Driving forces of carbon dioxide emissions in China’s cities: An empirical analysis based on the geodetector method

Xu, Li; Du, Hongru; Zhang, Xiaolei

doi:10.1016/j.jclepro.2020.125169

Cited by 101 publications

(39 citation statements)

References 81 publications

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“…Statistical models, such as Geodetector and GWR, are used to explore the driving mechanism on habitat quality [36,42]. Geodetector is a widely used spatial analysis model to measure the degree of spatial stratification heterogeneity [43][44][45]. It can not only explore the influence degree of driving factors, but also detect the interactive influence of two factors on dependent variables, with the advantages in analyzing combined natural-human driving forces of habitat quality [46].…”

Section: Billion Andmentioning

confidence: 99%

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Zhang

Lyu

Fan

et al. 2022

Land

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China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and, the construction land expansion in Liulin County should be restricted and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality.

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Section: Billion Andmentioning

confidence: 99%

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Zhang

Lyu

Fan

et al. 2022

Land

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“…Third, during urbanisation, many people migrate from rural to urban areas, and this substantial increase in the urban population also increases CO 2 emissions [33]. Improvements in economic development and urbanisation can help achieve low-carbon development in an urban agglomeration [34].…”

Section: Urban Developmentmentioning

confidence: 99%

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3 and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

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“…The key to reducing carbon emissions is to identify and analyze the key sectors for carbon reduction in each province. And at the city level, Xu et al (2021) selected Chinese prefecturelevel cities as the research object and analyzed its data in 2005 and 2012. The final result showed that gross domestic product, research and development, direct foreign investment, and urban built-up area were major factors affecting CO2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

Liu

Jiang

Liu

et al. 2021

Environ Sci Pollut Res

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Driving forces of carbon dioxide emissions in China’s cities: An empirical analysis based on the geodetector method

Cited by 101 publications

References 81 publications

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

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