Analysis of Impacting Factors of Regional Carbon Emissions Based on the STIRPAT Mode - A Case Study of Shaanxi Province

As a matter of fact, carbon emission is a hot topic in contemporary society. This paper focuses on the carbon emission problem in Anhui Province, and adopts the IPCC method to measure the industrial carbon emission and total carbon emission in each region of Anhui Province, and analyses the decoupling of GDP and carbon emission in each region of the province from 2011 to 2020 based on the Tapio decoupling model. In addition, this study constructs a prediction model for the trend of carbon emission in the industrial sector of Anhui Province by using the STIRPAT model, and sets up three different scenarios for the indicators of the size of the industrial economy, the industrial output value per capita, the energy structure, the energy intensity, and the intensity of the industrial sector's carbon emission in Anhui, including the energy saving, the baseline, and the aggressive scenarios. According to the analysis, weakly decoupled relationship between GDP and carbon emissions in various regions of Anhui Province. In addition, the peak carbon emissions are 459.27 million tons, 493.25 million tons, and 562.48 million tons in 2030, 2035, and 2040 in the three different scenarios, namely, energy conservation, baseline, and aggressive scenarios, respectively.

Unintended consequences: China's pairing assistance policy and carbon emissions in administrative border areas -evidence from China.

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction.

By revealing the temporal and spatial differentiation of China's regional tourism carbon emissions and its decoupling relationship with tourism economic growth and identifying the key factors affecting tourism carbon emissions, this paper is expected to provide a reference for the formulation and implementation of China's regional tourism industry emission reduction policies and measures. Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data exhibit a primary characteristic of weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

In recent years, the issue of regional economics and carbon emissions has become a research hotspot in the cross field of economy, environment and ecology. This paper selects the regional economics and carbon emissions related literature collected in the Web of Science (WOS) database as the basis, and uses the bibliometric software Citespace and VOSviewer to visually analyze the time distribution, organization, author and keywords in this research field. This paper provides a more systematic analysis of how different regions of China could achieve carbon emission objectives, from the aspects of regional industrial transformation, energy consumption structure, policy implementation and regional coordinated development. The keywords with high frequency are carbon emissions, economic growth and energy consumption, etc. The research hotspots can be divided into structural decomposition analysis, low-carbon industry transformation path, policy framework and energy efficiency, etc. The results show that future research should strengthen multidisciplinary cross-integration in different universities and institutions. However, based on in-depth analysis, the key factors which affect regional carbon emissions are regional policy implementation, changes in industrial structures, optimization of energy consumption structure and carbon trade market mechanism. Finally, we suggest that institutions and scholars should conduct adequate interdisciplinary and cross-industry cooperation; industrial sector development should consider local endowment; there should greater use of clean energy to optimize the energy consumption structure; and an increase in R&D carbon capture and sequestration technology.

Using data for 30 provincial panels in China from the period 1997–2014, this study analyzes the impact of multi-dimensional industrial structures and technological progress on carbon emissions in the STIRPAT framework. A spatial autocorrelation test demonstrated that there were significant positive global spatial correlations and local spatial agglomerations among the regions that were assessed. The dynamic spatial regression results show that industrial structure rationalization, industrial structural transformation and industrial structural upgrading significantly reduced carbon emissions. Industrial structural transformation provided the greatest contribution to carbon emissions. Technological progress was also conducive to reducing carbon emissions. Furthermore, efficiency improvements and technological innovation reduced carbon emissions, and efficiency improvements played a relatively greater role. There was an inverted U-shaped relationship between regional affluence and carbon emissions. The energy consumption structure, population and urbanization had significantly positive effects on carbon dioxide emissions, but the impact of foreign direct investments on carbon reduction was insignificant. Finally, some policy recommendations are given.

Based on provincial panel data from 2008 to 2017 as the research sample in China, this study examines the impact of financial development on carbon emissions. Regression models are employed to estimate the influence of financial development on carbon emissions in different regions. The research reveals that the level of carbon emissions is influenced by differentiation and heterogeneity, with different regions showing varying sensitivities to factors such as financial development, technological innovation, and industrial structure. Financial efficiency has a negative impact on carbon emissions, being positive in regions with low carbon emissions. In areas with higher carbon emissions, the impact of financial efficiency on carbon emissions is greater. Industrial structure has a negative impact on carbon emissions, and in regions with lower carbon emissions, the upgrading of industrial structure has a greater impact on carbon reduction. Technological innovation has a negative impact on carbon emissions in regions with medium to high levels of emissions, while its impact in low-emission areas is positive but not significant. Per capita GDP, energy structure, and urbanization level overall have a positive impact, while foreign investment has a negative impact. The introduction of technological innovation as a moderating variable significantly enhances the significance level of financial efficiency.

