CO2 emission data for Chinese cities

摘要

Highlights ? Available CO2 emissions data is scarce in prefecture-level cities. ? Energy-related carbon emissions differ significantly across city typologies. ? There are big differences in carbon emissions inventories of the cities. ? There are big differences in the capabilities of cities for low-carbon development. Abstract Greenhouse gas (GHG) emission inventories and basic data sources for energy consumption in cities are important for the mitigation of carbon emissions and the development of low-carbon strategies. However, recent IPCC reports and other studies have highlighted significant data gaps in the place-based literature for cities’ carbon emissions and their drivers. In the last few years, literature related to cities’ carbon emissions in China has grown rapidly. This study reviews the literature on city-level carbon emissions in China and examines the emission estimates. A detailed literature search, a regression analysis, and an inductive analysis of city carbon emission data were conducted. Our findings showed that approximately 45% of prefecture-level cities have emission estimates with different levels. The CO2 emission data available for prefecture-level cities are scarce. The methodologies, sectoral coverage, and other aspects of the estimates are limited. The energy-related carbon emissions of the cities differ significantly across city typologies in China. The values of Intensity of Concern (IC) and Clustering Intensity (CI) are spatially aggregated, with the highest values in eastern China, middle values in the central area, and lowest values in western China. There are significant differences and gaps in the carbon emissions inventories and capabilities of low-carbon development in Chinese cities. A number of advanced cities could act as model cities for data collection, and reliable basic data sources could be provided for low-carbon development by perfecting the nation’s carbon emissions accounting system, the statistical system, and shared mechanisms. ]]>