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The cost of mitigation revisited

A Publisher Correction to this article was published on 29 November 2021

This article has been updated

Abstract

Estimates of economic implications of climate policy are important inputs into policy-making. Despite care to contextualize quantitative assessments of mitigation costs, one strong view outside academic climate economics is that achieving Paris Agreement goals implies sizable macroeconomic losses. Here, we argue that this notion results from unwarranted simplification or omission of the complexities of quantifying mitigation costs, which generates ambiguity in communication and interpretation. We synthesize key factors influencing mitigation cost estimates to guide interpretation of estimates, for example from the Intergovernmental Panel on Climate Change, and suggest ways to improve the underlying models. We propose alternatives for the scenario design framework, the framing of mitigation costs and the methods used to derive them, to better inform public debate and policy.

Main

The United Nations Framework Convention on Climate Change states that “policies and measures to deal with climate change should be cost effective to ensure global benefits at the lowest possible costs”1. Correspondingly, the government-approved outlines of Intergovernmental Panel on Climate Change (IPCC) reports often explicitly indicate that the macroeconomic costs of mitigation should be assessed. For example, the outline for the upcoming Sixth Assessment Report (AR6) requests that authors assess “Economics of mitigation and development pathways, including mitigation costs”2, reflecting concerns about the costs of climate policy. This concern is mirrored in national policy documents such as the 2007 Stern Review3, and more recently from the USA4, the UK5 and the European Union6.

For decades now, the IPCC has been tasked with assessing the literature on macroeconomic costs of mitigating climate change and has responded by publishing both estimates of, and the limitations inherent in, long-term macroeconomic projections7,8 (Fig. 1 and Supplementary Section 1). Making such estimates is a complex undertaking that, although rooted in economics, requires consideration of elements from engineering, political science and sociology. It is not surprising that these complexities have led to misunderstandings and controversy. For example, numerical estimates of the costs of climate mitigation reported in the IPCC’s Fifth Assessment Report (AR5) have elicited reactions ranging from “their bills have become enormous”9 to “salvation gets cheap”10. Relevant stakeholders, especially those more at risk from a transition to a low-carbon economy, have emphasized the interpretation that efforts to mitigate climate change will lead to substantial macroeconomic losses. This emphasis may have succeeded despite the cautious framing of the estimates in IPCC reports, especially in AR5. The caveats, clearly stated in the report, caution against taking these estimates at face value, but they risk getting lost when these numbers are used in the general audience discourse.

Fig. 1: Evolution of representation of mitigation costs in IPCC reports.
figure 1

Each successive IPCC report synthesizes the main messages emerging from recent developments in the underlying scenarios literature. *Instead of mitigation, ‘limitation’ was the term used in the early days of the IPCC to refer to reduction of GHG emissions. CoM, cost of mitigation; NRP, no-regret potential; CP, climate policies.

So that this discourse can become more critically informed by the underlying facts, we here review the process of developing and interpreting mitigation cost estimates and unpack key elements that form the basis for their estimation. Earlier IPCC Assessment Reports explored costs of mitigation more theoretically, but their treatment has gradually shifted towards a more quantitative basis (Fig. 1). Scenario quantification with models such as integrated assessment models (IAMs) has been part of IPCC assessment reports since the beginning11 (Box 1).

Currently, climate mitigation scenarios do not consider important determinants of net costs. In this Perspective, we discuss missing elements, highlighting the uncertainties involved and the ambiguities in the size and sign of the changes resulting from these deficiencies. We also illustrate opportunities for an improved presentation of mitigation costs that may help size opportunities for social, environmental and economic benefits beyond those from direct climate mitigation.

