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As a traditional energy and carbon intensive sector, it becomes imperative for the global paper industry to realize green development. This paper aims to provide guidance for the green transformation of global paper industry by evaluating the global paper industry green development level in a systematic and dynamic way. This paper employs text-mining method to develop paper industry green development evaluation indicator system and adopts market-extended LCA model and entropy-TOPSIS method to calculate the paper industry green development level of 48 countries/regions. The results show that during 2000–2030 the paper industry green development level of developing countries improves faster than developed countries, where those of developing and developed countries increase from 0.3369 to 0.3938 and from 0.5120 to 0.5517 respectively and the gap between them narrows from 0.1751 to 0.1579. The comparative analysis reveals that the paper industries of developed and developing countries are in different green development stages. Developing countries, represented by China, improve greatly in economic and social dimensions, but decline seriously in ecological dimension due to enlarging resource consumption and environmental damages. On the contrary, developed countries tend to saturate in economic and social dimensions and have been improving fast in ecological dimension, gradually realizing the decoupling of industrial economic development and environmental damages. To further promote the global paper industry green development, developing countries should focus on promoting the coordinated development between economic growth and environment protection and developed countries should play a leading role in the global green transformation by promoting optimal allocation of resources and cross-regional communications of green technologies. • A paper industry green development indicator system is developed based on text mining. • A global paper industry green development model is built with market-extended LCA. • Complex interactions among the indicators are internalized in the evaluation. • A spatial-temporal analysis is performed on global paper industry green development level. [ABSTRACT FROM AUTHOR]

As a traditional energy and carbon intensive sector, it becomes imperative for the global paper industry to realize green development. This paper aims to provide guidance for the green transformation of global paper industry by evaluating the global paper industry green development level in a systematic and dynamic way. This paper employs text-mining method to develop paper industry green development evaluation indicator system and adopts market-extended LCA model and entropy-TOPSIS method to calculate the paper industry green development level of 48 countries/regions. The results show that during 2000–2030 the paper industry green development level of developing countries improves faster than developed countries, where those of developing and developed countries increase from 0.3369 to 0.3938 and from 0.5120 to 0.5517 respectively and the gap between them narrows from 0.1751 to 0.1579. The comparative analysis reveals that the paper industries of developed and developing countries are in different green development stages. Developing countries, represented by China, improve greatly in economic and social dimensions, but decline seriously in ecological dimension due to enlarging resource consumption and environmental damages. On the contrary, developed countries tend to saturate in economic and social dimensions and have been improving fast in ecological dimension, gradually realizing the decoupling of industrial economic development and environmental damages. To further promote the global paper industry green development, developing countries should focus on promoting the coordinated development between economic growth and environment protection and developed countries should play a leading role in the global green transformation by promoting optimal allocation of resources and cross-regional communications of green technologies. • A paper industry green development indicator system is developed based on text mining. • A global paper industry green development model is built with market-extended LCA. • Complex interactions among the indicators are internalized in the evaluation. • A spatial-temporal analysis is performed on global paper industry green development level. [ABSTRACT FROM AUTHOR]

Based on the complex network of industry space, this paper investigates the industry structure optimization in Jiangxi Province in China. For the prefecture-level cities in Jiangxi Province, the location quotient is applied to obtain the specialized sub-industries. The conditional probability is used to analyze the interdependencies between industries. The complex network of industry space is presented to illustrate the relatedness among industries and analyze the space and structure of the regional overall industry. The index of transitional potential is proposed to measure the potential of a non-specialized industry which will become a specialized one in the future. The transformation difficulty index of industry is obtained to analyze the difficulty level of the transformation of the original specialized industry set to a new specialized one of a city. The results show that more industries have positive than negative interdependencies with one another in Jiangxi Province; rare industries specialize in cities at higher-level GDP, and common industries dominate the cities at lower-level GDP; it is more difficult for rarer non-specialized industries to become specialized ones, more common non-specialized industries can become specialized ones more easily, and the cities with higher GDP can transform industries more easily. By discussing the industries that have positive interdependencies with existing specialized industries, the detailed direction of industry structure optimization is analyzed. Finally, the policy implications are proposed, and these can be applied to other developing countries. • The conditional probability is used to analyze the interdependencies between industries. • The complex network of industry space is presented to illustrate the relatedness among industries. • The index of transitional potential is used to measure the potential of a non-specialized industry becoming specialized. • The transformation difficulty index is the difficulty level of the transformation of the originally specialized industry. • The industry structure can be optimized by developing the industries that have positive interdependencies with existing specialized industries. [ABSTRACT FROM AUTHOR]

The research investigates how the resumption of the China certified emission reduction (CCER) market affects Chinese manufacturers' stock market valuations from an operations and supply chain management (OSCM) perspective. The paper draws from a resource-based view to examine the moderating effects of supply chain concentration (SCC), operational slack, and operational efficiency (OE) on the relationship between the CCER market and manufacturers' stock market valuations. This paper employs the event study method to examine market responses to CCER. The results reveal that the resumption of the CCER market significantly enhances the market valuations of high-pollution manufacturers to a greater extent than those of manufacturers with lower pollution levels. Additionally, the research findings indicate that SCC and operational slack attenuate the positive effect of CCER market resumption while OE amplifies it. This paper contributes to the OSCM literature by urging businesses to focus on the roles of SCC, operational slack, and OE in the CCER framework to harness the advantages of the CCER system better. • Resumption of China certified emission reduction positively affects manufacturers. • Operations and supply chain management factors are important moderators. • Manufacturers with varying pollution levels face different institutional impacts. • The event study method is employed to examine manufacturers' market responses. [ABSTRACT FROM AUTHOR]

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market. [ABSTRACT FROM AUTHOR]

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time. [ABSTRACT FROM AUTHOR]

