{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T15:44:46Z","timestamp":1771256686853,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671352"],"award-info":[{"award-number":["41671352"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["1908085QF279"],"award-info":[{"award-number":["1908085QF279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Anhui Province, China","award":["41671352"],"award-info":[{"award-number":["41671352"]}]},{"name":"Natural Science Foundation of Anhui Province, China","award":["1908085QF279"],"award-info":[{"award-number":["1908085QF279"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China\u2019s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.<\/jats:p>","DOI":"10.3390\/rs14133014","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"3014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiwen","family":"Lin","sequence":"first","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0004-5025","authenticated-orcid":false,"given":"Jinji","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, China"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Anhui Normal University, Wuhu 241003, China"}]},{"given":"Fei","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, China"}]},{"given":"Safura","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7795-3630","authenticated-orcid":false,"given":"Zhengqiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.rser.2016.09.105","article-title":"Energy consumption, CO2 emissions, and economic growth: An ethical dilemma","volume":"68","author":"Antonakakis","year":"2017","journal-title":"Renew. Sust. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6948","DOI":"10.1038\/s41467-021-27252-1","article-title":"Location-specific co-benefits of carbon emissions reduction from coal-fired power plants in China","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"684","DOI":"10.3389\/fenvs.2021.824298","article-title":"Carbon emission trading Scheme, carbon emissions reduction and spatial spillover effects: Quasi-experimental evidence from China","volume":"9","author":"Yang","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/522279a","article-title":"Steps to China\u2019s carbon peak","volume":"522","author":"Liu","year":"2015","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109737","DOI":"10.1016\/j.rser.2020.109737","article-title":"Adjusting energy consumption structure to achieve China\u2019s CO2 emissions peak","volume":"122","author":"Xu","year":"2020","journal-title":"Renew. Sust. Energy Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.scitotenv.2019.05.352","article-title":"Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster","volume":"684","author":"Qin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"123427","DOI":"10.1016\/j.jclepro.2020.123427","article-title":"Estimating spatiotemporal dynamics of county-level fossil fuel consumption based on integrated nighttime light data","volume":"278","author":"Liu","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.apenergy.2017.01.007","article-title":"Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors","volume":"190","author":"Wang","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.apenergy.2013.01.036","article-title":"Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China","volume":"106","author":"Wang","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jclepro.2014.09.025","article-title":"A spatio-temporal decomposition analysis of energy-related CO2 emission growth in China","volume":"103","author":"Chen","year":"2015","journal-title":"J. Clean. Prod."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.apenergy.2014.09.059","article-title":"Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China\u2019s provinces","volume":"136","author":"Wang","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ecolind.2016.04.022","article-title":"CO2, economic growth, and energy consumption in China\u2019s provinces: Investigating the spatiotemporal and econometric characteristics of China\u2019s CO2 emissions","volume":"69","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.3390\/rs6021705","article-title":"Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data","volume":"6","author":"Shi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/15481603.2015.1124488","article-title":"World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery","volume":"53","author":"Xie","year":"2016","journal-title":"Gisci. Remote Sens."},{"key":"ref_15","first-page":"193","article-title":"Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data","volume":"28","author":"Cao","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103338","DOI":"10.1016\/j.scs.2021.103338","article-title":"Quantitative evaluation of urban expansion using NPP-VIIRS nighttime light and landsat spectral data","volume":"76","author":"Zheng","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103119","DOI":"10.1016\/j.scs.2021.103119","article-title":"Night-time light data based decoupling relationship analysis between economic growth and carbon emission in 289 Chinese cities","volume":"73","author":"Du","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/JSTARS.2015.2399416","article-title":"Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China","volume":"8","author":"Yu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, P., Zhang, X., Cheng, J., and Sun, Q. (2019). Modeling population density using a new index derived from multi-sensor image data. Remote Sens., 11.","DOI":"10.3390\/rs11222620"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Meng, X., Han, J., and Huang, C. (2017). An improved vegetation adjusted nighttime light urban index and its application in quantifying spatiotemporal dynamics of carbon emissions in China. Remote Sens., 9.","DOI":"10.3390\/rs9080829"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yue, Y., Tian, L., Yue, Q., and Wang, Z. (2020). Spatiotemporal variations in energy consumption and their influencing factors in China based on the integration of the DMSP-OLS and NPP-VIIRS nighttime light datasets. Remote Sens., 12.","DOI":"10.3390\/rs12071151"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, S., Wu, Y., Schlink, U., and Singh, R.P. (2020). Spatiotemporal variations of city-level carbon emissions in China during 2000\u20132017 using nighttime light data. Remote Sens., 