面對氣候變化所帶來的生存危機以及環境治理的復雜狀況,對中國碳排放強度時空演進的動態監測與預警治理的研究是實現碳排放強度下降目標的關鍵。文章測算1997—2015年中國大陸30個省區碳排放強度的空間面板數據,采用探索性時空數據分析(ESTDA)方法對中國碳排放強度的空間相關性、集聚特征及其時空躍遷進行空間統計分析,借助分位數回歸與時空躍遷嵌套模型,揭示在時間和空間推移的雙重作用下中國各省區碳排放強度的時空躍遷機制。研究結果表明:(1)中國30個省區的碳排放強度在時空分布上并不是完全隨機狀態,各個省區碳排放強度之間具有顯著的空間相關性特征,碳排放強度的變動趨勢會受到其相臨近省區碳排放強度的影響,省域間的碳排放強度在空間分布上呈現"集聚"與"分異"并存的時空演進特征。(2)中國碳排放強度空間集聚趨勢增強,具有高度的凝固性和較低的流動性,10個高碳排放強度省區碳排放強度的穩定性將成為制約中國碳排放強度整體躍遷的重點省區,相關省區的躍遷性將成為驅動中國碳排放強度整體躍遷的關鍵省區。(3)各省區的碳排放強度空間集聚過程中存在時空躍遷的驅動模式和制約模式,分位數回歸模型能夠很好地解釋各驅動因素對碳排放強度時空躍遷的驅動機制,不同響應階段的驅動因素的分位數與碳排放強度時空躍遷類型之間具有很強的嵌套性。(4)根據各省區碳排放強度時空演進及其躍遷機制的分析結果,進一步提出加強對關鍵省區碳排放強度的有效監測與治理,加大碳排放的約束力度等差異化的碳減排調控措施。

In face of the crisis of survival and the complicated situations of environmental governance both caused by climate changes,the key solution to reduce the intensity of carbon emissions is to study the dynamic monitoring and early warning control of the spatiotemporal evolution of the carbon intensity in China. This paper estimated the spatial panel data of the carbon intensity in 30 provinces in China during 1997-2015,and did spatial statistical analysis of the spatial correlation,agglomeration characteristics and space-time transition of the carbon intensity by the means of exploratory spatio-temporal data analysis( ESTDA). It also adopted quantile regression and the space-time transition nested models to reveal the spatiotemporal transition mechanism of the carbon intensity in the Chinese provinces under the dual functions of time and space. The results show that:(1)The carbon intensity of the 30 provinces in China is not completely random in the spatial and temporal distribution. There are distinct spatial correlation characteristics between the carbon intensity of the provinces,and the change trend of the carbon intensity in the various provinces is affected by that in the adjacent provinces. The carbon intensity among provinces embodies the spatio-temporal evolution characteristics,both‘agglomeration’and ‘differentiation’in the spatial distribution.(2)The spatial agglomeration of the carbon intensity in China is on the increase,with high solidification and low liquidity. The stability of the 10 provinces with high carbon intensity will critically restrict the overall transition of carbon intensity in China. In the contrast,the transition of related provinces will critically drive the overall transition of carbon intensity in China.(3) There are driving modes and control modes of the space-time transition in the process of spatial agglomeration of the carbon intensity in the different provinces. The quantile regression model can well explain the driving mechanism caused by different driving factors in the space-time transition of carbon intensity. There are strong nesting between the quantile of the driving factors and the different types of space-time transition about the carbon intensity in the different stages of the response.(4)According to the results of carbon intensity evolution and transition mechanism in the different provinces,we should put forward further different control measures to reduce the carbon emissions,such as strengthening the effective monitoring and management of key provincial carbon intensity and further restricting the carbon emissions.