As China's economy continues to grow rapidly, the importance of green and sustainable development is increasingly prominent. In this context, enterprises have undergone significant changes in all aspects of the value creation process. Based on the data of Chinese a-share listed companies from 2011 to 2021, this paper links the digital transformation of enterprises with the green technology innovation of enterprises. The focuses on the relationship between the enterprises' digital transformation and the enterprises' green innovation and its Local-neighborhood effect. Research shows that the digital transformation of enterprises will promote the enterprise green technology innovation, and this relationship will have a Local-neighborhood effect in different regions, and this spatial spillover effect is mainly achieved through the transfer of high-tech industries between regions. The results of heterogeneity test show that the effect of green technology innovation is the strongest in Beijing-Tianjin-Hebei economic circle, and the weakest in Pearl River Delta economic circle. Based on the spatial econometric model, this paper provides empirical evidence for the government and the state to formulate the regional development strategy of enterprises' digital transformation and green innovation. • Spatial panel Durbin model reveals digital transformation's impact on green technology innovation and regional spillover. • The effect of digital transformation of enterprises on their own green technology innovation has a "local-neighborhood" effect. • The spatial spillover effect is mainly realized through the transfer of high-tech industries between regions. • The strongest green technology innovation effect is found in the Beijing-Tianjin-Hebei economic circle. [ABSTRACT FROM AUTHOR]

The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

As the battery recycling market in China is still in an embroynic stage, the mechanisms, channels, pricing, and efficiency of battery recycling are shrouded in opacity. Battery recycling in China is currently dominated by electric vehicle manufacturer, supplemented by other market players such as battery producers and electric vehicle retailers. Due to the lack of information, battery recycling lacks cooperation and communication, and the whole market is dealing with "Information Island." Therefore, based on information sharing and channel competition, this paper designs a Stackelberg game model with an electric vehicle manufacturer, an electric vehicle retailer, and a third-party recycler as the primary participants. This study found that effective information sharing can reduce intensified market competition and promote the transformation of market players into "collaboration state". To promote partnership in the battery recycling market, this paper proposes a revenue-sharing contract incentive for the electric vehicle retailer to share demand information about battery recycling. It finds a revenue-sharing contract based on information sharing can help companies and competitors make appropriate behavioral choices in the battery recycling market. This study demonstrates that information sharing benefits the electric vehicle manufacturer and the third-party recycler while offering no advantages to the electric vehicle retailer. The decreasing accuracy of demand signals results in the diminishing value of information sharing for the electric vehicle manufacturer and the third-party recycler. The revenue-sharing contract can incentivize the electric vehicle retailer to share information. [Display omitted] • A battery recycling mode of electric vehicles is constructed. • Effective information sharing would encourage market players to be more collaborative. • A revenue-sharing contract is designed to incentivize the electric vehicle retailer to share information. • Inaccurate information and channel competition reduce the attractiveness of information sharing. • Inter-firm information sharing is beneficial for building an efficient battery recycling system. [ABSTRACT FROM AUTHOR]

This paper studies examines how a firm's position in the global value chain affects its pollutant emissions. This paper integrates China's firm-level trade data into China's multi-regional input-output tables to provide an index to measure a firm's position in global value chains. Based on a firm-level pollution dataset (2000–2014) in China, this paper conducts an empirical analysis and find that the most-downstream firms on the global value chain have lower pollutant emission intensity. Our results also suggest that the production factor structure effect, the knowledge absorption effect and the abatement investment effect together contribute to the emission intensity gap between the most-upstream firms and the most-downstream firms. These novel results are significant in guiding firms towards reducing their emissions and promoting a green global value chain, with the ultimate goal of achieving multinational environmental protection. [Display omitted] • The most-downstream firms have lower pollutant emission intensity. • A supply chain disruption shock is used as an instrumental variable. • Factor structure, knowledge absorption and abatement investment are channels. • Effects on firms with diverse ownership and trade patterns are heterogeneous. [ABSTRACT FROM AUTHOR]

With the growing global energy demand and limited production of conventional crude oil, the extraction of unconventional heavy and extra-heavy oil is receiving unprecedented attention. China is the fourth largest producer of heavy oil. High viscosity and complex formation environments increase the difficulty of exploiting heavy oil. To alleviate the economic cost and environmental pollution pressure brought by heavy oil production, China has achieved significant advancement in the innovation of heavy oil recovery, including accelerating the transformation of thermal technology to non-thermal technology and the use of new energy technology to achieve the goal of low-carbon economy and green environmental protection. Based on the background of heavy oil in China, this paper recharacterizes the high-viscosity mechanism of heavy oil. From different aspects, including basic principles, main characteristics, applicability, limitations, and challenges, this paper comprehensively reviews existing heavy oil recovery technologies, including thermal recovery, in-situ upgrading, cold recovery, and new energy technologies. Currently, in China, the main target of heavy oil recovery is achieving the "carbon peaking and carbon neutrality" goals, which can be described as low energy consumption, low emission, and low pollution. To facilitate the low-carbon transition in the extraction of heavy/extra-heavy oil and attain environmental sustainability goals, it is essential to increase the proportion of non-thermal factors in thermal recovery technologies, develop efficient in-situ catalysts, adopt clean cold extraction techniques, and advance the development of new energy technologies. [ABSTRACT FROM AUTHOR]

Compared to cast-in-situ construction, prefabricated construction reduces the generation of construction waste; however, construction waste cannot be entirely eliminated through prefabrication and can be generated at each stage of its life cycle. This paper generates a system dynamics model, comprising two causal-loop diagrams and one stock-flow diagram, to simulate the effectiveness of policies on decarbonizing prefabricated concrete building waste. The model can predict the annual and total construction waste, along with the associated carbon emissions, under different policy scenarios in China by 2030. This paper focuses on prefabricated concrete buildings in China, with specific attention to the wastage of concrete, bricks, mortar, and metal. In single-factor scenario simulations, increasing landfilling fee is the most effective policy for waste decarbonization, potentially reducing carbon emissions by 31%. Increasing prefabrication rate and enhancing regulation degree at the design phase of prefabrication can directly reduce prefabricated building waste generation, subsequently resulting in an 18% and 7% carbon emissions reduction. Multi-factor scenario simulations demonstrate that the simultaneous application of all four policies is most effective, achieving a 44% reduction in carbon emissions. This research establishes a theoretical framework for prefabricated concrete building waste management and provides the Chinese government with practical insights on optimizing policies for waste decarbonization in the dynamic landscape of prefabrication. • This paper develops the prefabricated building waste generation and disposal systems. • The model predicts the construction waste and associated carbon emissions by 2030. • Raising landfill fee is an effective policy for decarbonizing construction waste by 31.06%. • Prefabrication rate reaching 100% can reduce 18.04% of carbon emissions from waste. • Applying all policies is most effective, achieving a 44.34% carbon emissions reduction. [ABSTRACT FROM AUTHOR]