Costs of a changing climate

Perhaps the most important omission from estimates of economic impacts of mitigation is that calculated costs do not include impacts from climate change itself, and the associated economic benefits of avoided impacts11,12,13,14. That is, reported estimates represent the gross costs of mitigation. Impacts include loss of agricultural productivity15, heat-induced mortality and morbidity16,17 and loss of labour productivity18,19, infrastructure losses from extreme events and sea-level rise20, biodiversity losses21 and many others22. Climate stress also has a complex relationship with migration and related geopolitical instability23 and with financial instability24,25. The omission of impacts from estimates of economic costs of mitigation reflects the historical structure of the IPCC, with mitigation benefits (that is, avoided impacts) and mitigation costs featured in different so-called Working Groups II and III. However, this separation has created room for scholars and policy-makers to focus only on the cost side of mitigation, ignoring the benefits26,27,28,29,30. Moreover, this separation also results in unrealistic reference scenarios that ignore climate damage.

The challenge of estimating the aggregate economic effects of the physical impacts of climate change lies in a dearth of data, high uncertainties in regional climate change and the controversial or impossible nature of assigning costs to human lives, biodiversity or cultural heritage. We do not assess these complex aspects here in detail. However, studies that also include economic impacts of climate change in detailed-process-based IAMs are emerging in the literature31,32,33, but robust comprehensive estimates are not available. Bringing new elements such as non-market damages34 into the analysis adds further value to avoiding damages, but also adds sources of uncertainty to the overall outcome. Continuing use of no-impact baselines in most studies and assessments is therefore likely. Still, refining the granularity of climate impacts, and bridging results of bottom-up approaches (for example, ref. 35), which start from detailed biophysical impact modules, and econometric top-down methods36 provide fruitful avenues for future research37.

While methodological improvements in estimating economic impacts of climate change are welcome, available literature already indicates that structural uncertainty about damages and the risk of tipping points warrant ambitious climate action38,39. Furthermore, climate change poses serious risks to economic and geopolitical stability, via, for example, risk transmission channels in the financial24,25,40,41 and agricultural42,43 sectors. Finally, climate change increases the risks of extreme events at the ‘tail end’ of distributions—low-probability but high-impact events potentially causing catastrophic and irreversible damage38,44. However, the high uncertainty attached to the economic implications of such events38 means that including them in numerical cost estimates may further obscure rather than clarify policy options. Still, the avoided impacts resulting from mitigation must be present in the framing of the economic impacts of climate policy, and the social cost of carbon remains an important concept, particularly when political commitment is uncertain45,46.

Crucially, estimating costs of mitigation by comparing against a hypothetical reference without climate impacts provides a skewed image to policy-makers and stakeholders. A more relevant question might be how to implement mitigation in a way that is compatible with improving human welfare or promoting sustainable development. One way to abstract from climate change impacts in the discussion on mitigation pathways is to explore sets of scenarios that achieve similar cumulative emissions, as this would compare scenarios with similar climate impacts. Such temperature-clustered scenarios could differ in how they achieve their climate goals (timing, technology and instrument choices), and would therefore provide insight into the corresponding costs and how they are distributed across society. Although IAMs routinely produce these types of scenario (see, for example, ref. 47 or the illustrative scenarios in the IPCC 1.5 °C report48), currently the macroeconomic costs of mitigation are calculated by comparing mitigation scenarios with a baseline with very different temperature outcomes. While climate impact variability is reduced across temperature-clustered scenarios, it is not necessarily eliminated altogether. Temperature overshoot may imply strong impacts, particularly when thresholds for tipping points are crossed. For this, recent literature49 can guide design of temperature-clustered scenario ensembles. Furthermore, how climate policy itself is implemented may influence impacts of global warming by affecting the capacity of vulnerable socioeconomic groups and regions to adapt to changing climate conditions. This should be acknowledged in future work, for instance by revealing the economic impacts for heterogeneous agents and regions in the world50,51.

