global Environmental Change 74 (2022) 102528 impacts are perceived accurately by the agents. Unlike standards, car­ 2.1. Policy-design module bon taxation generates revenue that can increase public support if it is used to reduce inequality. We argue that because of likely initial The policy-design module works as follows. The objective of the misperception of effects, climate policies are best designed to ensure a policy is to keep greenhouse gas (GHG) emissions within a carbon high public support during the first periods of implementation We budget consistent with the 2030 mitigation target of the Spanish gov­ further show that social interactions help generating public support for ernment. In particular, Spain has the objective to decrease carbon di­ policies that are beneficial for well-connected individuals. Finally, we oxide equivalent emissions by 47% with respect to 2017 levels (Spanish demonstrate that a higher income inequality has an ambiguous effect on Congress, 2019). Assuming that emissions decrease at a constant rate, public support for progressive climate policies. On the one hand, higher this target defines a trajectory of emissions with a mitigation rate of income inequality implies that redistributive policies will have a more approximately 5% per year to achieve the required emissions reduction. positive distributional effect, hence increasing the support for such We model two widely used policies, namely carbon taxation and per­ policies. On the other hand, given that social influence tends to increase formance standards. A carbon tax is a pricing instrument that directly with income, higher income inequality means that richer agents increase affects the production costs of high-carbon goods. A performance stan­ their influence on the opinion of other agents. Since high-income earners dard is a non-price instrument that fixes a certain intensity of emissions do not benefit from progressive climate policies, high income inequality on the production of goods, which is used to regulate the transportation can substantially diminish public support for policies with positive or energy sectors, e.g. the European Emission Standards, the Corporate distributional impacts. Average Fuel Economy and Clean Energy standards in the U.S., or the The remainder of this article is organized as follows. Section 2 de­ various white certificates schemes. An important difference between the scribes the model and its three modules, namely the policy-setting two policies is that a tax generates revenue that can be used in a variety module, policy-impact module and policy-support module. Section 3 of ways, among which we consider three types: contains the empirical parametrization. Section 4 presents numerical simulations to identify effective and acceptable climate policies and 1. Return the revenue through progressive transfers that favour poorer discusses the role of distributional impacts, opinion dynamics and in­ households (labelled hereafter as progressive). come inequality. Section 5 concludes, discusses policy implications and 2. Return the revenues proportionally to household income, i.e., suggests directions for further research. through a reduction of income taxes (labour tax reduction). 3. Fund projects that reduce GHG emissions (green spending). 2. A model of dynamic public support for climate policy For details on how revenue uses are implemented in the model, see The model consists of three modules to determine emissions and Section 2.2. economic impacts, public support, and changes in the policy stringency, The policy-maker sets an initial stringency τ0 for either a tax or the as illustrated in Fig. 1: performance standard. Adaptation of the stringency level then depends on the policy support. If more than 50% of the population supports the 1. A policy-design module initiates and adapts the policy design (see policy at the end of a period, consistent with a political majority-voting Section 2.1). It evaluates the policy in place based on the policy rule, the stringency is increased. This is implemented in the model by support. If more than half of the agents supports the policy, consis­ setting a fixed growth rate, ρ : τt+1 = τt (1 + ρ). Assuming that the tent with the widely practiced democratic majority rule, it is stringency increases over time at a fixed rate is consistent with what we strengthened; if not, its stringency remains the same. see already in various countries – notably Sweden, Argentina, Canada 2. A policy-impact module using a general-equilibrium model calcu­ (British Columbia) and South Africa (Ramstein et al., 2019). If public lates emissions reduction and distribution of wellbeing effects among support for the current policy design is less or equal the support households in accordance with the policy setting in the policy-design threshold (50%), then the stringency remains unaltered: τt+1 = τt . module affecting the consumption of low- and high-carbon goods Implicitly, we assume that the stringency cannot decrease even if public and services (see Section 2.2). The general-equilibrium nature of the support is very low. This is in line with the case of France where in model allows accounting for labour and goods market dynamics that response to public resistance the carbon tax was halted but not reduced underlie the ultimate policy effects. (Criqui et al., 2019). A complementary argument is that