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Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning

by 1,2, 1,2,*, 3, 1,2, 1,2 and 4
1
School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China
2
Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241003, China
3
College of Physics and Electronic Information, Anhui Normal University, Wuhu 241003, China
4
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Academic Editor: Brian Alan Johnson
Remote Sens. 2022, 14(13), 3014; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133014
Received: 30 April 2022 / Revised: 11 June 2022 / Accepted: 21 June 2022 / Published: 23 June 2022
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’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies. View Full-Text
Keywords: carbon emissions; machine learning; multi-dimensional data; spatiotemporal analysis; China carbon emissions; machine learning; multi-dimensional data; spatiotemporal analysis; China
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MDPI and ACS Style

Lin, X.; Ma, J.; Chen, H.; Shen, F.; Ahmad, S.; Li, Z. Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning. Remote Sens. 2022, 14, 3014. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133014

AMA Style

Lin X, Ma J, Chen H, Shen F, Ahmad S, Li Z. Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning. Remote Sensing. 2022; 14(13):3014. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133014

Chicago/Turabian Style

Lin, Xiwen, Jinji Ma, Hao Chen, Fei Shen, Safura Ahmad, and Zhengqiang Li. 2022. "Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning" Remote Sensing 14, no. 13: 3014. https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133014

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