Economics at your fingertips  

Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model

Shaojian Wang, Chenyi Shi, Chuanglin Fang and Kuishuang Feng

Applied Energy, 2019, vol. 235, issue C, 95-105

Abstract: 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.

Keywords: CO2 emissions; City-level; DMSP/OLS; Geographically Weighted Regression Model; China (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-05-11
Handle: RePEc:eee:appene:v:235:y:2019:i:c:p:95-105