Carbon emissions, the industrial structure and economic growth: Evidence from heterogeneous industries in China

Do circular economy, public-private Partnership and carbon policy manage the environmental stress? Developed countries' situation under the Prism of COP27

Since the Industrial Revolution, the economies have played well to make progress in economic growth. Besides, rapid growth has brought severe challenges, and environmental degradation is one of them. Therefore, the globe has introduced several green initiatives, such as the Kyoto Protocol, the Paris Agreement, and the Sustainable Development Goals, but the problem remains intact. Specifically, this study focuses on COP27 and highlights the key challenges and their best solutions. Undoubtedly, most nations have tried to meet their settled targets by 2030, but these have different priorities to facilitate their populace. Therefore, international cooperation has been introduced as a logical solution to collaborate across borders or within the region to deal with sustainability themes. However, developed nations have environmental problems due to industrial, income, and population growth, directly associated with environmental risks. Thus, under the SDGs, this empirical research tries to cover the critical problems (income, population aging, & industrial development) and their best alternative (public-private partnership, emission taxes & circular economy) to minimize environmental issues. Similarly, the current study utilizes an advanced series of estimators to investigate the study's objective for 17 developed nations from 2000 to 2021. Investigated outcomes describe income, population aging, and industrial activities that bring carbon emissions. Conversely, carbon policy and public-private partnerships support the sustainability theme for specified economies. Under the base model, the circular economy declines the environmental pressure by 0.016 %, 0.002 %, and 0.019 %, respectively, under the specified estimators. Moreover, this empirical research investigates the mediating role of carbon policy, public-private partnership & circular economy on industrial development. It brings a significant decline in emissions only for carbon policy & circular economy. However, this study also proposes some green policies to become clean & green shortly.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Agricultural carbon reduction in China: The synergy effect of trade and technology on sustainable development

The control of carbon emissions from agriculture is imperative in addressing the challenges posed by the greenhouse effect. China must develop a specific pathway for reducing its agricultural carbon emissions, accounting for its unique circumstances and considering the impacts of trade liberalization. (1) The study revealing that agricultural trade liberalization (ATL) has a marked effect on lowering China's agricultural carbon emission intensity (ACEI), with robustness and endogeneity tests supporting these findings. (2) In the pursuit of emissions reduction, the crucial role of technology spillovers (TS) and the optimization of industrial structure (OIS) are essential. (3) The reduction in ACEI is particularly notable in coastal regions, areas with low environmental regulations and during periods characterized by more stable agricultural tariffs. This study shows a synergistic association between ATL and ACEI, indicates the potential for a mutually beneficial situation with advantages in both economic and environmental aspects.

Collaborative effect of the energy conservation and emission reduction fiscal policy in China

Government-led national comprehensive demonstration cities for Energy Conservation and Emission Reduction Fiscal Policy (ECERFP) are pivotal for China in addressing environmental governance. Using a panel dataset covering 278 Chinese cities from 2003 to 2019, this study adopts the staggered difference-in-differences (DID) approach to investigate the synergistic impacts of ECERFP on pollution and carbon reduction. The findings indicate that ECERFP contributes to a 3% improvement in pollution reduction performance, a 1.5% enhancement in carbon reduction performance, and a 4% overall increase in combined pollution and carbon reduction efforts. Furthermore, the study examines the heterogeneous effects of ECERFP on environmental performance. ECERFP significantly influences the synergistic efforts in pollution and carbon reduction by fostering green innovation, enhancing energy allocation, and optimizing industrial structures. This study both theoretically and empirically outlines the specific pathways and mechanisms through which "incentive-based" green fiscal policy promotes synergistic pollution and carbon reduction, thus providing a pragmatic foundation for enhancing the role of fiscal policy in environmental governance.

Forecasting carbon prices in China's pilot carbon market: A multi-source information approach with conditional generative adversarial networks

In recent years, the Chinese government has actively pursued the implementation of its 'dual-carbon' strategy, concurrently establishing a national carbon emissions trading market. Accurate carbon price forecasts have become essential for policymakers and investors involved in related initiatives. Nevertheless, influenced by the interaction of various information sources, carbon trading prices exhibit non-linear and non-stationary characteristics, posing challenges for accurate prediction. Current research, centered around deep learning models, predominantly emphasizes intricate network structures, optimisation algorithms, and data decomposition. However, these models face a developmental bottleneck in extracting carbon price features and efficiently leveraging multi-source information. Consequently, novel ideas and methodologies are imperative. This study focuses on the Hubei and Guangdong regional carbon markets as research subjects. It develops a prediction framework based on a generative adversarial network model to capture the time-series change characteristics of carbon trading prices and the condition matrix. First, a generator prediction model is used to obtain the input matrix features and extract the time series features through a complex network to predict the carbon price data at the next moment using a fully connected layer. Second, a discriminator is utilised to distinguish between the actual values and the predicted values. The generator and the discriminator undergo continuous iterative training and alternate optimisation. This process aims to bring the generated prediction distributions closer to the actual sample data, resulting in more accurate final predictions. The empirical results convincingly show that the proposed model achieves unparalleled forecasting precision in both markets. The proposed model demonstrates the lowest MAE (0.804 and 0.839), lowest MAPE (0.023 and 0.018), lowest RMSE (1.174 and 1.383), and highest R2 (0.971 and 0.989) across both markets, indicating superior predictive accuracy. Additionally, the proposed model consistently outshines traditional forecasting approaches across one-step, five-step, and ten-step forecasts, affirming its robustness and universal applicability in modelling carbon trading price series. The findings suggest that this study can aid policymakers in optimizing the carbon pricing system. Furthermore, it offers a reference for policymakers to comprehensively leverage external factors, such as regulating traditional energy prices, leveraging international carbon market experiences, and monitoring economic dynamics. This comprehensive strategy can streamline the exploration and management of carbon price fluctuations, ultimately strengthening the carbon market's risk control system.

