Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China

Yan Wang and Guangdong Li

Environment and Planning A, 2017, vol. 49, issue 2, 248-251

Abstract: China, the world’s top CO 2 emitter, is faced with pressure of energy-saving emission reduction. In the 2015 Paris Climate Conference (COP21), China announced its plan, aiming to cut down CO 2 emissions by 60%–65% per unit of GDP in comparison to 2005’s level by 2030. To achieve this ambitious goal, reliable national, provincial, and city-level statistics are fundamental for multi-scale mitigation policy-makings as well as for the allocation of responsibilities among different administrative units. However, China implemented a top-down energy statistical system. The National Bureau of Statistics only publishes annually both national and provincial energy statistics. Only part of cities released their statistics, which results in missing data in city-level energy statistics. This also affects data transparency and accuracy of energy and CO 2 emission statistics, and as a result, increases difficulty in allocation of CO 2 emission reduction responsibilities. In order to fill this lacuna, we employed a standardized remote sensing inversion approach for estimating China’s city-level CO 2 emissions from energy consumptions by integrating DMSP/OLS ‘city lights’ satellite data and statistical data. The end product is a map of city-level CO 2 emissions in China. The most topping CO 2 emitters are located in the major urban agglomerations along the more economically developed eastern coast (e.g. Yangtze River Delta, Beijing–Tianjin–Hebei, Shandong Peninsula, and Pearl River Delta). Other regions with high CO 2 emissions are Shanxi and Henan in Central China, as well as the Chengdu–Chongqing and Shaanxi in West China. Regions with low CO 2 emissions are western China, and most of Central China and South China.

Keywords: CO2 emissions; DMSP/OLS light satellite data; cartogram (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:49:y:2017:i:2:p:248-251

DOI: 10.1177/0308518X16656374

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