In the domain of global socio-economic dynamics, the pivotal role of food production (FP) cannot be overstated. It constitutes the bedrock of fundamental human necessities while being inexorably linked with critical aspects such as economic expansion, societal equilibrium, and geopolitical interplays. The multifaceted nature of FP appears through its direct impact on macroeconomic indicators and its latent influence on international relations and policy-making. This critical nexus underscores the imperative for a comprehensive understanding and strategic management of food production processes to sustain and enhance global socio-economic stability and growth. Investigating the nexus between green finance (GF) and FP is pivotal for ensuring national security and promoting sustainable economic development. This research thus paper examines GF's impact on FP in China as outlined in the 2017 green finance policies. It employs the difference-in-differences (DID) method and analyzes a balanced panel dataset comprising 282 cities in China from 2009 to 2020. The results show that GF has an inhibitory effect on FP, and this remains valid after a range of reliability checks, including parallel trend assessments and placebo tests. Further findings of transmission mechanism analysis indicate that non-agricultural factors and financial exclusion are critical for GF's inhibitory effect on FP. Heterogeneity analyses show that GF's inhibitory effect on FP is more pronounced in the main food-producing areas, the main food-marketing areas, and cities with better infrastructure. The empirical results of this paper comprehensively illustrate GF's impact on FP and how it contributes to the achievement of food security objectives. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

Resilience is an important aim of climate adaptation in urban development planning. Since 2015, sponge city (SC) pilot construction in China has highlighted the importance of urban water systems, but has paid insufficient attention to extreme and long-term climate change risks. This paper aims to provide a new method for evaluating the urban water system climate resilience (UWSCR), classify China cities in combination with climate hazards, conduct case studies on SC pilots, and propose ways to improve the UWSCR in SCs. Firstly, Taking heavy rain and drought as hazards, an evaluation index system of UWSCR is established, including engineering resilience (ENR), ecological resilience (ECR) and evolutionary resilience (EVR). A combination model based on the analytic hierarchy process and entropy weight method is used to measure the UWSCR of 27 SC pilot cities and 253 non-pilot cities at or above the prefecture level in China. Secondly, coordinating hazard index and UWSCR index, cities are divided into four types. The results show that the ENR and EVR of SC pilots exhibited an increasing trend from 2010 to 2020 and were greater than those of non-pilots. Among the four types, Resilient Cities and Low Risk Cities with high climate resilience (CR) were mainly distributed in the eastern and central regions of China. High Risk Cities and Vulnerable Cities with low CR were mostly distributed in the central and western regions. Among the cities of high CR, SC pilots accounted for 63.0% of the pilot samples, while non-pilots only accounted for 26.5% of the non-pilot samples. Finally, this paper analyzes the typical cases of SC pilots, and puts forward some suggestions, such as considering the UWSCR systematically and holistically, improving the ECR of UWSCR, making medium-term to long-term adaptation planning and providing differentiated policy support. The originality of this work is to identify four types of cities from the perspective of UWSCR and provide reference for improving the CR of SCs. [Display omitted] • Against climate change, security risks of urban water systems have intensified. • Sponge city pilot projects in China highlighted the importance of urban water system. • The climate resilience of urban water system in China was evaluated and classified. • The proportion of high-resilience cities in the sponge city pilot sample is high. • The evaluation of urban water system climate resilience can guide sponge city constructions. [ABSTRACT FROM AUTHOR]

The relationship between government and enterprise has attracted extensive attention. Under the increasingly severe requirement of sustainable development, there is still a lack of research on the economic effect of political acuity from the perspective of enterprise's positive party value creation. We built a dictionary of political acuity and demonstrated reliability through machine learning algorithms such as K-Nearest Neighbor (KNN). Then, based on the annual reports of China's listed companies from 2013 to 2020, the relationship between political acuity, ESG atmosphere, and corporate performance is empirically discussed. Examination through the formal institutional perspective of informal institutionalization shows that the positive acuity of the Communist Party of China(CPC) and the democratic parties(DPC) will improve corporate performance, while the negative acuity will have an early warning effect on performance. Furthermore, the stronger ESG atmosphere synergized this relationship. The CPC's negative acuity has supervision effect on non-state-owned companies, and this supervisory effect still exists in the ESG atmosphere. However, whether the moderating effect is considered or not, the positive political acuity of state-owned enterprises doesn't significantly improve performance. We found that the CPC's embedding in corporate governance will lead to both economic and political benefits, and have thresholds. We constructed a political acuity dictionary and introduced text analysis and nonparametric estimation method in this paper, which provided a basis and reference for future research on political acuity at the enterprise level. Our findings also enrich the institutional theory and the embeddedness theory. Meanwhile, our research broadened the boundary of political parties, ESG, and enterprises, the integration of ideological values and formal institutions, and had practical implications for the green transformation of firms and the precise positioning of political party functions. • We build a dictionary of political acuity for the first time. This dictionary provides a new measurement perspective for the study of corporate political sentiment and sustainable development. • From the perspective of positive value creation, we examine the impact path of a company's party acuity, ESG atmosphere, and financial performance. This paper helps to expand the governance boundaries of ideology and formal institutions, political concepts, and corporate behavior. • We explored the accounting consequences of political acuity for different types of firms, and measured the economic and political effects thresholds. • We introduced machine learning, python crawler, text analysis, and nonparametric estimation methods into this paper, which helped to expand the technical approach to corporate governance and green transformation. [ABSTRACT FROM AUTHOR]

Carbon trading is widely used as a carbon finance instrument to promote green transformation. This paper focuses on carbon trading's impact on the bond spread for high-carbon enterprises. Using variation in carbon trading access due to pilot policies in China, we find that bonds issued by high-carbon enterprises participating in the carbon trading pilots have lower spreads. Furthermore, the results suggest that carbon trading reduces bond spreads by encouraging high-carbon enterprises to improve their environmental performance. Another channel is the information asymmetry between bondholders and enterprises. More active carbon markets provide investors with more information about enterprises' motivations for reducing carbon emissions, leading to a greater reduction in bond spreads. In addition, the impact of carbon trading is more significant in local SOEs, high-innovation enterprises, and longer-term bonds. Our research helps us understand the importance of carbon trading in achieving a low-carbon economy. [ABSTRACT FROM AUTHOR]

