| 基于融合夜间灯光的城市群碳排放反演及时空格局动态 |
| 摘要点击 1990 全文点击 64 投稿时间：2025-02-17 修订日期：2025-05-07 |
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| 中文关键词 城市群 碳排放 夜间灯光 时空格局 协同减排 |
| 英文关键词 urban agglomerations carbon emissions nighttime lights spatiotemporal patterns collaborative mitigation |
| DOI 10.13227/j.hjkx.202502104 |
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| 中文摘要 |
| 中国城市贡献了约85%的碳排放，成为碳减排主战场. 以京津冀、中原和成渝城市群为例，基于融合的夜间灯光对能源碳排放进行反演，采用Slope指数、标准差椭圆和探索性时空数据分析探究城市群碳排放时空格局动态特征. 结果表明: ①2000~2022年，各城市群碳排放呈“快速增长-增速下降-趋于稳定”的变化特征. 邯郸、郑州和重庆为碳排放迅猛增长城市，天津、邢台、新乡、菏泽、洛阳、南阳和成都为碳排放较快增长城市; ②京津冀和成渝城市群碳排放标准差椭圆分别呈“东北-西南”和“西北-东南”方向分布，而中原城市群碳排放分布方向不明显. 受产业转移和核心城市影响，城市群碳排放重心动态迁移; ③各城市群县级碳排放呈空间集聚特征，以高-高和低-低集聚为主，京津冀和成渝城市群集聚特征较强，而中原城市群由于发展均衡集聚程度较弱，但逐渐增强; ④各城市群碳排放的局部空间结构总体上较稳定，但快速发展区和核心城市周边区域表现出较强的动态性. 研究结果强调应结合多源数据提高精细尺度碳排放估算精度，同时关注效率与公平性促进城市群协同碳减排. |
| 英文摘要 |
| Accounting for approximately 85% of carbon emissions, cities in China will play a crucial role in carbon reduction. Taking the Beijing-Tianjin-Hebei, Central Plains, and Chengdu-Chongqing urban agglomerations as examples, energy-related carbon emissions were estimated using intercalibrated nighttime lights. Slope index, standard deviation ellipse, and exploratory spatial-temporal data analysis were employed to explore the dynamics of carbon emission patterns in these urban agglomerations. The results indicated that: ① From 2000 to 2022, carbon emission growth in the three urban agglomerations showed a trend of "rapid growth, declining growth rate, and stabilization." Handan, Zhengzhou, and Chongqing were characterized by rapid growth in carbon emissions, while Tianjin, Xingtai, Xinxiang, Heze, Luoyang, Nanyang, and Chengdu were characterized by relatively rapid growth in carbon emissions. ② The carbon emission distribution in the Beijing-Tianjin-Hebei and Chengdu-Chongqing urban agglomerations followed "northeast-southwest" and "northwest-southeast" orientations, respectively, while no distinct directional pattern was observed in the Central Plains urban agglomeration. The migration trajectory of the carbon emissions gravity center was influenced by industrial transfer and core cities. ③ At the county level, the three urban agglomerations exhibited spatial autocorrelation characteristics, mainly high-high and low-low agglomeration. The autocorrelation of the Beijing-Tianjin-Hebei and Chengdu-Chongqing urban agglomerations was stronger than that of the Central Plains urban agglomeration due to its relatively balanced internal development. ④ The local spatial structures of carbon emissions in the three urban agglomerations were generally stable, but the rapidly developing areas and surrounding areas of core cities exhibited strong dynamism. The findings emphasize the importance of integrating multi-source data to improve the accuracy of carbon emissions estimation, while also focusing on efficiency and equity to promote collaborative carbon reduction in urban agglomerations. |
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