Minor losses to a wealthier world

A common instinctive reaction of an untrained reader to the estimates of numerical losses is that mitigation leads to a reduction in economic output and is not worth the cost. However, when presented differently, mitigation scenarios can highlight that decarbonizing the economy is understood to happen alongside persistent growth of per capita income over time. This key perspective of the economic impacts of climate change mitigation points to a communication opportunity for the upcoming IPCC AR6. We illustrate this in Fig. 2. At the basis of mitigation cost estimates typically lie annual global consumption (and gross domestic product, GDP) growth rates between 1% and 4% throughout the century (for example, Shared Socioeconomic Pathway 2 (SSP2) projections in ref. 52). As such, the consumption losses reported in AR5 represent a small reduction in wealth over the entire century, when considered in the context of a reference in which consumption “grows anywhere from 300% to more than 900% over the century”53. As is clearly explained in the AR5 text, the median annualized reduction in the growth rate of consumption is only 0.06 percentage points (0.04 to 0.14) compared with consumption that grows between 1.6% and 3% per year in the baseline53. The order of magnitude of this cost estimate arguably represents a negligible number when put in the perspective of economic growth over the century and the corresponding uncertainties involved in projecting long-term economic activity (Fig. 2a). This presentation of the economic impacts of mitigation could be reinforced in future estimates (including IPCC reports), emphasizing that steady economic progress is consistent with reaching the climate goals of the Paris Agreement, and that comparable levels of per capita income can be obtained while enhancing the economy’s carbon efficiency by a factor of five (Fig. 2b). Furthermore, highlighting channels that can bring economic gains of climate policy in key figures and headline statements in the report would provide a more balanced representation of the economics of mitigation.

Fig. 2: Mitigation costs in a growing economy.
figure 2

a, Consumption growth variation across baselines, models and mitigation scenarios. The green bar indicates the results range of the WITCH-GLOBIOM model. The grey wedge is the range of consumption growth across all SSP baselines from the SSP database. b, Producing more (GDP) with less (GHG emissions). Model results from four IAMs with endogenous GDP estimation for scenarios that combine middle-of-the-road socioeconomic assumptions (SSP2) with five different levels of climate change mitigation stringency. Thin black lines in b indicate GDP per capita mitigation frontiers for milestone years for each model. Perfectly vertical lines would indicate no reduction in GDP per capita. Negative slopes indicate decreasing GDP per capita with growing mitigation effort. See Supplementary Section 4 for variations of b using other SSP scenarios. Data source: SSP database135.

What is not evident in the panels in Fig. 2 is how this growing wealth, as well as the mitigation costs, are distributed across geographies, income classes and socioeconomic groups. In fact, moderate GDP changes hide deep transformations in economic structures that may lead to regionally and sectorally differentiated economic decline or prosperity8,54,55. Mitigation creates new low-carbon value chains (‘sunrise’ industries) and phases out old carbon-intensive industries and occupations (‘sunset’ industries). For example, levels of stranded fossil fuel assets will vary by region and by commodity56, with the lowest-cost producers potentially gaining or maintaining market share while higher-cost producers see sunset industries diminish or disappear completely57. While there is potential for well-designed policy to reduce undesired effects of mitigation, ill-designed transitions can cause rapid repricing of assets and economic uncertainty, raising the risks of financial instability25 and social unrest58. For instance, coal phase-out raises acute issues of just transition for coal-dependent communities59,60. Similarly, the avoided climate damage would be different across geographies and income classes. Climate action can potentially benefit vulnerable households that may be disproportionally impacted by climate change, if mitigation policies and complementary measures seek to strengthen the resilience of low-income households, reduce energy poverty and enhance social protection simultaneously61,62. Failing to do so would further exacerbate the challenges to adapt to climate change for vulnerable socioeconomic groups and regions63. Moreover, emission taxation has important distributive effects64. Revenues from emission taxation can be used to lessen its regressive distributional impacts or even turn the policy into a progressive policy, reducing inequality or improving wellbeing of lower-income households65,66,67,68.