gradually 3. A policy-support module uses the indicators generated by the policy- strengthening policy would avoid a ”shock therapy” for the economy impact module to update opinions of households. To this end, we use (Pahle et al., 2018), which would also contribute to stabilize policy an agent-based model (see Section 2.3). This takes into account support. The policy goal differs from traditional analysis in public eco­ personal weights assigned by individuals to each impact indicator, nomics, which seeks to identify welfare-maximizing climate policy tra­ which depend on their political views. Through interactions in a jectories. Our approach can be seen as more realistic in the sense that social network, individual agents then exchange opinions until these governments do not necessarily pursue optimal policies but ones that are converge to a steady-state. feasible to implement given public support. The approach is empirically operationalized for the case of Spain. As 2.2. Policy-impact module described in Section 3, this involves use of results from a recent public opinion survey (Maestre-Andrés et al., 2021) to calibrate how effec­ The policy-impact module relies on a general equilibrium model, tiveness, personal wellbeing and distributional effects influence policy which features two firms that produce either a low- or a high-carbon support, as well as how people share their opinion about the policy in a consumption good. Households then decide about their labour supply social network. We further calibrate the policy-impact module using and how to allocate their budget between the two consumption goods. Spanish national statistics, notably to reproduce the observed produc­ They require a minimal consumption level of the high-carbon good, tivity growth and income inequality. Because we do not model the reflecting the empirical regularity that low-income households spend a Spanish energy sector, we rely on a stylized calibration of the marginal larger share of their income on carbon-intensive subsistence goods abatement costs, and we abstain from interpreting the absolute values of (Klenert and Mattauch, 2016; Oswald et al., 2020). the tax or standards. We instead focus on the qualitative results by In line with previous modelling studies (Klenert et al., 2018), we comparing different policies and studying their temporal trajectories. model two representative profit-maximizing firms by a Cobb-Douglas production function with labour, L, and energy input, E. One firm pro­ duces the low-carbon – or clean– good, C, and the other the high-carbon 3 T. Konc et al. Global Environmental Change 74 (2022) 102528 – or dirty– one, D. Firms buy labour from the agents at the market wage, w. Without loss of generality, we assume that the clean energy input has a zero carbon intensity. GHG emissions Gt are linear in the use of the carbon intensive input, Gt ≡ κED,t . The carbon tax, τD , increases costs of the carbon-intensive energy input, ED . Firms sell their good at the market price, pj . The firms’ profit maximization problem at time t can then be formally described as: ( ) ( ) [ ] maxLj,t ,Ej,t F Lj,t , Ej,t pj,t − Ej,t qj,t + τj,t − Lj,t wt ∀j ∈ C, D ( ) with F Lj,t , Ej,t ≡ At Lαj,t Eζj,t Here, qj,t is the price of the energy input and At is the total factor productivity. The first-order conditions of the firms problem are: [ ] αAt Lαj,t− 1 Eζj,t = w∀j ∈ C, D (1) [ ] ζAt Lαj,t Eζ−j,t 1 = qj,t + τj,t ∀j ∈ C, D (2) Agents are heterogeneous in terms of productivity, which translates into heterogeneous income for workers. We introduce a minimal con­ sumption level of the high-carbon good, D (known as Stone-Geary preferences), to reflect that low-income households spend a larger share of their income on carbon-intensive basic goods, such as food and Fig. 2. Relation between the policy impact on wellbeing indicator and the heating. Agent i ∈ N maximizes its welfare in each period, subject to a relative change of individual welfare due to the policy. budget constraint, formalized as: ( ) ( )σ ( )1− σ− η Vi,t ≡ Ui,t (D☆ i,t , Ci,t ) to express the indirect utility (or wellbeing) of agent i ☆ maxDi,t ,Ci,t ,Lit Ui,t Di,t , Ci,t , Li,t ≡ Di,t − D Cηi,t 1 − Li,t in period t. ( ) ( ) ( ) s.t. Di,t pD,t − τS + Ci,t pC,t − τS ⩽ϕi wt Li,t 1 − τL,t + bi,t Implementing performance standards and a tax combined with a reduction in labour taxes is straightforward. Abatement through green Here σ and η represent the utility weights of the high- and low-carbon spending is assumed to cost 40 Euros per avoided ton of yearly CO2 goods, respectively, ϕi the productivity of agent i, (1 − Li ) the leisure emission (Gillingham and Stock, 2018). Finally, modelling a progressive time, τL the labour tax and bi potential direct transfers (based on carbon- redistribution of tax revenues through direct transfers is more ambig­ tax revenues) to agent i. The income of agent i is therefore equal to uous, since the redistribution scheme can be made more or less ϕi wt Li,t (1 − τL,t ) + bi,t . We model performance standards as a tax on favourable to low-income households (see Appendix). For the sake of carbon-intensive inputs and a subsidy on output, τS (Goulder et al., simplicity and comparability with other model studies, we assume that 2016). The first-order conditions of the households problem are: progressive redistribution takes the form of equal transfers to all