| 基于贝叶斯层次时空模型的碳排放时空轨迹及驱动因素 |
| 摘要点击 580 全文点击 37 投稿时间：2024-12-24 修订日期：2025-03-21 |
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| 中文关键词 碳排放 贝叶斯层次时空模型 时空特征 空间自相关 长江中游城市群 |
| 英文关键词 carbon emissions Bayesian hierarchical spatio-temporal model space-temporal characteristics spatial autocorrelation urban agglomerations in the middle reaches of the Yangtze River |
| DOI 10.13227/j.hjkx.20260209 |
| 作者 | 单位 | E-mail | | 丁于博 | 东华理工大学江西省流域生态过程与信息重点实验室, 南昌 330013
东华理工大学南昌市景观过程与国土空间生态修复重点实验室, 南昌 330013
东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | 994589863@qq.com | | 危小建 | 东华理工大学江西省流域生态过程与信息重点实验室, 南昌 330013
东华理工大学南昌市景观过程与国土空间生态修复重点实验室, 南昌 330013
东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | weixiaojian1988@ecut.edu.cn | | 蔡进 | 东华理工大学江西省流域生态过程与信息重点实验室, 南昌 330013
东华理工大学南昌市景观过程与国土空间生态修复重点实验室, 南昌 330013
东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | | | 郭锦 | 东华理工大学江西省流域生态过程与信息重点实验室, 南昌 330013
东华理工大学南昌市景观过程与国土空间生态修复重点实验室, 南昌 330013
东华理工大学测绘与空间信息工程学院, 南昌 330013
东华理工大学自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 南昌 330013 | |
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| 中文摘要 |
| 研究城市碳排放的时空轨迹以及驱动因素对于实现碳达峰、控制全球碳排放和保护生态环境具有重要意义. 贝叶斯层次时空模型相较于传统的碳排放驱动识别模型能够处理更加复杂的非线性和多边变量关系，较好地应对数据缺失，提高结果估算的准确性. 基于此，利用碳核算系数法对长江中游城市群的碳排放量进行测算，应用泰尔指数、重心迁移模型和空间自相关分析探索城市碳排放的时空轨迹，进一步采用贝叶斯层次时空模型分析影响碳排放的驱动因素. 结果显示：①研究区域的碳排放总量从2006年的78 459.68×104 t上升到2021年的123 350.56×104 t，碳排放增速由1.95%下降到1.61%；武汉城市圈是碳排放的核心区域，碳排放总量占比为47.48%；环鄱阳湖城市群的碳排放差异性最大；碳排放重心呈现由南向北的变化. ②长江中游城市群的碳排放量存在显著的空间相关性，集聚类型主要表现为高-高型或低-低型，并呈现西高东低的空间分布特征. ③碳排放量受驱动因素影响程度排序为：城市化率>产业结构>经济发展水平>实际利用外资>能源效率>科学技术支出>人口总量；城镇化率、经济发展水平、实际利用外资和能源效率对碳排放的正向影响逐渐增强，产业结构对碳排放的正向影响逐渐减弱，科学技术支出与人口总量对碳排放的正向影响呈现波动状态；研究区域碳排放局部变化差异明显，整体表现为“上弱下强”，热点主要集中在研究区域下方；研究区域碳排放局部趋势差异明显，快速增长区主要分布在武汉城市群. 研究结果对于认识碳排放时空变化特征及其驱动变量、对后续贝叶斯层次时空模型应用到碳排放领域具有重要的理论和实践意义. |
| 英文摘要 |
| The investigation of the spatiotemporal trajectory and driving factors of urban carbon emissions is crucial for achieving carbon peaking， controlling global carbon emissions， and protecting the ecological environment. Compared with the traditional carbon emission-driven identification， the Bayesian hierarchical spatio-temporal model can deal with more complex nonlinear and multilateral variable relationships， better cope with data missing， and improve the estimation accuracy of the results. Based on this， this study uses the carbon accounting coefficient method to measure the carbon emissions of urban agglomerations in the middle reaches of the Yangtze River. The Theil index， gravity center migration model， and spatial autocorrelation analysis are used to explore the spatial and temporal trajectory of urban carbon emissions. Further， the Bayesian hierarchical spatial-temporal model is used to analyze the driving factors affecting carbon emissions. The results showed that： ① The total carbon emissions of the study area increased from 78 459.68×104 t in 2006 to 123 350.56×104 t in 2021， with the rate of increase in carbon emissions slowing down from 1.95% to 1.61%. The Wuhan urban agglomeration was the core area for carbon emissions， accounting for 47.48% of the total emissions. The difference in carbon emissions in the Poyang Lake urban agglomeration was the largest； the focus of carbon emissions changed from south to north. ② The carbon emissions of the urban agglomeration in the middle reaches of the Yangtze River exhibited significant spatial correlation， primarily manifesting as high-high or low-low clustering types， and displayed a spatial distribution characteristic of higher emissions in the west and lower emissions in the east. ③ The degree of influence of driving factors on carbon emissions was ranked as follows： urbanization rate>industrial structure>level of economic development>actual use of foreign capital>energy efficiency>expenditure on science and technology>total population. The positive impact of urbanization rate， economic development level， actual utilization of foreign capital， and energy efficiency on carbon emissions was gradually increasing， the positive impact of industrial structure on carbon emissions was gradually weakening， and the positive impact of science and technology expenditure and total population on carbon emissions was fluctuating. The local changes in carbon emissions in the study area were obviously different， and the overall performance was ‘weak in the upper part and strong in the lower part’. The hot spots were mainly concentrated below the study area， the local trend of carbon emissions in the study area was obviously different， and the rapid growth area was mainly distributed in Wuhan urban agglomeration. The study results are significant for understanding the spatiotemporal characteristics of carbon emissions and their driving variables and hold important theoretical and practical implications for the subsequent application of Bayesian hierarchical spatiotemporal models in the field of carbon emissions. |
