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From least-cost to SDG-optimal sectoral allocation of Paris Agreement-compatible mitigation efforts

Nature Climate Change (2026)Cite this article

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Abstract

Limiting global warming to well below 2 °C necessitates profound decarbonization, but how to distribute mitigation efforts over sectors remains a widely debated issue. Although integrated assessment models traditionally rely on ‘least-cost’ optimization to answer this question, the resulting sectoral allocations vary widely and ignore impacts on other potential policy objectives. Here we connect an integrated assessment models with a portfolio analysis to evaluate how sector-specific mitigation actions impact key indicators from Sustainable Development Goals (SDGs) related to poverty, health, water, economy and land, and to identify Pareto-optimal and Paris-compliant mitigation portfolios that reveal the trade-offs between other sustainable development priorities. Furthermore, we define ‘SDG-balanced’ portfolios that, in most cases, outperform standard least-cost scenarios across all five SDG indicators for an equivalent carbon budget. Our findings demonstrate that the simultaneous evaluation of a broader set of policy priorities is crucial to provide truly policy-relevant guidance for the climate transition.

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Fig. 1: Analysis flow and scenario structure.
Fig. 2: Marginal impacts in five SDG indicators from incremental mitigation efforts in each sector (MSVs).
Fig. 3: Mitigation portfolios at the SDG-optimizing Pareto frontier.
Fig. 4: Comparison of SDG-balanced mitigation portfolios and least-cost portfolios achieving the same overall emissions target.
Fig. 5: Illustration of scenarios applying 2 °C-, 1.7 °C- and 1.5 °C-compatible SDG-balanced mitigation portfolios on top of NPi mitigation.

Data availability

The datasets generated during and analysed in the current study, as well as the full details of the NPi, least-cost and post-Pareto SDG-balanced scenarios (in IPCC-style format) are available via Zenodo at https://doi.org/10.5281/zenodo.18633223 (ref. 66). Model input data (equations, assumptions and parameters) are included in the online model repository referred to in the Code availability statement. The latest projections of the net income distribution used for this analysis are publicly available from Zenodo at https://doi.org/10.5281/zenodo.7474549 (refs. 67).

Code availability

The analysis has been developed using an enhanced version of the open-source GCAM and is available via GitHub at https://github.com/bc3LC/gcam-core/tree/bioaccounting_7p1. A detailed documentation for all these input assumptions used in the GCAM model is available via GitHub at https://github.com/JGCRI/gcam-doc. The post-processing code to calculate the mentioned SDG outcomes from GCAM scenarios is available via GitHub at https://github.com/bc3LC/gcam_sdg.

References

  1. Report of the Conference of the Parties on Its Twenty-First Session, Held in Paris from 30 November to 13 December 2015. Part One: Proceedings (UNFCCC, 2016).

  2. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nation, 2015); https://sdgs.un.org/2030agenda

  3. Schmidt Tagomori, I. et al. Climate policy and the SDGs agenda: how does near-term action on nexus SDGs influence the achievement of long-term climate goals?. Environ. Res. Lett. 19, 054001 (2024).

    Article  Google Scholar 

  4. van Soest, H. L. et al. Analysing interactions among Sustainable Development Goals with integrated assessment models. Glob. Transit. 1, 210–225 (2019).

    Article  Google Scholar 

  5. von Stechow, C. et al. 2 °C and SDGs: united they stand, divided they fall?. Environ. Res. Lett. 11, 034022 (2016).

    Article  Google Scholar 

  6. Soergel, B. et al. A sustainable development pathway for climate action within the UN 2030 Agenda. Nat. Clim. Change 11, 656–664 (2021).

    Article  Google Scholar 

  7. Fujimori, S. et al. Measuring the sustainable development implications of climate change mitigation. Environ. Res. Lett. 15, E085004 (2020).