12.","DOI":"10.3390\/rs12182916"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1080\/014311697218485","article-title":"Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption","volume":"18","author":"Elvidge","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.apenergy.2015.11.055","article-title":"Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis","volume":"168","author":"Shi","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"121841","DOI":"10.1016\/j.energy.2021.121841","article-title":"Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road","volume":"239","author":"Zhong","year":"2022","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134394","DOI":"10.1016\/j.scitotenv.2019.134394","article-title":"Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: Utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets","volume":"703","author":"Lv","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1109\/LGRS.2020.3014956","article-title":"NPP-VIIRS nighttime light data have different correlated relationships with fossil fuel combustion carbon emissions from different sectors","volume":"18","author":"Shi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106731","DOI":"10.1016\/j.eiar.2021.106731","article-title":"Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data","volume":"93","author":"Wu","year":"2022","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_29","first-page":"364","article-title":"Convolutional neural network with data augmentation for SAR target recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, M., Tseng, H., Hsu, Y., and Tsai, H.P. (2020). Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images. Remote Sens., 12.","DOI":"10.3390\/rs12040633"},{"key":"ref_31","first-page":"768","article-title":"An urban water extraction method combining deep learning and Google Earth Engine","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114696","DOI":"10.1016\/j.apenergy.2020.114696","article-title":"Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery","volume":"268","author":"Yang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"170201","DOI":"10.1038\/sdata.2017.201","article-title":"Data descriptor: China CO2 emission accounts 1997\u20132015","volume":"5","author":"Shan","year":"2018","journal-title":"Sci. Data"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rong, T., Zhang, P., Jing, W., Zhang, Y., Li, Y., Yang, D., Yang, J., Chang, H., and Ge, L. (2020). Carbon dioxide emissions and their driving forces of land use change based on economic contributive coefficient (ECC) and ecological support coefficient (ESC) in the lower Yellow River region (1995-2018). Energies, 13.","DOI":"10.3390\/en13102600"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102701","DOI":"10.1016\/j.scs.2020.102701","article-title":"Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region","volume":"66","author":"Zhou","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1038\/467909a","article-title":"Cities lead the way in climate-change action","volume":"467","author":"Rosenzweig","year":"2010","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, X., Yan, H., Huang, Y., Zhang, G., Lin, T., and Ye, H. (2021). Downscaling building energy consumption carbon emissions by machine learning. Remote Sens., 13.","DOI":"10.3390\/rs13214346"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ma, J., Guo, J., Ahmad, S., Li, Z., and Hong, J. (2020). Constructing a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light. Remote Sens., 12.","DOI":"10.3390\/rs12060937"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"595","DOI":"10.3390\/en20300595","article-title":"A fifteen year record of global natural gas flaring derived from satellite data","volume":"2","author":"Elvidge","year":"2009","journal-title":"Energies"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1080\/17538947.2021.1946605","article-title":"Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data","volume":"14","author":"Shi","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","article-title":"A new family of power transformations to improve normality or symmetry","volume":"87","author":"Yeo","year":"2000","journal-title":"Biometrika"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1080\/01621459.2000.10474340","article-title":"Generalized linear models","volume":"95","author":"McCulloch","year":"2000","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.isprsjprs.2021.09.014","article-title":"Simplified object-based deep neural network for very high resolution remote sensing image classification","volume":"181","author":"Pan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_50","first-page":"8001605","article-title":"Random and coherent noise suppression in DAS-VSP data by using a supervised deep learning method","volume":"19","author":"Dong","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, S., and Fan, F. (2021). Analysis of the response of long-term vegetation dynamics to climate variability using the pruned exact linear time (PELT) method and disturbance lag model (DLM) based on remote sensing data: A case study in Guangdong province (China). Remote Sens., 13.","DOI":"10.3390\/rs13101873"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/04353684.1971.11879353","article-title":"The Standard Deviational Ellipse; An updated tool for spatial description","volume":"53","author":"Yuill","year":"1971","journal-title":"Geogr. Ann. Ser. B Hum. Geogr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100947","DOI":"10.1016\/j.uclim.2021.100947","article-title":"Urban form, land use, and cover change and their impact on carbon emissions in the Monterrey Metropolitan area, Mexico","volume":"39","author":"Carpio","year":"2021","journal-title":"Urban Clim."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"190027","DOI":"10.1038\/sdata.2019.27","article-title":"An emissions-socioeconomic inventory of Chinese cities","volume":"6","author":"Shan","year":"2019","journal-title":"Sci. Data."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.rser.2012.10.042","article-title":"Energy conservation in China\u2019s Twelfth Five-Year Plan period: Continuation or paradigm shift?","volume":"18","author":"Lo","year":"2013","journal-title":"Renew. Sust. Energy Rev."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, F., Pilot, E., Yu, J., Nie, C., Holdaway, J., Yang, L., Li, Y., Wang, W., and Vardoulakis, S. (2018). Taking Action on Air Pollution Control in the Beijing-Tianjin-Hebei (BTH) Region: Progress, Challenges and Opportunities. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15020306"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.apenergy.2018.09.180","article-title":"Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets","volume":"235","author":"Zhao","year":"2019","journal-title":"Appl. Energy"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3014\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:36Z","timestamp":1760139516000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133014"],"URL":"https:\/\/doi.org\/10.3390\/rs14133014","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}