Abstract
The Paris Agreement calls for a cooperative response with the aim of limiting global warming to well below two degrees Celsius above pre-industrial levels while reaffirming the principles of equity and common, but differentiated responsibilities and capabilities1. Although the goal is clear, the approach required to achieve it is not. Cap-and-trade policies using uniform carbon prices could produce cost-effective reductions of global carbon emissions, but tend to impose relatively high mitigation costs on developing and emerging economies. Huge international financial transfers are required to complement cap-and-trade to achieve equal sharing of effort, defined as an equal distribution of mitigation costs as a share of income2,3, and therefore the cap-and-trade policy is often perceived as infringing on national sovereignty2,3,4,5,6,7. Here we show that a strategy of international financial transfers guided by moderate deviations from uniform carbon pricing could achieve the goal without straining either the economies or sovereignty of nations. We use the integrated assessment model REMIND–MAgPIE to analyse alternative policies: financial transfers in uniform carbon pricing systems, differentiated carbon pricing in the absence of financial transfers, or a hybrid combining financial transfers and differentiated carbon prices. Under uniform carbon prices, a present value of international financial transfers of 4.4 trillion US dollars over the next 80 years to 2100 would be required to equalize effort. By contrast, achieving equal effort without financial transfers requires carbon prices in advanced countries to exceed those in developing countries by a factor of more than 100, leading to efficiency losses of 2.6 trillion US dollars. Hybrid solutions reveal a strongly nonlinear trade-off between cost efficiency and sovereignty: moderate deviations from uniform carbon prices strongly reduce financial transfers at relatively small efficiency losses and moderate financial transfers substantially reduce inefficiencies by narrowing the carbon price spread. We also identify risks and adverse consequences of carbon price differentiation due to market distortions that can undermine environmental sustainability targets8,9. Quantifying the advantages and risks of carbon price differentiation provides insight into climate and sector-specific policy mixes.
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Data availability
Scenario data have been uploaded in Zenodo with the identifier https://doi.org/10.5281/zenodo.4010426. Source data are provided with this paper.
Code availability
The model codes of REMIND (identifier: ab2c995116e7fb402f6dd0183724496373af996e) and MAgPIE (identifier: 950bc7a08fd0e6c8f790c1399c7837133233e2fc) are open source (https://github.com/remindmodel/remind and https://github.com/magpiemodel/magpie).
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Acknowledgements
We acknowledge funding from the German Federal Ministry of Education and Research (BMBF) in the Funding Priority ‘Economics of Climate Change’ (DIPOL: 01LA1809A). This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement numbers 730403 and 821124.
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N.B., C.B., D.K. and A.S. developed the policy analysis framework and designed the experiments. N.B. and A.S. implemented the policy framework. N.B. wrote the manuscript (with assistance from C.B. and G.L.). N.B. led the analysis and writing of the manuscript with contributions from all authors.
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Extended data figures and tables
Extended Data Fig. 1
Graphical illustration of the distributional effects between advanced economy A and developing economy B for different policy frameworks characterized by different marginal abatement cost functions fA and fB. The case ‘Uniform price w/o transfers’ with carbon prices equal to p in both regions implies different mitigation costs. In the case ‘Uniform price w/ transfers’ these differences are neutralized by transfers T. Alternatively, in the case ‘No transfer’ the differentiation of policies leads to equal costs; this case is not depicted explicitly. The case ‘Hybrid’ differentiates carbon prices to reduce T. The change of global mitigation cost is only the difference between the regional mitigation costs ΔACa − ΔACb (indicated by the red triangles), whereas the changes of transfers are represented by the orange rectangles pΔR. As long as the differentiation of prices is relatively small, the decline of transfers exceeds the increase in global mitigation costs.
Extended Data Fig. 2 Illustration of the exponential compression function.
The x axis shows the 2030 carbon prices in the full differentiation case (see Fig. 2a). The example applies the parameter α = 0.5 to the compression function \({p}_{r}={\tilde{p}}^{{\rm{\min }}}{({\tilde{p}}_{r}/{\tilde{p}}^{{\rm{\min }}})}^{\alpha }\) that has been introduced in the Methods. The y axis shows the carbon prices after compression. The figure shows a subset of regions and the light grey line highlights the compression for Latin America and the EU. We note that the set of compressed carbon prices is scaled to comply with the global carbon budget (that is, the relative differences of the carbon price spread remain constant).
Extended Data Fig. 4 Time path of transfers in the default case with uniform carbon prices.
The transfers are expressed as percentages of GDP in the OECD and the non-OECD regions. The dashed line serves as a point of orientation. It represents the share of the US$100 billion relative to the OECD’s GDP in 2020.
Extended Data Fig. 5 Effect of carbon price differentiation on primary and final energy use.
a, b, Changes in the global energy mixes distinguished by energy carriers and regions. The figure depicts differences compared with the uniform carbon tax case for the 1,300 GtCO2 carbon budget. Primary energy (a) is measured according to the direct equivalence principle; final energy (b) is measured as delivered to final consumer. This means that fossil fuels, biomass and geothermal energy are measured in primary energy input, whereas renewables (hydro, wind and solar) as well as nuclear energy are measured by their electricity output. Notable results are as follows. First, the total amount of energy use increases. Second, OECD countries mostly reduce residual oil and gas consumption, but non-OECD countries mostly increase the use of coal; therefore, the total consumption of coal increases, whereas the global use of oil and gas decreases. Third, the total use of biomass increases due to increasing demand in OCED countries. Fourth, OECD countries accelerate modernization of final energy use by mainly reducing the use of liquids and gases, but increasing electricity and hydrogen. Finally, non-OECD countries delay modernization of energy use by mainly increasing the use of solids, liquids and gases with corresponding implications for air pollution and so on.
Extended Data Fig. 6 Effect of carbon price differentiation on land-use change and investments.
a, b, Changes in global land use (a) and investment and regions (b). The figure depicts differences compared with the uniform carbon tax case for the 1,300 GtCO2 carbon budget. The two regions show opposite changes in the variables; for example, OECD countries convert agricultural land into forests to remove carbon by afforestation, whereas non-OECD countries convert forests into cropland to grow biomass that is exported to OECD countries. Moreover, OECD countries increase investments in the energy sector substantially to facilitate the transition to low-carbon technologies and to invest into carbon-removal technologies (which are counted as part of the energy sector). The higher OECD investments crowd-out the macroeconomic investments in OECD countries and energy sector investments in non-OECD countries.
Extended Data Fig. 7 Sensitivity analysis of the trade-off curve.
a, The sensitivity of the ban on bioenergy trade. b, The sensitivity with respect to the carbon budget. c, The variation of the maximum annual retirement rate of fossil-fuelled infrastructure from 9% to 6% and the delayed availability of technologies that rely on underground geological storage of CO2 (that is, CCS including direct air capture). d, The application of the linear compression function; in this sensitivity analysis the case of uniform taxes and the no-transfer case are identical.
Extended Data Fig. 8 Regional aggregates used in REMIND and MAgPIE.
Regions and countries belonging to the OECD region are coloured in blue tones. See ‘Data availability’ section for more details. World map based on rworldmap package66.
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Bauer, N., Bertram, C., Schultes, A. et al. Quantification of an efficiency–sovereignty trade-off in climate policy. Nature 588, 261–266 (2020). https://doi.org/10.1038/s41586-020-2982-5
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DOI: https://doi.org/10.1038/s41586-020-2982-5
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