Sun Xiu-Feng, Shi Kai-Fang, Wu Jian-Ping
College of Horticulture and Landscape Architecture, Southwest University, Chongqing 400715, China.
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China.
Huan Jing Ke Xue. 2018 Jun 8;39(6):2971-2981. doi: 10.13227/j.hjkx.201711080.
China's CO emissions present obvious temporal and spatial distribution characteristics. Therefore, the study of spatiotemporal dynamics of CO emissions could provide useful information for the government and policy-makers on viable CO emissions mitigation in China. Using Chongqing as a case study, we investigated the spatiotemporal dynamics of CO emissions at the county level (38 counties) from 1997 to 2012.The mathematical statistical method, spatial autocorrelation, and rank size rule were employed to evaluate the CO emissions change in detail. The results showed that all of the counties in Chongqing have experienced a rapid growth of CO emissions, but the two dimensional structure of CO emissions has not changed. The Global Moran's I clearly decreases with a small fluctuation, and these values gradually decrease from 0.56 in 1997 to 0.40 in 2012.In addition, the HH clusters are concentrated in some counties in the downtown areas. Based on the rank size rule analysis, the slope values decrease from -1.35 in 1997 to -0.88 in 2012, indicating a clear scattered pattern of CO emissions in Chongqing at the county level. It has also been proven that the proportion of second industries and the urbanization rate are more important impact factors for CO emissions than the population.