The Spatial Pattern of the Prefecture-Level Carbon Emissions and Its Spatial Mismatch in China with the Level of Economic Development
Abstract
:1. Introduction
2. Data and Research Methods
2.1. Data Source
2.2. Research Methods
2.2.1. Spatial Autocorrelation Analysis
2.2.2. The Barycenter Model
2.2.3. The Spatial Mismatch Index
2.2.4. The Standard Deviation Ellipse
3. Result
3.1. Spatial Pattern of Carbon Emissions in China
3.1.1. The Overall Spatial Pattern Characteristics of the Prefecture-Level Carbon Emissions
3.1.2. Spatial Correlation Pattern Characteristics of the Prefecture-Level Carbon Emissions
3.2. Spatial Mismatch between Carbon Emission and Economic Development in China
3.2.1. The Spatial-Temporal Relationship between the Prefecture-Level City Carbon Emissions and Economic Development Level
3.2.2. The Characteristics of Spatial Mismatch between the Prefecture-Level Cities’ Carbon Emissions and Economic Development Level
- (1)
- The evolution characteristics of the spatial mismatch
- (2)
- The spatial evolution of the contribution of the prefecture-level cities
4. Discussion
- (1)
- In the future, the level of economic development will be ahead of carbon emissions in relatively developed regions such as the eastern and central regions. In this regard, these areas should adhere to the orientation of high-quality development, take innovation as the cornerstone, continuously accelerate the pace of industrial transformation and upgrading [71], and promote the continuous improvement of the level of economic growth based on following the laws of economic and social development and ecological environmental protection. At the same time, the construction of the ecological environment shall be strengthened by these means of environmental regulation [72], technological innovation [73,74], environmental governance investment [75], and other means under the background of the continuous improvement of economic development level and the accelerating pace of industrial transformation and upgrading. Ultimately, it will promote the coordinated evolution of carbon-emission reduction and economic and social development, and complete the goal of dual carbon as soon as possible.
- (2)
- Relatively underdeveloped regions such as the western region have the real dilemma that the level of economic growth lags behind carbon emissions, and carbon productivity was low. In this regard, it should change gradually the traditional model of economic development relying on resource exploitation [76]. Currently, these areas should play the role of market forces in reshaping industrial locations actively [77]. In addition, these areas should promote the geographical agglomeration of enterprises and industries in regions with factor endowments [78], and be integrated into the national and global industrial systems. Ultimately, these areas should exploite a new source for the coordinated and high-quality development of the economy, society, and ecological environment. At the same time, these areas should reduce and neutralize carbon emissions using environmental governance investment, technological innovation, and afforestation.
5. Conclusions
- (1)
- China’s city carbon emissions showed the characteristics of continuous expansion over time. It showed a trend of decreasing from the north to the south and increasing from the southeast to the northwest in space. Specifically, the prefecture-level cities with carbon emissions in the first and second gradients were mainly in the southern region, and the prefecture-level cities in the third and fourth gradients were primarily in the northern areas. In addition, there was a transition of the first and second gradients to the third and fourth gradients from the southeast to the northwest. Secondly, the prefecture-level cities in the third and fourth gradients showed the characteristics of aggregation in the mid-southern Liaoning, Beijing-Tianjin-Hebei, and the Yangtze River Delta, the Pearl River Delta, the junction of Inner Mongolia and Shanxi, and the northeast China. In addition, Chongqing’s carbon emissions have always been on the fourth gradient.
- (2)
- The high-value and low-value areas of the cities’ carbon emissions in China showed significant spatial aggregation and positive spatial correlation. The high-high (H-H) areas were mainly distributed in city agglomerations of the mid-southern Liaoning, Beijing-Tianjin-Hebei, HueBaoyu, central Shanxi, Shandong Peninsula, and the Yangtze River Delta. High-low (H-L) areas were mainly distributed in Chongqing, Chengdu, Wuhan, and other leading inland cities. Low-high (L-H) and high-high areas (H-H) cross-aggregate in the above six-city agglomerations. The low-low (L-L) areas were primarily distributed in the city agglomerations of the middle reaches of the Yangtze River, Guangdong, Fujian, Zhejiang and Macao, the Pearl River Delta, Beibu Gulf, Central Yunnan, Central Guizhou, and Chengdu-Chongqing, and there was a specific growth trend. Finally, China’s prefecture-level carbon emissions were clustered in six city agglomerations in northern China and were scattered across all the economic growth poles of southern China.
- (3)
- The barycenter of China’s carbon emissions moved first to the northwest and then to the east, but the overall change was not significant. The barycenter of economic development level showed the characteristics of first moving to the northwest, then to the southwest, and the migration to the west was significant. Moreover, the barycenter of economic development level was always in the south of the barycenter of carbon emissions. Furthermore, the mismatch distance of the barycenter between carbon emissions and economic development level presented an N-type change over time from the perspective of the distance of spatial mismatch.
- (4)
- The mismatch index of carbon emissions and economic development level in the prefecture-level cities showed a clear regional differentiation pattern, which decreased continuously from the east to the west. The positive mismatch areas with higher mismatch indexes were primarily distributed in the eastern coastal-city agglomerations, such as the mid-southern Liaoning, Beijing-Tianjin-Hebei, Shandong Peninsula, the Yangtze River Delta, the west bank of the Taiwan Strait, and the Pearl River Delta, as well as the city agglomerations of Chengdu-Chongqing and the middle reaches of the Yangtze River. Conversely, the negative mismatch areas with a lower mismatch index were mainly distributed in the vast inland areas except for the city agglomerations mentioned above. The areas of a positive mismatch were primarily located in the eastern and central regions, and continued to migrate to the south and expand to the west overall. In addition, the mismatched barycenter had the characteristic of moving northward and then westward, and then southward all the time. The contribution of China’s city units to the spatial mismatch showed the characteristics of continuous migration from the eastern coastal areas to the central and western regions.
6. Limitations and Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | 2005 | 2010 | 2015 | 2020 |
---|---|---|---|---|
Moran’s I | 0.0910 | 0.1059 | 0.1261 | 0.1356 |
p value | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Year | The Barycenter of Carbon Emissions | The Barycenter of Economic Development Level | Distance (Km) | ||
---|---|---|---|---|---|
Longitude | Latitude | Longitude | Latitude | ||
2005 | 115°01′ E | 34°38′ N | 115°79′ E | 32°88′ N | 183.90 |
2010 | 115°01′ E | 34°31′ N | 115°68′ E | 32°98′ N | 161.88 |
2015 | 114°93′ E | 34°61′ N | 115°36′ E | 32°66′ N | 223.77 |
2020 | 115°14′ E | 34°51′ N | 115°08′ E | 31°96′ N | 187.11 |
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Yang, Z.; Sun, H.; Yuan, W.; Xia, X. The Spatial Pattern of the Prefecture-Level Carbon Emissions and Its Spatial Mismatch in China with the Level of Economic Development. Sustainability 2022, 14, 10209. https://doi.org/10.3390/su141610209
Yang Z, Sun H, Yuan W, Xia X. The Spatial Pattern of the Prefecture-Level Carbon Emissions and Its Spatial Mismatch in China with the Level of Economic Development. Sustainability. 2022; 14(16):10209. https://doi.org/10.3390/su141610209
Chicago/Turabian StyleYang, Zedong, Hui Sun, Weipeng Yuan, and Xuechao Xia. 2022. "The Spatial Pattern of the Prefecture-Level Carbon Emissions and Its Spatial Mismatch in China with the Level of Economic Development" Sustainability 14, no. 16: 10209. https://doi.org/10.3390/su141610209