agents. ( σ 1 − Li,t ) p We parametrize the economic model to match the income distribu­ ( ) = ( D,t )∀i ∈ N (3) tion and carbon intensity of production in Spain (National Statistical 1 − σ − η Di,t ϕi wt 1 − τL,t Office of Spain, 2020), and recent estimates of the social cost of carbon σ Ci,t pD,t by integrated assessment models (Metcalf and Stock, 2017; Quinet, = ∀i ∈ N (4) 2019; Kaufman et al., 2020). In line with empirical estimates, we set the ηDi,t pC,t minimal consumption level of the high-carbon good such that a doubling ( ) Di,t pD,t + Ci,t pC,t = ϕi wt Li,t 1 − τLa,t + bi,t ∀i ∈ N (5) in income corresponds to an increase in emissions of 70% (Büchs and Schnepf, 2013). As a result, low-income households have a more carbon- Finally, we impose equilibrium conditions on supply and demand for intensive consumption basket than high-income ones, even though they the goods and labour, and on the government budget: emit less in absolute terms. This implies that low-income households ( ) will experience a higher relative wellbeing loss from the carbon tax. ∑ F LD,t , ED,t = Di,t (6) The module generates three indicators of policy impact: (1) personal i∈N wellbeing Wi , defined as the welfare variation due to the policy; (2) ( ) distributional effects Qi defined as the change in the Gini coefficient of ∑ the welfare distribution M due to the policy; and (3) policy effectiveness F LC,t , EC,t = Ci,t (7) i∈N Ei , defined as the ratio of the remaining annual carbon budget Bt ≡ ∑ ∑t G and the current level of emissions Gt . Wi is heteroge­ initialbudget− k=1 k LD,t + LC,t = ϕi Li,t (8) Tfinal − t neous across households. Qi and Ei are also indexed by i as they may i∈N ∑ ( ) account for potential heterogeneity in people’s perceptions about ED,t τD,t + τS,t Di,t + Ci,t + ϕi wt τL,t − bi,t = 0 (9) i∈N distributional effects and effectiveness. Yet, we assume that everyone has access to the same information about the effects of the policy so that We use the fact that Stone-Geary utility functions have a Gorman Qi and Ei are the same across individuals. While one could assume di­ polar form (Jacobs and van der Ploeg, 2019) and solve the model versity among agents in terms of information access and interpretation, defined by Eqs. (1)–(9) for a representative household with productivity the approach we follow seems a logical starting point for a first study of ∑ ϕ = i∈N ϕi . The distribution of consumption among households is this kind. determined ex post by the distribution of productivity. We find the Each indicator is normalized between 0 and 1, by capping E at 1 and equilibrium prices and wage as well as the equilibrium consumption of transforming W and Q with a logistic function. A value 0 indicates the the two goods, denoted by D☆ i,t and Ci,t . In addition, we use the notation ☆ worst outcome and 1 the best. Following the findings in behavioural 4 T. Konc et al. Global Environmental Change 74 (2022) 102528 Fig. 3. Distributions of number of peers and political ideology. economics about the perceptions of gains and losses (Kahneman and Table 2 Tversky, 1979), we assume that personal wellbeing and distributional Regression Table effects are concave in gains and convex in losses, and formulate the indicators using a logistic function (Eqs. 10 and 11). If an agent’s welfare Dependent Variable: Policy opinion or the welfare distribution are unchanged by the policy, the indicators Independent Variables OLS equal 0.5 (see Fig. 2). We set the steepness parameter of the logistic Personal wellbeing effects 0.365*** function to a = 0.2, so that the indicators converge to 0 as the change in (0.018) welfare or inequality approaches − 100%. Finally, effectiveness is Distributional effects 0.205*** defined as the completion of the climate target. It is maximal if the (0.014) Effectiveness 0.511*** emissions at time t (Gt ) are smaller or equal than the annual remaining (0.013) carbon budget (Bt ), and below 1 if they exceed it (Eq. 12). Personal wellbeing × Political ideology 0.008** (0.003) 1 Wi,t = ( ) (10) Distributional effects × Political ideology − 0.005* (0.003) V − Vli,0 1 + exp a i,tVi,0 Effectiveness × Political ideology − 0.011*** (0.003) 1 Qi,t = ( ) (11) N 2004 1 + exp a MtM− 0M0 R2 0.902 ⎧ [ ] ***p < 0.01, *p < 0.05, p < 0.1 ⎪ ⎪ Bt Bt ⎪ ⎨ if ∈ 0, 1 Gt Gt Ei,t = (12) ⎪ Bt ⎪ ⎪ ⎩1 if > 1. about the policy, in line with evidence from psychology (Howe and Gt Krosnick, 2017) and demonstrated by research on carbon-tax percep­ tions (Douenne and Fabre, 2022). This feature of opinion dynamics may have been at work in the case of carbon pricing in Australia, where 2.3. Policy-support module opinions have remained relatively stable despite public debate about the policy (Dreyer et al., 2015). The policy-support module uses an agent-based model (ABM) to We estimate the intrinsic opinion on climate policy based on the represent heterogeneous, dynamic and interactive opinions about the three indicators from the policy-impact module: personal wellbeing, policy. ABMs are flexible tools which allow to depart from traditional distributional effects and effectiveness. The relative importance of these assumptions about representative and socially isolated agents, namely factors depends on political ideology Ii of the agents. The empirical by describing a population of heterogeneous agents with a wide range of value of political ideology is assessed by the