Environmental protection and sustainable development are now profoundly ingrained in the public consciousness in the modern era of the global green revolution. First, this paper uses an evolutionary game model to theoretically analyse the impact of environmental regulation and green finance on enterprises by dissecting the game behaviour between local governments, banks and enterprises. Then, we incorporate entrepreneurial spirit into the model to determine whether entrepreneurial spirit accelerates the achievement of green sustainability objectives. Environmental regulation and green finance are found to contribute substantially to the innovation of green technologies by local enterprises, while entrepreneurial spirit accelerates this process. Finally, we empirically test the findings based on a prefecture-level panel dataset from 2007 to 2020. We find that green finance has a positive impact in Central and Western cities of China, while Eastern cities are in greater need for government environmental regulation controls. For entrepreneurial spirit, the incentive effect of entrepreneurial spirit on the level of green technology innovation is more significant in Central and Western regions of China. This paper enriches the field of research on entrepreneurial spirit and regional green sustainable development by providing empirical evidence for the evaluation of environmental regulatory and green financing policy impacts. [ABSTRACT FROM AUTHOR]

The mitigation of climate change requires that agricultural development would proceed in accordance with green and sustainable practices. This implies that economic performance should be improved while minimizing the impact on the environment. One way to assess the underlying costs of sustainable agricultural development, and to model the relationship between the environment and the economy in general, is to use the carbon shadow price (CSP). In this paper, we utilize a by-production model within a non-parametric framework to estimate the agricultural CSP from 1997 to 2020 in 31 provinces of China. The patterns of the agricultural CSP are unveiled through kernel density analysis and convergence tests. Additionally, this paper constructs a comprehensive agricultural informatization indicator using the entropy method. We then empirically investigate its influence on agricultural CSP. The results reveal that China's agricultural CSP has demonstrated consistent growth over the past two decades which suggests serious improvement in environmental performance. Furthermore, the CSP show cross-province variation, albeit with conditional β convergence. Our empirical findings indicate that agricultural information technology contributes to an increase in CSP, thereby indicating its positive role on the green growth of agriculture in China. [ABSTRACT FROM AUTHOR]

With the increasing severity of global warming, the goal of carbon sequestration and emission reduction in various industries has become an important responsibility for sustainable development. As an important part of carbon sink, the improvement of net carbon efficiency in the plantations industry will make a great contribution to the realization of the "double carbon" target in agriculture. However, there are large spatial and temporal differences in the net carbon efficiency of the plantations sector in various provinces, and there is still a large scope for increasing carbon sink and reducing carbon emission. In this paper, we have analyzed spatial and temporal trends in the net carbon sink efficiency of the plantations' production processes in 31 provinces in China (excluding Hong Kong, Macao and Taiwan) from 2000 to 2020. On this basis, we have analyzed the influencing factors of net carbon efficiency in the plantations' production process through an extended Kaya constant equation. Through the geographically and temporally weighted regression model, we explore the spatio-temporal heterogeneity of the influencing factors, and further analyze the driving and restraining factors of net carbon efficiency in plantations' production process, in order to propose appropriate countermeasures. The study shows that: (1) The net carbon efficiency of plantations sector in China increase between 2000 and 2020, and the development of net carbon efficiency is uneven across regions. (2) The geographic center of net carbon efficiency of China's plantations industry shift from the northeast to the southwest and is consistent with the migration direction of agricultural inputs. (3) The influencing factors of net carbon efficiency in the plantations sector show large regional differences, and can be specifically divided into four major types of zones. In this regard, this paper provides more appropriate countermeasure suggestions for increasing carbon sink and reducing carbon emission in each zone, with a view to achieving the goal of double carbon in agriculture with practical significance. • Net carbon efficiency is a new objective indicator to measure the carbon sink level of cropland. • Cultivated land carbon sink capacity is closely related to agricultural inputs. • Regional differences in the factors influencing net carbon efficiency. [ABSTRACT FROM AUTHOR]

China has committed to peak carbon emissions by 2030 and become carbon neutral by 2060. Hebei, as a major energy province in China, has more challenging carbon peaking and carbon neutrality targets. Based on the extended STIRPAT model, this paper evaluates the time to peak carbon and post-peak carbon trend changes in Hebei province under different scenarios from 2020 to 2060. Based on the support vector machine model with particle swarm optimization, this paper evaluates the carbon neutral pathways under different scenarios in Hebei province from 2020 to 2060 in terms of forests, croplands, wetlands, grasslands and Carbon Capture, Utilization and Storage (CCUS). The results show that a monotonically flat trend without inflection point, a single-inflection-point plateau-period trend and a multi-inflection-point inverted U-wave trend occur after carbon peaks. Hebei Province can achieve carbon neutrality by 2060 under the policy scenario and the enhanced scenario, and the enhanced scenario is better. Comparing with existing studies, the innovation of this paper is to design different trends of changes after carbon peak, combining carbon absorption and carbon emission, and providing more diversified development paths for carbon neutral strategies through a more integrated perspective and multi-scenario comprehensive analysis. Therefore, In the future, Hebei Province should establish a platform for carbon emissions monitoring and carbon footprint assessment, and set up an early warning and assessment mechanism for carbon peaking, so as to prevent a rebound trend in carbon emissions after peaking. At the same time, it should introduce and develop carbon capture and conversion technologies and promote the low-carbon transformation of the industrial and energy sectors, thereby forcing the early realization of peak carbon. Pay attention to the role of forestry resources in carbon neutrality, increase carbon sink resources such as forests and wetlands, promote the development of CCUS, and enhance the implementation of the policy. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the water-energy-food nexus (WEFN) system, focusing on its interlinked structure and dynamic characteristics. This paper proposes a dynamic series-loop data envelopment analysis (DEA) model with a directional distance function for measuring WEFN efficiency in China. A spatial Durbin model is constructed to investigate the impact of extreme temperatures on WEFN efficiency. The results are as follows. (1) Compared with the black-box DEA model and series network DEA model, a series-loop network DEA model can reflect the "networking effect" and "looping effect" in the WEFN system. (2) The WEFN system performs well with an average series-loop efficiency of 0.875, but the efficiency of the water subsystem scores low and needs improvement. (3) At the national level, extremely high and low temperatures have no significant effect on the WEFN efficiency in local or adjacent provinces, whereas average temperature promotes the WEFN efficiency in local provinces but hinders it in neighboring provinces. In the regions with a low temperature difference, average temperature has no impact on the WEFN efficiency of both the province itself and its neighboring provinces. Overall, this study provides valuable insights into the complex interlinkages within the WEFN system and highlights the importance of considering these relationships when improving the system efficiency. [ABSTRACT FROM AUTHOR]