In addition to highlighting the small relative consumption losses overall, IPCC assessments could put more emphasis on distributional issues of climate policies and corresponding complementary policy measures that ensure an equitable transition to a low-carbon economy. Regional cost estimates are presented in AR5 Chapter 6 (ref. 8) but, due to political sensitivity, were excluded from the Summary for Policymakers. Scenarios exploring how to mitigate distributional inequities could help increase ambition in the revised nationally determined contributions (NDCs). Furthermore, clearly acknowledging the caveats of GDP as a mitigation cost metric, and reporting broader and additional welfare metrics such as distribution of income, will enable a science-based societal debate and the design of appropriate complementary measures to ensure a fair transition.

Imperfections define reality

Climate action in line with the Paris Agreement will require structural changes to the economy69,70,71. Rather than isolated climate policies, this deep transition will need to be supported by policy packages containing sector-specific instruments, which can, and arguably should, be designed in coordinated ways that enhance cross-sectoral synergies and minimize trade-offs. Such packages can concomitantly reduce emissions and improve economic efficiency by enhancing policy coordination across sectors and geographies, lifting information barriers and removing incumbent power, ensuring a stable climate for long-run investments through credible government signals or enabling innovators to be rewarded for socialized benefits of their investments. A broad-based policy package approach can help accelerate the transition to meet ambitious societal objectives72,73,74. This transition also probably requires a full quiver of fiscal, financial and monetary policy instruments to be deployed to enable a favourable financial environment to unlock required investments across geographies and sectors75. It stands to reason then that such far-reaching policy packages should be aimed at also removing existing inefficiencies by including pro-development measures that ensure broader human welfare gains.

The reference scenario against which the costs of climate action are calculated by design reflects the assumptions and concepts underlying the modelling approach with which it was created. Currently, models that assume well-functioning economic systems dominate the literature (although there are notable exceptions76,77,78,79,80,81). Assumptions of such ‘first-best’ or idealized economies often include that agents make rational choices under perfect information, markets operate under perfect competition (no market power) and goods, capital and workers move across sectors of the economy without transaction costs11,82. Clearly, this represents an overly stylized view of the real-world economy, which is characterized by biases and imperfections in information, competition and access to capital83, as well as by limitations to the flow of goods, capital76 and labour, across regions, sectors and social classes. Such imperfections are often referred to as market failures84,85,86.

These imperfections imply that resources are allocated in suboptimal ways by the economy. This keeps the economy from operating at its production frontier and may lead to a misallocation of capital from its most productive uses as well as persistent unemployment. However, typically, these market failures are not explicitly represented in studies estimating the macroeconomic costs of climate policy. When limits on greenhouse gas (GHG) emissions are introduced into such an idealized reference economy, model simulations will invariably result in economic losses. The constraint restricts the choice set of economic agents (for example, no fossil fuel use in a production process) and the benefits of mitigation are not accounted for. Hence, models that take a simplified first-best economy—without distortions, imperfections and market failures—as a starting point of their analysis tend to limit the potential range of outcomes at both ends. On the one hand, such models exclude the economic gains that would result from correcting the market failures and imperfections. On the other, they do not include the economic losses that would arise if the climate transition did not resolve economic inefficiencies, or even exacerbated them by, for example, further concentrating market power. In this sense, the current estimates span a narrow range of economic outcomes, which will depend on the way in which climate policies are implemented. Capturing and quantifying a broad set of behavioural imperfections and market failures, however, is a daunting task, while a stylized or simplified representation of the economy makes it possible to model the transformation and to explain the results transparently. More research effort is needed to explore the size and sign of the change in economic activity that results from including second-best elements in a modelling framework.

