Abstract
China has clearly put forward the strategic goals of reaching the “Carbon Emission Peak” by 2030, and achieving “Carbon Neutrality” by 2060. To achieve these goals, it is necessary to precisely understand the spatial distribution characteristics of historical carbon emissions in different regions. This paper has selected a representative national-level urban agglomeration in China, the Harbin–Changchun urban agglomeration, to study the temporal and spatial distribution characteristics of carbon emissions in its counties. This paper has constructed global and local Moran’s I indexes for the 103 counties in this urban agglomeration by using the carbon emission values reflected by night light data from 1997 to 2017 to perform global and local autocorrelation analysis on a spatial level. The results show that: (1) the main characteristic of carbon emission clustering in the Harbin–Changchun urban agglomeration is similar clustering; (2) the changes in carbon emissions of the Harbin–Changchun urban agglomeration have a strong correlation with relevant policies. For example, due to the impact of the “Twelfth Five-Year Plan” policies, in 2013, the global county-level Moran’s I index of the carbon emissions in the Harbin–Changchun urban agglomeration decreased by 0.0598; (3) the areas where high carbon emission values cluster together (“High–High Cluster”) and low carbon emission values cluster together (“Low–Low Cluster”) in the Harbin–Changchun urban agglomeration are highly concentrated, and the clusters are closely related to the development level of different regions.
Highlights
Since the industrial revolution, the extensive use of fossil fuels by human society has emitted a large amount of carbon dioxide which has posed a serious threat to the ecological environment [1,2,3]
This paper focuses on the 103 counties in this urban agglomeration, and performs global and local autocorrelation analysis on a spatial level by utilizing the carbon emission values reflected by night light data from 1997 to 2017
This paper takes the county-level carbon emission data of the Harbin–Changchun urban agglomeration from 1997 to 2017 as the research object, and conducts global and local autocorrelation analysis on a spatial level by utilizing the spatial autocorrelation analysis method based on the carbon emission values estimated from the night light data
Summary
The extensive use of fossil fuels by human society has emitted a large amount of carbon dioxide which has posed a serious threat to the ecological environment [1,2,3]. According to statistics from the International Energy Agency, global energy-related carbon dioxide emissions reached 33 gigatonnes (Gt) in 2019, which has greatly affected the global environment [4]. Chinese President Xi Jinping has mentioned that China will scale up its Intended Nationally Determined Contributions by adopting more vigorous policies and measures, aiming to reach the “Carbon Emission Peak” by 2030 and achieve “Carbon Neutrality” by 2060 [5]. This is a higher goal set by China regarding the timing of carbon emission peak and long-term carbon neutrality on the basis of the commitments of the Paris Agreement. As the world’s largest carbon dioxide emitter, balancing the relationship between economic growth and carbon emissions is a major challenge currently facing China
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