长江与黄河流域碳排放效率时空演变特征及路径识别探究

为深入探究同类型地理单元碳排放效率的区域异质性，利用考虑非期望产出的MinDS模型、Malmquist指数分析2005—2017年长江与黄河流域城市碳排放效率的静态与动态特征与差异，从流域间、流域内比较视角探究长江与黄河流域碳排放效率的空间集聚特征与演化规律，通过随机效应模型对不同城市类型碳排放效率的影响因素进行面板回归分析. 结果表明:①2005—2017年，长江与黄河流域碳排放效率平均值分别为0.785、0.747，碳排放效率总体处于较低水平；碳排放效率呈先降后升的“U”型变化趋势，且2012—2017年处于“U”型上升区段. ②长江流域碳排放效率呈下游>上游>中游的中间低、两端高的空间分布格局特征，黄河流域呈下游>中游>上游的空间递增格局特征. 长江流域碳排放效率高值区呈现城市群集聚趋势，低值区较分散；黄河流域碳排放效率低值区以宁夏沿黄城市群为中心沿黄河干流向周边扩散，高值区规模较小且分散. ③长江与黄河流域碳排放效率的Malmquist指数均呈上升趋势，表征技术革新的技术进步指数是长江与黄河流域碳排放效率提升的主要内生驱动力，而表征要素组合、管理水平的技术效率指数则对碳排放效率提升作用不显著. ④根据技术效率指数与技术进步指数在碳排放效率提升中的作用差异，可将研究对象划分为六类城市. 经济发展水平、产业结构是影响两大流域碳排放效率提升的共同因素. 研究显示，长江与黄河流域碳排放效率变动既有整体的相似性又有内部的差异性，既要考虑产业结构等因素对两大流域碳排放效率提升的普遍影响，还要注意城镇化水平等因素的差异化影响，以实现两大流域碳减排与效率提升政策设计的“因地制宜、分类施策”. 

Comparative Study of Spatial-Temporal Evolution and Growth Path of Carbon Emissions Efficiency in Yangtze River Basin and Yellow River Basin

In order to further explore the regional heterogeneity of carbon emission efficiency of the same type of geographical unit, this study employs the MinDS model considering unexpected output and Malmquist index approach to investigate the static and dynamic urban carbon emissions efficiency of the Yangtze River Basin and the Yellow River Basin during the period 2005-2017, and further examine the spatial agglomeration characteristics and evolution law of carbon emission efficiency in the two basins from the perspective of the inter- and intra-basin comparison. Also, the influencing factors of different city types are evaluatedby using the random effect panel data regression model. The results show that: (1) The average carbon emission efficiencies of the Yangtze River Basin and the Yellow River Basin were 0.785 and 0.747 respectively during the underlying period. In general, the carbon emission efficiencies of the two Basins show a U-shaped curve. Both of them were in the rising stage of the curve from 2012 to 2017, but still stayed at a relatively low-efficiency level. (2) The carbon emission efficiency of the Yangtze River Basin presents a spatial distribution pattern of ‘downstream > upstream > midstream’, which is low in the middle and high at both ends, and the carbon emission efficiency of the Yellow River Basin presents the spatial distribution pattern of ‘downstream > midstream > upstream’. The high value areas of carbon emission efficiency in the Yangtze River Basin show the trend of agglomeration, and the low value areas are more scattered. While the low value areas of the Yellow River Basin are centered on the Ningxia urban agglomeration along the Yellow River and spread along the main flow of the Yellow River, the high value areas are small and scattered. (3) The Malmquist index shows an upward trend. The technological progress index, which denotes technological innovation level, is the main factor contributing to the improvement of carbon emission efficiency in the two Basins. The technological efficiency index, which represents the combination of production factors and management level, has no significant effect. (4) Based on the difference in the role of technological efficiency and technological progress in the improvement of carbon emissions efficiency, the research objects could be divided into six types of cities. The level of economic development and industrial structure are common factors that affect the improvement of carbon emission efficiency in the two major river basins. The study shows that the changes of carbon emission efficiency in the two basins have both overall similarities and intraregional heterogeneity. We should consider not only the general impact of industrial structure on the improvement of carbon emission efficiency in the two river basins, but also the differentiated impact of factors such as the level of urbanization, to realize the ‘local conditions and classified measures’ in the policy design of carbon emissions reduction and efficiency improvement.