Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional "double carbon" goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15'57″~112°25'43″ E and 27°43'13″~27°49'21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the "northwest-southeast" pattern to the "north-south" pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

[Spatiotemporal Dynamic Evolution and Gravity Center Migration of Carbon Emissions in the Main Urban Area of Chongqing over the Past 20 Years].

Global warming caused by carbon emissions is an environmental issue that is of great concern to all walks of life. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important part of achieving the regional double-carbon goals. Taking the main urban area of Chongqing as an example, based on the data of land use and energy consumption, this study estimated the carbon emissions of 153 townships and streets in the main urban area of Chongqing from 2000 to 2020 by using the carbon emission coefficient method. Additionally, using the ESTDA framework to pass the LISA time path, spatiotemporal transition, and the standard deviation ellipse model from the perspective of spatiotemporal interaction, the spatiotemporal dynamic evolution of carbon emissions in the main urban area and the shift in the center of gravity over the past 20 years were analyzed. The results showed that: ① in the past 20 years, the carbon emissions in the main urban and rural areas have had a significant positive spatial correlation, and the spatial convergence showed a trend of first decreasing and then increasing. ② In the past 20 years, there were 126 township streets with low and medium relative lengths (accounting for 82%), indicating that the local spatial structure of township carbon emissions in the main urban area had strong stability; the total number of township streets with low and medium curvatures was 138 (accounting for 90%), indicating that the volatility of the main urban and rural carbon emissions in the direction of spatial dependence was relatively stable; there were 113 township streets (accounting for 74%) of the synergistic growth type, indicating that the main urban and rural carbon emissions were relatively stable. The emission pattern had strong spatial integration. ③ In the past 20 years, the spatiotemporal agglomeration index was greater than 70%, indicating that the local spatial correlation pattern and agglomeration characteristics of carbon emissions in the main urban and rural areas had strong stability. 4 In the past 20 years, the center of carbon emission in the main urban area had been distributed between 106°30'43″-106°32'42″E, 29°33'34″-29°35'56″N, and the center of gravity shifted to the northeast as a whole. The spatial distribution changed from the "northwest-southeast" pattern to the "northeast-southwest" pattern. These results can provide reference for the green and low-carbon sustainable development of Chongqing and the formulation of differentiated emission reduction policies, as well as provide reference for other similar mountain cities in western China.