Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Sustainability (2026)
Harnessing rooftop photovoltaic (RPV) generation to power electric vehicles (EVs) can substantially accelerate the renewable energy transition and carbon mitigation. Yet, the mismatch between electricity generation and charging demand, exacerbated by rapid EV adoption, introduces large uncertainties in charging capacity, economic feasibility and decarbonization potential. Here we assess the spatiotemporal scalability of PV-powered EV charging across 40 global cities, analysing 3.38 billion charging records from 22,000 charging piles. Under three charging strategies, influential factors affecting daily charging capacity (generation-to-demand ratio) across all urban microgrids of varying sizes consistently followed an exponential scaling law. By 2050, RPV generation is projected to double in each city (1.6–434.7 TWh yr−1), supported by rooftop area expansions aligned with the shared socioeconomic pathways and charging demand will rise 4–1,759-fold (1.5–10,451.7 GWh yr−1), driven by increased EV adoption under International Energy Agency scenarios. Under these evolving conditions, the annual charging capacity of each city declines but remains sufficient to meet 2050 charging demands. Across all cities, total revenue is projected at US$3,173.2 (±99.5) billion with accumulative carbon mitigation of 11.9(±0.4) Gt from 2025 to 2050. These results suggest that RPV-powered EV charging can remain economically viable and sufficient to meet growing demand across diverse urban settings through 2050.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
The historical EV charging data and simulated RPV potential of the 40 cities are available via GitHub at https://github.com/IntelligentSystemsLab/SolarCityEV/tree/main/data. Source data are provided with this paper.
The code used to manipulate the data and generate the results is available via GitHub at https://github.com/IntelligentSystemsLab/SolarCityEV/tree/main/code.
Wang, R., Lam, C. M., Alvarado, V. & Hsu, S.-C. A modeling framework to examine photovoltaic rooftop peak shaving with varying roof availability: a case of office building in Hong Kong. J. Build. Eng. 44, 103349 (2021).
Article Google Scholar
Wang, Y. et al. Accelerating the energy transition towards photovoltaic and wind in China. Nature 619, 761–767 (2023).
Article CAS Google Scholar
Kammen, D. M. & Sunter, D. A. City-integrated renewable energy for urban sustainability. Science 352, 922–928 (2016).
Article CAS Google Scholar
Zhang, Z. et al. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nat. Commun. 14, 2347 (2023).
Article CAS Google Scholar
Horesh, N., Trinko, D. A. & Quinn, J. C. Comparing costs and climate impacts of various electric vehicle charging systems across the United States. Nat. Commun. 15, 4680 (2024).
Article CAS Google Scholar
Wu, J., Powell, S., Xu, Y., Rajagopal, R. & Gonzalez, M. C. Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns. Cell Rep. Sustain. 1, 100006 (2024).
Google Scholar
Nie, Y., Zelikman, E., Scott, A., Paletta, Q. & Brandt, A. Skygpt: probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained videogpt. Adv. Appl. Energy 14, 100172 (2024).
Article Google Scholar
Zhu, R. et al. The effect of urban morphology on the solar capacity of three-dimensional cities. Renew. Energy 153, 1111–1126 (2020).
Article Google Scholar
Powell, S., Cezar, G. V., Min, L., Azevedo, I. M. L. & Rajagopal, R. Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption. Nat. Energy 7, 932–945 (2022).
Article Google Scholar
Hassan, M. Machine learning optimization for hybrid electric vehicle charging in renewable microgrids. Sci. Rep. 14, 13973 (2024).
Article CAS Google Scholar
Joshi, S. et al. High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation. Nat. Commun. 12, 5738 (2021).
Article CAS Google Scholar
Zhang, Z. et al. Worldwide rooftop photovoltaic electricity generation may mitigate global warming. Nat. Clim. Change 15, 393–402 (2025).
Article Google Scholar
Wang, W. et al. Electric vehicle charging load forecasting considering weather impact. Appl. Energy 383, 125337 (2025).