survey (see Fig. 3 in Section possible behaviours and interactions (Castro et al., 2020). We parame­ 3) and lies in the interval [1,10], with 1 denoting far-left and 10 far- trize the module using empirical data from a survey conducted among right. The intrinsic opinion of climate policy in the absence of social the population of Spain in August 2019 through a web-based question­ influence is calculated as follows: naire (see Section 3). The intrinsic opinions of agents in the model ( ) ( ) ( ) depend on the impacts of the policy and political orientation. This is POi,t = β1 + β4 Ii Wi,t + β2 + β5 Ii Qi,t + β3 + β6 Ii Ei,t (13) motivated by ample evidence that political views strongly correlate with attitudes to climate change and climate policy (McCright et al., 2016; The weights β are obtained by means of an econometric estimation of Cruz, 2017). The module reflects that evaluating public support is more Eq. (13). From the data, we obtain β4 > 0, β5 < 0, and β6 < 0, which complex than merely assessing the impacts of a policy, by recognizing means that agents with a right-wing political ideology put a higher than that an agent is influenced by opinions of its peers in a social network. average weight on personal wellbeing and lower than average weights We assume that agents are more influenced by opinions of peers that on effectiveness and distributive effects, while the opposite holds true share a similar political stance. Overall public support for climate policy for left-wing agents. The estimated coefficients are reported in Table 2 in depends on the ability of agents who (do not) support it to influence Section 3. other agents into supporting (rejecting) it. We also address opinion We need to make several assumptions to estimate the intrinsic stability. We assume that agents imperfectly learn about the actual ef­ opinion function. The survey only provides information about the fects of climate policy and are resistant to change their initial support perception of hypothetical policies. We assume that the difference 5 T. Konc et al. Global Environmental Change 74 (2022) 102528 between hypothetical and real-life policies is small, such that it is Table 1 reasonable to use the estimates provided by the survey as weights for Correspondence between survey questions and modelled variables actual policy support. Our method also assumes that people’s assess­ Variables Survey question Measurement Mean ment of climate policy is driven by objective information through (SD) observable indicators. In reality, a difference between objective and Policy Opinion How acceptable do you Likert scale from 1 3.00 perceived effects could arise, for example, due to media outlets reporting (PO) find a carbon tax? (completely (1.34) biased news or people not being able or willing to observe all relevant unacceptable) to 5 information. Despite this limitation, we think it is reasonable to assume (completely acceptable) Personal How do you think a Likert scale from 1 (much 2.24 that there is a correlation between the objective effects of climate pol­ wellbeing (W) carbon tax will affect worse off) to 5 (much (0.99) icies and their perception by the public (see Appendix). you personally? better off) Next, the initial opinion of an agent is subjected to social influence. Distributional How do you think a Likert scale from 1 (much 1.67 We represent social interactions as a similarity-biased process, where effects (Q) carbon tax will affect worse off) to 5 (much (0.88) low-income better off) individuals give a higher weight to the opinion of peers with similar households? political views. Policy How effective do you Likert scale from 1 (very 3.16 The social interactions influence policy opinion in the following effectiveness think a carbon tax is for ineffective) to 5 (very (1.04) manner (Konc and Savin, 2019): (E) reducing CO2 effective) ∑ emissions? θi,j POj,t Political Where would you Scale ranging from 1 to 4.47 j∈Ni SOi,t = (1 − γ)POi,t + γ ∑ (14) ideology (I) situate yourself 10, where 1 is ’left-wing’ (2.44) θi,j ideologically? and 10 is ’right-wing’ j∈Ni ( ⃒ ⃒) θi,j = exp − ⃒Ii − Ij ⃒ Here SOi,t is the current opinion of the policy, Ni is the set of peers in the social network of agent i, and θi,j is the measure of ideological sim­ ilarity between the agent i and its peers. The parameter γ ∈ [0, 1] cap­ tures the weight of social influence in an individual’s opinion formation. Note that Eq. (14) defines a steady state, denoting the state after repeated social interactions when no further change in policy accept­ ability is observed. We parametrize the social network in line with the household survey (see Section 3 for details). According to it, richer households tend to have more social peers and thus exert more social influence. Assuming that affluent agents have a higher political influ­ ence better represents feasibility constraints. It is reminiscent of studies showing that richer agents have more lobbying power to influence certain policy scenarios (Gilens and Page, 2014; Isley et al., 2015; Berthe and Elie, 2015). Finally, we assume that agents are resistant to changing their initial support about the policy. The support of agent i for the policy imple­ mented at time t is: ( ) Si,t = δSi,t− 1 + 1 − δ SOi,t (15) with δ the weight of the opinion in the previous period. The opinion Si,t Fig. 4. Co-dynamics between tax stringency, effectiveness, wellbeing