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Reframing incentives for climate policy action

Nature Energy volume 6, pages 1133–1143 (2021)Cite this article

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Abstract

A key aim of climate policy is to progressively substitute renewables and energy efficiency for fossil fuel use. The associated rapid depreciation and replacement of fossil-fuel-related physical and natural capital entail a profound reorganization of industry value chains, international trade and geopolitics. Here we present evidence confirming that the transformation of energy systems is well under way, and we explore the economic and strategic implications of the emerging energy geography. We show specifically that, given the economic implications of the ongoing energy transformation, the framing of climate policy as economically detrimental to those pursuing it is a poor description of strategic incentives. Instead, a new climate policy incentives configuration emerges in which fossil fuel importers are better off decarbonizing, competitive fossil fuel exporters are better off flooding markets and uncompetitive fossil fuel producers—rather than benefitting from ‘free-riding’—suffer from their exposure to stranded assets and lack of investment in decarbonization technologies.

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Fig. 1: Diffusion of technology and evolution of energy use and emissions in key sectors.
Fig. 2: The evolving geography of energy demand and supply.
Fig. 3: World oil and gas reserves and resources.
Fig. 4: Evolution of key energy and macroeconomic variables.
Fig. 5: Cumulated macroeconomic gains and losses by country.

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Data availability

The data needed to replicate and interpret the study are included in a supplementary data file with this article. Additional data from the various models used in this study for variables not included in the supplementary data file can be obtained from the authors upon reasonable request. Original data from Rystad and the IEA are licensed by these owners, but the datasets derived by the authors from these datasets and used in the study are included in the supplementary data file.

Code availability

The computer code and algorithm needed to replicate the study for the E3ME-FTT model is licensed and not publicly available, but can be obtained from the authors upon reasonable request.

Change history

  • 10 November 2021

    In the version of this article originally published, the description of the Supplementary Information file was incorrect and has now been updated as of 10 November 2021.

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Acknowledgements

We acknowledge the Global Systems Institute (Exeter), C-EERNG and Cambridge Econometrics for support and funding from UK NERC (J.-F.M., N.R.E., P.S., G.S. and J.E.V., ‘FRANTIC’ project grant no NE/S017119/1), the Leverhulme Trust Leverhulme Research Centre Award (N.V., N.R.E. and P.B.H., no. RC-2015-029) and the Newton Fund (J.-F.M., P.S., J.E.V., H.P. and U.C., EPSRC grant no. EP/N002504/1 and ESRC ‘BRIDGE’ grant no. ES/N013174/1). P.S. acknowledges funding from the CISL, Paul and Michelle Gilding (Prince of Wales Global Sustainability Fellowship in Radical Innovation and Disruption). The team acknowledges N. Seega at CISL for critical support in effectively engaging stakeholders, as well as S. Sharpe, R. Svartzman, R. Barrett, E. Schets and colleagues at Ortec Finance and Federated Hermes for insightful discussions.

Author information

Authors and Affiliations

  1. Global Systems Institute, Department of Geography, University of Exeter, Exeter, UK

    J.-F. Mercure & P. Vercoulen

  2. Cambridge Centre for Environment, Energy and Natural Resources Governance (C- EENRG), University of Cambridge, Cambridge, UK

    J.-F. Mercure, P. Salas, A. Lam, H. Pollitt, N. R. Edwards & J. E. Vinuales

  3. Cambridge Econometrics, Cambridge, UK

    J.-F. Mercure, P. Vercoulen, H. Pollitt & U. Chewpreecha

  4. University of Cambridge Institute for Sustainability Leadership (CISL), Cambridge, UK

    P. Salas

  5. Political Economy Research Institute and Department of Economics, University of Massachusetts Amherst, Amherst, MA, USA

    G. Semieniuk

  6. Department of Economics, SOAS University of London, London, UK

    G. Semieniuk

  7. Department of Economics, Faculty of Social Sciences, University of Macao, Taipa, China

    A. Lam

  8. School of Environment, Earth & Ecosystem Sciences, The Open University, Milton Keynes, UK

    P. B. Holden, N. Vakilifard & N. R. Edwards

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Contributions

J.-F.M. designed, coordinated and performed the research, with contributions from G.S., P.S., P.B.H. and H.P. J.-F.M. wrote the article with support from N.R.E., G.S., J.E.V., H.P., P.S., P.B.H. and N.V. J.-F.M. and P.V. ran the E3ME-FTT simulations, with support from U.C. and H.P. J.-F.M. and P.S. developed the updated FTT:Power and the fossil resource depletion model, and integrated the Rystad dataset into the framework. A.L. developed the updated FTT:Transport model, its data and the policy assumptions. P.S. and J.-F.M. developed and applied the game theory model. N.V. ran the GENIE simulations with support from P.B.H. J.E.V. contributed geopolitical expertise. N.R.E. coordinated the overall FRANTIC NERC project.

Corresponding author

Correspondence to J.-F. Mercure.

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The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks Michael Bradshaw, Andreas Goldthau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Technology dynamics for solar photovoltaic and electric vehicles.

The dashed and dotted lines, associated with the left-hand side vertical axes, show technological costs for chosen regions given in the legend. The dashed lines show PV and EV levelised costs (the break-even service costs for one unit of electricity or transport), while the dotted lines show the levelised costs of the best fossil alternative, gas turbines and petrol vehicles (for vehicles, the mid-range class was used). The solid lines, associated with the right-hand side vertical axes, show the diffusion of solar PV and EVs. The dynamics show that costs going down incentivise more technology uptake, which generates cost reductions, in a positive reinforcing cycle. Fossil technologies are mature, without substantial learning, their cost dominated by resource costs. In the case of gas turbine costs, the fluctuations are related to variations in capacity factors (or load hours) that vary according to how the plants are used to balance the electricity grid.

Extended Data Fig. 2 Projections for all scenarios of all major energy vectors in the economy.

Dashed lines are guide to the eyes indicating totals of other scenarios in the same quantity.

Extended Data Fig. 3

Projections for all scenarios of non-renewable energy use by region. Dashed lines are guide to the eyes indicating totals of other scenarios in the same quantity.

Extended Data Fig. 4

Projections for all scenarios of renewable energy use by region. Dashed lines are guide to the eyes indicating totals of other scenarios in the same quantity.

Extended Data Fig. 5 Cumulated gains and losses in the value of fossil fuel assets, GDP, investment and fossil fuel production across chosen economies.

(a) for the Net-zero SO scenario, relative to the InvE scenario, expressed in absolute, and (b) for the EU-EA Net-zero SO scenario relative to the InvE undiscounted. Gains are positive and losses negative. Values are cumulated over 15 years, between 2022 and 2036, using a 6% discount rate.

Extended Data Fig. 6 Structure of the game and possible scenario outcomes.

Importers can decide between a high or low-carbon energy system. OPEC can decide between observing quotas or flooding fossil fuel markets. High-Cost Exporters (HCE) can choose between high or low-carbon energy systems. The combinations of decisions leading to overall scenarios are shown at the bottom. N/A are infeasible scenarios, where HCE deciding unilaterally to decarbonise is ruled out by existing low-carbon policy in importer countries.

Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Supplementary Tables 1–3.

Supplementary Data 1

This spreadsheet contains all the data shown in the graphs, however with more detailed geographical disaggregation, and offers options to re-calculate some values using alternate discount rates and cumulation periods.

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Mercure, JF., Salas, P., Vercoulen, P. et al. Reframing incentives for climate policy action. Nat Energy 6, 1133–1143 (2021). https://doi.org/10.1038/s41560-021-00934-2

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