Useful policy insights can be provided by including such channels in models and scenarios, which are useful tools with which to explore the interlinkages and ramifications of policy packages. Overlooking these opportunities in models that intend to inform policies may come at the risk of mitigation cost estimates that are biased high, and potentially diminishing both societal support for strong climate action and the identification of win–win opportunities. Conversely, it can also highlight transition assistance costs that add to the mitigation burden. Reskilling workers, reindustrializing states that lose their vital fossil fuel revenues and other such policies will take coordinated effort and additional resources.

Comparing with appropriate benchmarks

A no-climate policy world does not exist, and assuming away all existing policies is neither trivial nor desirable. A reference that ignores already adopted climate policies artificially inflates the divergence with ambitious pathways12,87,88, driving up the mitigation cost. Recent research88,89 shows that current policies are compatible with global temperature increases that are lower than projected warming in scenarios that neglect any existing climate policy measures. Starting from a reference scenario that represents a plausible future emission pathway, including technological progress and climate impacts, is a first step in ensuring mitigation cost estimates are realistic. The next step is designing a reference scenario accounting for economic imperfections that can be potentially resolved with smart climate policy packages.

When imperfections and multiple externalities are introduced in a model-based assessment and the implications studied explicitly (a situation referred to as a ‘second-best’ setting), well-designed policy interventions could enhance economic efficiency and generate positive economic impacts90. We next explore some of the relevant mechanisms by which this can be done. However, including real-world features does not automatically imply that mitigation costs will be lower. Accounting for some types of market failure in models may actually work in the opposite direction, since some mechanisms may raise estimates of the costs of climate action, as is the case of potential short- to medium-run frictions in the transition to a low-carbon economy. For example, frictions to reallocation of workers from one sector to another or other rigidities in labour markets have been found to increase cost estimates if left unresolved77,91. Conversely, by explicitly including such dynamics, it becomes possible to assess how specific compensatory policies can alleviate these burdens58,79,91,92,93.

Capturing real-world features

Explicitly modelling the key channels affecting the cost of mitigation will improve our understanding of the implications of any effective set of climate policy measures. We identify and review five categories of institutional or behavioural imperfections, instances in which the idealized world view often adopted in modelling exercises (first best) behaves markedly and often persistently differently from reality (second best). These categories include co-benefits, behavioural imperfections, knowledge spillovers, investment and finance, and pre-existing distortions (we summarize the categories here and provide a detailed discussion in Supplementary Section 5).

In addition to avoided climate impacts, well-designed climate policies can result in co-benefits such as reduced air pollution. These synergies and co-benefits may offset costs and potentially deliver net benefits (no-regret potentials in SAR7). Moreover, they are desirable from a welfare standpoint and should be considered in drafting and evaluation of policy measures, whether in monetized form94,95 or not: for example, simply as health outcomes96,97,98.

Humans often behave in ways detrimental to our health, wellbeing and purses, outright irrationally in some instances and boundedly rationally in others. For example, food and energy consumption may deviate from the optimum for welfare maximization due to habit formation and myopic views. A first-best reference based on rational behaviour implies optimal decision-making for energy and health, leaving no margin for welfare gains from climate policies that spur energy efficiency or nudge towards healthier diets. However, it is challenging to steer decisions towards energy efficiency and healthy diets, mitigation options typically considered very cheap in IAMs. Importantly, bringing this kind of behavioural bias into the analysis has implications for the optimal mix of policy instruments99. As many existing models and scenarios rely on (implicit) carbon pricing as the primary policy lever, they do not represent the opportunities of alternative instruments explicitly.

First-best references also imply optimal research and development investment levels to produce innovation in new technologies and market design. However, innovators may not fully capture the benefits of their innovation, since knowledge spillovers allow other agents to benefit from the new knowledge and capture some of the benefits (known as positive externalities). Second-best reference scenarios may imply low research and development, providing an opportunity for climate policy packages to address this imperfection through incentives.