What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Digital transformation and carbon intensity reduction in transportation industry: Empirical evidence from a global perspective

Digital transformation has become an inevitable trend in industrial development, but research on its environmental benefits has not been conducted in-depth. This paper focuses on the impact and mechanisms of the digital transformation of the transportation industry on its carbon intensity. Empirical tests are conducted based on the panel data of 43 economies from 2000 to 2014. The results show that the digital transformation of the transportation industry reduces its carbon intensity, but only the digital transformation that relies on domestic digital sources is significant. Second, technological progress, upgrading the industry's internal structure and improving energy consumption are the main channels through which the digital transformation of the transportation industry reduces its carbon intensity. Third, in terms of subdividing industries, the digital transformation of basic transportation has a more significant effect on reducing carbon intensity. For segmentation digitization, the carbon intensity reduction from digital infrastructure is more significant. This paper serves as a reference for countries to formulate development policies for the transportation industry and implement the Paris Agreement.

Empirical linkages of the construction sector, intensive energy consumption, and economic openness with chemical oxygen demand pollution

Though construction sector development and economic openness contribute to regional economic development, they have also been debated to pose some environmental challenges. Along these lines, we explored the long- and short-term connections of intensive energy consumption, economic openness, and construction sector development with the chemical oxygen demand throughout the scales of regional development of China's 30 provincial units over the 2004-2021 period. Theoretically, we contribute to the existing knowledge by incorporating chemical oxygen demand pollution, construction sector development, and economic openness to the Kaya identity's baseline framework. Empirically, we apply a series of advanced methods of panel data econometrics for robust results. Our key findings are as follows: First, we revealed a long-term stable cointegrating association among our variables of interest. Second, using the common correlated effect mean group estimator, we unfolded that the intensive energy consumption showed a chemical oxygen demand pollution reduction influence in both the long and short term, demonstrating the most substantial influence in the high regional development panel while expressing the least powerful influence the least regional development setting. Third, we unveiled that economic openness and construction sector development showed a linear chemical oxygen demand pollution enhancement influence in moderately and least developed regions. Nevertheless, both established an inverted U-shaped linkage with chemical oxygen demand pollution for the whole country as well as for high regional development data samples. Eventually, we found consistent estimates across long- and short-term investigations regarding signs of relationships; however, long-term effects remained more powerful than short-term ones. These findings would serve as factual scientific knowledge to help local as well as national governments create the optimal environmental regulations for the construction sector to achieve the sustainable development goals (SDGs), especially the Climate Action Plan (i.e., SDG-13).

Under the different sectors: the relationship between low-carbon economic development, health and GDP

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China's carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO₂ emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 10⁶ tce and [0.16, 1.12] × 10⁸ m³, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m³/10⁴ kWh and 0.04 ton-CO₂/10⁴ kWh, respectively.

A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China

Introduction

With China's rapid industrialization and urbanization, China has been increasing its carbon productivity annually. Understanding the association between carbon productivity, socioeconomics, and medical resources with cardiovascular diseases (CVDs) may help reduce CVDs burden. However, relevant studies are limited.

Objectives

The study aimed to describe the temporal and spatial distribution pattern of CVDs hospitalization in southeast rural China and to explore its influencing factors.

Methods

In this study, 1,925,129 hospitalization records of rural residents in southeast China with CVDs were analyzed from the New Rural Cooperative Medical Scheme (NRCMS). The spatial distribution patterns were explored using Global Moran's I and Local Indicators of Spatial Association (LISA). The relationships with influencing factors were detected using both a geographically and temporally weighted regression model (GTWR) and multiscale geographically weighted regression (MGWR).

Results

In southeast China, rural inpatients with CVDs increased by 95.29% from 2010 to 2016. The main groups affected were elderly and women, with essential hypertension (26.06%), cerebral infarction (17.97%), and chronic ischemic heart disease (13.81%) being the leading CVD subtypes. The results of LISA shows that central and midwestern counties, including Meilie, Sanyuan, Mingxi, Jiangle, and Shaxian, showed a high-high cluster pattern of CVDs hospitalization rates. Negative associations were observed between CVDs hospitalization rates and carbon productivity, and positive associations with per capita GDP and hospital beds in most counties (p < 0.05). The association between CVDs hospitalization rates and carbon productivity and per capita GDP was stronger in central and midwestern counties, while the relationship with hospital bed resources was stronger in northern counties.

Conclusion

Rural hospitalizations for CVDs have increased dramatically, with spatial heterogeneity observed in hospitalization rates. Negative associations were found with carbon productivity, and positive associations with socioeconomic status and medical resources. Based on our findings, we recommend low-carbon development, use of carbon productivity as an environmental health metric, and rational allocation of medical resources in rural China.