    Article  Google Scholar 

  8. McCollum, D. L. et al. Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals. Nat. Energy 3, 589–599 (2018).

    Article  Google Scholar 

  9. Iyer, G. et al. Implications of sustainable development considerations for comparability across nationally determined contributions. Nat. Clim. Change 8, 124–129 (2018).

    Article  Google Scholar 

  10. Moreno, J. et al. Assessing synergies and trade-offs of diverging Paris-compliant mitigation strategies with long-term SDG objectives. Glob. Environ. Change 78, 102624 (2023).

    Article  Google Scholar 

  11. Moreno, J. et al. The impacts of decarbonization pathways on Sustainable Development Goals in the European Union. Commun. Earth Environ. 5, 1–14 (2024).

    Article  Google Scholar 

  12. Hermwille, L. et al. Ensuring an Effective Global Stocktake with a Sectoral Perspective (Wuppertal Institute, 2022); https://doi.org/10.48506/opus-8033

  13. van de Ven, D. J. et al. Energy and socioeconomic system transformation through a decade of IPCC-assessed scenarios. Nat. Clim. Change 15, 218–226 (2025).

    Article  Google Scholar 

  14. Fuhrman, J. et al. Ambitious efforts on residual emissions can reduce CO2 removal and lower peak temperatures in a net-zero future. Environ. Res. Lett. 19, 064012 (2024).

    Article  CAS  Google Scholar 

  15. Stenzel, F. et al. Irrigation of biomass plantations may globally increase water stress more than climate change. Nat. Commun. 12, 1512 (2021).

    Article  CAS  Google Scholar 

  16. Hirata, A. et al. The choice of land-based climate change mitigation measures influences future global biodiversity loss. Commun. Earth Environ. 5, 259 (2024).

    Article  Google Scholar 

  17. Hof, C. et al. Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proc. Natl Acad. Sci. USA 115, 13294–13299 (2018).

    Article  CAS  Google Scholar 

  18. Madhu, K., Pauliuk, S., Dhathri, S. & Creutzig, F. Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment. Nat. Energy 6, 1035–1044 (2021).

    Article  CAS  Google Scholar 

  19. Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).

    Article  CAS  Google Scholar 

  20. Edelenbosch, O. Y. et al. Reducing sectoral hard-to-abate emissions to limit reliance on carbon dioxide removal. Nat. Clim. Change 14, 715–722 (2024).

    Article  Google Scholar 

  21. Nikas, A., Doukas, H. & Papandreou, A. in Understanding Risks and Uncertainties in Energy and Climate Policy: Multidisciplinary Methods and Tools for a Low Carbon Society (eds Doukas, H., Flamos, A. & Lieu, J.) (Springer, 2019); https://doi.org/10.1007/978-3-030-03152-7

  22. Dekker, M. M. et al. Identifying energy model fingerprints in mitigation scenarios. Nat. Energy 8, 1395–1404 (2023).

    Article  Google Scholar 

  23. Kowarsch, M. in Understanding and Evaluating the IAM-Based Economics (Springer, 2016); https://doi.org/10.1007/978-3-319-43281-6_7

  24. Gambhir, A., Ganguly, G. & Mittal, S. Climate change mitigation scenario databases should incorporate more non-IAM pathways. Joule 6, 2663–2667 (2022).

    Article  Google Scholar 

  25. Pindyck, R. S. The use and misuse of models for climate policy. Rev. Environ. Econ. Policy 11, 100–114 (2017).

    Article  Google Scholar 

  26. Sampedro, J. et al. Health co-benefits and mitigation costs as per the Paris Agreement under different technological pathways for energy supply. Environ. Int. 136, 105513 (2020).

    Article  CAS  Google Scholar 

  27. Soergel, B. et al. Combining ambitious climate policies with efforts to eradicate poverty. Nat. Commun. 12, 2342 (2021).

    Article  CAS  Google Scholar 

  28. Ambrósio, G., Doelman, J. C., Schipper, A. M., Stehfest, E. & van Vuuren, D. Global sustainability scenarios lead to regionally different outcomes for terrestrial biodiversity. Environ. Res. Lett. 19, 104055 (2024).