On finance and investment, first-best references or scenarios that assume optimal allocation of resources at all times are ill equipped to explore policies that address capital underallocation, a situation in which we are currently living100,101,102 as indicated by negative interest rates. This was true even before COVID-19 and is relevant for stimulus package discussions. Some models operate under equilibrium paradigms, which limit annual investments to the amount of savings available each year. In reality, fiscal and monetary policies such as quantitative easing aimed to stimulate the economy inject cash beyond available savings and increase available funds for debt financing through loans103,104. In times of low growth, low interest rates and apparent underinvestment, taxing carbon emissions rather than capital can increase economic efficiency105.

Finally, pre-existing distortions are often the result of inefficient taxation, and some constituencies with particularly inefficient tax systems can leverage climate policy to deliver ‘double dividends’106,107 and improve economic performance by using revenues from carbon taxation to, for example, remove labour market imperfections by lowering labour taxes79 or raising the efficiency of other types of tax (see Supplementary Section 7 for a discussion on the European Union’s energy excise tax reform).

The assessment of policy design results from the ability to compare costs and benefits—and how they are distributed across sectors, households and regions—between scenarios that differ in terms of instrument choice, policy coverage and speed of implementation. An encompassing approach to climate policy may provide the leverage and momentum to address some of these imperfections through institutional reform and broad policy packages. Studies that start from a second-best situation explicitly incorporating these channels can identify positive economic outcomes and inform policy design. In addition, these mechanisms affect GDP through total factor productivity (TFP). TFP is an exogenous input to many models because endogenizing it involves complexities and uncertainties, but doing so can provide policy-relevant insights (Box 2).

Although we do not enter into a detailed discussion here, shortcomings of GDP as a metric for progress have widely been acknowledged (including in AR554), along with potential alternatives108,109,110. Recent work suggests that decreasing consumption111, such as reductions in final energy demand112 and food waste113, can form an integral part of the climate solution with desirable features from a societal point of view. The narrower concept of economic activity is still used as a measure of impact in policy documents, such as the UK Climate Change Commission’s report on reaching net zero5. While economic growth and the associated living standards and fiscal revenues remain important, there are other considerations that should weigh in on policy assessment. As noted, GDP is a poor metric for welfare, and the underlying structure of the economic flows that make up GDP should be unpacked and assessed for their desirability or alignment with broader policy objectives. Although there are tensions between the concepts of green growth and degrowth, there are also synergies114, suggesting that climate action can benefit from wider-scope policies115. When extending the concept of GDP to properly account for the environment, evidence from the USA suggests that environmental regulation brings macroeconomic benefits, not costs116,117. Recent evidence from Europe indicates that the direct link between air pollution and GDP growth may be larger than thought previously118. Conversely, GDP is sometimes linked to welfare-reducing activities, creating opportunities to decouple GDP from resource use and GHG emissions119.

Net welfare is what matters

The mechanisms explored above map onto three transmission channels for the impacts of mitigation action on economic activity: avoided impacts from climate change, co-benefits of mitigation measures and resolution of socioeconomic distortions and imperfections (including behavioural imperfections, knowledge spillovers and suboptimal investment and finance). This Perspective argues that measures feeding into these channels are expected to increase economic activity and welfare, potentially offsetting mitigation costs such that net gains arise. It is not possible to say ex ante whether the benefits exceed the costs or vice versa, that is, whether mitigation action will lead to higher or lower economic activity. This will depend on the measures being analysed and the context into which new policies are introduced.

Accounting for uncertainties, Fig. 3 is a conceptual representation of the effect on aggregate economic activity through each channel. Rather than absolute values, the arrows indicate the direction of change resulting from these effects. Mitigation applied to a first-best reference that does not account for avoided damages, co-benefits, underinvestment and other imperfections in the economic system invariably leads to losses to the aggregate economy (grey arrow pointing down). These losses can be offset by the three transmission channels. The first two involve the inclusion of avoided impacts and co-benefits (green and blue arrows pointing up). The third channel involves the implementation of second-best features into the reference scenario that are corrected via policy packages (yellow arrows pointing up). Insight on each of these four arrows can inform policy design and investment decisions. The more successfully the policy packages resolve reference scenario imperfections, the larger the positive contributions of the green, blue and yellow arrows. If the magnitude of these gains is larger than the direct losses typically captured by economic models, the scenario results in welfare gains or higher economic activity.