On the urban resource and environment carrying capacity in China: A sustainable development paradigm

As urbanization progresses, the number of resource and environmental problems that impede sustainable growth in cities is increasing. The urban resource and environment carrying capacity (URECC) serves as a crucial indicator for understanding the interaction between human activities and urban resource and environmental systems, guiding the practice of sustainable urban development. Thus, accurately comprehending and analyzing the URECC and coordinating the balanced growth of the economy and the URECC is essential to ensure cities' sustainable development. In this research, we combine DMSP/OLS and NPP/VIIRS night-time light data to assess the economic growth of Chinese cities using panel data for 282 prefecture-level cities in China from 2007 to 2019. The findings reveal the following outcomes: (1) Economic growth significantly contributes to the enhancement of the URECC, and the economic expansion of neighboring areas also promotes the URECC within the region. Economic growth can indirectly improve the URECC by fostering internet development, industrial upgrading, technological progress, opening up opportunities, and educational advancements. (2) The results from the threshold regression analysis suggest that as the level of internet development improves, the influence of economic growth on the URECC is initially constrained and then facilitated. Similarly, as financial development improves, the effect of economic growth on the URECC is initially constrained and subsequently promoted, with the promotion effect gradually increasing. (3) The relationship between economic expansion and the URECC varies across regions with different geographic locations, administrative levels, scales, and resource endowments.

Spatial characteristics and coupling coordination between carbon emission efficiency and industrial structure in three metropolitan areas of Jiangsu Province, China

It is of great practical significance to study the spatial characteristics of carbon emission efficiency, industrial structure, their coupling and coordination relationship for China's green development and industrial structure transformation in the new era. From the perspective of coupling, coordination and space, this paper analyzes and summarizes the spatial characteristics of carbon emission efficiency and industrial structure of 19 cities in three metropolitan areas of Jiangsu Province during 2009-2019 and their coupling and coordination relationship. The carbon emission efficiency in this study is represented by the carbon emission economic efficiency index and carbon emission social efficiency index. The results show that (a) the high-emission centers in the three metropolitan areas developed from "three centers" in 2009 to "five centers" in 2019. The continuous high-energy consumption of the secondary industry and the growth of the economic aggregate of the third industry kept the regional high carbon dioxide emissions. (b) The average value of carbon emission economic efficiency in 19 cities continued to increase, indicating that the contribution rate of the same amount of carbon emissions to economic income gradually increased; the growth range of carbon emission economic efficiency index is greater than that of carbon emission social efficiency index, indicating that carbon emission has a more significant effect on the improvement of regional economic development than on the improvement of public service level and residents' living quality. (d) The solidification degree of carbon emission efficiency is greater than that of the industrial structure (solidification degree carbon emission social efficiency > carbon emission economic efficiency > industrial structure). The high-grade industrial structure in Xuzhou metropolitan area is closely related to the improvement of carbon emission economic efficiency and carbon emission social efficiency, and both are in moderate antagonism. The rationalizing industrial structure in Nanjing metropolitan area is closely related to the improvement of carbon emission economic efficiency, which is in high coordination run-in. The concentration degree of industrial structure in Suzhou-Wuxi-Changzhou metropolitan area is closely related to the improvement of carbon emission economic efficiency and carbon emission social efficiency, which are in polar coordination coupling and high coordination run-in, respectively. The proposed coupling path of carbon emission efficiency-industrial structure can not only alleviate the dynamic disharmony in different cities but also effectively improve the coupling degree in cities.

Development zone policy and urban carbon emissions: empirical evidence from the construction of national high-tech industrial development zones in China

As a key strategy to promote system reform, improve the investment environment, and encourage industrial agglomeration, the national high-tech industrial development zone (NHTDZ) policy in China can not only reduce energy consumption through the scale effect but also improve energy efficiency by modernizing industrial structure and fostering technological innovation, thereby alleviating environmental pollution. Existing studies, however, focus solely on the effects of NHTDZ policy on social and economic development, ignoring their impact on the ecological environment, especially carbon (CO₂) emissions that contribute to global warming. Thus, this article analyzes a panel data of 285 prefecture-level cities and above in China from 2003 to 2019 to assess the influence of NHTDZ policy on CO₂ emissions, treating the NHTDZ construction since 1988 as a quasi-natural experiment. The results indicate that the NHTDZ policy would mitigate urban carbon emissions, particularly in middle, southeastern, medium-sized, resource-based (RB), non-key environmental protection (non-KEP), and non-two control zone (non-TCZ) cities. In addition, the mediation mechanism test demonstrates that the environmental benefits of the NHTDZ policy in China are attributable to the scale effect, the structural upgrading effect, and the technology innovation effect. The NHTDZ policy would lower per capita CO₂ emissions by reducing energy consumption, upgrading industrial structure, and promoting green technology innovation.

Study on Spatiotemporal Features and Factors Influencing the Urban Green Total Factor Productivity in the Yellow River Basin under the Constraint of Pollution Reduction and Carbon Reduction

Whether cities can attain a win-win situation with simultaneous environmental protection and economic growth is a compelling issue in current urban development. It will be of great practical significance to comprehensively evaluate the implementation effect of the multi-dimensional goals of an urban development from the perspective of a green total factor productivity (GTFP) evaluation. The paper places pollution reduction and carbon reduction into the research framework of GTFP, introduces the SBM-DDF model to evaluate the urban GTFP of 58 cities in the Yellow River Basin (YRB) from 2006 to 2020, and employs the panel regression model to empirically study the factors influencing the urban GTFP of the region. The results are obtained as follows: (1) from the perspective of time range, the urban GTFP in the basin displays an evolutionary trend of first declining and then mounting, demonstrating the highest GTFP in the downstream, the second-highest in the midstream and the lowest in the upstream; (2) regarding the spatial distribution characteristics, the urban GTFP in the basin presents obvious spatial differences, showing the regional differences by increasing from the upstream to the downstream; (3) from the perspective of the whole basin, the advancement of economic development, urbanization processes, environmental regulations and the ecological background have significantly positive effects in improving the urban GTFP, while the improvement of the industrial structure, opening-up and energy intensity affects the urban GTFP of the basin negatively; and (4) from the perspective of the regional heterogeneity of the effects of the various influencing factors, the improvement of the opening-up and industrial structure expedites the growth of the urban GTFP of the downstream, the advancement of urbanization process restrains the urban GTFP in the upstream and the impact of the ecological background on the urban GTFP in different regions is relatively complex. This study is of great importance to improve the urban GTFP and boost the high-quality development of the cities in the basin.