    Article  Google Scholar 

  29. Séférian, R., Rocher, M., Guivarch, C. & Colin, J. Constraints on biomass energy deployment in mitigation pathways: the case of water scarcity. Environ. Res. Lett. 13, 054011 (2018).

    Article  Google Scholar 

  30. Emissions Gap Report 2023: Broken Record—Temperatures Hit New Highs, Yet World Fails to Cut Emissions (Again) (United Nations Environment Programme, 2023); https://doi.org/10.59117/20.500.11822/43922

  31. van de Ven, D.-J. et al. A multimodel analysis of post-Glasgow climate targets and feasibility challenges. Nat. Clim. Change 13, 570–578 (2023).

    Article  Google Scholar 

  32. Forouli, A., Nikas, A., Van de Ven, D. J., Sampedro, J. & Doukas, H. A multiple-uncertainty analysis framework for integrated assessment modelling of several sustainable development goals. Environ. Model. Softw. 131, 104795 (2020).

    Article  Google Scholar 

  33. Nikas, A. Projecting progress in sustainable development goals vis-à-vis climate action in climate–economy models. PLoS Clim. 3, e0000449 (2024).

    Article  Google Scholar 

  34. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  35. Gupta, A. in Handbook of Environmental and Sustainable Finance (Elsevier, 2016); https://doi.org/10.1016/B978-0-12-803615-0.00001-7

  36. Shoemaker, J. K., Schrag, D. P., Molina, M. J. & Ramanathan, V. What role for short-lived climate pollutants in mitigation policy?. Science 342, 1323–1324 (2013).

    Article  CAS  Google Scholar 

  37. Fekete, H. et al. A review of successful climate change mitigation policies in major emitting economies and the potential of global replication. Renew. Sustain. Energy Rev. 137, 110602 (2021).

    Article  Google Scholar 

  38. Koasidis, K., Koutsellis, T., Xexakis, G., Nikas, A. & Doukas, H. Understanding expectations from and capabilities of climate-economy models for measuring the impact of crises on sustainability. J. Clean. Prod. 414, 137585 (2023).

    Article  Google Scholar 

  39. van Vuuren, D. P. et al. Defining a sustainable development target space for 2030 and 2050. One Earth 5, 142–156 (2022).

    Article  Google Scholar 

  40. Zimm, C. et al. Justice considerations in climate research. Nat. Clim. Change 14, 22–30 (2024).

    Article  Google Scholar 

  41. Peng, W. et al. Climate policy models need to get real about people—here’s how. Nature 594, 174–176 (2021).

    Article  CAS  Google Scholar 

  42. Byers, E. et al. AR6 scenarios database. Zenodo https://doi.org/10.5281/ZENODO.5886912 (2022).

  43. Fuss, S. et al. Negative emissions—part 2: costs, potentials and side effects. Environ. Res. Lett. 13, 063002 (2018).

    Article  Google Scholar 

  44. Crippa, M. et al. GHG emissions of all world countries (Publications Office of the European Union, 2023); https://doi.org/10.2760/953322

  45. Hausfather, Z. & Peters, G. P. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).

    Article  CAS  Google Scholar 

  46. Calvin, K. et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci. Model Dev. 12, 677–698 (2019).

    Article  CAS  Google Scholar 

  47. Zhao, X. et al. Core Model Proposal #399: Updating the SSP Database (v3.0) (Population, GDP, and Labor Force) and Labor Productivity (KLEM) (US Department of Energy Office of Scientific and Technical Information, 2024); https://doi.org/10.2172/2999947

  48. Sampedro, J. et al. Residential energy demand, emissions, and expenditures at regional and income-decile level for alternative futures. Environ. Res. Lett. 19, 084031 (2024).

    Article  Google Scholar 

  49. Waldhoff, S. et al. Analyzing the distributional impacts of global change on food access and availability in a multi-sector dynamics, human–earth system model. In AGU Fall Meeting Abstracts GC22B-06 (AGU, 2023).