effects takes values in the interval [0,1]. We assume that the policy has suffi­ and public support. Note that the revenue of the tax is returned in the form of a progressive transfer. cient public support if the median support is above 0.6, in line with the majority-voting rule. The value above of 0.6 corresponds to the response ”somewhat acceptable” of the 5-point Likert scale we use to parametrize ”completely ineffective/unacceptable” to ”completely effective/accept­ the model, see Section 3. The initial support Si,0 is calibrated on the able” in the case of the first set of questions and from ”I would be much survey data. It means that in our model the initial opinion of climate worse off/ they would be much worse off” to ”I would be much better policy can be biased by inaccurate perceptions. We assume that the ef­ off/ they would be much better off” in the case of the second set of fects of policies reduce the initial bias at the rate δ. questions (see Table 1). From this information we derive the weights of personal wellbeing effects, distributional effects and policy effectiveness in the support of climate policies (see Eq. 13). We normalize the weights 3. Parametrization in Table 2 so that POi always lies in the interval [0,1]. We also derive from the survey the characteristics of the opinion Our model uses empirical data from a survey conducted among the social network. To this end, we use responses to the survey question population of Spain in August 2019 through a web-based questionnaire ”With how many of [your peers] do you talk about climate change or (Maestre-Andrés et al., 2021). The sample of citizens was restricted to climate policy? You can enter a value between 0 and 100.” The resulting individuals over 18 years old. The survey had 2004 completed responses network has an average degree of 8 and resembles a ”scale free” topol­ (response rate 59%) obtained using a quota sampling technique repre­ ogy, with a highly asymmetric degree distribution where few so-called sentative of the Spanish population in terms of age, gender and ’star agents’ have a high number of peers and the majority of other geographical location. Respondents were first asked to evaluate the ef­ agents have few connections (Fig. 3). The survey further shows a posi­ fects of a climate policy and then to rate their acceptability of it. The tive correlation (0.10) between income and the number of peers. We survey data include respondents’ perception about the effectiveness, and reproduce this feature in our model, which implies that wealthier citi­ how they consider the carbon tax may affect them personally (personal zens tend to be more central in social networks (Fafchamps and Gubert, wellbeing) and low-income households in general (distributional ef­ 2007). fects). They could respond on 5-point Likert scales, ranging from 6 T. Konc et al. Global Environmental Change 74 (2022) 102528 Table 4 with progressive recycling of revenues achieves the highest public Support for different policies ensuring that emissions stay within the carbon support, because of high wellbeing and distributional effects. It also budget. receives more support from households in low-income deciles (Fig. 5). Policy Maximum Maximum Maximum Maximum The redistributive aspect of the progressive policy explains these effects. support wellbeing distributional effectiveness As low-income households are overcompensated for what they origi­ effects effects nally paid in carbon taxes, they are better off thanks to the policy. On the Carbon tax other hand, high-income households face a net loss due to the policy. with: Because the marginal utility decreases with consumption, the utility Progressive 0.76 0.58 0.64 1 gains of low-income households outweigh the utility losses of high- recycling Labour tax 0.66 0.49 0.46 1 income households, resulting in a higher aggregate wellbeing in the reduction population. Green 0.62 0.30 0.49 1 The performance standards and the carbon tax associated with a spending reduction in labour taxes have negative distributional effects. The sim­ Standards 0.65 0.47 0.46 1 ilarity between these policies is due to both favouring higher income Note: ”Maximum” before each indicator in the title row refers to the maximum agents with a lower carbon-intensity of consumption. The average average value over time achieved for the respective indicator. Note that wellbeing effect across the population is lower than with progressive maximum values for different indicators do not necessarily relate to the same tax recycling. As noted by Levinson (2018) and Goulder et al. (2016), trajectory. standards introduce an implicit tax on inefficiency and an implicit subsidy on efficiency, and thus benefit agents with a lower carbon- 4. Results: Identifying feasible policy trajectories intensity of consumption. Recycling the tax revenue through a labour tax cut yields a similar outcome, since the tax payment in proportion of 4.1. Comparison of performance standards and carbon taxation their income is lower for agents with higher incomes. As a result, stan­ dards and carbon taxation with a labour tax cut receive lower public We simulate the model with different initial values and annual support than with progressive redistribution. growth rates of stringency. For each initial stringency level, we assess Finally, a carbon tax with revenues used for green spending has