Decoupling analysis and peak projection of manufacturing CO2 emissions from the perspective of investment

Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003-2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03047-w.

Can economic growth and carbon emissions reduction be owned: evidence from the convergence of digital services and manufacturing in China

Under the background of the deep convergence of China's digital services and manufacturing, it is of great significance to investigate the effects of the convergence of digital services and manufacturing on economic growth and carbon emissions reduction to the application of digital technology in the whole world. This paper constructs a simultaneous equation model and uses three-stage least squares to estimate the effect and mechanism of industrial convergence on economic growth and carbon emissions. The results show that (i) industrial convergence improves the change of total factor productivity (TFP) and the change of technical efficiency, and the reduction of carbon emissions is the main factor driving the growth of TFP and technical efficiency; (ii) industrial convergence and carbon emissions show a significant U-shaped relationship; (iii) the heterogeneity analysis shows that the convergence of capital-intensive, technology-intensive and labor-intensive manufacturing with digital services will help to improve the growth of TFP, it can inhibit carbon emissions first and then promote it. Therefore, the government should take targeted measures to promote industrial convergence of digital services and manufacturing according to the economic development and industry characteristics, so as to give full play to its positive role in economic growth and emissions reduction.

Identification of factors influencing net primary productivity of terrestrial ecosystems based on interpretable machine learning --evidence from the county-level administrative districts in China

Global climate change is rooted in the imbalance between carbon sources and sinks, and net-zero greenhouse gas emissions should focus not only on the source-side drivers but also on the sink-side influencing factors. Taking the county-level administrative districts in China as the sample, this study uses machine learning models to fit the relationship between socioeconomic development (SED) and net primary productivity (NPP) of terrestrial ecosystems. Moreover, it identifies key influencing factors and their effects based on the SHapley Additive exPlanations (SHAP) algorithm. The results show that the districts with low terrestrial NPP show the characteristics of agglomeration distribution. The eight key factors, in order, are as follows: agricultural development level, latitude, population size, longitude, animal husbandry development level, economic scale, time trend and industrialization level. In this study, via SHAP interaction plots, we found that the effects of population, economic growth, and industrialization on terrestrial NPP are regionally heterogeneous; via cluster analysis, we found the stage characteristics of the mode of SED affecting terrestrial NPP. Therefore, the conservation of terrestrial NPP needs to be combined with the stage changes of SED, as well as inter-regional differences, to develop a regionally coordinated and time-coherent ecological carbon sink conservation plan.

Government Intervention, Structural Transformation, and Carbon Emissions: Evidence from China

Government intervention and structural transformation play an important role in both the economy and carbon emissions. Based on provincial panel data from China from 2003 to 2020, this paper employs econometric models to investigate the impact of government intervention and structural transformation on carbon emissions. In particular, structural transformation is divided into two indicators: The rationalization of the industrial structure and the upgrading of the industrial structure. According to the research findings, government intervention has significantly promoted carbon emissions and structural transformation has had dual effects on carbon emissions; meanwhile, the rationalization of the industrial structure has significantly increased carbon emissions, while the upgrading of the industrial structure has slowed down carbon emissions, with these findings passing the corresponding robustness test. The relationship between government intervention, structural transformation, and carbon emissions varies significantly over time and across regions. Further investigations revealed that government intervention and structural transformation have a significant impact on carbon emissions in various panel quantiles. Finally, the paper makes policy recommendations in order to provide empirical support for promoting China's high-quality economic development and achieving the "double carbon" goal.

Study of the impact of industrial restructuring on the intensity of air pollutant and greenhouse gas emissions from high-energy-consuming sectors: empirical data from China

Environmental pollution and climate change have become nontraditional global security threats. As China's economy grows, the country faces an increasing number of challenges associated with improving atmospheric quality and reducing greenhouse gas emissions. Based on China's dynamic noncompetitive input-output tables and data on energy consumption and emissions from 1994 to 2016, a hybrid input-output model is constructed to identify high-energy-consuming sectors and to quantify the impact of industrial restructuring on the intensity of air pollutant and greenhouse gas emissions from these sectors. The empirical results indicate that the impact of industrial restructuring on the intensity of air pollutant and greenhouse gas emissions from high-energy-consuming sectors was nonlinear and has undergone a "promotion reduction" shift. This study also found that the impact of industrial restructuring is more significant on the intensity of greenhouse gas emissions than on the intensity of air pollutant emissions; furthermore, the reduction in greenhouse gas emission intensity achieved by industrial restructuring after 2008 began to show results. Based on the findings of this study, we make recommendations such as the need for the Chinese government to continue to promote supply-side structural reforms in the energy sector.

How does digital village construction influences carbon emission? The case of China

Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an "inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.

