Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Overview and Data Processing
2.2. Research Methods
2.2.1. Global Spatial Autocorrelation Analysis
2.2.2. Geodetector
- (1)
- It can explore both quantitative and qualitative data.
- (2)
- It can detect the interaction between two factors on the dependent variable [39].
2.2.3. Geographically Weighted Regression Model
- In terms of analysis results, the global regression model can only obtain the overall status of the entire study area and may ignore the relationships between variables. The GWR model introduces the spatial geographic location of the data in the model, which can reflect the specific situation of the distribution of the relationships between variables in different geographic spaces.
- In terms of visualisation, GWR can use ArcGIS to present the numerical values of various parameters on the map, making it easier to study the changes in parameters as the spatial geographic relationship changes.
3. Results
3.1. Spatiotemporal Characteristics of Carbon Emissions
3.2. Global Spatial Autocorrelation
3.3. The Result of Geodetector
- (1)
- Single-factor detection results (Table 3).
- (2)
- Interaction factor detection results (Figure 3).
3.4. Geographically Weighted Regression Model
3.4.1. The Impact of Population on Carbon Emissions
3.4.2. The Impact of GDP per Capita on Carbon Emissions
3.4.3. The Impact of Energy Intensity on Carbon Emissions
4. Conclusions
- (1)
- In terms of total carbon emissions, the total carbon emissions of the middle reaches of the Yangtze River urban agglomeration show fluctuations between 2010 and 2020, the carbon emission reduction effect is unstable, and there is still room for more carbon emission reduction.
- (2)
- In terms of the spatial and temporal distribution of carbon emissions, those in the middle reaches of the Yangtze River urban agglomeration show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan and this remains unchanged.
- (3)
- In terms of the spatial distribution of factors influencing carbon emissions in the middle reaches of the Yangtze River urban agglomeration, in 2010, 2014, and 2017, population size was the most important factor influencing carbon emission divergence, and in terms of interaction, the interaction between energy intensity, GDP, and urbanisation was the cause of increasing carbon emissions. The impact of population size on carbon emissions has decreased from north to south, and the impact of energy intensity on carbon emissions has shown a spread from the most influential region from in the northwest to the centre and then to the northeast, while the GDP per capita has had little impact on the differences in the spatial distribution of carbon emissions.
- (4)
- In terms of the explanatory power of the factors influencing carbon emissions in the middle reaches of the Yangtze River urban agglomeration, given that the impact of population size on carbon emissions is weakening and will no longer be an important factor related to carbon emissions by 2020, and that the interaction between energy intensity, population size, urbanisation, and GDP capita gdp is the cause of increasing carbon emissions, carbon reduction strategies should focus on controlling energy intensity, and policies should be tailored to local conditions based on spatial differences in the degree of influence of different factors on carbon emissions.
5. Recommendations and Prospects
- (1)
- Strengthen technological innovation and improve energy use efficiency. The interaction between energy intensity and the level of economic development and urbanisation is the main driver of carbon emissions in the middle reaches of the Yangtze River urban agglomeration. The key factor influencing energy intensity is the development in the level of technology. The impact of energy intensity in the middle reaches of the Yangtze River urban agglomeration shows a distribution of high in the north and low in the south, and the high values are slowly shifting from the northwest to the northeast. this indicates that in the carbon emission control of the middle reaches of the Yangtze River urban agglomeration, the adjustment for Hubei cities should be prioritised more in the region, i.e., strengthening the technological innovation of Hubei cities and improving energy use efficiency, while allowing for more high-precision industries from the east coast to the central part, especially regarding the cities in the middle reaches of the Yangtze River cities in the northern part of the Yangtze River urban agglomeration.
- (2)
- Promote new urbanisation and build a green development city. This study also found that the urbanisation rate has been the main driver of carbon emissions in the middle reaches of the Yangtze River urban agglomeration since 2017 and 2020. Therefore, how to promote the intensive development of urban agglomeration and promote urban-rural integration and realise the promotion and popularisation of new green urbanisation in the middle reaches of the Yangtze River urban agglomeration in the process of urbanisation, are priority issues.