a the lowest stringency growth rate such that total emissions stay within relatively low support. Here the revenue of the tax is not given to the carbon budget until 2030. We examine how the resulting trajectories households but is used to further reduce emissions. As a result, this compare in terms of public support and its components, namely personal policy scenario results in the largest wellbeing loss, in turn undermining wellbeing, distributional effects and effectiveness. Fig. 4 illustrates the policy support. Therefore, even though this policy could reach the output in terms of stringency and support for the trajectory starting with mitigation targets with the lowest tax, it enjoys the lowest public sup­ a relatively low carbon tax. Initially, effectiveness decreases because port, i.e. compared to the other policy scenarios considered. These policy stringency does not keep up with the emissions target. The target findings suggest that any carbon tax that is not associated with transfers is fulfilled thanks to high tax rates in later years. These cause wellbeing to households is bound to face strong public resistance. impacts to decrease quickly, also as progressive transfers do not Our results are somewhat in contrast with survey-based insights compensate the welfare losses of the high taxes. showing that people tend to prefer green spending among all the po­ We find that both performance standards and carbon tax can tential revenue uses (e.g. Kotchen et al., 2017). This contradiction can be potentially count on sufficient public support, if designed adequately. explained by the positive wellbeing effect of some form of green Table 4 reports the outcomes in terms of public support and its three spending, such as through improved public transport or lower air constituent components for the carbon tax combined with the different pollution, which falls outside the scope of our analysis. Another expla­ revenue uses, as well as for the performance standards. The carbon tax nation is that in our model agents form their opinion based on actual Fig. 5. Average public support for each income decile under different uses of carbon-tax revenue. As the simulations use random numbers, we report average results ± one standard deviation over 50 runs. 7 T. Konc et al. Global Environmental Change 74 (2022) 102528 Fig. 6. Carbon tax trajectories with progressive revenue recycling, for δ = 0.5 and γ = 0.5. We show only tax trajectories that receive sufficient support during the entire period and achieves the maximum average value over time for either the policy support or at least one of the policy-impact indicators (distributive effects and personal wellbeing). As the simulations use random numbers, we report average results ± one standard deviation over 50 runs. policy impacts, while the survey reflects respondents’ perception. Since support for standards is likely to be higher than for taxes (Rhodes et al., households generally fail to recognize fully the systemic effects of a 2017) even though they are significantly more costly (Parry et al., carbon tax on the economy as a whole, they tend to regard green 2014), which contrasts with our results. It is unlikely to be caused by a spending of the tax revenues as the main way to support emission re­ preference for non-pricing instruments over pricing instruments because ductions (Kallbekken and Aasen, 2010). Some surveys indicate that the willingness to pay may be even higher for a pricing instrument than 8 T. Konc et al. Global Environmental Change 74 (2022) 102528 Table 5 as high as a policy with lower initial tax and a steeper increase (Fig. 6). Support for different tax revenue uses in function of the strength of social in­ The explanation is that households are resistant to change their opinion fluence, γ. about a policy and do not fully update their opinion about a policy every Use of carbon-tax revenue γ =0 γ = 0.25 γ = 0.75 time new evidence about its effects arrives (see Eq. 15). A policy starting Carbon tax with: with a lower tax gathers more public support early on and can exploit Progressive recycling 0.82 0.76 0.74 this support in later periods due to the path dependence of opinions. Labour tax reduction 0.65 0.66 0.71 Green spending 0.65 0.65 0.62 4.2. The role of opinion stability and social influence Standards 0.65 0.65 0.71 Note: We refer to maximum support as the maximum average support over time In our model, the public support for the policy depends on its direct achieved with the tax recycling policy. impacts on households and on opinion dynamics. We discuss two mechanisms that affect the aggregate support for climate policy, namely opinion stability and social influence. Table 3 Fig. 4 shows that when opinions of agents are more stable, tax tra­ Parameter values jectories that maximize public support and personal wellbeing differ. Parameter Baseline Source The more resistant the opinions of agents are to change (i.e. the higher is value δ), the steeper is the tax trajectory that maximizes policy support. The Number of agents 1000 reason is that a lower initial tax gathers more support early on, which Number of periods 13 Difference between the target year translates into higher support later because of stable opinions. Hence, 2030 and the starting year 2017 Number of social links 4000 Re-analysis of data from Maestre- due to opinion dynamics, welfare-maximizing