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Nature Climate Change (2026)
24
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Climate change is intensifying global energy demands and amplifying exposure to extreme heat. Building façade-integrated photovoltaics (FIPV) present a largely untapped opportunity to supply renewable electricity while enhancing urban climate resilience. Here we show that deployable FIPV systems worldwide could generate 732.5 ± 4.5 TWh of electricity annually, based on a global synthesis of building datasets, climate projections and façade-scale simulations, with theoretical bounds of 8.9–7,671.3 TWh under conservative-to-optimistic assumptions. Although FIPV deployment costs exceed those of conventional photovoltaics, over 80% of urban districts exhibit lifetime expenditure savings due to combined electricity generation and cooling-load reductions. Under a gradual S-curve adoption reaching upper-bound potential by 2050, FIPV could deliver cumulative emission reductions of up to 37.7 GtCO2, corresponding to 0.0519 ± 0.0111 °C of avoided warming under currently announced national policies. These results identify FIPV as a complementary mitigation–adaptation strategy, highlighting the need for targeted policies to address regional and economic disparities in climate-resilient urban transition.
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The Bing Maps Global Building Footprints are available via GitHub at https://github.com/microsoft/GlobalMLBuildingFootprints. The East Asia building dataset is available via Zenodo at https://doi.org/10.5281/zenodo.8174931 (ref. 76). The 30-m urban expansion dataset is available via Figshare at https://doi.org/10.6084/m9.figshare.21792209.v2 (ref. 77). The Overture Maps POI dataset can be downloaded at https://overturemaps.org/. Hourly ERA5 reanalysis data, including solar radiation, air temperature, wind speed and relative humidity, were obtained from the Copernicus Climate Change Service (C3S) via the Climate Data Store (CDS) at https://doi.org/10.24381/cds.adbb2d47. The Typical Meteorological Year version 3 (TMY3) data are available at https://energyplus.net/weather. CMIP6 model outputs are available via the Earth System Grid Federation (ESGF) at https://esgf-node.llnl.gov/projects/cmip6/. Country-level penetration rates of clean electricity are sourced from the Statistical Review of World Energy 2024, available at https://www.energyinst.org/statistical-review. National-scale emissions data were obtained from the EDGAR database at https://edgar.jrc.ec.europa.eu/report_2023. Country-level electricity price ranges were compiled from data available at https://www.globalpetrolprices.com/. Country boundaries were obtained from the Natural Earth dataset available at https://www.naturalearthdata.com/. Source data are provided with this paper.
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This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB0740200, L.Y.) and the National Natural Science Foundation of China (grant nos. 42571482, H.J.; 42471386, L.Y.).
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Hou Jiang (姜侯), Ling Yao (姚凌), Tang Liu (刘唐), Fangyu Ding (丁方宇), Xingxing Zhang (张星星), Fenzhen Su (苏奋振) & Chenghu Zhou (周成虎)
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Ling Yao (姚凌) & Fenzhen Su (苏奋振)
Faculty of Geography, Yunnan Normal University, Kunming, China
Jun Qin (秦军) & Ning Lu (吕宁)
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Wenli Zhao (赵文利)
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, China
Rui Zhu (朱瑞)
Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
Rui Zhu (朱瑞)
Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing, China
Jia Wang (王佳)
Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing, China
Fan Zhang (张帆)
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H.J. conceived of and designed the study. H.J., L.Y. and J.Q. developed the modelling framework and performed the simulations. W.Z. and R.Z. contributed to data curation and validation. H.J., T.L. and F.D. conducted the analysis. H.J. wrote the first draft of the paper. All authors contributed to the interpretation of the results and revision of the paper. L.Y., F.S. and C.Z. supervised the project and secured funding.
Correspondence to Ling Yao (姚凌) or Jun Qin (秦军).
The authors declare no competing interests.
Nature Climate Change thanks Zhiling Guo, Moritz Wussow and the other, anonymous, reviewer(s) 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.
a, Spatial distribution of maximum FIPV potential under the optimistic scenario at 0.25°×0.25° resolution. Insets show the dynamic diffusion (analogous to Fig. 1c), climate impacts (analogous to Fig. 1b), and annual potential for the top ten countries. Linear trends in inset time series were estimated using ordinary least squares regression. Statistical significance of regression slopes was assessed using two-sided t-tests. b, As in a, but for the conservative scenario. c, Projected global linear trend of FIPV potential under SSP1–2.6 during 2020–2050. Statistical significance was assessed using two-sided ordinary least squares regression at the grid-cell level (n = 47,175). Grid-cells with p < 0.05 are indicated. d, Absolute change in mean FIPV potential between 2046–2050 and 2020–2024 under the base scenario and SSP1–2.6 climate projection. e–f, Spatial characteristics of annual electricity demand (e) and the ratio of FIPV potential to electricity demand (f) for buildings modelled under the base scenario. Basemap data from Natural Earth (https://www.naturalearthdata.com).
