Mapping urban CO2 emissions using DMSP/OLS 'city lights' satellite data in China
Bibliographic Data
| ID | 4139579 |
|---|---|
| Authors | Yan Wang (0009-0009-9633-0023, Chinese Academy of Sciences), Guangdong Li (0000-0001-5691-6112, Chinese Academy of Sciences, corresponding author) |
| Year | 2017 |
| Volume | 49 |
| Issue | 2 |
| Pages | 248-251 |
| Publication date | 2017-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0308518x16656374 |
| OpenAlex | W2468891705 |
| Language | EN |
| Citations received | 10 |
| References cited | 4 |
China, the world's top CO2 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 CO2 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 CO2 emission statistics, and as a result, increases difficulty in allocation of CO2 emission reduction responsibilities. In order to fill this lacuna, we employed a standardized remote sensing inversion approach for estimating China's city-level CO2 emissions from energy consumptions by integrating DMSP/OLS 'city lights' satellite data and statistical data. The end product is a map of city-level CO2 emissions in China. The most topping CO2 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 CO2 emissions are Shanxi and Henan in Central China, as well as the Chengdu-Chongqing and Shaanxi in West China. Regions with low CO2 emissions are western China, and most of Central China and South China
Air quality index · Beijing · China · Delta · Economic geography · Emission inventory · Geography · Meteorology · Urban agglomeration · Air Quality and Health Impacts · Engineering · Environmental Science · Impact of Light on Environment and Health · Urban Transport and Accessibility
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Spatiotemporal Dynamics of Ecological Total-Factor Energy Efficiency and Their Drivers in China at the Prefecture Level
The Slowdown in China’s Carbon Emissions Growth in the New Phase of Economic Development
Land use data can improve the accuracy of carbon emission spatial inversion model
Mapping the spatial-temporal changes in energy consumption-related carbon emissions in the Beijing-Tianjin-Hebei region via nighttime light data
Towards low-carbon cities
Unveiling the link
Spatiotemporal evolutionary characteristics and influencing factors of carbon emissions in Central Plain urban agglomeration
The decoupling relationship between land use efficiency and carbon emissions in China
Visualizing the evolution of per capita carbon emissions of Chinese cities, 2001-2016
| Citations per year | 1.43 |
|---|---|
| Citation span | 2019 - 2026 (8) |
| Highly cited | No |