| 京津冀城市群人为碳排放时空格局演变与驱动机制分析 |
| 摘要点击 818 全文点击 27 投稿时间：2025-07-14 修订日期：2025-09-14 |
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| 中文关键词 人为碳排放 STIRPAT模型 岭回归 驱动机制 京津冀城市群 |
| 英文关键词 anthropogenic carbon emissions STIRPAT model ridge regression driving mechanisms Beijing-Tianjin-Hebei Urban Agglomeration |
| DOI 10.13227/j.hjkx.202507186 |
| 作者 | 单位 | E-mail | | 武爱彬 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | wu.ai.bin@163.com | | 康园园 | 邯郸市土地储备中心, 邯郸 056011 | | | 刘美茹 | 河北地质大学土地科学与空间规划学院, 石家庄 050031 | | | 陈辅国 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 罗健盈 | 河北地质大学土地科学与空间规划学院, 石家庄 050031 | | | 沈会涛 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 秦彦杰 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | | | 赵艳霞 | 河北省科学院地理科学研究所, 河北省地理信息开发应用工程技术研究中心, 石家庄 050011 | zhyx8698@163.com |
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| 中文摘要 |
| 掌握城市群人为碳排放时空格局演变规律与驱动机制，可以为区域降碳减排政策制定提供依据. 基于碳排放因子法与多源遥感数据，系统分析了2000~2020年京津冀城市群人为碳排放的时空演变特征及驱动机制. 结果表明：①2000~2020年京津冀城市群人为碳排放呈波动增长趋势，总人为排放量（以C计）由105.90 Tg增长至358.58 Tg，其中河北省是排放的主要贡献者，增长显著，北京市人为碳排放于2010年达到峰值后呈下降趋势，天津市呈先增后降再增的波动态势. ②人为碳排放结构方面，能源消费占主导地位，工业过程与废弃物排放占比呈明显上升趋势，农业排放占比相对稳定. ③空间格局上，人为碳排放呈现明显的“核心-外围”结构，特大城市与产业交通走廊是人为碳排放的集中区域，空间极化趋势加剧. ④驱动机制分析表明，经济发展、城镇化水平与碳排放强度是三地共同的主导因素；人口规模与产业结构作用存在区域差异，天津市和河北省的人口增长和工业化特征显著，北京市的产业转型较明显；能源强度在天津市和河北省表现出显著的减排效应，北京市减排潜力相对较低；科技创新的减排效应尚未有效释放，整体对人为碳排放贡献有限. 研究构建的扩展STIRPAT-岭回归模型验证了其在区域人为碳排放驱动机制分析中的稳定性与解释能力，可为京津冀城市群精准减排与差异化碳管理提供科学依据. |
| 英文摘要 |
| Understanding the spatiotemporal evolution and driving mechanisms of anthropogenic carbon emissions in urban agglomerations is critical for formulating regional carbon reduction policies. Based on the carbon emission factor method and multi-source remote sensing data， this study systematically analyzed the spatiotemporal evolution characteristics and driving mechanisms of anthropogenic carbon emissions in the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2020. The results showed that： ① The total anthropogenic carbon emissions （in terms of C） in the Beijing-Tianjin-Hebei Urban Agglomeration exhibited a fluctuating upward trend， increasing from 105.90 Tg in 2000 to 358.58 Tg in 2020. Hebei Province was the main contributor， showing significant growth. Beijing municipality's anthropogenic carbon emissions peaked in 2010 and then exhibited a downward trend， while Tianjin municipality showed a fluctuating trend of initial increase， subsequent decrease， and renewed increase. ② Regarding anthropogenic emission structure， energy consumption was the dominant contributor， while emissions from industrial processes and waste showed a clear increasing trend， and agricultural emissions remained relatively stable. ③ Spatially， anthropogenic carbon emissions displayed a clear “core-periphery” structure. Megacities and industrial-transportation corridors were the main concentration areas， and spatial polarization trends intensified over time. ④ Analysis of driving mechanisms revealed that economic development， urbanization， and anthropogenic carbon emission intensity were the primary influencing factors across all three areas. However， population scale and industrial structure showed regional differences： Tianjin municipality and Hebei Province were notably influenced by population growth and industrialization， while Beijing municipality experienced more significant industrial transformation. Energy intensity showed substantial emission reduction effects in Tianjin municipality and Hebei Province， but Beijing municipality's emission reduction potential was relatively lower. Additionally， the emission-reducing effect of technological innovation had not yet been effectively realized， contributing little overall to reducing anthropogenic carbon emissions. The extended STIRPAT-ridge regression model constructed in this study demonstrated strong stability and explanatory power for analyzing the driving mechanisms of regional anthropogenic carbon emissions， thus providing scientific support for precise emission reduction and differentiated carbon management strategies in the Beijing-Tianjin-Hebei Urban Agglomeration. |
