Carbon Emission and Sustainable Development of the city Coupling in Shandong Province

The new quality productive forces is an advanced force committed to high-quality development and a significant driving force in achieving the "double carbon goal." To explore the impact of the new quality productive forces on carbon emissions in the Beijing-Tianjin-Hebei Region and enhance the positive effects of region's new quality productive forces on carbon emission levels and efficiency， this study aims to promote high-quality development in the Beijing-Tianjin-Hebei Region. Initially， based on urban panel data from the Beijing-Tianjin-Hebei Region from 2007-2022， in accordance with the industrial structure and current policies of the Beijing-Tianjin-Hebei region， a comprehensive evaluation method is utilized to calculate the index of new quality productive forces in the region， and the data envelopment analysis method is employed to measure the carbon emission efficiency. The development patterns and correlations between these two indices are analyzed across both temporal and spatial dimensions. Subsequently， an econometrics model is applied to examine the impact of new quality productive forces on carbon emissions and emission efficiency， and a mediation effect model is established to assess the mechanisms by which new quality productive forces affect carbon emissions through industrial structure and enhance carbon emission efficiency through technological innovation. The study produced several interesting results： ① The new quality productive forces can significantly reduce carbon emissions and improve carbon emission efficiency in the Beijing-Tianjin-Hebei Region， with results remaining robust after a series of tests， including winsorization test， alteration of control variables， and changes in estimation methods. ② After passing the mediation effect test， it is realized that new quality productive forces can influence carbon emissions through the regional industrial structure and enhance carbon emission efficiency by elevating the level of scientific and technological innovation. ③ Classifying cities in the Beijing-Tianjin-Hebei Region into resource-based cities and low-carbon pilot cities and conducting stratified regression analyses shows that the impact of new quality productive forces on carbon emissions and emission efficiency exhibits significant heterogeneity among different types of cities， providing crucial references for differentiated governance in the region. Based on the conclusions above， the study proposes recommendations from three aspects： constructing a coordinated development pattern of carbon emission reduction and efficiency， promoting the governance process of carbon emission reduction with the new quality productive forces， and developing and governing methods tailored to local conditions. These suggestions can provide new research perspectives and practical guidance for China to achieve the "double carbon goal."

Spatiotemporal analysis of carbon emission efficiency across economic development stages and synergistic emission reduction in the Beijing-Tianjin-Hebei region.

Achieving carbon peaking and neutrality is critical for global sustainability efforts and addressing climate change, yet improving land use carbon emission efficiency (LUCE) remains a challenge, especially in resource-dependent regions like Shanxi Province. Existing studies often overlook the spatial heterogeneity of LUCE and the mechanisms behind its driving factors. This study assesses LUCE disparities and explores low-carbon land use pathways in Shanxi to support its sustainable transition. Based on county-level land use data from 1990 to 2022, carbon emissions were estimated, and LUCE was measured using a three-stage super-efficient SBM-DEA model, with stochastic frontier analysis (SFA) to control for external noise. eXtreme Gradient Boosting (XGBoost) with SHAP values was used to identify key socio-economic and environmental drivers. The results show the following: (1) emissions rose 2.46-fold, mainly due to expanding construction land and shrinking cultivated land, with hotspots in Taiyuan, Jinzhong, and Linfen; (2) LUCE improved due to gains in technical and scale efficiency, while pure technical efficiency stayed stable; (3) urbanization and government intervention promoted LUCE, whereas higher per capita GDP constrained it; and (4) population density, economic growth, urbanization, and green technology were the dominant, interacting drivers of land use carbon emissions. This study integrates LUCE assessment with interpretable machine learning, demonstrating a framework that links efficiency evaluation with driver analysis. The findings provide critical insights for formulating regionally adaptive low-carbon land use policies, which are essential for achieving ecological sustainability and supporting the sustainable development of resource-based regions.

Purpose As one of the three key fields of global carbon emissions, building carbon emissions are increasingly emphasized in all sectors of society. In the building field, improving the carbon emission efficiency (CEE) of residential buildings in urban areas is key to achieving China’s carbon reduction targets. Design/methodology/approach Using a three-stage data envelopment analysis model, carbon emissions, capital and labor were taken as inputs and residential population and residential floor area were taken as outputs, while the income of urban residents, urban population density and level of urban service facilities were selected as environmental variables. This study calculated the CEE of urban residential buildings in 30 provinces and cities in China from 2012 to 2021 and explored the regional spatial status and temporal evolution of CEE in urban residential buildings in China. Findings The results show that there are clear disparities in the CEE of urban residential buildings between regions in China, presenting a clear spatial distribution described as high in the east region and low in the west area and high in the south region and low in the north part. The CEE of residential buildings in the central and western China has been improved dramatically. However, the CEE and resource input efficiency of residential buildings in Chinese urban areas can still be improved. Originality/value This study comprehensively describes the current status of the CEE of residential buildings in Chinese urban areas and provides a decision-making basis for government and industry managers to formulate targeted building carbon emission reduction strategies.