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain. [Display omitted] • Energy consumption is divided into six parts based on the intermediate destination. • Fifteen factors is decomposed based on index decomposition analysis. • The contributions of the structure, proportion, and efficiency changes is considered. • This paper shows a way of identifying the composition of energy consumption. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality, China must grasp the intricate factors driving coal market, as it addresses coal consumption and reduces the carbon footprint. While previous studies have identified certain factors, they have largely overlooked the impact of the overall industry sector on the coal market. In this paper, a hybrid causal machine learning model was proposed that combine with least absolute shrinkage and selection operator (LASSO), multiple linear regression (MLR), peter and clark momentary conditional independence (PCMCI) and interpretable machine learning. The approach employed a complex network perspective to identify 10 key factors in China's coal market from three dimensions: industrial sector, foreign trade and energy market. The hierarchical pattern of these factors is classified into two levels: direct factors, primarily encompassing crude oil futures, exchange rates and architectural decoration, and intermediary factors, mainly including the chemical industry and building material. Interestingly, this paper reaffirms the roles of crude oil futures and exchange rates in the coal market, as identified in prior studies. Additionally, it uncovers new influential factors primarily driven by the chemical industry and architectural decoration, previously unexplored. Furthermore, this paper found a significant contemporaneous relationship between crude oil futures and coal price index (0.151) as well as coal consumption (0.102), and the strong relationship between coal consumption and the Indonesian Rupiah to Chinese Yuan exchange rate (0.063) was also confirmed. Among the emerging dominant factors, the coal price index and the chemical industry (0.145), along with coal consumption and building materials (0.250), also exhibit a strong contemporaneous relationship. These findings highlight the importance of considering the entire industry sector while analyzing the coal market and promoting a cleaner transformation of the industry in China, and the paradigm presented not only applicable to the coal market, but can be extended to other industries. [Display omitted] • Better grasping key factors in China's coal market critical to carbon neutrality. • A comprehensive study encompassing industrial sector, foreign trade and energy market. • A novel hybrid machine learning models and industry-wide features are introduced. • Identification and categorization of direct and intermediary factors. • Findings offer a new paradigm for decision-making, driving cleaner production reforms. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of China's environmental regulations, with a focus on the green credit policy, on green innovation in pursuit of the "carbon peak and carbon neutrality" goal. Given the implementation of the 2012 Green Credit Guidelines, this paper employs the Difference-in-Difference Method (DID) and conducts a quasi-natural experiment to empirically examine the interactions among the central government, local governments, and high-pollution and high-emission enterprises by exploring the impact of green credit policies on green innovation of these enterprises. Relying on 4503 samples of 237 high-pollution and high-emission enterprises listed in the main board in China from 2003 to 2021, this study reveals that the green credit policy can improve green innovation of these enterprises. Specifically, this study explores the detailed mechanism through which the green credit policy enables these enterprises to increase their market concentration and then the monopoly profits, which in turn promotes their green innovative productivity. In addition, this paper also reveals how local governments' political and financial incentives may serve as positive and negative moderating factors, respectively, within the impact mechanism. This study has valuable implications for how to effectively formulate and implement environmental regulations by balancing the interests of the central government, local governments and enterprises. [ABSTRACT FROM AUTHOR]

It is a controversial question of whether environmental regulation can achieve the double dividend of improving environmental quality and increasing employment and income. This paper takes Chinese migrant population as the main research object and uses the Chinese Household Income Projects data (CHIPS) and the emission data of major pollutants at the prefecture-level city jointly. Based on the classic Mincer wage equation, we investigate the impact and mechanisms of environmental regulation on the income of Chinese migrant population. The results show that: (1) The intensity of environmental regulation significantly improves the income level of the migrant population, and for every 1% increase in the intensity of environmental regulation, the wage income of the mobile population will rise by 4.96%. (2) The documented positive effect of environmental regulation is more significant in non-public firms, the eastern regions and high-skilled workers. Conversely, for the female and elderly group, 1% increase in environmental regulations will restrain their income growth by 4.95% and 0.37%, respectively. (3) The influencing mechanisms include both the direct effect of labor demand and the indirect effects of firm performance and technological innovation. (4) we find that 1% increase in environmental regulations respectively raise the income of the low- and moderate-income migrant population by 10.834% and 3.579%, while having no significant impact on the high-income group, thus reducing the income gap of the migrant population. The findings of this paper provide theoretical support and practical evidence for achieving a coordinated development of ecological environmental protection and income improvement for socially disadvantaged groups in China. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

Carbon trading market is a central policy tool for promoting cleaner enterprises' energy consumption and investment behaviors. It is crucial for enterprises to establish a green and low-carbon development model by clarifying carbon trading impact mechanism on enterprise behaviors and the interactive relationship between entities in the carbon market. China's contract-based carbon market is still in the development stage, the impact of the mainstream allowance mechanism and government regulation on enterprises' energy behaviors has yet to be determined. In view of this, this paper describes mathematical mechanisms of the influence of carbon prices and carbon quotas on enterprises' energy consumption and investment, and the evolutionary game model of government-enterprise behaviors is constructed, the evolution process and appropriate strategies of enterprises' energy behaviors in the development of the carbon market are discussed. This paper uses real data of China carbon market to simulate the model, it is found that strategies such as relaxation of carbon quotas and carbon subsidies dynamization are needed during the initial development of the market. In the mature stage of the carbon market, strategies are needed to increase the proportion of government carbon revenue and reduce its regulatory cost. The higher the initial willingness of government-enterprise, the better the effect of strategies and the constraints of public willingness. The model and results have theoretical implications for enterprises' energy transformation and government regulation, and provide a reference for the development of China's carbon market. • Constructed benefit-cost analytical framework for enterprises' energy behaviors. • Described mechanisms of carbon trading affecting enterprises' energy behaviors. • Constructed government-enterprise behaviors model by the evolutionary game theory. • Found new conclusions of enterprises' energy behaviors under the carbon market. [ABSTRACT FROM AUTHOR]

