Spatio-Temporal Variation of Carbon Emission Intensity and Spatial Heterogeneity of Influencing Factors in the Yangtze River Delta

The Yangtze River Delta plays an important strategic role in China's economic development pattern, and its carbon emission intensity, which reflects the development of a low-carbon economy, has attracted much attention. From the perspective of the city-level, this study uses the coefficient of variation, spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model to study the spatio-temporal characteristics of carbon emission intensity in the Yangtze River Delta from 1997 to 2017 and the spatial heterogeneity of its influencing factors. The results indicated that: (1) the carbon emission intensity in the Yangtze River Delta increased first and then decreased during the sample period, and the number of low-carbon emission intensity zones decreased first and then increased. (2) Through the coefficient of variation analysis, it is known that the ratio of nugget value to base value is much less than 25%, indicating that the correlation between the cities in the Yangtze River Delta is becoming more and more obvious, the spatial difference is becoming smaller, and the integration level is growing higher and higher. (3) The carbon emission intensity of the Yangtze River Delta has a strong positive spatial correlation, and the carbon emission intensity of the Yangtze River Delta decreases from the north to the south. (4) The effect of population size on carbon emission intensity is bidirectional, but the inhibition effect is greater than the promotion effect, and the average regression coefficient is -0.0796; the average regression coefficient of economic development level is 0.3674, and the average regression coefficient of industrial structure is 0.1702, both of which have a positive impact on carbon emission intensity. The degree of urbanization has a bidirectional effect, and the regression coefficient ranges from -0.920 to 0.091, and the negative effect is quite strong. Additionally, each factor has spatial heterogeneity.