| 黄河流域高碳制造业减污降碳协同效应及其影响因素 |
| 摘要点击 1988 全文点击 270 投稿时间：2024-09-11 修订日期：2024-11-19 |
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| 中文关键词 高碳制造业 减污降碳 协同效应 面板回归 时空地理加权回归 黄河流域 |
| 英文关键词 high-carbon manufacturing pollution reduction and carbon reduction synergistic effects panel regression geographically and temporally weighted regression the Yellow River Basin |
| DOI 10.13227/j.hjkx.202409143 |
| 作者 | 单位 | E-mail | | 岳文慧 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | yuewh@henu.edu.cn | | 张丽君 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | zlj7happy@163.com | | 秦耀辰 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | | | 郝天阳 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | | | 王玉香 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | | | 刘秀芳 | 河南大学地理与环境学院, 黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学应对气候变化与碳中和实验室, 郑州 450046 | |
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| 中文摘要 |
| 高碳制造业减污降碳协同效应研究有助于深入理解低碳转型与空气质量提升的协同机制，对“双碳”目标推进与清洁空气行动具有重要意义. 然而，减污降碳的协同机制尚未得到充分讨论. 因此，利用多尺度排放清单数据集和工业企业普查数据库等数据，采用协同控制交叉弹性系数、面板回归及GTWR模型，分析2000~2020年间城市高碳制造业协同效应变化及其影响因素. 结果发现：①黄河流域高碳制造业污染物排放呈倒U型变化，排放量降低58.14%. 碳排放量持续增加，但是2010年后增速放缓，倒U型和直线型变化的城市比例相当. ②黄河流域高碳制造业协同控制交叉弹性系数呈现阶段性与区域异质性的变化特征，协同控制效应由反协同控制为主导转变为非协同与正向协同控制并重. 协同控制效应的地理邻近性不断增强，组团分布格局明显. ③从全局层面看，企业数量扩张和国有企业比例增加有利于减污降碳协同水平提高，固定资产投资、地方企业和对外出口比例的扩大以及专业化程度提高显著抑制减污降碳协同水平. ④从时空异质性特征来看，各因素的空间异质性变化比较复杂，但是企业数量、专业化、固定资产投资及地方企业比例的作用方向稳定，多样化、国有企业及对外出口比例作用方向的波动性较大. 研究结果有助于从规模扩张、产业集聚、环境管制及对外出口层面为提高减污降碳协同水平提供政策参考. |
| 英文摘要 |
| Studying the synergistic effects of pollution reduction and carbon reduction in high-carbon manufacturing industries can help deepen understanding of the synergistic mechanisms between low-carbon transformation and air quality improvement， which has great significance for promotion of the “dual carbon” goal and clean air action. However， the synergistic mechanisms of pollution reduction and carbon reduction have not been discussed fully. Therefore， using data such as the multi-scale emission inventory dataset and the industrial enterprise census database， the synergy control cross-elasticity coefficient， panel regression， and GTWR models were used to analyze the changes in the synergistic effect of the urban high-carbon manufacturing industry and its influencing factors during the period from 2000 to 2020. The study produced several useful results： ① In the Yellow River Basin， the pollutant emissions of the high-carbon manufacturing industry showed an inverted U-shaped change， and the emissions decreased by 58.14%. Carbon emissions continued to increase， but the rate of growth slowed after 2010， and the proportions of cities with inverted U-shaped and linear changes were equal. ② The synergistic control cross-elasticity coefficient of high-carbon manufacturing industries in the Yellow River Basin showed a stage-specific and spatially heterogeneous change pattern， with the synergistic control effect shifting from predominantly counter-synergistic control to a balance of non-synergistic and positive synergistic control. The geographical proximity of synergistic control effects increased constantly， and the agglomeration distribution pattern was obvious. ③ From a broader perspective， the expansion of the number of enterprises and the increase in the proportion of state-owned enterprises are conducive to improving the synergy between pollution reduction and carbon reduction. The expansion of fixed asset investment， the proportion of local enterprises， the proportion of exports， and the improvement in specialization， significantly suppress the synergy between pollution reduction and carbon reduction. ④ From the perspective of spatial heterogeneity characteristics， the spatial heterogeneity changes of various factors are relatively complex， but the direction of the effects of enterprise number， specialization， fixed asset investment proportion， and local enterprise proportion are stable. The directions of the effects of diversification， state-owned enterprise proportion， and export proportion are more volatile. The research results of this study provide policy references for improving the synergy level of pollution reduction and carbon reduction from the aspects of scale expansion， industrial agglomeration， environmental regulation， and foreign exports. |
