Abstract

Due to the lack of city-level energy consumption statistics and the inconsistency between national, provincial and city-level data, we developed a normalized approach for assessing China׳s city-level CO 2 emissions of energy consumptions using DMSP/OLS nighttime light imageries and explored major driving forces for proposing feasible mitigation policies. China׳s CO 2 emission process was always consistent with its economic development and was classified into two rapid periods (1999–2002 and 2007–2010) and two slow periods (1992–1999 and 2002–2007). Most cities in Western, Central and Northern China belonged to the slow growth type, while most cities in Eastern China belonged to the rapid growth type. Cities with huge CO 2 emissions mainly congregated in southern and eastern coastal areas. Contrarily, cities with small amount of CO 2 emissions were mainly located in southwestern inlands. The CO 2 emission per capita (PCCE) in Northeastern and Eastern China was higher than that of Western and Central China. While the CO 2 emission per GDP (PGCE) of Northeastern and Western China was higher than that of Eastern and Central China. GDP increment was the major factor determining the carbon-emission growth rate, while industry structures and energy efficiencies were the major factors influencing regional CO 2 emission intensities. Therefore, in order to decrease the growth rate of China׳s CO 2 emissions but not hinder its economic development, major efforts should be focused on optimizing the industrial structures in Eastern and Central China where industries mainly belonged to technology-intensive, labor-intensive and light industry types, and guiding companies to increase the energy efficiencies in Northeastern and Western China, where industries mainly belonged to heavy and energy-related types. In particular, more attention should be paid to prevent the CO 2 emissions per energy consumption (PECE) of underdeveloped cities or provinces from increasing rapidly rather than only focusing on reducing the PECE of developed regions.

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