Analysis of the decoupling state and driving forces of China's construction industry under the carbon neutrality target

The essential to achieving the 2060 carbon neutrality target in China lies in the performance of the construction industry. Decoupling economic development from CO₂ emissions is the main strategy for reducing emissions in the construction industry. This paper is based on panel data for China and its 30 provinces during 2009-2019. A Tapio decoupling model is constructed to analyze the decoupling state of economic development and CO₂ emissions in the construction industry. The logarithmic mean Divisia index model is constructed to continue the decomposition of the drivers of the decoupling state and CO₂ emissions. The results show that (1) the economic development level of most provinces is positively correlated with their CO₂ emissions; (2) Beijing and Jiangsu reach the ideal strong decoupling state, and Heilongjiang has the worst decoupling state. The same type of decoupling state shows a certain aggregation phenomenon in space; (3) economic output plays a critical role in promoting CO₂ emissions and decoupling of the construction industry in China and the provinces. The main driver of decoupling is indirect carbon intensity; (4) energy intensity has a greater impact on CO₂ emissions reduction in regions with more developed economic levels. Understanding the drivers of the decoupling state in China's construction industry provides a valuable basis for energy efficiency and emission reduction efforts in China and other countries.

Identifying the spatial heterogeneity in the effects of the construction land scale on carbon emissions: Case study of the Yangtze River Economic Belt, China

Low-carbon emissions are a major research focus to solve the problem of global warming and an important area that China needs to focus on to achieve high-quality development. Construction land scale is a non-negligible factor affecting carbon emissions. However, carbon emission impacts of county-scale spatial heterogeneity in construction land scale are under addressed in contemporary research. To address this gap, this paper took 1042 counties in China's Yangtze River Economic Belt (YREB) and developed datasets of the influencing factors including the construction land scale, GDP, secondary industry output proportion in GDP, residential population, and fixed asset investment. After comparing the ordinary least squares and geographically weighted regression (GWR) models, we applied GWR for more in-depth analyses. The global regression model results showed that the effect of the scale of construction land on carbon emissions was exceedingly significant and that the directions of the impacts coincided with the predictions. Further, local regression model results showed that construction land scale had significant spatial heterogeneity in the impact on carbon emissions and most counties (69.58%) showed significant positive correlations. The counties with significant construction land scale impacts on carbon emissions were concentrated and contiguous in spatial distribution and spatially clustered areas varied, with the clearest impact in the downstream region. The findings help to further identify the spatial heterogeneity of construction land scale impacts on carbon emissions, which provides evidence-based and theoretical support for policymakers to develop spatially differentiated emission reduction measures.

Research on the relation of Economy-Energy-Emission (3E) system: evidence from heterogeneous energy in China

To achieve China's carbon peak and carbon neutrality goals, conducting systematic research on the energy, economy, and emission factors that affect the sustainable development of society is of great significance. This paper first uses the vector error correction model (VECM)-based Granger causality test to analyze the joint causal relations and feedback correction mechanisms among energy consumption, economic growth, and CO₂ emissions in China from 1980 to 2019 at the energy heterogeneity level; then, analyzes the decoupling effect of China's four major energy sources (coal, oil, natural gas, and electricity) and economic growth from the perspective of energy heterogeneity; finally, the Tapio decoupling elastic model is decomposed into the emission reduction elasticity and energy saving elasticity to analyze the decoupling causality chain of the economy, energy, and carbon emissions. The research results show that there is a long-term, two-way causal relation between coal consumption and CO₂; coal consumption has a one-way causal relation with economic growth; and long-term, two-way causal relations exist between oil and CO₂, natural gas and CO₂, electricity and CO₂, electricity, and economic growth. In addition, when energy consumption, economic growth, and CO₂ emissions deviate from their equilibrium states in the short term, various energy consumption, economic growth, and CO₂ emissions in the previous year will be adjusted by 19.5%, 0.6%, …, 7.7%, and 3.4% to bring the nonequilibrium states back to the long-term equilibrium states. Furthermore, the energy-saving elasticity of China's total energy, coal, oil, and natural gas is the main factor affecting the corresponding decoupling elasticity, but the emission reduction elasticity of electricity has a stronger impact on the decoupling elasticity than the emission reduction elasticity does.

Heterogeneous Dynamic Correlation Research among Industrial Structure Distortion, Two-Way FDI and Carbon Emission Intensity in China

The increase in carbon emissions year by year poses a severe challenge to the high-quality development and sustainability of China’s economy. How to reduce the intensity of carbon emissions has become a prominent issue to promote green growth. Based on the provincial panel data from 2011 to 2020, this paper uses Exploratory Spatial Data Analysis (ESDA), the spatial econometric model and intermediary effect test as analysis methods. The following results are drawn. Firstly, China’s industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west of China, followed by the middle of China and is the lowest in the east of China. Secondly, the distortion of the industrial structure will not only lead to the increase in local carbon emission intensity but also produce reverse spillover to adjacent areas. Thirdly, the results of intermediary effect analysis show that industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI. This paper has a profound practical significance for promoting the process of industrial upgrading by insisting on developing foreign trade to achieve carbon emission reduction. The main innovation of this paper is to put forward the concept of industrial structure distortion and bring it into a unified research framework with two-way FDI and carbon emission intensity.

Can the Adjustment of China's Grain Purchase and Storage Policy Improve Its Green Productivity?