Digital transformation has the potential to significantly impact household energy consumption (HEC) by promoting the adoption of energy-efficient technologies. This paper utilizes the time-varying Difference-in-Differences (DID) model to evaluate the causal effect of digital transformation on HEC within China's households. We find that HEC is positively impacted by digital transformation, as embodied by the "Broadband China" strategy; and the results remain reliable when many robustness tests are taken into account. Specifically, digital transformation has the potential to indirectly increase HEC by consumption upgrade and internet development mechanisms. The heterogeneity analysis reveals that the impact of digital transformation on HEC varies significantly across different household and regional. High-income, low skilled, rural areas, as well as households in areas with higher levels of development and marketization, are more likely to be affected by the digital revolution in production and consumption, which will inevitably lead to an increase in HEC. These insights are crucial for developing effective strategies to reduce HEC and promote sustainable development. [Display omitted] • Assesses digital transformation's impact on China's household energy use. • Identifies significant increase in energy consumption due to digitalization. • Examines mechanisms of how digital transformation affects energy use. • Provides insights for harmonizing digital and green transformations. [ABSTRACT FROM AUTHOR]

Among the existing modes of "Internet plus tree-planting" in China, the government-led mode is represented by "Internet plus national voluntary tree planting", and the enterprise-led mode is represented by "Ant Forest". Now, the public enthusiasm for participating in both modes has declined. We investigated the factors influencing public participation in "Internet plus tree-planting" activities of the two modes, with a view to providing better suggestions for solving the dilemma of public participation. This paper examines the factors that influence public participation in "Internet plus tree-planting" of these two different modes. We combine the theory of planned behavior (TPB) with Knowledge-Attitude-Practice (KAP). Structural Equation Modeling (SEM) is used to explore the formation of public intention and behavior practice for the two modes and compares the differences between them. Results show that subjective norm has the greatest effect on public participation in national voluntary tree planting but has no effect on public participation in Ant Forest. On the contrary, the total effect of outcome efficacy on public participation behavior has the least weight in national voluntary tree planting, while the conclusion is the opposite for Ant Forest. This paper contributes to researchers' understanding of the two participation behaviors. In addition, suggestions on promoting integrated forms of the two modes were proposed for policymakers: (1) Keep channels of communication open and attract emerging forces. (2) Popularize environmental knowledge and improve environmental protection literacy. (3) Enrich interactive content and improve incentive policies. (4) Create an integration platform and foster two-way linkage between government and enterprises. • Combined KAP and TPB to construct the conceptual model of two "Internet plus tree-planting" modes. • Explored the different influencing factors of public participation between two modes. • In the government-led mode, subjective norm and outcome efficacy both had mediating effect on intention, and the total effect of subjective norm was greatest. • In the enterprise-led mode, outcome efficacy had a mediating effect on intention, while subjective norm had neither any direct nor indirect effect. [ABSTRACT FROM AUTHOR]

To promote the realization of the "dual carbon target", the implementation of rooftop distributed photovoltaic (RDPV) projects in the whole county will usher in large-scale development in China. However, comprehensive investigations into the economic benefits of RDPV projects in the whole county are still rare. This paper thus aims to evaluate the economic benefits of RDPV projects when considering multiple factors. The net present value (NPV), internal rate of return (IRR) and payback period (PBP) were used to analyze the financial feasibility of RDPV projects. The particle swarm optimization (PSO) algorithm was adopted to optimize the economic benefits of RDPV projects by calculating the different installation ratios of different building types, including industrial and commercial buildings, government buildings, public buildings, and residential buildings. A case study verified the feasibility of the optimization model of the economic benefits of RDPV projects in the whole county in China. The results revealed that the self-use ratio of power generation greatly impacted the economic benefits of RDPV projects and a single RDPV project became economically feasible when the self-use ratio of power generation was greater than 87.753%. When considering the limitations of the self-use ratio of power generation, installed capacity, and grid connection mode of different building types, compared to the single RDPV project, the economic benefits of RDPV projects on a county scale were not financially feasible based on the optimization model, which had an NPV of about −10.35 million yuan. Industrial and commercial buildings had the highest proportion of RDPV systems installed among the four building types, while residential buildings had the lowest proportion. The current results demonstrate the economic benefits of RDPV projects in the whole county, which will promote the development of RDPV projects and reduce carbon emissions in China. Therefore, this study is expected to provide investors of RDPV with recommendations for both decision-making and making scientific plans for the government. • This paper aims to evaluate economic benefits of RDPV projects in a whole county when considering multiple factors. • Self-use ratio of power generation greatly effects RDPV projects' economic benefits. • Maximizing the use of roofs can improve RDPV projects' large-scale effects. • Installing RDPV on industrial and commercial building has good economic benefits. • Investing in RDPV systems on residential buildings needs for caution. [ABSTRACT FROM AUTHOR]

The support of digital technology is urgently needed to promote low-carbon development and green economic and social transformation and to realize the goal of "carbon neutrality" early. This paper utilizes the "Golden Tax III" project for the digital transformation of China's tax collection and administration and empirically confirms that digital tax collection and administration significantly reduces carbon emissions based on the panel data of 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2010 to 2020, i.e., digital technology plays a positive role in carbon emission reduction. The mechanism test shows that digital technology indirectly affects carbon emissions through industrial structure, technological innovation, and tax structure. Heterogeneity analysis shows that digital technology has a significant inhibitory effect on carbon emissions in the northern region, non-Yangtze River region, provincial administrative areas with heavy local government debt, and a large proportion of tax revenue from the secondary industry and tax revenue from state-owned enterprises. Under the requirement of high-quality and green development of the economy, this paper is of great theoretical and practical significance for using digital technology to ensure the governance functions of tax, help low-carbon development, and reach the goal of "carbon neutrality" at an early date. • This study assesses the impact of digital technology on carbon emissions from the government's perspective. • This study enriches the mechanism by which digital technology affects carbon emissions. • This study provides suggestions to promote the digitization of government affairs and carbon emission reduction. [ABSTRACT FROM AUTHOR]