Article Google Scholar
Tian, J. et al. Short-term electric vehicle charging load forecasting based on TCN-LSTM network with comprehensive similar day identification. Appl. Energy 381, 125174 (2025).
Article Google Scholar
Martin, H., Buffat, R., Bucher, D., Hamper, J. & Raubal, M. Using rooftop photovoltaic generation to cover individual electric vehicle demand—a detailed case study. Renew. Sustain. Energy Rev. 157, 111969 (2022).
Article Google Scholar
Ji, N., Zhu, R., Huang, Z. & You, L. An urban-scale spatiotemporal optimization of rooftop photovoltaic charging of electric vehicles. Urban Inform. 3, 4 (2024).
Article Google Scholar
Perera, A. T. D. et al. Challenges resulting from urban density and climate change for the EU energy transition. Nat. Energy 8, 397–412 (2023).
Article Google Scholar
Jung, H. et al. Electric vehicle charging system to reduce carbon emissions using photovoltaic power generation and ESS. In Proc. 2022 IEEE 5th Student Conference on Electric Machines and Systems (IEEE, 2022); https://ieeexplore.ieee.org/document/9990052
Le-The, C. et al. Optimizing photovoltaic power distribution for EV charging in EV charging station. In Proc. 2024 11th International Conference on Power and Energy Systems Engineering (IEEE, 2024); https://ieeexplore.ieee.org/document/10840501
Liao, X., Zhu, R. & Wong, M. S. Simplified estimation modeling of land surface solar irradiation: a comparative study in Australia and China. Sustain. Energy Technol. Assess. 52, 102323 (2022).
Google Scholar
Yang, Z.-C. Modeling and forecasting monthly movement of annual average solar insolation based on the least-squares Fourier-model. Energy Convers. Manag. 81, 201–210 (2014).
Article Google Scholar
Sebastianelli, A. et al. Machine learning forecast of surface solar irradiance from meteo satellite data. Remote Sens. Environ. 315, 114431 (2024).
Article Google Scholar
Buffat, R., Grassi, S. & Raubal, M. A scalable method for estimating rooftop solar irradiation potential over large regions. Appl. Energy 216, 389–401 (2018).
Article Google Scholar
Huang, P. & Ma, Z. Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: insights from real-world data in Sweden. Renew. Energy 236, 121511 (2024).
Article Google Scholar
Baek, K., Lee, E. & Kim, J. A dataset for multi-faceted analysis of electric vehicle charging transactions. Sci. Data 11, 262 (2024).
Article Google Scholar
Chen, Q., You, L., Qu, H., Abdelmoniem, A. M. & Yuen, C. AFML: an asynchronous federated meta-learning mechanism for charging station occupancy prediction with biased and isolated data. IEEE Trans. Big Data 11, 1772–1786 (2025).
Article Google Scholar
You, L. et al. Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning. Innovation 5, 100587 (2024).
Google Scholar
Ko, Y., Jang, K. & Radke, J. D. Toward a solar city: trade-offs between on-site solar energy potential and vehicle energy consumption in San Francisco, California. Int. J. Sustain. Transp. 11, 460–470 (2025).
Article Google Scholar
Liu, X. et al. Charging private electric vehicles solely by photovoltaics: a battery-free direct-current microgrid with distributed charging strategy. Appl. Energy 341, 121058 (2023).
Article Google Scholar
Then, J., Agalgaonkar, A. P. & Muttaqi, K. M. Coordinated charging of spatially distributed electric vehicles for mitigating voltage rise and voltage unbalance in modern distribution networks. IEEE Trans. Indust. Applic. 59, 5149–5157 (2023).
Google Scholar
Alyami, S., Almutairi, A. & Alrumayh, O. Novel flexibility indices of controllable loads in relation to EV and rooftop PV. IEEE Trans. Intell. Transp. Syst. 24, 923–931 (2023).