While the sustainability of grain production has been extensively studied, there have been few studies focusing on the impact of grain policy adjustment on its sustainable production, and the quantitative relationship between these two aspects and the internal mechanism is not completely clear. The main objective of this paper was to explore the impact of grain purchase and storage policy (GPSP) adjustment on its green productivity by expounding the evolution logic and influence mechanism of GPSP. Therefore, taking maize production as an example, this paper constructs the analysis framework of the evolution logic and influence mechanism, and the super-epsilon-based measure model (Super-EBM) is adopted to measure maize green productivity (MGP) in main producing areas from 1997 to 2019, then two groups of difference-in-differences (DID) models are constructed to study the influence of the temporary purchase and storage policy (TPSP) and the producer subsidy policy (PSP) on MGP. The main conclusions include: the implementation of TPSP reduces MGP in Heilongjiang, Jilin, Liaoning and Inner Mongolia (experimental group), whereas the implementation of PSP improves MGP in these provinces is due to the difference in policy effects under the different regulatory objectives and measures; under the demonstration effect of two policies, the increase in effective irrigation and agricultural financial expenditure are important factors to improve MGP, but the backwardness of agricultural mechanization has been hindering the improvement of MGP; after the reform from TPSP to PSP, the continuous increase in production capacity hinders the improvement of MGP under the support effect, the impacts of farmers’ income and agricultural production price on MGP both shift from negative to positive under the wealth effect, and the influence of production agglomeration on MGP shifts from negative to positive under the siphon effect. The excessive implementation of GPSP has seriously affected the sustainability of grain production, thus, this study has certain practical significance and guiding value. The paper emphasizes that the effective way to achieve sustainable food production is to combine the adjustment of GPSP with improving the subsidy mechanism, enhancing the agricultural mechanization and maintaining the appropriate scale of operation.

Detecting Differences in the Impact of Construction Land Types on Carbon Emissions: A Case Study of Southwest China

The area with the highest concentration of carbon emission activities is construction land. However, few studies have been conducted that investigated the different effects of various types of construction land on carbon emissions and the extent of their impact. To address this shortcoming, this study constructed a multi-indicator evaluation system with 393 counties in Southwest China and integrated ordinary least squares and spatial regression models to deeply analyze the different impacts of construction land types on carbon emissions. The results revealed that (1) in Southwest China, carbon emissions were generally distributed in clusters, with significant spatial variability and dependence; (2) the distribution of urban land scale, rural settlement land scale, and other construction land scale all showed obvious spatial clustering differences; (3) all three types of construction land’s effect on carbon emissions was positive, and the direction of impact was in line with theoretical expectations; and (4) the other construction land scale had the highest effect on carbon emissions, followed by rural settlement land scale, while the urban land scale was slightly lower. The findings help to further explain the different impacts of construction land types on carbon emissions and provide theoretical references for the government to formulate more refined emissions reduction policies.

Convergence of energy carbon emission efficiency: evidence from manufacturing sub-sectors in China

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. It is of great significance to study the convergence of manufacturing energy carbon emission efficiency for realizing high-quality development of manufacturing in China. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses the coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China's manufacturing energy carbon emissions and its efficiency demonstrate an increasing trend. Coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not have σ convergence, but has [Formula: see text] convergence, and its convergence has industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emission efficiency and promote high-quality economic development.

The Impact of Economic Growth, Industrial Transition, and Energy Intensity on Carbon Dioxide Emissions in China

Carbon emission reduction has become a worldwide concern on account of global sustainability issues. Many existing studies have focused on the various socioeconomic influencing factors of carbon dioxide (CO2) emissions and the corresponding transmission mechanisms, while very few models have unified the scale effect, structure effect, and technique effect in the context of China. This paper attempted to analyze the impact of economic growth, industrial transition, and energy intensity on CO2 emissions in China by constructing an autoregressive distributed lag (ARDL) model. The results showed that there are long-term cointegration relationships between the three factors mentioned above and CO2 emissions. There is an inverted U-shaped relationship between economic growth and CO2 emissions, which not only verifies the environmental Kuznets curve (EKC) hypothesis, but also upholds the scale effect. In addition, the proportion of added value of secondary industry and energy intensity has significant positive impacts on CO2 emissions. On one hand, this confirms the structure effect and technique effect; on the other hand, it implies that the reduction effect is the dominant effect in the case of China, instead of the rebound effect. This paper is expected to make a valuable contribution to research in the field of sustainable development by providing both theoretical support and implementation of path choice for CO2 reduction in China.

Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions

Applying the principles of healthy products through agriculture practices has become an important issue due to significant environmental impacts of agrochemicals application. The agrochemicals have been recognized as an essential component of modern agriculture, but they are also an important source of environmental pollution that threatens the human's health and are main sources of carbon emissions. Pesticides and fertilizers application are important in the process of Iran's food production. In Iran, intensifying the agricultural production has led to overuse of chemical fertilizers and pesticides. This work is the first effort to quantify and compare the decoupling index pollution from agricultural sector using Tapio decoupling indicator and predict climate change impacts on this index by using Bayesian network across the whole country of Iran. For this purpose, required annual data of predictor variables for the period of 2008 to 2018 was used to calculate the decoupling index. For projecting climate change impacts on this index by using Bayesian network, monthly mean values of climatic variables were used. While Iranian farmers are criticized for pesticide overuse, these study findings showed that during the period of 2008-2018, decoupling index for pesticides (agricultural pollution by using pesticide) and decoupling index for fertilizer (agricultural pollution by using fertilizer) in the selected provinces fluctuate between RD-SD, SD-SD, SD-SD, and RD-SD. Therefore, the decoupling states show that in most study years, there is a strong decoupling of agricultural growth in selected provinces. This means that in the selected provinces, pollutant emissions of chemical fertilizer and pesticides use for agricultural productions have decreased and it has been well controlled. Therefore, by expansion of agricultural sector, the situation of agricultural pollution in these provinces in most years has not been intensified. Control of agricultural pollution in these provinces has shown a positive and significant impact on public health. In selected provinces, the cleaner agricultural products and application of organic fertilizers have been increased. This study results also showed that the climate change will accelerate increment of pests population and thus pesticides application in different climatic regions.

