Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model
文献类型：期刊论文
| 作者 | Wang, Shaojian1; Shi, Chenyi1; Fang, Chuanglin2 ; Feng, Kuishuang3
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| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2019-02-01 |
| 卷号 | 235页码:95-105 |
| ISSN号 | 0306-2619 |
| 关键词 | CO2 emissions City-level DMSP/OLS Geographically Weighted Regression Model China |
| DOI | 10.1016/j.apenergy.2018.10.083 |
| 通讯作者 | Fang, Chuanglin(fangcl@igsnrr.ac.cn) ; Feng, Kuishuang(kfeng@umd.edu) |
| 英文摘要 | Cities produce over 70% of the global CO2 emissions that result from energy use, and thus play a key role in climate mitigation and adaptation. While the factors influencing CO2 emissions have been subject to extensive study, via research that has explored the path of developing a low-carbon economy, little work has been undertaken at the city level as a result of a deficiency in data availability. Addressing this gap, this study firstly estimated CO2 emissions of cities in China using Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light imagery. We then analyzed spatial variations in the estimated CO2 emissions at the city level, using a spatial analytical model, finding significant spatial autocorrelation in CO2 emissions. Subsequently, we compared the effects of different socioeconomic factors on CO2 emissions, using both global and local regression models. The results from the global regression model revealed that private car ownership, economic growth, and energy consumption were the major factors promoting CO2 emissions in China's cities, while population density had an effect in reducing CO2 emissions. The use of a Geographically Weighted Regression (GWR) model provided more detailed results, revealing significant spatial heterogeneity in the impacts of different factors. Economic growth, private car ownership, and energy consumption all posed positive effects on CO2 emissions while the remainder of the factors studied were found to pose a bidirectional impact on CO2 emissions in different areas of China. Economic growth and private car ownership were to found to exert the strongest positive effects in the cities of western and central China, and energy consumption was shown to significantly and positively influence CO2 emissions in the southernmost part of China. Urban expansion and road density were identified as key promoting factors in CO2 emissions in the northeast of China; and the industrial structure demonstrated significantly positive effects in relation to CO2 levels in cities located in the Beijing-Tianjin-Hebei region. The role of foreign direct investment (FDI) was not found to be significant in most cities expect Guangdong, where a significant positive relationship appeared. |
| WOS关键词 | CARBON-DIOXIDE EMISSIONS ; ENVIRONMENTAL KUZNETS CURVE ; PEARL RIVER DELTA ; ECONOMIC-GROWTH ; SPATIOTEMPORAL VARIATIONS ; PANEL COINTEGRATION ; EMPIRICAL-EVIDENCE ; PROVINCIAL-LEVEL ; IMPACT FACTORS ; URBANIZATION |
| 资助项目 | National Natural Science Foundation of China[41590842] ; National Natural Science Foundation of China[41601151] ; Natural Science Foundation of Guangdong Province[2016A030310149] ; Pearl River S&T Nova Program of Guangzhou[201806010187] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000458942800009 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province ; Pearl River S&T Nova Program of Guangzhou |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/49347]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Fang, Chuanglin; Feng, Kuishuang |
| 作者单位 | 1.Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Sch Geog, Guangzhou 510275, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA |
| 推荐引用方式 GB/T 7714 | Wang, Shaojian,Shi, Chenyi,Fang, Chuanglin,et al. Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model[J]. APPLIED ENERGY,2019,235:95-105. |
| APA | Wang, Shaojian,Shi, Chenyi,Fang, Chuanglin,&Feng, Kuishuang.(2019).Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model.APPLIED ENERGY,235,95-105. |
| MLA | Wang, Shaojian,et al."Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model".APPLIED ENERGY 235(2019):95-105. |
入库方式： OAI收割
来源：地理科学与资源研究所
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