Article Google Scholar
Tripathi, A. K. et al. Integration of solar pv panels in electric vehicle charging infrastructure: benefits, challenges, and environmental implications. Energy Sci. Eng. 13, 2135–2152 (2025).
Article Google Scholar
Šimaitis, J. et al. Battery electric vehicles show the lowest carbon footprints among passenger cars across 1.5–3.0 ∘C energy decarbonisation pathways. Commun. Earth. Environ. 6, 476 (2025).
Article Google Scholar
Lukuyu, J., Shirley, R. & Taneja, J. Managing grid impacts from increased electric vehicle adoption in African cities. Sci. Rep. 14, 24320 (2024).
Article CAS Google Scholar
Meinshausen, M. et al. Realization of Paris agreement pledges may limit warming just below 2 ∘C. Nature 604, 304–309 (2022).
Article CAS Google Scholar
You, L. et al. FMGCN: Federated meta learning-augmented graph convolutional network for EV charging demand forecasting. IEEE Internet Things J. 11, 24452–24466 (2024).
Article Google Scholar
Zhong, T. et al. A city-scale estimation of rooftop solar photovoltaic potential based on deep learning. Appl. Energy 298, 117132 (2021).
Article Google Scholar
Huang, S., Rich, P. M., Crabtree, R. L., Potter, C. S. & Fu, P. Modeling monthly near-surface air temperature from solar radiation and lapse rate: application over complex terrain in Yellowstone National Park. Phys. Geogr. 29, 158–178 (2008).
Article CAS Google Scholar
Zhu, R. et al. An economically feasible optimization of photovoltaic provision using real electricity demand: a case study in New York City. Sustain. Cities Soc. 78, 103614 (2022).
Article Google Scholar
Ye, Y. et al. Planning the installation of building-integrated photovoltaic shading devices: a GIS-based spatiotemporal analysis and optimization approach. Renew. Energy 216, 119084 (2023).
Article Google Scholar
Global Solar Atlas (World Bank Group, 2025); https://globalsolaratlas.info/
Photovoltaic Geographical Information System (European Commission, 2025); https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html
Bruno, M., Monteiro Melo, H. P., Campanelli, B. & Loreto, V. A universal framework for inclusive 15-minute cities. Nat. Cities 1, 633–641 (2024).
Article Google Scholar
Yap, W., Stouffs, R. & Biljecki, F. Urbanity: automated modelling and analysis of multidimensional networks in cities. npj Urban Sustain. 3, 45 (2023).
Article Google Scholar
Kiss, G., Jansen, H., Castaldo, V. L. & Orsi, L. The 2050 city. Procedia Eng. 118, 326–355 (2015).
Article Google Scholar
Fernandes, M. S., Coutinho, B. & Rodrigues, E. The impact of climate change on an office building in Portugal: measures for a higher energy performance. J. Clean. Prod. 445, 141255 (2024).
Article Google Scholar
Joshi, S. et al. Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020–2050. Sci. Data 11, 563 (2024).
Article Google Scholar
Merollari, J. & Dervishi, S. Analyzing the impact of urban morphology on solar potential for photovoltaic panels: a comparative study across various European climates. Sustain. Cities Soc. 115, 105854 (2024).
Article Google Scholar
Global EV Data Explorer (International Energy Agency, 2024); https://www.iea.org/data-and-statistics/data-tools/global-ev-data-explorer
Cano, Z. P. et al. Batteries and fuel cells for emerging electric vehicle markets. Nat. Energy 3, 279–289 (2018).
Article Google Scholar
Cavalcante, I. et al. Dataset on electric road mobility: historical and evolution scenarios until 2050. Sci. Data 11, 1019 (2024).
Article Google Scholar
Understanding GEC Model Scenarios (International Energy Agency, 2025); https://www.iea.org/reports/global-energy-and-climate-model/understanding-gec-model-scenarios
Esch, T. et al. World settlement footprint 3D—a first three-dimensional survey of the global building stock. Remote Sens. Environ. 270, 112877 (2022).
Article Google Scholar
Wan, H. et al. Areal interpolation of population projections consistent with different SSPs from 1-km resolution to block level based on USA structures dataset. Comput. Environ. Urban Syst. 105, 102024 (2023).
Article Google Scholar
Download references
Correspondence and requests for materials should be addressed to R.Z. This work was supported by Jiangsu Provincial Double Initiative Project (grant no. 164080H00265 (R.Z.)), the National Natural Science Foundation of China (grant nos. 62576366 (L.Y.), 42325107 (M.C.) and 625B2185 (Z.G.)), the Guangdong Basic and Applied Basic Research Foundation (grant no. 2026A1515011721 (Z.G.)) and the RISE Project of the Hong Kong Polytechnic University (grant no. P0051003 (J.Y.)).
School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China
Linlin You & Zihan Guo
Guangdong Provincial Key Laboratory of Intelligent Transportation Systems, Sun Yat-sen University, Guangzhou, China
Linlin You & Zihan Guo
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, China
Rui Zhu, Min Chen & Guonian Lü
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
Rui Zhu, Min Chen & Guonian Lü
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
Rui Zhu & Zheng Qin
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
Paolo Santi & Carlo Ratti
Istituto di Informatica e Telematica del CNR, Pisa, Italy
Paolo Santi
ABC Department, Politecnico di Milano, Milan, Italy
Carlo Ratti
Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA
A. T. D. Perera
Shanghai Innovation Institute, Shanghai, China
Zihan Guo
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Ziyi Huang
Department of Geography, National University of Singapore, Singapore, Republic of Singapore
Shixiang Xing
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Jinyue Yan
International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University, Hong Kong, China
Jinyue Yan
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
PubMed Google Scholar
R.Z. proposed the research idea and led the project. L.Y., R.Z., P.S., C.R. and A.T.D.P. designed the research. Z.G., R.Z., Z.H., L.Y. and S.X. performed the research. R.Z. and L.Y. wrote the first version of the paper. M.C., G.L., Z.Q. and J.Y. provided scientific and technical guidance. L.Y. provided source data. L.Y., R.Z., P.S., C.R., A.T.D.P., Z.G., Z.H., S.X., M.C., G.L., Z.Q. and J.Y. were involved in data production and provided feedback on the paper.
Correspondence to Rui Zhu.
The authors declare no competing interests.
Nature Sustainability thanks Muhammad Irfantheir, Martin Raubalfor 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.
The top-down stage predicts the daily EV charging demand by applying a federated meta-learning framework and estimates the RPV electricity generation to explore the spatial patterns of daily solar EV charging capacity. The bottom-up stage uses an integrated scenario framework to estimate the projected annual EV charging capacity and derive the economic feasibility, enabling the assessment of carbon mitigation potential by 2050.
Several cities experienced dramatic increases in EVCDs, such as AMS, BER, CPH, DUB, etc. This is because the installation of new charging stations immediately followed by intensive charging demands, revealed from the geospatial analysis investigating the locations of charging stations over time. Large variations of the real EVCD imply great challenges in accurately forecasting daily EVCD at each station.
Source data
The prediction and observation values were normalised between 0 and 1 for easy comparison. The linear regression fits are shown with 95% confidence intervals (light red bands). EVS and R2 are close to 1 and the biases are notably small, demonstrating outstanding performance in daily EVCD forecasting for the investigated cities.
