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      Carbon mitigation potential afforded by rooftop photovoltaic in China

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          Abstract

          Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km 2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

          Abstract

          Potential rooftop photovoltaic in China affords 4 billion tons of carbon mitigation in 2020 under ideal assumptions, equal to 70% of China’s carbon emissions from electricity and heat. Yet most cities have exploited the potential to a limited degree.

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          How China could be carbon neutral by mid-century

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            100% Clean and Renewable Wind, Water, and Sunlight All-Sector Energy Roadmaps for 139 Countries of the World

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              Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data

              High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
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                Author and article information

                Contributors
                chenmin0902@njnu.edu.cn
                gnlu@njnu.edu.cn
                jjyan@polyu.edu.hk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 April 2023
                24 April 2023
                2023
                : 14
                : 2347
                Affiliations
                [1 ]GRID grid.260474.3, ISNI 0000 0001 0089 5711, Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), , Nanjing Normal University, ; Nanjing, 210023 China
                [2 ]GRID grid.41156.37, ISNI 0000 0001 2314 964X, School of Geography and Ocean Science, , Nanjing University, ; Nanjing, 210023 China
                [3 ]GRID grid.260474.3, ISNI 0000 0001 0089 5711, School of Geography, , Nanjing Normal University, ; Nanjing, 210023 China
                [4 ]International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094 China
                [5 ]GRID grid.511454.0, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, ; Nanjing, 210023 China
                [6 ]GRID grid.260474.3, ISNI 0000 0001 0089 5711, Jiangsu Provincial Key Laboratory for NSLSCS, School of Mathematical Science, , Nanjing Normal University, ; Nanjing, 210023 China
                [7 ]GRID grid.418742.c, ISNI 0000 0004 0470 8006, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), ; 1 Fusionopolis Way, Singapore, 138632 Republic of Singapore
                [8 ]GRID grid.24515.37, ISNI 0000 0004 1937 1450, Department of Civil and Environmental Engineering, , The Hong Kong University of Science and Technology, ; Hong Kong, China
                [9 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Senseable City Laboratory, Department of Urban Studies and Planning, , Massachusetts Institute of Technology, ; Cambridge, MA 02139 USA
                [10 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, , Peking University, ; Beijing, 100871 China
                [11 ]Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, 210023 China
                [12 ]GRID grid.440785.a, ISNI 0000 0001 0743 511X, Research Institute of Carbon Neutralization Development, School of Mathematical Sciences, , Jiangsu University, ; Zhenjiang, 212013 China
                [13 ]GRID grid.260474.3, ISNI 0000 0001 0089 5711, Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, , Nanjing Normal University, ; Nanjing, 210023 China
                [14 ]GRID grid.16890.36, ISNI 0000 0004 1764 6123, Department of Building Environment and Energy Engineering, , The Hong Kong Polytechnic University, ; Kowloon, Hong Kong China
                [15 ]GRID grid.411579.f, ISNI 0000 0000 9689 909X, Future Energy Center, , Mälardalen University, ; Västerås, 72123 Sweden
                Author information
                http://orcid.org/0000-0002-3898-0863
                http://orcid.org/0000-0001-8922-8789
                http://orcid.org/0000-0001-7994-6488
                http://orcid.org/0000-0002-0423-7430
                http://orcid.org/0000-0002-8942-8702
                http://orcid.org/0000-0001-6072-4199
                http://orcid.org/0000-0003-0300-0762
                Article
                38079
                10.1038/s41467-023-38079-3
                10126133
                37095101
                26b183b4-14d0-4ffe-b57e-658e86d47f78
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 September 2022
                : 14 April 2023
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                © The Author(s) 2023

                Uncategorized
                energy conservation,developing world
                Uncategorized
                energy conservation, developing world

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