Fig. 3: Economic impacts of mitigation action through three transmission channels.
figure 3

Impacts are shown in the short term (top row) and long term (bottom row) and across variations in mitigation timing. The three channels include avoided climate change impacts (green), co-benefits of the mitigation policies (blue) and resolution of socioeconomic distortions and imperfections (yellow). Light shading represents the economic impacts through each channel. The additional dark-shaded tips represent the impacts that earlier action may have through each channel (see text).

Models that explicitly represent these channels can provide deeper insight to inform policy decisions. Quantifying each of the channels individually and transparently may help identify policy options that justify lower temperature targets, earlier mitigation or different combinations of policy instruments (the variations in Fig. 3). Although these actions entail costs (for example higher short-term costs from earlier mitigation, dark tips on the grey arrows in the variation case), they generate economic benefits that accrue through the three other transmission channels (dark tips on the yellow, green and blue arrows).

If scenarios do not account for any of these channels, this should be clearly acknowledged when providing estimates for costs of mitigation action. Better yet, scenarios can be designed in ways that account for the channels (we have provided some examples) or minimize the consequences of excluding them. As mentioned, temperature-clustered scenarios can circumvent the challenges in modelling economic impacts of climate damages, by exploring alternative policy packages that achieve the same temperature outcomes. For example, such a framework could use as benchmark (or base case) a (second-best) scenario that achieves its climate objective via a globally uniform carbon price or emissions cap. This could then serve as the counterfactual to possible policy intervention scenarios including progrowth measures that, for example, improve resource efficiency, eliminate unfair market power or use carbon tax revenues to boost employment opportunities, enhance labour mobility by reskilling workers or ensure progressivity of a broader tax reform. This way, various policy interventions can be tested to identify economic trade-offs or synergies across potential policy packages and mitigation strategies. As such, a temperature-clustered scenario framework could help focus the policy debate on the appropriate combination of instruments to reach a given emission reduction target effectively.

An important concern for policy-making is the net outcome of the costs of action and the benefits that may accrue, including the results of their interactions. However, this is not to say that it is possible to determine a single best policy alternative or temperature target that is free from value judgements or political decisions regarding the distribution of “impacts over time and across individuals when values are heterogeneous”54. However, a reasonable range of cost estimates is useful and should not rule out potential positive outcomes. Ethical considerations of intergenerational justice should also inform the risk appetite towards, for example, large-scale tail events that may lead to irreversible changes in the Earth system. These considerations can be part of multicriterion analysis approaches that enable the assessment of conflicting priorities. When facing uncertainty, adaptive decision-making allows for dynamic realignment to changing circumstances120. Most policies can be amended if their costs are found to be too high, but this does not apply to the climate system (AR5 Synthesis Report54, page 79).

The cost of mitigation reloaded

To refine the role of economic analyses of the cost of mitigation in support of policy and the societal debate, we offer three suggestions for future work.

First, and starting immediately, existing and upcoming scenario studies should provide appropriate context and framing of findings, including not only caveats, but also risks and opportunities, surrounding the cost of climate change mitigation, supported by the literature and discussion provided in this Perspective. Reallocating economic resources from activities that have undesirable causes (for example, healthcare spending due to diseases related to air pollution) or consequences (for example, global warming induced by fossil fuel subsidies) to productive and sustainable uses will improve welfare outcomes, and modelling frameworks should differentiate accordingly to enable exploration of policy alternatives that maximize the latter. Emphasizing the risks associated with inaction places mitigation costs within a context of potential irreversibility of impacts and the more profound consequences for welfare and economic activity. Highlighting opportunities from alternative climate policy outcomes can help guide transition decisions.