policies are not neces­ Andrés et al. (2021) sarily the ones gathering largest public support. Wellbeing effect weights β1 = 0.365; β4 Re-analysis of data from Maestre- Social interaction also has an effect on the support for the carbon tax. = 0.008 Andrés et al. (2021) Richer agents tend to have more influence in the social network. Distributional effect β2 = 0.205; β5 Re-analysis of data from Maestre- Therefore, if a policy is beneficial to them, they are able to influence weights = − 0.005 Andrés et al. (2021) Effectiveness weights β3 = 0.511; β6 Re-analysis of data from Maestre- their connections to get support for it. Schemes of revenue recycling = − 0.011 Andrés et al. (2021) which favour low-income agents count on less support the higher is the Weight of past opinions δ 0.5 social influence exerted by rich agents. On the other hand, when the Dreyer et al. (2015), Howe and Krosnick (2017), Douenne and latter benefit from the revenue use, stronger social influence increases Fabre (2022) the support for the climate policy (Table 5). The effect is stronger for rich Intensity of social 0.25 Re-analysis of data from Becker households with right-wing political orientation, as they put more influence γ et al. (2017) weight on their personal wellbeing effects than left-wing households Abatement cost of green 40 Euros per Gillingham and Stock (2018) (see Table 3 in Section 3). Fig. 7. spending ton Annual productivity 1% OECD (2020) growth rate 4.3. The role of income inequality Gini coefficient of income 0.34 Spanish National Statistics distribution Institute (2020) Income inequality influences public support for climate policies Share of labour in 0.4 production function, α through two main channels. On the one hand, higher income inequality Share of energy in 0.4 implies that the distribution of consumption of the high-carbon good production function, ζ changes: more agents are close to their subsistence level of consumption Price of polluting energy, 0.5 and agents with the highest income are responsible for a higher share of qD total emissions. As a result, a carbon tax with progressive recycling has Price of clean energy, qC 1 Share of polluting good in 0.4 better distributional – and wellbeing – effects, because it benefits the utility function, σ bulk of the population (and similarly regressive policies have worse Share of clean good in 0.2 distributional effects). Over time, this increases the public support for utility function, η climate policies with positive distributional effects. Share of leisure in utility 0.4 function, σ On the other hand, higher income inequality means that wealthier Subsistence consumption 0.0031 agents have a stronger influence on the opinions of other agents. This of polluting good, D stems from the empirically motivated assumption that social influence increases with income. As stated above, with high income inequality, a carbon tax with progressive recycling benefits a larger number of agents for standards (Aldy et al., 2012; Kotchen et al., 2017). A possible but negatively affects high-income earners. Depending on the strength explanation is that households misperceive the costs associated with a of social influence, they are able to convince other agents that pro­ stringent standard because they are not as salient as with pricing in­ gressive policies do not merit support, which drives down the public struments. Given a lack of both theory and data, such perception issues support for climate policies with positive distributional effects. are difficult to capture in the model. The link between income inequality and public support for different Since the carbon tax with progressive revenue recycling performs climate policies is ambiguous, and strongly depends on the capacity of best on public support, we consider it in more detail. Fig. 6 shows the tax the wealthiest agents to influence the implementation of policies (Gilens trajectories for the policy designs which score best on the distinct impact and Page, 2014). Fig. 8 shows that for high income inequality and indicators or maximize policy support, while ensuring that emissions strength of social influence, regressive policies gather the largest public stay within the carbon budget and public support for each trajectory in support. When people have different weights to influence political any period exceeds the support threshold of 50%. feasibility, a minority of individuals can block a policy that would be The increase rate that maximizes personal wellbeing is relatively beneficial to the majority. It shows that calculating direct effects of low. This slow growth of the tax rate is driven by economic growth, as climate policies is not sufficient to assess public support. The addition of households due to increasing consumption pollute more and should then other mechanisms that increase the power of certain groups could be subject to a higher absolute tax in order to stay within the carbon reverse the positive feedback loop between stringency and public sup­ budget. This tax trajectory, however, does not achieve a public support port. For instance, companies or people who dislike climate policy could 9 T. Konc et al. Global Environmental Change 74 (2022) 102528 (A) (B) Fig. 7. Carbon tax trajectories maximizing support and wellbeing for two values of δ reflecting distinct resistance to opinion change. Results pertain to progressive revenue