A step towards sustainable path: The effect of globalization on China's carbon productivity from panel threshold approach

Surfacing the stress of global CO₂ emission reduction and the change into a low-emission economy has become one of the prominent economic concerns in the twenty-first century. The essence of evolving a low-emission economy is to raise carbon productivity that can be estimated as the cost-effective paybacks of CO₂ emissions. A panel threshold model was applied to approximate the threshold effect of globalization on carbon productivity under the development of human capital by using the panel data of thirty provinces of China from 2009 to 2017. The empirical findings demonstrate that China's carbon productivity increases, while economic growth shape moves towards sustainable development with low-carbon emission. Moreover, the driving force of globalization on carbon productivity is not tediously decreasing/increasing, but it has a double threshold effect of human capital. In line with this, this study finding found a single and double threshold of 9.3478 and 10.8800, respectively, as a benchmark where the relationship turns positive. The empirical findings have suggested several policy implications for the Chinese Government, policymakers, and regulatory authorities regarding this critical issue.

Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019

Curbing carbon emissions by restricting economic growth could decrease human well-being across the world and especially in developing countries, suggesting that we need to find alternative approaches to reducing carbon emissions. Against this background, this paper investigates the relationship between urban spatial structure and carbon emissions in the Chinese context from 2002 to 2019. Specifically, urban spatial structure of 286 Chinese cities, represented by the two dimensions of polycentricity and compactness, are calculated based on the gridded (1 km × 1 km) LandScan dataset on population, while carbon emissions of these cities are aggregated from the gridded (1 km × 1 km) Open-source Data Inventory for Anthropogenic CO2 (ODIAC) dataset on carbon emissions. The empirical results based on different regression models find that overall (1) more dispersed and less monocentric (i.e., less compact and more polycentric) cities are often associated with lower levels of carbon emissions, ceteris paribus; (2) the impact of polycentricity on carbon emissions could be moderated by the economic development levels of Chinese cities. For cities with gross domestic product of more than 173 billion yuan, a more polycentric spatial structure is usually associated with a higher level of carbon emissions; (3) a city’s urban spatial structure could have positive spatial spillovers on carbon emissions of its neighboring cities.

How does economic policy uncertainty affect CO2 emissions? A regional analysis in China

More recently, the COVID-19 pandemic outbreak has created massive economic policy uncertainty (EPU). EPU and its economic fallout have been a hot topic of study; however, the impact of EPU on CO2 emissions has been seldom addressed to date. This paper investigates the effects of the EPU on CO2 emissions. It elucidates the role of EPU in moderating the environmental regulation-CO2 emissions nexus at the national and regional levels using the panel data model and provincial panel data from 2003 to 2017 in China. The main empirical results are as follows. The EPU has a negative impact on carbon emissions; however, this relationship is non-significant even at the 10% level in the central and western region datasets. Environmental regulation positively increases the CO2 emissions implying that the green paradox occurs in the whole and western regions datasets. From the perspective of the moderating effect of uncertainty, EPU exerts a positive impact upon the environmental regulation-CO2 emissions nexus in the whole and western region datasets. The moderating effect is not significant in the eastern and central regions. The results demonstrate that the re-examination of the EKC hypothesis is inconclusive. Kuznets relationship between economic growth and CO2 emissions for the national, eastern, and central samples was confirmed. In contrast, CO2 emissions monotonically rise as GDP grows for western datasets. Based on the overall findings, some policy implications were put forward. We recommend that the local government should consider EPU to improve the institutional environment. Further, different regions should implement various environmental policies according to regional conditions maximizing the emission reduction potential.

Influencing factors and decoupling analysis of carbon emissions in China's manufacturing industry

The manufacturing industry directly reflects national productivity, and it is also an industry with high energy consumption and severe carbon emissions. This study decomposes the influential factors on carbon emissions in China's manufacturing industry from 1995 to 2018 into industry value added, energy consumption, fixed asset investment, carbon productivity, energy structure, energy intensity, investment carbon intensity, and investment efficiency by Generalized Divisia Index Model. The decoupling analysis of carbon emissions and industry value added is carried out to investigate the states of the manufacturing industry under the pressure of "low carbon" and "economy." Results show that first, fixed asset investment is the driving force of carbon emissions, followed by industry value added; investment carbon intensity, carbon productivity, investment efficiency, and energy intensity are the mitigating factors; simultaneously, the impacts of energy consumption and energy structure are fluctuating. Second, the decoupling of manufacturing has improved, especially in the light industry. Third, the decoupling of carbon emissions and economic development is mainly dominated by the decoupling of energy consumption and industry added value. Therefore, reducing the proportion of coal consumption and optimizing the energy structure are significant ways to promote the low-carbon development of the manufacturing industry.

Do industrialization, energy importations, and economic progress influence carbon emission in Pakistan

The largest challenge still remains in enhancing the living conditions and economic progress, while growing the environmental footprint is related to energy. The move towards renewable sources means that energy utilization can be increased and environmental impacts reduced. The key purpose of the present analysis was to investigate the CO2 emissions interaction to industrialization, energy imports, carbon intensity, economic progress, and gross capital formation by using time span data ranging from 1971 to 2019. Variable stationarity was confirmed by utilizing the unit root tests, while quantile regression analysis was utilized to check the CO2 emission influence on the independent variables. Outcomes showed that industrialization has constructive influence with CO2 emission having coefficient (0.161636) with probability value (0.0000). Similarly the variable energy imports, carbon intensity, and gross capital formation have positive coefficients (0.206843), (0.895212), and (0.442922) with probability values (0.2171), (0.0004), and (0.0002) correspondingly that exposed the positive interaction with CO2 emission in Pakistan. The variable economic progress exposed an adverse impact to CO2 emission with having coefficient (-0.002841) with probability value (0.8795). In directive to improve the economic progress, the government of Pakistan should take future action to minimize carbon dioxide emission from different sectors that cause the climate change.