In communicating climate policy, the choice of words can skew public opinion121. Properly communicating the benefits of climate action and the stakes involved helps dissipate public opposition, as demonstrated for the case of British Columbia, Canada, where carbon revenues were redistributed directly to families via carbon dividend cheques122,123. Framing the policy as a ‘carbon dividend’ instead of a ‘carbon tax’ allows for an explicit discussion of the benefits of climate action rather than just the costs. This is relevant in current debates around the European Union’s Green Deal, the USA’s Green New Deal and the inclusion of sustainability criteria in post-COVID-19 recovery efforts124.

Second, a temperature-clustered second-best scenario framework allows exploration of alternative climate policy packages and their associated macroeconomic costs. A scenario protocol could describe an ensemble of 1.5-°C- or 2-°C-compatible scenarios with alternative climate policy implementations. These alternatives can be measured against a benchmark scenario with similar temperature outcome that, for example, assumes the immediate introduction of globally uniform, comprehensive carbon prices. Relying only on carbon taxes has notable welfare costs125 but by enabling explicit exploration of mechanisms that may lead to welfare gains, this framework may help capture the opportunities presented by the deep transformations that a low-carbon transition entails. Importantly, it also paves the way for an open discussion of the limitations of current estimates of macroeconomic costs of mitigation. This framework’s central idea is to compare welfare and development outcomes of climate trajectories that are similar but stem from different policy packages. It resembles recent proposals126 in the field of impacts and adaptation, translated to the context of mitigation.

Third, combining various approaches to estimate mitigation costs can provide a more comprehensive view. Different model types each have their strengths; they are complementary tools, but the research community could put more effort into learning from one another127,128. In this respect, including relevant insights and tools from financial economics may help to better capture risk and uncertainty129. A more diverse modelling landscape with fertilization across different fields could result in improved understanding of the costs of climate change mitigation. Embracing uncertainty in scenario design to explore risks and opportunities130,131 and endogenizing key parameters can broaden the possibility space132,133 (Box 2). Importantly, while diversity is desirable and a lot can be learned from it, the risk that broadening the range of estimates may create confusion, misinterpretation and even distrust, calls for nuanced communication. Empirical work134 on the propagation of policy effects can provide important input.

Further work on the cost of climate action is important for several reasons. Costs need to be analysed to inform smart policy design that strives for effective emission reductions in an efficient manner and with the largest benefits to society. Importantly, the cost of climate policy needs to be acknowledged to develop complementary measures that guide vulnerable people and regions in the transition towards a carbon-neutral economy.

The framework proposed here will not invariably reveal that there are net welfare gains from climate mitigation policy. However, by not including the mechanisms and channels that could lead to growth, an overly pessimistic picture is sketched—one that suggests irreconcilable trade-offs between climate action and development. This framework rebalances the odds by introducing options to align the climate action narrative with one of increasing welfare and sustainable development. Recovery from the COVID-19 recession is an opportunity for policy-makers around the world to revive flailing economies through public investments (for example in renewable energy) at a time when they are likely to have large positive impacts. We hope the ideas proposed here will contribute to a better understanding of how to use the recovery and climate policy packages to spur growth that is green, inclusive and self-sustaining.

Change history

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Acknowledgements

The authors acknowledge support from the following European Union’s Horizon 2020 research and innovation programme projects: PARIS REINFORCE (grant agreement no. 820846) for A.C.K., A.G. and J.R.; ENGAGE (grant agreement no. 821471) for V.B.; NAVIGATE (grant agreement no. 821124) for C.G. and M.T.

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Köberle, A.C., Vandyck, T., Guivarch, C. et al. The cost of mitigation revisited. Nat. Clim. Chang. 11, 1035–1045 (2021). https://doi.org/10.1038/s41558-021-01203-6

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