recycling option. carbon taxation under three distinct revenue uses that fulfil the re­ quirements of effectiveness and acceptability, and identified the one enjoying the highest average public support. We find that carbon taxation generates more public support than performance standards and that transfers to households are key to ensuring maximum support for climate policy. Progressive redistribu­ tion is the most supported revenue-recycling policy because of its posi­ tive wellbeing effects for the majority of the population. Social influence helps gathering public support for policies that are favourable to well- connected individuals. This is detrimental for transfers aimed at low- income households because these tend to exert less social influence. Agents’ tendency to resist opinion change translates into higher public support for a tax trajectory with a lower initial tax that increases fast in later periods. This suggests that a carbon tax is best designed in a way to win considerable public support early on, which can then be exploited in later periods. Finally, income inequality has a double-edged effect on public support. On the one hand, higher income inequality generally increases public support for progressive policies because of their positive distributional effects for a large share of the population. On the other hand, social influence of the richest agents increases with income inequality, meaning that support for policies that are detrimental to them, such as progressive policies, decreases. There are several avenues for further research. First, more attention Fig. 8. Policy enjoying maximum public support as a function of the Gini index is needed to understand the relation between policy impacts and public of the income distribution and the strength of social influence, γ. For combi­ support. In particular, longitudinal data of public support for carbon nations of Gini index and γ below the blue curve, a carbon tax with progressive taxes and other instruments could provide valuable information, notably recycling gathers most public support. For combinations above the curve, a if it would allow measuring the change in public support before and after carbon tax with a carbon tax with labour tax reduction, i.e. regressive recycling, an increase in the stringency of climate policy. The role of income as a gathers most public support. To aid interpretation of the Gini index values, note factor co-determining the perception of policies also merits further that South Africa has a Gini index around 0.60, China around 0.44, the United analysis. Because our results are sensitive to parameters in the policy States around 0.41, Japan around 0.32, and the European Union around 0.30 (World Bank, 2020; Eurostat, 2020). opinion module, conducting similar surveys in countries other than Spain would contribute to robustness of the results. Second, instead of focusing on a single policy instrument, such as a carbon tax or a per­ engage in lobbying action and have a direct influence on policy de­ formance standard, one could adapt our model to deal with a policy mix cisions (Isley et al., 2015). In this case, increasing the stringency of involving several instruments, which more realistically captures the climate policy might create more resistance, despite having positive reality of climate policy. Third, our framework could also be applied to effects for most people. non-climate policies for which effectiveness and public support are essential, such as taxes on alcohol, tobacco and food (Reynolds et al., 5. Conclusions 2019) or road pricing (Schade and Schlag, 2003). Fourth, while here we focused on citizen support, one could extend the framework with Traditionally, models of climate policy focus on efficiency. We additional mechanisms underlying political feasibility, such as lobbying designed a model to identify carbon taxes and performance standards by firms and NGOs. Finally, further assumptions regarding policy sup­ that are both effective and acceptable. This was motivated by substantial port can be explored. For instance, one could assume that incomplete evidence that public support for climate policy is critical to its imple­ information due to media framing or the social network creates a biased mentation. We developed an innovative model consisting of three perception of policy impacts. In this case information policies correcting modules – for policy design, calculation of impacts, and derivation of the misperceptions could be studied as complementary instrument next public support. We assessed trajectories of performance standards and to more traditional climate policies. 10 T. Konc et al. Global Environmental Change 74 (2022) 102528 Declaration of Competing Interest Castro, J., Drews, S., Exadaktylos, F., Foramitti, J., Klein, F., Konc, T., Savin, I., Bergh, J. v.d., 2020. A review of agent-based modeling of climate-energy policy. WIRES Clim. Change 11, e647. doi:10.1002/wcc.647. The authors declare that they have no known competing financial Clayton, S., 2018. The Role of Perceived Justice, Political Ideology, and Individual or interests or personal relationships that could have appeared to influence Collective Framing in Support for Environmental Policies. Soc. Just. Res. 31, the work reported in this paper. 219–237. https://doi.org/10.1007/s11211-018-0303-z. 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