Author
Listed:
- Zhixin Zhang
(Nanjing Normal University
Potsdam Institute for Climate Impact Research
Technical University Berlin)
- Zhen Qian
(Nanjing Normal University
Potsdam Institute for Climate Impact Research
Technical University of Munich)
- Min Chen
(Nanjing Normal University
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
International Research Center of Big Data for Sustainable Development Goals)
- Rui Zhu
(Agency for Science, Technology and Research (A*STAR))
- Fan Zhang
(Peking University)
- Teng Zhong
(Nanjing Normal University)
- Jian Lin
(Nanjing Normal University)
- Liang Ning
(Nanjing Normal University)
- Wei Xie
(Nanjing Normal University)
- Felix Creutzig
(Potsdam Institute for Climate Impact Research
Technical University Berlin
University of Sussex Business School)
- Wenjun Tang
(Chinese Academy of Sciences)
- Laibao Liu
(The University of Hong Kong
The University of Hong Kong)
- Jiachuan Yang
(The Hong Kong University of Science and Technology)
- Ye Pu
(Chinese Academy of Sciences)
- Wenjia Cai
(Tsinghua University)
- Yingxia Pu
(Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
Nanjing University
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology)
- Deer Liu
(Jiangxi University of Science and Technology)
- Hui Yang
(China University of Mining and Technology)
- Hongjun Su
(Hohai University)
- Mingyue Lu
(Nanjing University of Information Science and Technology)
- Fei Li
(Zhongnan University of Economics and Law)
- Xufeng Cui
(Zhongnan University of Economics and Law)
- Zhiwei Xie
(Shenyang Jianzhu University)
- Tianyu Sheng
(Nanjing Normal University)
- Kai Zhang
(Nanjing Normal University)
- Paolo Santi
(Massachusetts Institute of Technology
Istituto di Informatica e Telematica del CNR)
- Lixin Tian
(Jiangsu University
Nanjing Normal University)
- Guonian Lü
(Nanjing Normal University
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)
- Jinyue Yan
(Kowloon
Mälardalen University)
Abstract
Rooftop photovoltaic (RPV) is often understood as a niche contribution to climate change mitigation. However, the global potential of RPVs to mitigate global warming is unknown. Here we map the global rooftop area at 1-km resolution, quantifying 286,393 km2 of rooftops worldwide through geospatial data mining and artificial intelligence techniques. Using nine advanced Earth system models from the coupled model intercomparison project phase 6, we reveal that RPVs could substantially contribute to reducing global temperatures by 0.05–0.13 °C before 2050. Region-specific analysis underscores the variability in RPV potential and the necessity of tailored approaches to optimize RPV deployment, considering local solar resources, existing infrastructure and grid carbon intensity. Our findings reveal that leveraging RPV systems offers a viable and impactful strategy for reducing carbon footprints and combating climate change globally, while advocating targeted interventions to enhance the benefits of RPVs, particularly in areas with high solar radiation or rapid urbanization.
Suggested Citation
Zhixin Zhang & Zhen Qian & Min Chen & Rui Zhu & Fan Zhang & Teng Zhong & Jian Lin & Liang Ning & Wei Xie & Felix Creutzig & Wenjun Tang & Laibao Liu & Jiachuan Yang & Ye Pu & Wenjia Cai & Yingxia Pu &, 2025.
"Worldwide rooftop photovoltaic electricity generation may mitigate global warming,"
Nature Climate Change, Nature, vol. 15(4), pages 393-402, April.
Handle:
RePEc:nat:natcli:v:15:y:2025:i:4:d:10.1038_s41558-025-02276-3
DOI: 10.1038/s41558-025-02276-3
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