Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm
文献类型：期刊论文
| 作者 | Zhou, Lei; Song, Jun; Chi, Yonggang; Yu, Quanzhou |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 15期号:2 |
| 关键词 | carbon emission energy conservation genetic algorithm nighttime light data urban agglomerations |
| DOI | 10.3390/rs15020404 |
| 文献子类 | Article |
| 英文摘要 | Urban agglomerations, such as Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta, are the key regions for energy conservation, carbon emission reduction and low-carbon development in China. However, spatiotemporal patterns of CO2 emissions at fine scale in these major urban agglomerations are not well documented. In this study, a back propagation neural network based on genetic algorithm optimization (GABP) coupled with NPP/VIIRS nighttime light datasets was established to estimate the CO2 emissions of China's three major urban agglomerations at 500 m resolution from 2014 to 2019. The results showed that spatial patterns of CO2 emissions presented three-core distribution in the Beijing-Tianjin-Hebei Region, multiple-core distribution in the Yangtze River Delta, and null-core distribution in the Pearl River Delta. Temporal patterns of CO2 emissions showed upward trends in 28.74-43.99% of the total areas while downward trends were shown in 13.47-15.43% of the total areas in three urban agglomerations. The total amount of CO2 emissions in urban areas was largest among urban circles, followed by first-level urban circles and second-level urban circles. The profiles of CO2 emissions along urbanization gradients featured high peaks and wide ranges in large cities, and low peaks and narrow ranges in small cities. Population density primarily impacted the spatial pattern of CO2 emissions among urban agglomerations, followed by terrain slope. These findings suggested that differences in urban agglomerations should be taken into consideration in formulating emission reduction policies. |
| WOS关键词 | PEARL RIVER DELTA ; RESIDENTIAL ENERGY-CONSUMPTION ; ELECTRIC-POWER CONSUMPTION ; CARBON-DIOXIDE EMISSIONS ; DRIVING FORCES ; IMPACT FACTORS ; LEVEL ; CITIES ; POPULATION ; DYNAMICS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000918909100001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/189845]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Institute of Geographic Sciences & Natural Resources Research, CAS 2.Liaocheng University 3.Chinese Academy of Sciences 4.Zhejiang Normal University |
| 推荐引用方式 GB/T 7714 | Zhou, Lei,Song, Jun,Chi, Yonggang,et al. Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm[J]. REMOTE SENSING,2023,15(2). |
| APA | Zhou, Lei,Song, Jun,Chi, Yonggang,&Yu, Quanzhou.(2023).Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm.REMOTE SENSING,15(2). |
| MLA | Zhou, Lei,et al."Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm".REMOTE SENSING 15.2(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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