How does industrial restructuring influence carbon emissions: City-level evidence from China

Carbon emission reduction effects of green finance reform and innovation pilot zones policy: evidence from the prefecture-level city in China

The establishment of green finance reform and innovation pilot zones is an important practical exploration to achieve carbon emission reduction goals through green finance in China. Based on the panel data of 282 Chinese prefecture-level cities from 2006 to 2019, this paper examines the mechanism of China's green financial reform and innovation pilot zone policy (GFRI) on urban carbon emissions (CE) and carbon emission efficiency(CEE) using difference-in-differences model. The study shows that GFRI has a significant carbon emission reduction effect, which is reflected in the significant reduction of urban CE and the improvement of urban CEE. GFRI achieves carbon emission reduction by promoting urban green innovation, while the mediating effect of financial agglomeration has not been verified. The results of heterogeneity analysis show that GFRI has more significant effects on carbon emission reduction in non-resource-based cities, large-scale cities and cities with strict environmental regulation. Financial development and digital infrastructure play a positive moderating role on the carbon emission reduction effect of GFRI. This study provides empirical evidence and policy insights from the Chinese city level for deepening the green finance policy and promoting urban low-carbon development.

Driving factors and emission reduction scenarios analysis of CO2 emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO₂ emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO₂ emissions in the GBA and surrounding cities. The results show that (a) CO₂ emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO₂ emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO₂ emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO₂ emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

Effective Conditions for Achieving Carbon Unlocking Targets for Transport Infrastructure Development-Joint Analysis Based on PLS-SEM and NCA

It is important to investigate how to achieve carbon unlocking in the transport sector, especially in transport infrastructure, in order to contribute to the achievement of carbon neutrality targets and the 2030 Sustainable Development Goals. This study aims to investigate the necessary and sufficient conditions to achieve carbon unlocking in transport infrastructure. To achieve this, a combination of partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) methods have been used to examine whether there are unidentified necessity factors beyond the currently recognized 'technology-in-institution' (TIC) lock-in. This study also explores how the carbon unlocking of transport infrastructure can be achieved through the unlocking of relevant factors. The study includes 366 points from a subjective questionnaire from the government, transport infrastructure researchers, and relevant businesspeople. We found that, at the adequacy level, achieving institutional and technological unlocking is sufficient and economic factors have little impact on transport infrastructure (0.06), and that institutional and technical factors have a large impact on carbon unlocking (0.453, 0.280); however, from the necessary point of view, carbon unlocking at the economic level is necessary to achieve the goal of a medium to high level of carbon unlocking. To achieve carbon unlocking at this level (over 50%), a combination of technological, institutional, and economic factors is required. To achieve full carbon unlocking, the technology, system, and economy need to be at least 0.533, 0.791, and 0.63 unlocked. Therefore, we can conclude that by using the joint analysis of PLS-SEM and NCA, we have achieved an extension of the traditional TIC and identified sufficient and necessary conditions to achieve a medium to high degree of carbon unlocking.

Effect of environmental regulation policy synergy on carbon emissions in China under consideration of the mediating role of industrial structure

A comprehensive understanding of the effect of environmental regulation policy synergy on carbon emissions is essential for the design and decision-making of the other policy combinations. In this paper, the panel data from 30 provinces in China during 2000-2019 are used as samples to investigate the heterogeneity of different policy combinations and compare the differences using a Bayesian non-parametric generalized additive quantile method. The research results imply that the environmental regulation policy synergy has a significant heterogeneous effect on carbon emissions on different levels. Specifically, the effect of environmental regulation policy synergy on carbon emissions has an inverted U-shape trend at the low quintiles, i.e., before the turning point, it shows a "green paradox" effect, and after the turning point, it shows an "emission reduction" effect. However, the effect of environmental regulation policy synergy on carbon emissions shows a significant N-shape trend at the high quintiles, i.e., before the second turning point, it shows an "emission reduction" effect, and after the second turning point, it shows a "green paradox" effect. Moreover, the effect of environmental regulation policy combinations on carbon emissions reduction has been found to vary significantly across regions.

Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect

Carbon emissions trading (CET) is now one of China’s key policy tools for achieving the goals of carbon peak and carbon neutrality. To comprehensively explore the consequences of China’s CET policy, the study first evaluated the content validity of CET policies across China’s 31 provinces, autonomous regions, and municipalities falling directly under the central government using policy strengths, tools, and measures from 2011 to 2020. The spatiotemporal drivers of regional carbon efficiency improvements from aspects of actual policy effect, average policy content validity effect, and policy quantity effect were also evaluated via the logarithmic mean Divisia index. This analysis revealed that the policy content validity was increasing in general and was higher in pilot regions. On average, the policy quantity effect was the primary driver of carbon efficiency improvements in both pilot and non-pilot regions, but the actual policy effect failed to promote carbon efficiency in both regions. Beijing’s carbon emissions reduction effect was superior to that of other pilot regions, and where actual policy effect and policy quantity effect were the primary and secondary drivers of local carbon efficiency improvements, respectively. These findings suggest that when formulating CET policies, each region should not only focus on improving policy content validity, but also pay attention to the actual carbon emissions reduction effects produced by policies as well.

Spatial analysis of carbon dioxide emissions from producer services: an empirical analysis based on panel data from China

This study focuses on producer services based on data from 30 provinces in China from 2005 to 2018. The spatial panel STIRPAT-Durbin model was used to analyze the spatiotemporal evolution characteristics and spatial effects of CO₂ emissions. The empirical results reveal four key findings. First, there are significant differences in CO₂ emissions between eastern and western China, and the agglomeration areas gradually evolved from east to central and south China. Second, the increase in industrial agglomeration level leads to increased carbon emissions from producer services and produces negative spatial spillover effects. Third, urbanization, employment scale, and per capita wealth significantly increase the provinces' carbon emissions and produce positive spatial spillover effects. Fourth, technology can significantly reduce CO₂ emissions, but the positive spatial spillover effect is not significant.

Does tourism industry agglomeration improve China's energy and carbon emissions performance?

The tourism industry is seen as having great potential, but tourism development and tourism activities may increase energy consumption and environmental pressure. Based on the provincial panel data of China from 2000 to 2017, we calculate the energy and carbon emission performance by using the non-radial distance function (NDDF) and further investigate the impact of tourism industry agglomeration on energy and carbon emission efficiency by combining the panel fixed effect model, mediation effect model and quantile regression. Our research results show that there is an inverted U-shaped relationship between tourism industry agglomeration and energy and carbon emission efficiency, and tourism industry agglomeration can improve energy and carbon emission efficiency at present. At the same time, the impact of tourism industry agglomeration on energy and carbon emission efficiency has regional heterogeneity. The industrial structure upgrading plays an important role in the process of tourism industry agglomeration. In addition, with the improvement of energy and carbon emission performance, the impact of tourism industry agglomeration is also different. These findings suggest that policymakers should promote tourism industry agglomeration to realize energy conservation and emission reduction. The Chinese government should focus on the tourism resources and advantages of different regions and formulate differentiated regional policies to improve ecological performance.

Does technical progress curb India's carbon emissions? A novel approach of combining extended index decomposition analysis and production-theoretical decomposition analysis

Technical progress is one of the best strategies to meet the dual challenges of economic growth and carbon emissions control in developing countries such as India. In this research, an empirical study of India's carbon emissions was conducted to understand the effects of various technical factors on total and industrial carbon emission intensity. Ten driving factors of carbon emission in 56 sectors of India are decomposed by combining Index Decomposition Analysis (IDA) and Production-theoretical Decomposition Analysis (PDA). The results reveal that (i) the pure carbon-reduction technical efficiency and the scale efficiency of carbon-reduction technology in India have not improved, and the technical progress of pure carbon-reduction and the scale efficiency of carbon-reduction technology has played a positive role in carbon emission. (ii) The scale efficiency of pure energy-saving technology progress is the most important factor in restraining carbon emissions, and pure energy-saving technology progress has had an overall promoting effect. However, the effect of energy-saving technology progress is not as effective as the scale efficiency through improving energy technology progress. (iii) Per capita value added and population size are powerful factors that promote carbon emissions. This study also makes a targeted analysis of the driving factors of carbon emissions in different industries, which provide a reference for the process of carbon-reduction in different industries.

Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.

Carbon neutrality, bank lending, and credit risk: Evidence from the Eurozone

The development of a green financial intermediation channel is imperative to achieve zero-carbon economies. In this study, we assess the impact of carbon-neutral lending on the credit risk in the Eurozone. We employ quarterly data for a sample of 344 lending institutions of 19 member states spanning over ten years from 2011 to 2020. Using two specific credit risk measures, the findings show that the exposure to carbon-neutral lending is negatively related to the default risk. The results remain consistent for the various size sorts, depicting that regardless of the bank size, the impact of green financing on the credit risk is the same. We attribute the credit risk reduction to the lower volatility of the borrowers' earnings and cash flows emanating from their sustainable business model. As a consequence of lower credit risk, financial institutions can benefit from lower loan loss provisions and economic capital requirements. This incentive is vital to increase the carbon neutral credit and contribute towards pro-environmental goals.

Towards sustainability path in Argentina: the role of finance, energy mix, and industrial value-added in low or high carbon emission-application of DARDL simulation

The interconnection between environmental protection and sustainable development is at the heart of discussion among all the intergovernmental agencies around the globe. Such discussion is considered highly important considering the role of finance, an abundance of fossil fuel and industrial value-added on economic activities and environmental issues. Meanwhile, few empirical studies in this line of discussions have documented policy options for projecting the path towards sustainable development in Argentina from 1971 to 2018. To contribute to the extant literature in filling this gap, this study examines whether finance can escalate a long-lasting economic shift that will change the path of carbon emission in Argentina using the novel econometric technique, dynamic Autoregressive Distributed Lag simulations. The modelling protocol incorporates the impact of the following economic agents such as population, economic growth, trade openness, and government consumption expenditure. Our result suggests that all the variables are cointegrated under the ARDL-bounds testing framework. The long and short-term estimates from the dynamic ARDL simulation show that finance and industrial value added interestingly offer policy options for CO2 mitigation in Argentina. Fossil fuel, population, economic growth, and government consumption expenditure have increasing an impact on CO2 emissions, exacerbating sustainability challenges in Argentina. In sum, improved finance and industrial restructuring are needed economic acumen that can accelerate a quick transition to a low-carbon development in Argentina, while fossil fuel, population, economic growth, and government consumption expenditure generate environmental challenges. Policy options in consideration of investors' safety in carbonated companies in Argentina, these companies owe shareholders an obligation to invest in a resilient carbon capture and storage technology in a bid to decrease environmental degradation and align with environmental goals set by the Argentine government.

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Spatiotemporal changes in efficiency and influencing factors of China's industrial carbon emissions

The industrial sector is the backbone of China's national economy. The industrial carbon emission efficiency (ICEE) of China is directly related to the achievement of carbon emission reduction targets. This paper reports on the use of the minimum distance (min-SBM) method to determine the ICEE of 30 provinces in China during 1998-2015, as well as the use of a spatial econometric method to investigate the convergence and influencing factors of the regional ICEE. The results indicate significant regional differences in the ICEE. The provinces with higher average values of ICEE are located in the eastern coastal areas, whereas the provinces with lower average values of ICEE are located in the central and western inland regions. The results of the spatial autocorrelation index reveal that China's inter-provincial ICEE exhibits significant spatial autocorrelation characteristics, and its spatial distribution demonstrates a certain regularity. The local indicators of spatial association diagram further illustrate that most provinces in China have high and low agglomeration values. With the introduction of the spatial effect, the absolute and conditional convergence rates increase. In addition to the non-significant industrial structure effect, the level of economic development, foreign direct investment, technological progress, and government intervention demonstrate a positive impact on the ICEE convergence, whereas the energy consumption structure has a negative impact. This work investigates the cause for the regional gap in China's current ICEE. Suggestions for improving the efficiency of China's industrial carbon emissions and narrowing the regional gap are provided, which serve as a reference value for China to achieve the peak of carbon dioxide emissions before 2030.