  50. gcam_sdg. GitHub https://github.com/bc3LC/gcam_sdg (2025).

  51. Bertram, C. et al. Carbon lock-in through capital stock inertia associated with weak near-term climate policies. Technol. Forecast. Soc. Change 90, 62–72 (2015).

    Article  Google Scholar 

  52. Sampedro, J. et al. rfasst: an R tool to estimate air pollution impacts on health and agriculture. J. Open Source Softw. 7, 3820 (2022).

    Article  Google Scholar 

  53. Van Dingenen, R. et al. TM5-FASST: a global atmospheric source-receptor model for rapid impact analysis of emission changes on air quality and short-lived climate pollutants. Atmos. Chem. Phys. 18, 1–55 (2018).

    Google Scholar 

  54. Stanaway, J. D. et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1923–1994 (2018).

    Article  Google Scholar 

  55. Jerrett, M. et al. Long-term ozone exposure and mortality. N. Engl. J. Med. 360, 1085–1095 (2009).

    Article  CAS  Google Scholar 

  56. Kim, S. H. et al. Balancing global water availability and use at basin scale in an integrated assessment model. Clim. Change 136, 217–231 (2016).

    Article  Google Scholar 

  57. Birnbaum, A., Lamontagne, J., Wild, T., Dolan, F. & Yarlagadda, B. Drivers of future physical water scarcity and its economic impacts in Latin America and the Caribbean. Earths Future 10, e2022EF002764 (2022).

    Article  Google Scholar 

  58. Byers, E. et al. Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett. 13, 055012 (2018).

    Article  Google Scholar 

  59. Edmonds, J. et al. Sensitivity of future regional and global energy markets and macroeconomic activity to a hypothetical global energy market disruption. iScience 28, 111449 (2025).

    Article  Google Scholar 

  60. Chaudhary, A., Verones, F., de Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).

    Article  CAS  Google Scholar 

  61. Chaudhary, A. & Brooks, T. M. Land use intensity-specific global characterization factors to assess product biodiversity footprints. Environ. Sci. Technol. 52, 5094–5104 (2018).

    Article  CAS  Google Scholar 

  62. Vernon, C. R. et al. Demeter—a land use and land cover change disaggregation model. J. Open Res. Softw. 6, 15 (2018).

    Article  Google Scholar 

  63. Forouli, A. et al. AUGMECON-Py: a Python framework for multi-objective linear optimisation under uncertainty. SoftwareX 20, 101220 (2022).

    Article  Google Scholar 

  64. Nikas, A., Fountoulakis, A., Forouli, A. & Doukas, H. A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Oper. Res. 22, 1291–1332 (2022).

    Google Scholar 

  65. Hoaglin, D. C., Iglewicz, B. & Tukey, J. W. Performance of some resistant rules for outlier labeling. J. Am. Stat. Assoc. 81, 991–999 (1986).

    Article  Google Scholar 

  66. Van de Ven, D.-J., Rodés-Bachs, C., Rouhette, T. & Koasidis, K. Intermediary and final data output from: ‘From least-cost to SDG-optimal sectoral allocation of Paris-compatible mitigation effort’. Zenodo https://doi.org/10.5281/zenodo.18633223 (2026).

  67. Narayan, K. B., O´Neill, B. C., Waldhoff, S. & Tebaldi, C. Data supplement to "Non-parametric projectins of national income distribution consistent with the SSPs". Zenodo https://doi.org/10.5281/zenodo.7474549 (2022).