Source data
a, The microgrids are modelled by a set of hexagons with the edge length r equalling 2 km. The maps show noticeably heterogeneous distributions of EV charging stations across cities, in terms of the number of charging stations, their density, and location. b, Annual mean charging capacities (a dimensionless quantity) vary from hundreds to a few thousand under the MES strategy. c, Annual mean charging capacities are around hundreds of thousands under the woESS strategy. There were three microgrids in Shenzhen (SZH) that did not produce rooftop PVEG as rooftops were unavailable in these regions.
a, Regression under the AES strategy. b, Regression under the MES strategy. φ* and (rm{cc}_{m}^{* }) represent the mean ln(φ) and the mean ln(ccm) across all 40 cities, respectively, at a given resolution of microgrids. Statistical significance was assessed by using two-sided Pearson correlation across the eight resolution-level aggregated data points (n = 8, df = 6). No adjustment for multiple comparisons was made. Pearson correlation coefficients (R) and p-values are shown in the plots. For AES, a strong negative correlation between φ* and (rm{cc}_{m}^{* }) was observed (95% confidence interval, in [ − 0.993, − 0.795]; t(6) = − 8.54). For MES, a similarly strong negative correlation was observed (95% confidence interval in [ − 0.997, − 0.904]; t(6) = − 12.95).
Source data
The scatter plots show strong positive correlations between ln(φ) and ln(ccm), with Pearson correlation coefficient (R) ranging between 0.53 and 0.64 (p < 0.0005) when r varies from 2 km and 16 km. Statistical significance was assessed for each plot using two-sided Pearson correlation across 40 cities (n = 40, df = 38). No adjustment for multiple comparisons was made. The 95% confidence interval and t statistics are [0.270, 0.723] and 3.86 for r = 2 km, [0.279, 0.728] and 3.93 for r = 4 km, [0.32, 0.764] and 4.48 for r = 6 km, [0.432, 0.804] and 5.31 for r = 8 km, [0.303, 0.742] and 4.13 for r = 10 km, [0.372, 0.774] and 4.73 for r = 12 km, [0.362, 0.769] and 4.66 for r = 14 km, and [0.410, 0.796] and 5.15 for r = 16 km.
Source data
The bar plot shows that the charging demands under the APS are larger than under the STEPS. Surprisingly, 6 cities are expected to grow more than 100 times, followed by 5 cities between 50 and 100 times, 15 cities between 25 and 50 times, and the remaining 14 cities smaller than 25 times.
Source data
a–e, The bar plots show the differences in annual Eg across five SSPs and present large differences across cities under the same scenario, with the smallest demand around 1.10-1.31 TWh yr−1 in RVK and the largest demand around 323.13-447.72 TWh yr−1 in LOA in 2050.
Source data
a,b, cca under the SSP2. c,d, cca under the SSP3. e,f, cca under the SSP4. g,h, cca under the SSP5. Overall, cca is the largest under the SSP5, followed by SSP2, SSP4, and SSP3, which indicates the most substantial urbanisation and consequently, the largest rooftop area expansion and PVEG. cca is smaller under the APS than STEPS since APS suggests a larger penetration of EVs and thus, a larger EVCD.
Source data
a,b,c, LOA, SZH, MEL, SPO, and DBA under the SSP5 obtain the largest carbon mitigation from RPVs, followed by SSP1, SSP2, SSP4, and SSP3. d,e,f, SZH, MEL, SYD, MIL, and LDN under the APS (Gasoline) achieve the largest carbon mitigation from EVs, followed by APS (Diesel), STEPS (Gasoline), and STEPS (Diesel). g,h,i, SZH, MSL, SYD, TLV, BER under the APS make the largest carbon emission from ESSs, compared to the STEPS.
Source data
Supplementary discussion, Tables 1–9, Figs. 1–19 and references.
Statistical source data for Figs. 1–5 and Extended Data Figs. 2, 3 and 5–10.
Springer Nature or its licensor (for example a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
You, L., Zhu, R., Santi, P. et al. Rooftop photovoltaic-powered electric vehicle charging for accelerated decarbonization. Nat Sustain (2026). https://doi.org/10.1038/s41893-026-01854-3
Download citation
Received:
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1038/s41893-026-01854-3
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Nature Sustainability (Nat Sustain)
ISSN 2398-9629 (online)
© 2026 Springer Nature Limited
Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.