- (3)
- Based on resource endowment, optimise energy structure. The six high-energy-consuming industries in the middle reaches of the Yangtze River urban agglomeration account for a high proportion of industry and have still been on the rise in recent years. The consumption of fossil energy still accounts for a large proportion of the energy structure, and to achieve the goal of carbon peaking and carbon neutrality, it is necessary to adopt a variety of clean energy sources such as solar, wind, and geothermal energy according to local conditions. For example, Hubei province is not in a good position for photovoltaics; thus, the advantages of hydropower should be fully exploited, and wind power arrangements should be strengthened to adjust its energy structure. The economy of the Jiangxi province relies heavily on high carbon emission industries and it is difficult to adjust the industrial structure in the short term; thus, it should promote the conversion of coal to gas and coal to electricity and develop clean coal power where appropriate.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Province | City |
---|---|
Hubei Province | Wuhan, Jingmen, Huanggang, Xianning, Yichang, Xiangyang, Ezhou, Huangshi, Xiaogan, Jingzhou |
Hunan Province | Changsha, Yueyang, Changde, Zhuzhou, Xiangtan, Hengyang, Yiyang |
Jiangxi Province | Nanchang, Jiujiang, Yichun, Fuzhou, Shangrao, Jingdezhen, Ji’an, Pingxiang, Yingtan |
Variable | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
---|---|---|---|---|---|---|---|---|---|---|---|
Moran’I | −0.19 | −0.20 | −0.22 | −0.20 | −0.17 | −0.17 | −0.15 | −0.15 | −0.11 | −0.07 | −0.09 |
Z-value | −1.63 | −1.70 | −1.82 | −1.73 | −1.55 | −1.39 | −1.14 | −1.17 | −0.71 | −0.33 | −0.52 |
p-value | 0.10 | 0.09 | 0.07 | 0.084 | 0.12 | 0.16 | 0.26 | 0.242 | 0.48 | 0.74 | 0.61 |
Impact Factor | q-Value | |||
---|---|---|---|---|
2010 | 2014 | 2017 | 2020 | |
Population size (pop) | 0.7012 | 0.7012 | 0.7013 | 0.4666 |
GDP per capita (p_gdp) | 0.3777 | 0.3774 | 0.3610 | 0.7225 |
Energy intensity (c_gdp) | 0.4019 | 0.4031 | 0.3112 | 0.2862 |
Share of secondary sector (gdp2) | 0.2432 | 0.2431 | 0.3246 | 0.1727 |
Urbanisation rate (ur) | 0.2430 | 0.2693 | 0.7408 | 0.7064 |
2010 | 2014 | 2017 | 2020 | ||||
---|---|---|---|---|---|---|---|
Interaction Factor | q | Interaction Factor | q | Interaction Factor | q | Interaction Factor | q |
ur∩c_gdp | 0.7452 | pop∩c_gdp | 0.7087 | pop∩c_gdp | 0.7799 | ur∩c_gdp | 0.8025 |
c_gdp∩gdp2 | 0.7400 | c_gdp∩p_gdp | 0.7024 | c_gdp∩p_gdp | 0.6490 | ur∩p_gdp | 0.7947 |
pop∩c_gdp | 0.7215 | ur∩c_gdp | 0.7520 | p_gdp∩gdp2 | 0.6063 | pop∩c_gdp | 0.7688 |
c_gdp∩p_gdp | 0.6737 | p_gdp∩gdp2 | 0.6624 | ur∩c_gdp | 0.5401 | c_gdp∩p_gdp | 0.6868 |
p_gdp∩gdp2 | 0.6624 | ur∩c_gdp | 0.6121 | ur∩p_gdp | 0.5244 | p_gdp∩gdp2 | 0.5666 |
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Zhang, H.; Lei, Y. Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River. Sustainability 2023, 15, 10176. https://doi.org/10.3390/su151310176
Zhang H, Lei Y. Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River. Sustainability. 2023; 15(13):10176. https://doi.org/10.3390/su151310176
Chicago/Turabian StyleZhang, Huang, and Yidong Lei. 2023. "Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River" Sustainability 15, no. 13: 10176. https://doi.org/10.3390/su151310176