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A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings

An Author Correction to this article was published on 29 November 2021

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Despite the importance of evaluating all mitigation options to inform policy decisions addressing climate change, a comprehensive analysis of household-scale interventions and their emissions reduction potential is missing. Here, we address this gap for interventions aimed at changing individual households’ use of existing equipment, such as monetary incentives or feedback. We have performed a machine learning-assisted systematic review and meta-analysis to comparatively assess the effectiveness of these interventions in reducing energy demand in residential buildings. We extracted 360 individual effect sizes from 122 studies representing trials in 25 countries. Our meta-regression confirms that both monetary and non-monetary interventions reduce the energy consumption of households, but monetary incentives, of the sizes reported in the literature, tend to show on average a more pronounced effect. Deploying the right combinations of interventions increases the overall effectiveness. We have estimated a global carbon emissions reduction potential of 0.35 GtCO2 yr−1, although deploying the most effective packages of interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation pathways with energy-demand reductions.

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Fig. 1
Fig. 2: Estimated average effect size of different categories of reviewed interventions.

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information and on GitHub ( All the information collected in this project is publicly available in line with the systematic reviews reporting protocol40,41, providing the transparency and reproducibility required to conform with Open Synthesis principles42 (see the ROSES checklist35). Source data are provided with this paper.

Code availability

We used the NACSOS software36 to manage search results, remove duplicates, screen records and extract data, and the metafor package in R (ref. 38) for the meta-regressions. All the software packages used are open source and freely accessible. The code developed for the paper and its Supplementary Information is available on GitHub (

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Funding by the German Ministry for Education and Research (BMBF) from the START (ref. no. 03EK3046B, A.L. and J.C.M.) and Ariadne (ref. no. 03SFK5J0, J.C.M.) projects is gratefully acknowledged. T.M.K. is supported by a PhD stipend from the Hertie School.

Author information

Authors and Affiliations



J.C.M., T.M.K. and M.M.Z.D. designed the research. N.R.H., T.M.K. and M.M.Z.D. developed the literature screening strategy. T.M.K., M.M.Z.D., S.L., A.J. and H.G. manually screened the literature and collected the data. M.C. performed the machine learning-enabled screening. T.M.K. performed the meta-analysis. T.M.K., J.C.M., F.C., N.K., L.H., A.L. and G.B. analysed the results. T.M.K. and J.C.M. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to Jan C. Minx.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Energy thanks Paul Stern, Massimo Tavoni and Jeroen van den Bergh for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1–7, Notes 1 and 2, and references.

Reporting Summary

Supplementary Table

ROSES checklist.

Supplementary Data

Underlying data for funnel plot.

Source data

Source Data Fig. 2

Estimated average effect size by intervention and combination of them.

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Khanna, T.M., Baiocchi, G., Callaghan, M. et al. A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings. Nat Energy 6, 925–932 (2021).

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