The following paper about regional climatic changes over China with RegCM has recently been accepted for publication by Climate Dynamics:
Lu, C., G. Huang, and X. Wang. Projected Changes in Temperature, Precipitation, and Their Extremes over China Through the RegCM. Climate Dynamics, accepted in July 2019.
More details will come soon once the paper is published.
Title: CO2 emissions patterns of 26 cities in the Yangtze River Delta in 2015: Evidence and implications
Journal: Environmental Pollution
Abstract: As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China’s effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran’s I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran’s I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.