Carbon emissions have become a global challenge that threatens human development. Governments have taken various measures to reduce carbon emissions, and green finance is an important and innovative way to realize carbon emission reductions. This paper uses data on a prefecture-level city in China to explore the impact of green finance on carbon emission intensity from both theoretical and empirical perspectives, and analyzes the mechanisms by which green finance affects carbon emission intensity. On this basis, this paper further analyzes the impact of green finance on carbon emission efficiency. In addition, this paper introduces variables related to the digital economy to perform a comprehensive examination of the moderating effect of digital economy development on the relationship between green finance and both carbon emission intensity and efficiency. The results indicate that green finance reduces carbon emission intensity and that green innovation, green total factor productivity and the transformation and upgrading of industry are important mediating mechanisms. Meanwhile, analysis shows that green finance improves carbon emission efficiency. This paper also finds that the digital economy significantly enhances the role of green finance in reducing carbon emission intensity and promoting carbon emission efficiency, and makes a positive contribution to promoting carbon emission reduction. The findings will contribute to strengthening the government’s capacity for environmental protection, developing green finance, and reducing carbon emissions.

Sustainable development is regarded as the common goal of development by all the countries of the world. There are many approaches on evaluating the level of regional sustainable development. In this paper, back propagation artificial neural networks and GIS were applied to evaluate the levels of the sustainable development of 17 cities in Shandong province. Firstly, the writers choose 28 indexes belonging to 8 subsystems and 3 systems. Secondly, the writers processed the data of the 28 indexes with 2 methods according to the characteristic of each index. Thirdly, the writers evaluated the sustainable development of 17 cities in Shandong province with the method of BP-ANN by the neural network toolbox of MATLAB7.0, and showed the location of cities of different levels of sustainable development with ARCVIEW3.2. The results show that the high level cities locate along the Jiaoji railway and the east coast and that the low level ones locate at southwest and northwest of the province.

Carbon emission efficiency of China’s industry sectors: From the perspective of embodied carbon emissions

Land use/cover change is the second major contributor to carbon emissions, following energy emissions. Studying provincial land-use carbon emissions is crucial for achieving the “double carbon” goal. This study selects 16 prefecture-level cities in Shandong Province as the research object. It analyzes the spatial and temporal distribution pattern of carbon emissions in Shandong Province based on land-use data and energy consumption. In terms of net carbon emissions, this study utilizes the standard deviation ellipse and kernel density estimation to analyze net carbon emissions change from the municipal and regional perspectives. In terms of carbon ecological carrying capacity, not only the carbon ecological carrying capacity of forest and grassland was considered, but also the carbon ecological carrying capacity of crops in Shandong Province, which is a large grain province. Using the geographic detector to explore the drivers. Research findings indicate that carbon sources and sinks show a clear spatial and temporal distribution pattern, with the center of gravity of net carbon emissions extending to the northeast. Areas with high carbon ecological carrying capacity have high forest coverage, grassland coverage, and crop yields. Regarding driving factors, the urbanization rate, economic aggregate, and technological progress demonstrate significant explanatory power through single and interaction tests, suggesting that these factors are critical drivers of land-use carbon emissions within Shandong Province. Based on the spatiotemporal pattern analysis of land-use carbon emissions in Shandong Province, each city's growth rate and spatial distribution characteristics can be clarified, providing a scientific basis for the local government to formulate regional and differentiated emission-reduction policies. In addition, by exploring the driving factors of land-use carbon emissions in Shandong Province, the influence level of factors on carbon emissions can be revealed to provide references for formulating regional sustainable development strategies.

Rapid urbanization and industrialization promote economic growth as well as bring carbon emissions, which seriously threaten the eco-environment and socioeconomic sustainable development. Facing increasing resource constraints, improving carbon emissions efficiency (CEE) is conducive to promote coordinated development of economy and environmental protection. In recent years, regional economic integration (REI) has rapidly developed. It can not only promote factors flow between regions but also achieve industrial and economic agglomeration. However, few studies have been reported in the literature about the relationship between the REI and CEE. In this study, we first illustrate how the REI influences CEE in theory, then take the Yangtze River Delta (YRD) as a case study to conduct empirical research. The results show that (1) the overall CEE value in the YRD has exhibited an upward trend from 2000 to 2020, and its spatial distribution has revealed a significant auto-correlation pattern. (2) On the whole, the REI act a noteworthy positive impact on CEE. When considering types of cities, it is found to have significant positive impacts for the CEE in economically developed cities, while it exhibits a negative impact in the less-developed ones. (3) Upgrading industrial structure and increasing per capita GDP can promote the CEE, but hinder its growth in surrounding areas. Our findings suggest that the government should formulate a unified overall plan to facilitate REI development and establish a modern industrial system of clean and low-carbon to promote regional sustainable development.

