Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing
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| 出版年: | Remote Sensing vol. 16, no. 1 (2024), p. 23 |
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| その他の著者: | , , , , |
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MDPI AG
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| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs16010023 |2 doi | |
| 035 | |a 2912802037 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Zhang, Zhaoxu |u School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China; <email>zhangzhaoxu@tiangong.edu.cn</email> (Z.Z.); <email>2113620105@tiangong.edu.cn</email> (S.F.); <email>2213620136@tiangong.edu.cn</email> (J.L.); ; The Eighth Geological Brigade, Hebei Bureau of Geology and Mineral Resources Exploration, Qinhuangdao 066000, China; Marine Ecological Restoration and Smart Ocean Engineering Research Center of Hebei Province, Qinhuangdao 066000, China | |
| 245 | 1 | |a Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions. | |
| 651 | 4 | |a Macao | |
| 651 | 4 | |a Hong Kong China | |
| 651 | 4 | |a China | |
| 651 | 4 | |a Tibet | |
| 651 | 4 | |a Taiwan | |
| 653 | |a Logarithms | ||
| 653 | |a Global warming | ||
| 653 | |a Datasets | ||
| 653 | |a Greenhouse gases | ||
| 653 | |a 21st century | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a Energy resources | ||
| 653 | |a Energy consumption | ||
| 653 | |a Radiation | ||
| 653 | |a Climate change | ||
| 653 | |a Center of gravity | ||
| 653 | |a Pollution monitoring | ||
| 653 | |a Remote sensing | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Greenhouse effect | ||
| 653 | |a Carbon | ||
| 653 | |a Economic development | ||
| 653 | |a Emissions | ||
| 653 | |a Statistics | ||
| 653 | |a Research | ||
| 653 | |a Volatile organic compounds--VOCs | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Emission analysis | ||
| 653 | |a Data processing | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Environmental quality | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Sustainable development | ||
| 653 | |a Nighttime | ||
| 653 | |a Night | ||
| 653 | |a Light | ||
| 653 | |a Satellites | ||
| 653 | |a Nitrogen dioxide | ||
| 653 | |a Cities | ||
| 700 | 1 | |a Fu, Shihong |u School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China; <email>zhangzhaoxu@tiangong.edu.cn</email> (Z.Z.); <email>2113620105@tiangong.edu.cn</email> (S.F.); <email>2213620136@tiangong.edu.cn</email> (J.L.); | |
| 700 | 1 | |a Li, Jiayi |u School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China; <email>zhangzhaoxu@tiangong.edu.cn</email> (Z.Z.); <email>2113620105@tiangong.edu.cn</email> (S.F.); <email>2213620136@tiangong.edu.cn</email> (J.L.); | |
| 700 | 1 | |a Qiu, Yuchen |u School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China; <email>zhangzhaoxu@tiangong.edu.cn</email> (Z.Z.); <email>2113620105@tiangong.edu.cn</email> (S.F.); <email>2213620136@tiangong.edu.cn</email> (J.L.); | |
| 700 | 1 | |a Shi, Zhenwei |u Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China; <email>shizw@aircas.ac.cn</email> | |
| 700 | 1 | |a Sun, Yuanheng |u Environmental Information Institute, Navigation College, Dalian Maritime University, Dalian 116026, China | |
| 773 | 0 | |t Remote Sensing |g vol. 16, no. 1 (2024), p. 23 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2912802037/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2912802037/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2912802037/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |