China’s county-level monthly CO2 emissions during 2013–2021
Abstract The top-down method is widely used to estimate China’s CO2 emissions at the county level. However, studies have relied on a single indicator of regional total nighttime light brightness as an instrumental variable for prediction, leading to the assumption that there is a positive correlatio...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05461-3 |
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| author | Ming Gao Chaofan Tu Miaomiao Liu Jiandong Chen Xingyu Chen Hong Zou Thomas Shiu Tong Long Chen Shuke Fu |
| author_facet | Ming Gao Chaofan Tu Miaomiao Liu Jiandong Chen Xingyu Chen Hong Zou Thomas Shiu Tong Long Chen Shuke Fu |
| author_sort | Ming Gao |
| collection | DOAJ |
| description | Abstract The top-down method is widely used to estimate China’s CO2 emissions at the county level. However, studies have relied on a single indicator of regional total nighttime light brightness as an instrumental variable for prediction, leading to the assumption that there is a positive correlation between CO2 emissions and total nighttime light brightness in all regions within the same province. This assumption overlooks other heterogeneous relationships and does not correspond to reality. Therefore, this study constructed a dataset of potential feature variables based on multisource data (improved and calibrated nighttime light data, urban and rural human settlement data, and socioeconomic indicator data based on statistical yearbooks). After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO2 emissions. Compared with the total nighttime brightness, it has a stronger linear relationship with CO2 emissions. Using the top-down algorithm, we estimated China’s monthly CO2 emissions at the county level from 2013 to 2021. This dataset provides a solid foundation for predicting the achievement of China’s county-level “dual carbon” strategy. The methods used in this study can be generalized to other global regions. |
| format | Article |
| id | doaj-art-09598397fbaa4de2aa76563e3af7e990 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-09598397fbaa4de2aa76563e3af7e9902025-08-20T03:04:22ZengNature PortfolioScientific Data2052-44632025-07-0112111510.1038/s41597-025-05461-3China’s county-level monthly CO2 emissions during 2013–2021Ming Gao0Chaofan Tu1Miaomiao Liu2Jiandong Chen3Xingyu Chen4Hong Zou5Thomas Shiu Tong6Long Chen7Shuke Fu8School of Public Administration, Southwestern University of Finance and EconomicsSchool of Computer Science, Chengdu UniversitySchool of Public Administration, Southwestern University of Finance and EconomicsSchool of Public Administration, Southwestern University of Finance and EconomicsSchool of Public Administration, Southwestern University of Finance and EconomicsSchool of Economics, Southwestern University of Finance and EconomicsDepartment of Architecture and Civil Engineering, City University of Hong KongDepartment of Architecture and Civil Engineering, City University of Hong KongSchool of Law & Business, Wuhan Institute of TechnologyAbstract The top-down method is widely used to estimate China’s CO2 emissions at the county level. However, studies have relied on a single indicator of regional total nighttime light brightness as an instrumental variable for prediction, leading to the assumption that there is a positive correlation between CO2 emissions and total nighttime light brightness in all regions within the same province. This assumption overlooks other heterogeneous relationships and does not correspond to reality. Therefore, this study constructed a dataset of potential feature variables based on multisource data (improved and calibrated nighttime light data, urban and rural human settlement data, and socioeconomic indicator data based on statistical yearbooks). After the main feature variables were identified, a hybrid regression algorithm combining deep neural networks and CatBoost was constructed to generate instrumental variable for predicting CO2 emissions. Compared with the total nighttime brightness, it has a stronger linear relationship with CO2 emissions. Using the top-down algorithm, we estimated China’s monthly CO2 emissions at the county level from 2013 to 2021. This dataset provides a solid foundation for predicting the achievement of China’s county-level “dual carbon” strategy. The methods used in this study can be generalized to other global regions.https://doi.org/10.1038/s41597-025-05461-3 |
| spellingShingle | Ming Gao Chaofan Tu Miaomiao Liu Jiandong Chen Xingyu Chen Hong Zou Thomas Shiu Tong Long Chen Shuke Fu China’s county-level monthly CO2 emissions during 2013–2021 Scientific Data |
| title | China’s county-level monthly CO2 emissions during 2013–2021 |
| title_full | China’s county-level monthly CO2 emissions during 2013–2021 |
| title_fullStr | China’s county-level monthly CO2 emissions during 2013–2021 |
| title_full_unstemmed | China’s county-level monthly CO2 emissions during 2013–2021 |
| title_short | China’s county-level monthly CO2 emissions during 2013–2021 |
| title_sort | china s county level monthly co2 emissions during 2013 2021 |
| url | https://doi.org/10.1038/s41597-025-05461-3 |
| work_keys_str_mv | AT minggao chinascountylevelmonthlyco2emissionsduring20132021 AT chaofantu chinascountylevelmonthlyco2emissionsduring20132021 AT miaomiaoliu chinascountylevelmonthlyco2emissionsduring20132021 AT jiandongchen chinascountylevelmonthlyco2emissionsduring20132021 AT xingyuchen chinascountylevelmonthlyco2emissionsduring20132021 AT hongzou chinascountylevelmonthlyco2emissionsduring20132021 AT thomasshiutong chinascountylevelmonthlyco2emissionsduring20132021 AT longchen chinascountylevelmonthlyco2emissionsduring20132021 AT shukefu chinascountylevelmonthlyco2emissionsduring20132021 |