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Acknowledgements

We thank the Horizon Europe European Commission Projects ‘IAM COMPACT’ (grant no. 101056306 to D.-J.V.d.V., C.R.-B., T.H., R.H., J.S., A.N., N.F. and K.K.), DIAMOND (grant no. 101081179 to D.-J.V.d.V., C.R.-B., T.H., R.H., J.S., A.N., N.F. and K.K.) and ACCLIMATE (grant no. 101184374 to D.-J.V.d.V., T.H., N.F. and A.N.) and the Horizon 2020 European Commission Project ‘NDC ASPECTS’ (grant no. 101003866 to D.-J.V.d.V. and T.H.). G.I. and X.Z. are also affiliated with Pacific Northwest National Laboratory, which did not provide specific support for this paper. The views and opinions expressed in this paper are those of the authors alone and do not necessarily state or reflect those of the affiliated organizations or the US Government, and no official endorsement should be inferred.

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Authors and Affiliations

  1. Basque Centre for Climate Change, Leioa, Spain

    Dirk-Jan Van de Ven, Clàudia Rodés-Bachs, Théo Rouhette, Russell Horowitz, Jon Sampedro & Jorge Moreno

  2. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Jon Sampedro

  3. National Technical University of Athens, Athens, Greece

    Alexandros Nikas, Natasha Frilingou & Konstantinos Koasidis

  4. Center for Global Sustainability, University of Maryland, College Park, MD, USA

    Xin Zhao & Gokul Iyer

  5. School of Management and Law, Zurich University of Applied Sciences, Winterthur, Switzerland

    Abhishek Chaudhary

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  1. Dirk-Jan Van de Ven
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Contributions

D.-J.V.d.V., K.K. and A.N. coordinated the study design. D.-J.V.d.V., C.R.-B., T.R., R.H., J.S., A.N., N.F. and K.K. were responsible for the compilation of the analysis and figures. D.-J.V.d.V. coordinated the conception and writing of the paper with notable contributions and feedback from all other authors, including X.Z., A.C., G.I. and J.M.

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Correspondence to Dirk-Jan Van de Ven.

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Nature Climate Change thanks Shu Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Table 1 Sectoral decomposition and SDG performance of selected SDG-balanced mitigation pathways
Full size table
Extended Data Table 2 Cumulative sectoral emission saving potentials
Full size table
Extended Data Table 3 Land use type mapping for PSL indicator
Full size table

Extended Data Fig. 1 Stocktake of sectoral emission and mitigation potentials.

Sectoral emission stocktake for 2010, 2030 and 2050 for NPi baselines, 2 °C and 1.5 °C scenarios from IPCC AR6 (n values reflect number of models reporting, taking mean of all relevant scenarios from the same model; ranges refer to interquartile ranges), observed values in 2010 and 2020 (taken as average between 2019 and 2021 emissions to avoid Covid-19 distortions; no historical value for AFOLU due to uncertainty), and applied “minimal benchmark” in this study. The 2050 point in the minimal benchmark is calculated as the lower 10% point in the total emissions range in 1.5 °C compatible scenarios for each sector, while the 2030 value in this benchmark is calculated adapting the path to the 2050 level departing from observed 2020 emissions.

Extended Data Fig. 2 NPi reference scenarios by SSP.

A. Sectoral CO2 emissions (for SSP2, nearly identical in other SSPs as determined by defined emission constraints based on stocktake analysis visualised in Table 1 and Extended Data Fig. 1). B. Carbon prices by sector and SSP.

Extended Data Fig. 3 Graphical example of mitigation within transportation sector.

A. Graphical example of how mitigation targets in the transportation sector (for the NPi level, see Table 1, and a 20 Gt cumulative CO2 reduction, equal to the 1.5C-compatible SDG-balanced portfolio reflected in Fig. 5) translate to a mix of mitigation options (electrification, biofuels, mode switch, demand reduction) for passenger transport. B. Idem for freight transport (excluding freight shipping for illustrative purposes). C. Heterogenous carbon prices, depending on the SSP narrative applied.

Extended Data Fig. 4 Measuring cumulative mitigation blocks.

Graphical sketch of how emission budgets by sector are calculated and split in blocks.

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Van de Ven, DJ., Rodés-Bachs, C., Rouhette, T. et al. From least-cost to SDG-optimal sectoral allocation of Paris Agreement-compatible mitigation efforts. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02602-3

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