Short-term effects of land consolidation of dryland-to-paddy conversion on soil CO2 flux

To improve grain production capacity, many areas in the world are shifting from rainfed agriculture to irrigated agriculture. One example of such land consolidation is dryland-to-paddy conversion. The conversion of land use pattern largely affects the stability of farmland soil, especially the soil carbon cycle. However, the mutual feedback mechanisms between carbon flux variation and environmental factors during the farmland consolidation process are still poorly known. Located in the Huang-Huai-Hai Plain China, Xuzhou is a typical area where dryland-to-paddy conversion are most widely distributed. Therefore, in this study, we have carried out dryland-to-paddy conversion by setting up two isolated rectangular fields one group planting corn in dryland (DL) and another group planting in paddy field (PF) in Xuzhou. Here, we determined the effect of dryland-to-paddy consolidation on soil CO₂ flux in two isolated rectangular fields - the dryland (DL) cultivated with corn and the paddy field (PF) cultivated with rice. Our results showed that the soil carbon flux and temperature followed similar unimodal curves with greater soil CO₂ flux of in PF than in DL. Surprisingly, the land conversion significantly reduced soil microbial biomass carbon and easily oxidized organic carbon by 28.55% and 29.09%, respectively. The structural equation modeling results demonstrated that the changes in soil environmental factors, including temperature, and fungal OTU numbers, were the primary drivers for the soil CO₂ flux and soil carbon pool (P < 0.05). Overall, this study improves the understanding of the ecological impact of dryland-to-paddy conversion, providing insights into low-carbon agriculture and climate mitigation.

Sustainability Assessment Based on Integrating EKC with Decoupling: Empirical Evidence from China

In September 2020, the Chinese government proposed a climate change commitment that aims to make carbon emissions peak before 2030 and achieve carbon neutrality by 2060. In this context, it is important to examine the relationship between economic growth and carbon emissions. The Environmental Kuznets Curve (EKC) and decoupling analysis are commonly used assessment methods for regional sustainable development. Each method has a particular emphasis: the former focuses on long-term trends and the latter on short-term change. Integrating the EKC hypothesis with decoupling analysis is helpful to diagnose the relationship between economic growth and the carbon emissions of the manufacturing industry from the perspective of long-term trends and short-term changes. The results showed that the EKC passed the inflection point for both China’s entire manufacturing industry and manufacture of nonmetallic mineral product subsector (MNM), but not in the other four main subsectors from 1995 to 2017. Strong decoupling, weak decoupling, and expansive coupling were observed between CO2 emissions and the value added in China’s entire manufacturing industry, in which weak decoupling accounted for the largest proportion. The decoupling index showed a downward trend on the whole. The decoupling status of subsectors from 1995 to 2017 was mainly weak decoupling, but different subsectors also showed characteristics of differentiation. At present, integrating EKC with decoupling has only occurred across the entire manufacturing industry and MNM. This study will provide suggestions for carbon reductions in China and will enrich the assessment methods of sustainable development.

How to achieve a win-win situation between economic growth and carbon emission reduction: empirical evidence from the perspective of industrial structure upgrading

Over the past 40 years since China's reform and opening up, the industrial structure has undergone tremendous changes. The rapid development of the economy has been accompanied by a surge in carbon emissions. How to achieve a win-win situation for economic growth and carbon emissions reduction has aroused widespread concern from all sectors of society. Here, this paper discusses the dynamic relationship of industrial structure upgrading, economic growth, and carbon emission reduction. Results show that there is a long-term equilibrium relationship among industrial structure upgrading, economic growth, and carbon emissions. In the short term, when the three variables deviate from the long-term equilibrium state, the non-equilibrium state will be pulled back to equilibrium with the adjustment strength of - 0.0633, - 0.0097, and 0.0013. Carbon emission reduction promotes industrial structure upgrading. Industrial structure upgrading has a greater positive impact on economic growth. Industrial structure upgrading and economic growth have a negative impact on carbon emissions, thereby promoting emission reduction. And at the 10% significance level, there is a one-way Granger causality from carbon emissions to industrial structure upgrading, economic growth can cause one-way changes in carbon emissions, and industrial structure upgrading is a one-way Granger cause of economic growth. Finally, several carbon emission reduction policies are proposed promote industrial restructuring and sustainable economic development.

China's Provincial Eco-Efficiency and Its Driving Factors-Based on Network DEA and PLS-SEM Method

This study aims to estimate the eco-efficiencies of China at provincial levels. The eco-efficiencies of production and treatment stages are disentangled by the network data envelopment analysis (DEA) method. The key driving factors are identified by the integrative use of driving force-pressure-state-impact-response frame model (DPSIR) model and partial least squares structural equation modeling (PLS-SEM) method. This study provides several important findings. In general, the eco-efficiencies of most regions in China are inefficient and show significant regional differences. All DPSIR factors have significant and strong impacts on the eco-efficiency of the treatment stage. The eco-efficiency of the production stage evidently outweighs the eco-efficiency in economically well-developed regions. The originality of this study lies in three aspects. First, using two-stage network DEA, this study dissects the overall eco-efficiency into production efficiency and treatment efficiency. Empirical results provide insights into the root cause of the low efficiency of each province (municipality). Second, on the basis of the DPSIR model, an expanded pool of driving factors is investigated. Third, using the PLS-SEM method to analyze eco-efficiency is more reliable and effective than applying other traditional regression models.