Spatio-temporal evolution and gravity center change of carbon emissions in the Guangdong-Hong Kong-Macao greater bay area and the influencing factors

Li, Lei; Li, Junfeng; Wang, Xilong; Sun, Shujie

doi:10.1016/j.heliyon.2023.e16596

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…Exploratory Spatial Data Analysis (ESDA) classified into two categories, namely, Global Spatial Autocorrelation and Local Spatial Aautocorrelation [ 70 , 71 ]. The former can reveal the heterogeneity of factor space, usually represented by Moran's I ; and the latter can reflect the cold and hot distribution of variables in local space, usually represented by

[ 72 , 73 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China

Li,

Pu,

Gao

2023

Heliyon

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[ 72 , 73 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China

Li,

Pu,

Gao

2023

Heliyon

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“…Finally, they analyzed the temporal and spatial evolution of carbon emissions through spatial autocorrelation theory [14]. In the study of Lei Li and Junfeng Li, an exploratory spatial data analysis (ESDA) framework was constructed through spatial autocorrelation, kernel density estimation and standard deviation ellipse to analyze the spatio-temporal evolutionary characteristics of carbon emissions in the Greater Bay Area, and to identify various influencing factors of carbon emissions in the Greater Bay Area by combining geographically and temporally weighted regression (GTWR) models [15]. Fuqiang Han and Alimujiang Kasimu et al used the arid regions of Northwest China as their research subject and analyzed the carbon balance under land use changes by combining top-down and bottom-up approaches [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Yang,

Wang,

2024

Sustainability

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Understanding the spatio-temporal differentiation of carbon intensity factors is crucial for setting scientific and reasonable carbon emission reduction targets. This study, based on relevant data from the western regions for the years 2010–2019, analyzes the influencing factors of the spatio-temporal distribution differences in carbon intensity in these areas. Additionally, the Grey Forecasting Model was utilized to predict the development trend of average carbon intensity in the western regions. The results indicate the following: (1) The temporal dimension of carbon intensity in the western regions shows an overall declining trend with local rebounds, while the high-value areas of spatial carbon intensity are concentrated in the northern part of the study area. (2) Per capita Gross Domestic Product, energy consumption per unit of Gross Domestic Product and investment in industrial pollution control have a positive impact on carbon intensity, whereas investment in the energy industry and per capita disposable income of residents have a negative impact. (3) Energy consumption per unit of Gross Domestic Product is the factor with the highest degree of explanation in univariate analysis; interaction detection results suggest that the core factors of spatial distribution differences in carbon intensity are energy consumption and urban development. (4) Predictions using the Grey Forecasting Model for the development of carbon intensity in the western regions show a year-by-year decline, consistent with carbon intensity control targets. Based on these conclusions, this paper proposes policy recommendations focusing on improving regional economic coordination mechanisms, increasing investment in industrial pollution control, managing energy industry expenditures, adjusting the proportion of the urban population, and enhancing the per capita disposable income of residents.

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“…Based on previous studies on the driving factors of carbon emissions in the construction industry (Du et al, 2017;Wu et al, 2019;Li et al, 2022b;Chen and Bi, 2022;Huo et al, 2022;Zhu et al, 2022;Guo and Fang, 2023;Huo et al, 2023;Li et al, 2023;Shi et al, 2023), and considering data availability and the characteristics of the research area, this study selected nine factors from four aspects: population, economy, technology, and the construction industry as independent variables to explore the driving factors of carbon emissions in the construction industry (see Table 1). Frontiers in Environmental Science frontiersin.org…”

Section: Data Sourcesmentioning

confidence: 99%

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

Yin,

Lin

2024

Front. Environ. Sci.

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To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction.

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Spatio-temporal evolution and gravity center change of carbon emissions in the Guangdong-Hong Kong-Macao greater bay area and the influencing factors

Cited by 10 publications

References 32 publications

Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China

Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

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