Population flow can affect regional carbon emissions. Based on the analysis of the dual transmission mechanism of population flow and its effect on carbon emissions, this paper empirically studies the impact of population flow and other related factors on China's carbon emissions through panel econometric regression and heterogeneity analysis with fixed effect model. The results show that, firstly, in the long or short term, China's population flow can reduce the growth of carbon emissions. Secondly, the regional population aging and knowledge structure improvement caused by population flow are helpful to reduce carbon emissions, while the regional urbanization improvement caused by population flow is not significantly correlated with the growth of household miniaturization on carbon emissions. Thirdly, from the perspective of heterogeneous geographical divisions, population flow promotes the increase of carbon emissions in the northwest region of the Hu Huanyong Line (Hu Line), while it is opposite in the southeast region of Hu Line. Fourthly, China's consumption level, per capita GDP, energy intensity, and energy consumption structure have contributed to the growth of carbon emissions, while carbon intensity has a negative effect on carbon emissions. Finally, this paper puts forward relevant suggestions from the perspective of coordinating population policy and energy conservation and emission reduction policy.

Carbon emission reduction is gaining increasing attention worldwide. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emission reduction. To this end, the spatial characteristics, spillover effects, and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model and STIRPAT model with the extension of an ARDL method that was utilized to demonstrate the linkage amid variables. The results show that digital agriculture development reduces agricultural carbon emissions. Firstly, the results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region. Secondly, agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Finally, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emission reduction effect.

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China’s energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future.

Excessive carbon emissions from energy consumption seriously restrict China's sustainable development and eco-environmental protection. Although the carbon emissions from the construction industry are less than that of the power, transportation, and manufacturing sectors, the carbon emissions released by the construction industry cannot be ignored due to its extensive development trend of high energy consumption and low efficiency. Based on this, this paper studies energy-related carbon emissions and emissions reduction of China's construction industry from 2007 to 2017 by adopting the input-output analysis method, energy consumption method, and structural decomposition model. The results show that within the sample range: (1) The optimization of the construction industry energy consumption structure has a significant reduction effect on the growth of energy carbon emissions from the construction industry in China, and the reduction effect has shown an increasing trend over time. However, it should be noted that in this sample range, the optimization of energy consumption structure in the construction industry is mainly reflected in the decrease of the proportion of high-carbon energy consumption such as raw coal, while low-carbon energy such as natural gas has not played a significant role. Therefore, the future energy optimization space of China's construction industry is still huge. (2) Energy intensity effect and input structure effect have a positive inhibitory effect on carbon emission growth of the construction industry, and the inhibitory effect of energy intensity effect is stronger than that of input structure effect. It shows that in the sample range, the generalized technological progress and energy efficiency of the construction industry have been better optimized and improved. (3) Except for 2015-2017, the final demand effect in other intervals has a positive effect on the growth of carbon emissions in the construction industry, and the secondary and tertiary industries play a major role in the final demand effect. It shows that the total demand for the construction industry in various industries still maintains a growth trend. This paper provides a theoretical analysis basis and practical guidance for China's construction industry to carry out more accurate and efficient emission reduction from the supply-side energy varieties and demand-side industry level, and further enriches the existing research on carbon emissions of the construction industry from the perspective of input-output analysis.

