Analysis of carbon emission performance and regional differences in China's eight economic regions: Based on the super-efficiency SBM model and the Theil index

Navigating the green shift with innovative techniques in petrochemical emission control

This analysis delves into the profound influence of the petrochemical sector on carbon emissions, highlighting the insufficient data that impedes the formulation of effective emission reduction strategies. By concentrating on the unique challenges within this industry, the study employs a sophisticated decision-making model to explore the complex interconnections between carbon emissions, mitigation approaches, and investment choices. The primary objectives include boosting energy efficiency, refining CO2 reduction initiatives, and cutting associated costs. To navigate the intricacies of carbon footprint assessment, the research introduces a novel hybrid framework that merges scientific modeling with a code-based prototype to aid in strategic planning. The efficacy of this approach is validated by experts, demonstrating its versatility and adaptability through CNHS mapping, which enables the customization of emission reduction strategies across different scenarios.

Study on the initial carbon quota allocation and spatial balance compensation strategy at the provincial level in China

With the official national unified carbon market launch on July 16, 2021, the allocation and trading of initial carbon quotas between regions will become the focus of research in the future. Based on a reasonable regional initial carbon quota allocation, introducing the concept of carbon ecological compensation and formulating differentiated emission reduction strategies according to the characteristics of different provinces can better guarantee the realization of China's carbon emission reduction goals. Based on this, this paper first analyzes the distribution effects under different distribution principles from the perspective of fairness and efficiency. Secondly, the Pareto optimal multi-objective particle swarm optimization (Pareto-MOPSO) algorithm is used to build the initial carbon quota allocation optimization configuration model to optimize the allocation results. The optimal initial carbon quota allocation scheme is determined through the comparative analysis of the allocation results. Finally, we explore the combination of carbon quota allocation and the concept of carbon ecological compensation and formulate the corresponding carbon compensation scheme. This study not only reduces the relative sense of exploitation of carbon quota allocation in different provinces but also contributes to the realization of a carbon peak in 2030 and carbon neutrality in 2060 (the "30.60" double carbon target).

Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities

Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

Spatial Evolutionary Characteristics and Influencing Factors of Urban Industrial Carbon Emission in China

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China's urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China's urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China's urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China's urban industrial carbon emissions was "low carbon-horizontal expansion" dominated by positive growth, and in 2016, it was "low carbon-vertical expansion" dominated by scale growth; (4) China's urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.

The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation

Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province

Industrial structure and regional innovation have a significant impact on emissions. This study explores, from the multivariate coupling and spatial perspectives, the degree of coupling coordination between three factors: industrial structure, carbon emissions, and regional innovation of 97 counties in Shandong Province, China from 2000 to 2017. On the basis of global spatial autocorrelation and cold and hot spots, this article analyzes the spatial characteristics and aggregation effects of coupled and coordinated development within each region. The results are as follows. (1) The coupling degree between carbon emissions, industrial structure, and regional innovation in these counties fluctuated upward from 2000 to 2017. Coupling coordination progressed from low coordination to basic coordination. Regional differences in coupling coordination degree are evident, showing a stepped spatial distribution pattern with high levels in the east and low levels in the west. (2) During the study period, the coupling coordination showed a positive correlation in spatial distribution. Moran’s I varies from 0.057 to 0.305 on a global basis. Spatial clustering is characterized by agglomeration of cold spots and hot spots. (3) The coupling coordination exhibited significant spatial differentiation. The hot spots were distributed in the eastern part, while the cold spots were located in the western part. The results of this study suggest that the counties in Shandong Province should promote industrial structure upgrades and enhance regional innovation to reduce carbon emissions.

Spatial-Temporal Heterogeneity for Commercial Building Carbon Emissions in China: Based the Dagum Gini Coefficient

There is great potential for carbon emission reduction in commercial buildings. Determining the spatial-temporal heterogeneity of CCBCE (China’s commercial building carbon emissions) is crucial for developing differentiated emissions mitigation policies. This paper estimated CCBCE and then adopted a method involving the visualization of spatial data, Dagum Gini coefficient, and kernel density estimation to analyze the spatial-temporal characteristics and regional differences in China’s eight economic regions in 2006–2019. The results indicate that: (1) The CCBCE displayed a general upward trend, increasing from 400.99 million t (tons) to 853.23 million t. The CCBCE from electricity accounted for the largest share (65.93% in 2009). Moreover, Guangdong was the only high-emission province in 2019 with 77.8 million t CCBCE. (2) The contribution rate of the different economic regions to incremental carbon emissions made a significant difference, and inter-regional differences (61.81%) were much higher than intra-regional differences (7.99%). (3) The greatest intra-regional differences were found in the Southern coastal economic region (average Gini coefficient up to 0.4782). For inter-regional differences, the disparity between the Northern coastal economic region and Northwest economic region was greatest. Further, the regional differences presented a trend of increase. The study concludes that effective measures should be taken to reduce the CCBCE in each region and narrow the regional gap of CCBCE.

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China's eight economic regions

To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China's eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.