Source data
a–b, Potential electricity savings under the optimistic (a) and conservative (b) scenarios (see Supplementary Table 2), assuming centralized or gas-based heating. c–d, Potential electricity savings (c) and increase in heating demand (d) under the base scenario if electric heating is applied in regions with average winter temperatures below 0 °C. Each panel presents the spatial distribution across 0.25°×0.25° grid-cells, temporal trends during 2020–2050 under SSP1–2.6 climate projections, and annual totals for the top 10 countries. Linear trends in inset time series were estimated using ordinary least squares regression. Statistical significance of regression slopes was assessed using two-sided t-tests. Basemap data from Natural Earth (https://www.naturalearthdata.com).
Source data
a, Self-consumption rates of FIPV generation at the 0.05° × 0.05° grid-cell level without storage. Insets show the distribution of grid-cell values (n = 47,175) using violin and box plots. The central line indicates the median; boxes represent the interquartile range (25th–75th percentile); whiskers denote the minimum and maximum values excluding outliers. b–d, Self-consumption rates (b), changes in electricity expenditures (c), and internal rate of return (d) under a reference storage configuration of 600 W (7.2 kWh) battery capacity per kW of PV capacity. e–g, Sensitivity of self-consumption rates (e), electricity expenditure changes (f), and internal rate of return (g) to storage configurations; labels indicate the median values under each configuration. All analyses were based on the base scenario. Basemap data in a–d from Natural Earth (https://www.naturalearthdata.com).
Source data
a, Self-consumption rates of FIPV generation at the individual building level (that is, without local grid integration). b, Comparison of self-consumption rates at the building level and after grid integration within grid-cells across three representative climate zones. c–d, Changes in cumulative electricity expenditures (c) and internal rate of return (d) at the building level. In a, c, and d, insets show the distribution of grid-cell values (n = 47,175) using violin and box plots. The central line indicates the median; boxes represent the interquartile range (25th–75th percentile); whiskers denote the minimum and maximum values excluding outliers. Compared with Fig. 3, these results highlight the benefits of local grid aggregation in enhancing self-consumption and economic performance. All analyses were based on the base scenario. Basemap data in a, c and d from Natural Earth (https://www.naturalearthdata.com).
Source data
a, Country-level cumulative carbon mitigation potential from 2026 to 2050 under the optimistic diffusion pathway, based on the Stated Policies Scenario (STEPS) for grid decarbonization (analogous to Fig. 4a). b, Annual trajectories of carbon mitigation under the optimistic diffusion pathway and three climate policy scenarios: STEPS, Sustainable Development Scenario (SDS), and Net Zero Emissions (NZE) (analogous to Fig. 4c). c, Global warming mitigation achieved by FIPV deployment under the optimistic diffusion pathway and three climate policy scenarios (analogous to Fig. 4e). The solid and dashed lines represent the CMIP6 model ensemble mean across nine Earth System Models (n = 9), and the shaded bands indicate the interquartile range (25th–75th percentile). d–f, Same as a–c, but for the conservative diffusion pathway. Basemap data in a and d from Natural Earth (https://www.naturalearthdata.com).
Source data
a, Relationship between building-related emission reductions and national grid emission factors. b, Relationship between maximum power-sector emission reductions and the penetration of solar and wind power in national grids. Circle size denotes total emission reductions under the base diffusion pathway and the Stated Policies Scenario (STEPS) for grid decarbonization. Countries are grouped by basic economic conditions (see Supplementary Fig. 8c).
Source data
Supplementary Notes 1–3, Figs. 1–24 and Tables 1–9.
Source data underlying Figs. 1b, 2c, 3a,c,d,f and 4c and Extended Data Figs. 1a,b, 2a–d, 3a, 4a,c, 5b,e and 6a,b.
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Jiang, H., Yao, L., Qin, J. et al. Building façade photovoltaics enhance global climate resilience. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02606-z
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