Using panel data of 30 provinces and regions in Mainland China (excluding Tibet) from 2006 to 2016, the Spatial Durbin Model was employed for the empirical research, and the spatial impact of fiscal decentralization and environmental decentralization on regional carbon emissions were analyzed from the perspective of promotion pressure of officials. The empirical study concludes: ① Fiscal decentralization, both within the region and in its neighborhood, will contribute to carbon emissions in the region; ② Environmental decentralization will help reduce carbon emissions, while environmental decentralization in neighboring regions will increase carbon emissions in the region; ③ The promotion pressure of officials plays a positive role in moderating the impact of fiscal decentralization on carbon emissions, and at the same time weakens the suppression of carbon emissions by environmental decentralization; ④ From a regional point of view, there is a positive relationship between fiscal decentralization and carbon emissions in various regions; but environmental decentralization has obvious spatial heterogeneity. The research suggests that reducing the degree of local fiscal decentralization, investment in major infrastructure projects involving high carbon emissions should be relatively centralized; appropriately increase the environmental management authority of local environmental protection agencies, fully use the advantages of local environmental protection departments to protect the environment according to local conditions; gradually improve the assessment system for local officials, moderately reduce the proportion of fiscal revenue and GDP assessment in areas with fragile ecological environment, and increase incentives for ecological performance assessment, put the development of low-carbon economy into practice.

To effectively address climate change, it is necessary to quantify the carbon emissions in high energy-consuming regions, analyze driving factors, and explore effective pathways for achieving green development. Therefore, this paper takes Liaoning Province as research area, using extended Kaya identity and LMDI method to analyze the driving factors of carbon emissions from energy consumption in five major industries and the residential consumption sector from 2011 to 2020 in Liaoning Province. Furthermore, this paper uses the Tapio model to explore the decoupling relationship between carbon emissions and economic development. The results show that: 1) From 2011 to 2020, total carbon emissions from energy consumption in five major industries showed a trend of initially declining and then rising, while carbon emissions from the residential consumption sector exhibited an upward trend. 2) For carbon emissions from the industrial sector, economic output and industrial structure are the primary factors that promote and inhibit carbon emissions respectively. The inhibitory effects of energy structure and energy intensity are not significant. Population scale has a certain promoting effect on carbon emissions. For residential energy consumption carbon emissions, Household consumption expenditure, residential energy structure, and residential population scale are driving factors that promote the growth of carbon emissions, while residential energy intensity restrains the growth of carbon emissions. 3) From 2011 to 2018, carbon emissions from the industrial sector have been decoupled from economic output, and the decoupling state is dominated by weak decoupling. However, carbon emissions are once again correlated with economic development in 2019–2020. Carbon emissions from residential energy consumption have not yet decoupled from consumption expenditure, and its decoupling state is unstable and has no obvious change rule.

Does regional corruption exacerbate regional carbon emissions? To answer this, based on the spatial Durbin model, this study empirically examines the impact of regional corruption on carbon emission, using panel data from 30 provinces in China during the period 2002–2017. The results show that: (1) there is an indistinctive N-shaped relationship between regional corruption and carbon emissions at the national level. Regional corruption tends to initially aggravate carbon emissions, then contributes to emission reduction, and then finally boosts carbon emissions. However, this effect is not statistically significant. The results suggest that the role of regional corruption on carbon emissions is twofold. Corruption can exacerbate and can also inhibit regional carbon emissions. (2) Pronounced regional heterogeneity exists with regard to the influence of corruption on carbon emissions. Regional corruption and carbon emissions show a significant N-shaped dynamic relationship in China’s central region, while the relationship is not significant in the eastern and western regions. (3) The impact of regional corruption on carbon emissions varies with time. For 2002–2009, regional corruption did not have a significant effect on carbon emissions. For 2010–2017, the direct effect became significant, and an apparent N-shaped relationship formed between regional corruption and carbon emissions. Based on the empirical results, this paper proposes several policy recommendations regarding corruption and carbon governance.Graphic abstract

The present article evaluates establishment of development zones and its association with carbon emissions. In the process of industrialization, carbon emissions in underdeveloped regions of the world increase with economic growth. In order to promote economic growth in the western region and strengthen the management of enterprise pollution emissions, the Chinese government has set up hundreds of development zones. Existing research shows that development zone establishment can promote economic growth; however, literature is scarce when the relationship is tested across region. Based on the panel data of five provinces with relatively backward economy in western China from 2001 to 2017, this paper constructs a "multi-period difference-in-difference" (DID) model with the establishment of development zones as a "quasi-natural experiment" to test the relationship. Findings reveal that development zone establishment increases carbon emissions in the region, and has a significant inhibitory effect on carbon emissions at national level. The conclusions of this paper provide empirical evidence and policy implications for reducing carbon emissions in economically underdeveloped areas.