Matches in SemOpenAlex for { <https://semopenalex.org/work/W2898892341> ?p ?o ?g. }
- W2898892341 endingPage "105" @default.
- W2898892341 startingPage "95" @default.
- W2898892341 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." @default.
- W2898892341 created "2018-11-09" @default.
- W2898892341 creator A5039858263 @default.
- W2898892341 creator A5044382297 @default.
- W2898892341 creator A5049493168 @default.
- W2898892341 creator A5088320866 @default.
- W2898892341 date "2019-02-01" @default.
- W2898892341 modified "2023-10-17" @default.
- W2898892341 title "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model" @default.
- W2898892341 cites W1266728252 @default.
- W2898892341 cites W1496422787 @default.
- W2898892341 cites W1840907900 @default.
- W2898892341 cites W1964788690 @default.
- W2898892341 cites W1966606556 @default.
- W2898892341 cites W1971820573 @default.
- W2898892341 cites W1972615792 @default.
- W2898892341 cites W1973749534 @default.
- W2898892341 cites W1975877897 @default.
- W2898892341 cites W1985143386 @default.
- W2898892341 cites W1985785857 @default.
- W2898892341 cites W1991067905 @default.
- W2898892341 cites W1998737852 @default.
- W2898892341 cites W2009583608 @default.
- W2898892341 cites W2011330606 @default.
- W2898892341 cites W2022875322 @default.
- W2898892341 cites W2030922582 @default.
- W2898892341 cites W2044074789 @default.
- W2898892341 cites W2046321290 @default.
- W2898892341 cites W2048686024 @default.
- W2898892341 cites W2056375010 @default.
- W2898892341 cites W2063025212 @default.
- W2898892341 cites W2072964811 @default.
- W2898892341 cites W2084517645 @default.
- W2898892341 cites W2093631778 @default.
- W2898892341 cites W2093855208 @default.
- W2898892341 cites W2108419099 @default.
- W2898892341 cites W2119642490 @default.
- W2898892341 cites W2122654608 @default.
- W2898892341 cites W2123866026 @default.
- W2898892341 cites W2129405474 @default.
- W2898892341 cites W2143796175 @default.
- W2898892341 cites W2183919650 @default.
- W2898892341 cites W2189128848 @default.
- W2898892341 cites W2192575451 @default.
- W2898892341 cites W2195033177 @default.
- W2898892341 cites W2211402078 @default.
- W2898892341 cites W2215412041 @default.
- W2898892341 cites W2221209262 @default.
- W2898892341 cites W2239249289 @default.
- W2898892341 cites W2275424541 @default.
- W2898892341 cites W2281206590 @default.
- W2898892341 cites W2288330981 @default.
- W2898892341 cites W2344059881 @default.
- W2898892341 cites W2408924253 @default.
- W2898892341 cites W2529717086 @default.
- W2898892341 cites W2547026077 @default.
- W2898892341 cites W2555639262 @default.
- W2898892341 cites W2608152993 @default.
- W2898892341 cites W2611381732 @default.
- W2898892341 cites W2614172635 @default.
- W2898892341 cites W2621412568 @default.
- W2898892341 cites W2742045889 @default.
- W2898892341 cites W2742487655 @default.
- W2898892341 cites W2745108377 @default.
- W2898892341 cites W2753363313 @default.
- W2898892341 cites W2760889115 @default.
- W2898892341 cites W2766597242 @default.
- W2898892341 cites W2768606410 @default.
- W2898892341 cites W2776072592 @default.
- W2898892341 cites W2780724679 @default.
- W2898892341 cites W2791151210 @default.
- W2898892341 cites W2800998285 @default.
- W2898892341 cites W2809753877 @default.
- W2898892341 cites W2883230022 @default.
- W2898892341 cites W2884181289 @default.
- W2898892341 doi "https://doi.org/10.1016/j.apenergy.2018.10.083" @default.
- W2898892341 hasPublicationYear "2019" @default.
- W2898892341 type Work @default.
- W2898892341 sameAs 2898892341 @default.
- W2898892341 citedByCount "167" @default.
- W2898892341 countsByYear W28988923412019 @default.
- W2898892341 countsByYear W28988923412020 @default.
- W2898892341 countsByYear W28988923412021 @default.
- W2898892341 countsByYear W28988923412022 @default.
- W2898892341 countsByYear W28988923412023 @default.
- W2898892341 crossrefType "journal-article" @default.
- W2898892341 hasAuthorship W2898892341A5039858263 @default.
- W2898892341 hasAuthorship W2898892341A5044382297 @default.
- W2898892341 hasAuthorship W2898892341A5049493168 @default.
- W2898892341 hasAuthorship W2898892341A5088320866 @default.
- W2898892341 hasConcept C105795698 @default.
- W2898892341 hasConcept C119599485 @default.
- W2898892341 hasConcept C127413603 @default.
- W2898892341 hasConcept C132651083 @default.
- W2898892341 hasConcept C144024400 @default.
- W2898892341 hasConcept C149923435 @default.
- W2898892341 hasConcept C152877465 @default.
- W2898892341 hasConcept C159620131 @default.