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations.

China faces the key issue of improving the efficiency of carbon emissions, in its endeavor of building a low-carbon economy and reducing carbon emissions. This paper adopts the super-slack-based measure (SBM) model with a bad output to measure the carbon emission efficiency of each Chinese province from 2000 to 2019, and further uses the Tobit model to analyze the impact of environmental regulation, technological progress, and the interaction between the two on carbon emission efficiency. The results show that: China's carbon emission efficiency presents a large inter-provincial difference. Only a few provinces like Shanghai and Beijing reached the efficient frontier, while all the other provinces failed to do so. Overall, most Chinese provinces have a huge potential for improving carbon emission efficiency. By dividing China into three regions, it could be seen that the eastern region had the highest carbon emission efficiency, followed in turn by the central region and the western region. According to the spatiotemporal variation of carbon emission efficiency, most provinces with a high carbon emission efficiency belong to the economically developed eastern region, while most central and western provinces did not realize satisfactory carbon emission efficiency. With the elapse of time, the carbon emission efficiency in most provinces declined to varied degrees, while that of a few provinces was on the rise. The results of the Tobit model show that both environmental regulation and technological progress both significantly promoted carbon emission efficiency, but their cross term clearly suppressed carbon emission efficiency. When it comes to the control variables, carbon emission efficiency has a significantly positive relationship with opening-up, and a significantly negative relationship with industrial structure, financial development, energy structure, and urbanization level.

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.

The excessive carbon emissions not only intensify the global climate change, but also seriously restrict the sustainable development of social economy. However, improving industrial carbon emissions efficiency is the most directly effective way to reduce emissions. Therefore, accurate measurement and analysis of carbon emissions efficiency and evolution characteristics of China’s industrial system is the basis for China to improve carbon emissions efficiency. Based on this, we adopted energy consumption method and input-output method to calculate and analyze the industrial carbon emissions efficiency and evolution characteristics of China from 2002 to 2015. The results show that (1) If carbon emissions from cement production are ignored and only energy-related carbon emissions are considered, the calculation results of carbon emissions efficiency of heavy industry will be overestimated about 30%. (2) Compared with 2002, China’s industrial carbon emissions efficiency increased by about twice in 2015. Specifically, the optimization of carbon emissions efficiency in agriculture, construction, light industry and service industry is very obvious, which has increased by 5.65, 5.00, 4.69 and 4.68 times respectively; The optimization of carbon emissions efficiency in power sector, heavy industry, transportation and chemical industry was obvious, which increased by 2.55, 2.01, 1.86 and 1.47 times respectively; The carbon emissions efficiency of the fossil energy sector has decreased by 0.36 times. (3) The carbon emissions efficiency of various industries has significant differences. Among them, agriculture, service industry, light industry and construction industry are generally industries with higher carbon emissions efficiency, while power sector, fossil energy sector, transportation industry, chemical industry and heavy industry are generally industries with lower carbon emissions efficiency, especially power and fossil energy sectors. Based on these, this paper provides policy implications and scientific evidence for accurately improving carbon emissions efficiency from the perspective of carbon emissions efficiency.

What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data

Jiangsu Province is not only a large province in terms of carbon emissions but also a pioneer in pursuing the goal of carbon neutrality. Improving carbon emission efficiency (CEE) is the key to lowering carbon emissions. Therefore, exploring CEE is of significance for balancing economic growth and successfully cutting carbon emissions. Based on the measurement of CEE in Jiangsu Province from 2008 to 2020, this paper explores its spatiotemporal pattern and influencing factors to propose corresponding policies. The results are as follows. (1) From 2008 to 2020, the CEE of Jiangsu Province exhibited a fluctuating increase, reaching 0.894 in 2020. The temporal variation trend of CEE in each region is consistent, whereas CEE in southern Jiangsu is greater. (2) The regional differences in CEE are evident. The low-high (LH) agglomeration region is mainly in northern Jiangsu, whereas the high-low (HL) agglomeration area is mainly in southern Jiangsu. (3) Technological progress is the primary way to raise CEE in Jiangsu Province, and the contribution of technical efficiency is relatively small. (4) The primary factors that promote CEE are economic growth and green technology progress, but environmental protection has an adverse effect. Therefore, all cities in Jiangsu Province should formulate carbon emission reduction policies that are in line with their development and provide a successful reference for regional green and low-carbon development and global climate governance.