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Decarbonization pathways for the residential sector in the United States

Nature Climate Change volume 12, pages 712–718 (2022)Cite this article

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An Author Correction to this article was published on 12 August 2022

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Abstract

Residential GHG emissions in the United States are driven in part by a housing stock where onsite fossil combustion is common, home sizes are large by international standards, energy efficiency potential is large and electricity generation in many regions is GHG intensive. In this analysis, we assess decarbonization pathways for the US residential sector to 2060, through 108 scenarios describing housing stock evolution, new housing characteristics, renovation levels and clean electricity. The lowest emission pathways involve very rapid decarbonization of electricity supply alongside extensive renovations to existing homes, including improving thermal envelopes and heat pump electrification of heating. Reducing the size and increasing the electrification of new homes provide further emission cuts and combining all strategies enables reductions of 91% between 2020 and 2050. The potential of individual mitigation strategies shows great regional variation. Reaching zero emissions will require simultaneous deployment of multiple strategies and greater reduction of embodied emissions.

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Fig. 1: Annual emission pathways for 108 scenarios.
Fig. 2: Cumulative 2020–2060 emissions for 108 scenarios.
Fig. 3: Annual and cumulative emissions by source for four selected scenarios.
Fig. 4: Percentage reductions in cumulative 2020–2060 emissions from individual strategies by state.

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Data availability

All input and postprocessed data supporting this analysis are available at https://doi.org/10.5281/zenodo.6651589. Source data are provided with this paper.

Code availability

All code to prepare and postprocess the results supporting this analysis is available in an archived repository at https://doi.org/10.5281/zenodo.6656201, the active version of this repository is available at https://github.com/peterberr/resstock_berrill/tree/feature/projections. Code to prepare the housing stock evolution scenarios can be found at https://github.com/peterberr/US_county_HSM. Source data are provided with this paper.

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Acknowledgements

P.B. acknowledges funding from the Yale School of the Environment doctoral programme. E.G.H acknowledges funding from the Research Council of Norway, project nos. 330300 and 257660. This work was authored in part by the NREL, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DE-AC36-08GO28308. Funding for NREL authors (E.J.H.W, J.L.R. and A.D.F.) was provided by US DOE Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the US government. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US government purposes. A portion of this research was performed using computational resources sponsored by the DOE Office of Energy Efficiency and Renewable Energy and located at the NREL.

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Authors and Affiliations

  1. Yale School of the Environment, Yale University, New Haven, CT, USA

    Peter Berrill

  2. Sustainability Economics of Human Settlements, Technische Universität Berlin, Berlin, Germany

    Peter Berrill

  3. Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

    Peter Berrill

  4. National Renewable Energy Laboratory, Golden, CO, USA

    Eric J. H. Wilson, Janet L. Reyna & Anthony D. Fontanini

  5. Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Edgar G. Hertwich

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Contributions

P.B. and E.G.H. conceived the study. P.B. designed the scenarios with input regarding practical energy simulation considerations from J.L.R., A.D.F. and E.J.H.W. P.B. ran the energy simulations with assistance from A.D.F. P.B. performed the postprocessing and graphical representation of the results. The writing of the manuscript was led by P.B. with substantive input from E.J.H.W. and E.G.H.

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Correspondence to Peter Berrill.

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Extended data

Extended Data Fig. 1 Mean floor area of housing by type and age cohort.

Values for cohorts up to 2010s are based on the housing stock as existing in 2020. Values for 2020s to 2050s cohorts are based on assumed characteristics of new housing in the Baseline and Reduced Floor Area (FA) new housing characteristics scenarios.

Source data

Extended Data Fig. 2 GHG emissions reduction by sequential strategy adoption.

Mitigation actions beyond the Baseline scenarios (black dashed line) are grouped into strategies affecting electricity supply (blue), renovation of existing homes (orange), and housing stock evolution (HSE)/new housing characteristics (NHC) (pink/purple). a) Strategies groups are ordered according to greatest cumulative emission mitigation potential. b) Strategy groups are ordered according to reverse cumulative emission mitigation potential.

Source data

Extended Data Fig. 3 CO2 intensity of electricity generation in 2020 and 2050.

a) 2020 CO2 intensities. b) 2050 CO2 intensities for the Low Renewable Electricity Cost electricity supply scenario. Emission intensities are aggregated into 20 Cambium Generation and Emission Assessment (GEA) regions55, weighted by total annual electricity generation in 134 balancing areas.

Source data

Extended Data Fig. 4 CO2 intensity of electricity generation, 2020-2050.

Cambium Generation and Emission Assessment (GEA) regions55 are grouped in two groups based on alphabetical order, to facilitate legible legends. a) Group 1 Mid-Case electricity supply, b) Group 1 Low Renewable Electricity Cost electricity supply, c) Group 2 Mid-Case electricity supply, d) Group 2 Low Renewable Electricity Cost electricity supply. Further reductions of CO2 intensity of electricity were assumed beyond 2050, as described in Supplementary Information Section 5.

Source data

Extended Data Fig. 5 Identification of best individual strategy by county.

a) Strategies compared are Extensive Renovation (Ext. Ren.), High Multifamily (High-MF) and High Turnover (High-TO) stock evolution, (Increased Electrification and Reduced Floor Area (IE &RFA) new housing characteristics, and Low Renewable Electricity Cost (LREC) electricity supply. b) Strategies compared include those considering in a) except for Low Renewable Electricity Cost (LREC) electricity supply which is excluded.

Source data

Extended Data Fig. 6 Estimated annual demand for new space heating equipment for two selected scenarios, 2025-2060.

Heat pump demand is shown by contributions from new construction (NewCon), replacement of units installed in new construction (NewCon_Rep), and Renovation. Efficiency gains from replacement of units in new construction is not considered in the energy and emissions analysis. The two selected scenarios represent two extremes in terms of low and high growth of heat pump demand; a) Baseline new housing characteristics (NHC) and Regular Renovation (Reg. Ren.) of existing homes, and b) Increased Electrification (Inc. Elec.) new housing characteristics and Extension Renovations (Ext. Ren.) of existing homes.

Source data

Extended Data Fig. 7 Mean floor area per capita by housing stock evolution and new housing characteristics scenarios.

DE = Deep Electrification, RFA = Reduced Floor Area.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–5, Figs. 1–36, Tables 1–9 and references.

Source data

Source Data Fig. 1

Source data for annual emissions in all scenarios 2020–2060.

Source Data Fig. 2

Source data for formatting for cumulative 2020–2060 emissions in all scenarios.

Source Data Fig. 3

Source data for formatting for annual emissions by source in selected scenarios.

Source Data Fig. 4

Source data for relative and absolute cumulative 2020–2060 reductions from selected individual and combined strategies by state.

Source Data Extended Data Fig. 1

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Berrill, P., Wilson, E.J.H., Reyna, J.L. et al. Decarbonization pathways for the residential sector in the United States. Nat. Clim. Chang. 12, 712–718 (2022). https://doi.org/10.1038/s41558-022-01429-y

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