Under the goal of "carbon peaking" and "carbon neutrality" in China, it is significant to explore whether emission reduction commitments contribute to carbon risk premium. Therefore, based on a sample of Chinese listed companies that publicly disclosed questionnaires in the CDP from 2009 to 2021, this paper empirically examines the relationship between emission reduction commitments and carbon risk premium and their formation mechanism. The results show that emission reduction commitments can effectively hedge the carbon risk premium, which pass several robustness tests. Specifically, the more comprehensive and ambitious the commitment is, the greater the suppression of the carbon risk premium. The mechanism test shows that disclosing emission reduction commitment information can reduce carbon risk by improving investor confidence and attracting political resources. Differences in enterprise emission levels, regional environmental regulation intensity and green financial development result in different risk-hedging effects of emission reduction commitments. Moreover, the greater the substantial pressure from stakeholders, the greater the tendency for firms to set up emission reduction commitments. Finally, the heterogeneity analysis reveals that confidence level of commitments has a significant impact on risk premium. Specifically, enterprises with SBTi validation, higher CDP ratings and higher target completion are more helpful in curbing carbon risk premium. Our findings contribute to corporate commitment to climate change and complement the carbon risk premium hypothesis. [Display omitted] The possible highlights of this study are as follows. • With the help of forward-looking information on emission reduction commitments, we re-examine the conclusion of the carbon risk premium and find that emission reduction commitments can effectively hedge the risk premium, enriching and developing the carbon risk premium hypothesis • This paper brings commitment levels and carbon emission levels into a unified analytical framework to study the correspondence between emission reduction commitments made under pressure to reduce emissions and the carbon risk premium, providing micro evidence for solving the mystery of the carbon risk premium. • Measure emission reduction commitment indicators from various angles, making the data on emission reduction commitments more objective and scientific. [ABSTRACT FROM AUTHOR]

Cities have emerged as critical and challenging places for promoting the green and low-carbon transformation of the economy and society in the context of the "dual carbon" targets. Meanwhile, fiscal policy is also an indispensable component of the public policy framework supporting the green and low-carbon transformation. This paper uses the pilot project of "Comprehensive Demonstration Cities for Energy Conservation and Emission Reduction Fiscal Policies" ("ECER-CD" policy) implemented in China since 2011 as a quasi-natural experiment. This research theoretically analyzes and empirically tests the energy conservation and emission reduction effects of fiscal policies supporting green and low-carbon transformation utilizing methodologies such as time-varying DID and multiple periods and multiple groups of DID. The study finds that the pilot cities effectively reverse the behavioural bias of local governments and promote energy conservation and emission reduction efforts. This conclusion holds after undergoing robustness tests such as parallel trend analysis and heterogeneity treatment effects. The effectiveness of the pilot policy is attributed to the upgrade of industrial structure, improvement of energy efficiency, and the effects of green technology innovation. However, cities' geographic location and resource dependency can interfere with the effective implementation of policies. This paper also further explores the environmental dividends and economic consequences of the policy and finds that the pilot cities have certain environmental dividends and do not significantly restrain economic and social development. This study broadens the research perspective on fiscal policies in energy conservation and emission reduction and clarifies the pathways through which fiscal policies promoting green, low-carbon transformation drive energy conservation and emission reduction in cities. Moreover, it also improves the use of the Difference-in-Differences method in evaluating policy effects in scenarios such as the overlapping implementation of pilot policies and policy withdrawal. Finally, this paper proposes policy recommendations to provide more empirical evidence for China to accelerate the green and low-carbon transformation of the economy and society. [ABSTRACT FROM AUTHOR]

Existing studies on emission accounting in global value chains (GVCs) have not focused on the relational nature of GVC activities manifested as the interaction of intermediate goods between enterprises. This paper proposes a relational GVC emissions accounting framework based on an inter-country input-output (ICIO) model distinguishing between domestic-owned enterprises (DOEs) and foreign-invested enterprises (FIEs), which is classified as trade-based, investment-based and hybrid-based emissions. Using AMNE-ICIO tables from 2005 to 2016, this paper traces emissions and carbon intensity in relational GVC activities at the global, economic, and industry levels. We find the following: (1) Investment-based emissions accounted for about 94.15% of the increase in relational GVC emissions, of which more than half comes from the interactive activities between DOEs and FIEs. (2) For the United States, high-income economies, and high-tech manufacturing, investment-based emissions were higher than trade-based emissions, and the reverse was true for upper-middle and lower-middle income economies and China. (3) Although relational GVC carbon intensity was high, the trend of decline was significant. (4) Compared to trade activities, investment-based activities were less carbon intensity in China and upper-middle income economies due to the interaction of production between FIEs and DOEs. The findings add new insights into the coordination of global emissions reductions and the allocation of emissions responsibility. • Emphasize the interdependence between enterprises in cross-border production. • Combine GVC linkages with carbon emissions and carbon intensity. • Investment-based emissions are the main factor of relational GVC emissions growth. • The carbon intensity of relational GVC decreased significantly. • Developing countries can access green spillover through investment-based GVC links. [ABSTRACT FROM AUTHOR]

The contradiction between industrial agglomeration and energy constrains the development of cities. Government competition can affect the flow of factors for industrial agglomeration. Based on the data of 274 cities in China from 2005 to 2020, this paper constructs improved non-radial directional distance function (NDDF) and dynamic spatial durbin model (SDM). The paper further uses a threshold effect model and a mediating effects model to examines the impact and spillover effects of local government competition (LGC), new energy industry clustering and their integration on ecological total factor energy efficiency (ETFEE) from the perspective of the role of knowledge. To solve the problem of endogeneity, this paper uses the presence or absence of highway entrances and exits in cities in 2004 as an instrumental variable. The study found that new energy industry agglomeration and LGC can promote ETFEE in the short term, but it will inhibit ETFEE in the long term. New energy industry agglomeration affects local ETFEE through two types of mediating effects: narrowing knowledge breadth and widening knowledge distance between cities. At the same time, new energy industry agglomeration and LGC has a significant spatial spillover effect on urban ETFEE in the short term. Areas with high urban ETFEE can promote the improvement of ETFEE in neighbouring areas. However, from the perspective of regional differences, it finds that the concentration of new energy industries and LGC have different effects on ETFEE. There is a significant impact in the eastern region, but not in the central and western regions. Finally, the paper gives constructive advice for the adaptation of the knowledge structure and the improvement of energy efficiency through the national and governmental levels. • First examination of local government competition, new energy industry agglomeration and energy efficiency from a perspective on the role of knowledge. • The relationships among new energy industry agglomeration, local government competition and energy efficiency are all inverted U-shape. • The level of government competition above 2.097 will inhibit the increase of ETFEE. [ABSTRACT FROM AUTHOR]

Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [ABSTRACT FROM AUTHOR]

The difficulty of the sulfur dioxide (SO 2) pollution control task has deepened step by step in recent years, while the marginal cost of environmental protection in China has also been higher. Therefore, it is necessary to study the main impact factors of China's industrial SO 2 pollution. Based on China's Industrial Environmental Statistics Database from 2004 to 2013, this paper extends the Index Decomposition Analysis (IDA) with firm entry and exit to conduct a micro-level decomposition study. The industrial SO 2 emission in China is decomposed into scale effect, composition effect, firm growth effect, and firm selection effect. Also, we use the Dagum Gini coefficient to analyze the spatial differences of firm growth effect and selection effect between the eastern, central, and western regions in China. This paper finds that the main contribution of industrial SO 2 emission control in China is the firm growth effect, which was −103% between 2004 and 2013. And the second largest contribution term is the firm selection effect, which is −18%, while the composition effect is −15%. In terms of temporal patterns, the size of the firm growth effect shrinks as time grows, and the composition effect changes from negative to positive after 2009. For spatial difference analysis, the Dagum Gini coefficient of technique effect and firm growth effect between different regions have become smaller and smaller over time, while the spatial differences of firm selection effect between regions remain unchanged. [ABSTRACT FROM AUTHOR]

This paper aims to study the significance of China's Industrial Structure Rationalization (ISR) in reducing carbon emissions. To this end, this study uses panel data from 30 Chinese province from 2000 to 2019 to measure the Industrial Structure Rationalization of each province by calculating the Thiel index, base on which a nonlinear autoregressive lagged distribution (NARDL-PMG) model is established to explore the impacts of industrial structure rationalization on carbon emissions. The results show that: (1) There still exist significant regional differences between Eastern and Central-Western regions in industrial structure as illustrated by the ISR index. The industrial structure in Eastern region is more rational; (2) In the long run, the carbon emission in China will drop when the industrial structure becomes more rational; (3) However, in the short run, the carbon emission in China will increase when the industrial structure becomes more rational. Besides the findings above, the major difference between this paper and the extant researches is that this paper pays attention to the relationship between industrial structure rationalization and environmental quality by investigating the asymmetric influences of industrial structure rationalization index's positive and negative shocks on carbon emission, which lays solid foundations for government aiming to set more detailed policies. • Focus on the impact of rationalization of industrial structure on CO2 emissions. • Focused on the linear relationship between industrial structure and carbon emissions. • Attempts to analyze three regional pairs from the East, Central and West regions. • Provide a reference for developing countries to achieve sustainable development. [ABSTRACT FROM AUTHOR]

This paper has observed pivotal changes in innovative frameworks at the intersection of the digital revolution and sustainable initiatives. Within Industry 4.0, the fusion between intelligent manufacturing and green innovation has become a central driver for sustainability. Integrating these two pivotal forces to harness their collective efficiency is crucial. However, current literature offers limited insights into their combined impact, and the direction of their evolving trends still needs to be defined. Considering this, the research elucidates the core concepts and traits of digital-green fusion (DGF) within regional advanced manufacturing (RAMIDGF). Moreover, this research proposes a double-helix fusion mechanism model. Utilizing data from the advanced manufacturing sectors in six key Chinese urban clusters, this paper adopts a comprehensive analytical toolkit that includes the Coupling Modified Model, Kernel Density Estimation, Markov Chain algorithms, and the Dagum Gini Coefficient for trend analysis. The findings indicate that between 2009 and 2021, RAMIDGF exhibited a trend of initial growth followed by a decline, showing a strong correlation with per capita GDP (RGDP). The principal source of variation in RAMIDGF stems from differences between urban agglomerations, with the Yangtze River Delta and the Beijing-Tianjin-Hebei clusters exhibiting transitional behavior and a 'ladder-type trend' in RAMIDGF. As metropolitan clusters become more economically integrated, the probability of transitional behavior in RAMIDGF heightens. However, achieving elevated leapfrog development proves challenging, with an inherent risk of decline. Spatial spillover implications, agglomeration characteristics, and club convergence tendencies mark the RAMIDGF process. These findings expand upon new paradigms for regional sustainable development, offering novel avenues for realizing high-quality growth. [Display omitted] • The conceptual connotation and characteristics of RAMIDGF are proposed. • The double helix fusion model is constructed to analysis the fusion mechanism. • The RAMIDGF level of urban agglomerations is evaluated. • The trend and characteristics of RAMIDGF in urban agglomerations are demonstrated. [ABSTRACT FROM AUTHOR]

The urgent need to address climate change, driven by the increasing concentrations of greenhouse gases, has made it imperative for the world to focus on enhancing carbon emission mitigation and carbon utilization. Consequently, carbon-negative technologies have garnered significant attention, such as Carbon Capture, Utilization, and Storage (CCUS). In order to achieve the circular utilization of carbon and improve the economic viability of conventional carbon-negative technologies, this paper proposes the innovative concept of Carbon Capture, Circular Utilization, and Sequestration (CCCUS). This multifunctional carbon-negative technology couples underground biomethanation of carbon dioxide and hydrogen-rich industrial by-product gases with geothermal energy production. CCCUS also offers several additional functions, such as renewable natural gas storage, natural gas displacement, and carbon dioxide geological sequestration. Furthermore, a preliminary assessment of the techno-economic feasibility of CCCUS is undertaken. A numerical case study, conducted using the XGS gas storage in the Sichuan Basin, China, unveiled that over a 425-day period, the conversion ratio of carbon dioxide to methane in the working gas can achieve 98.9%. Moreover, the net present value (NPV) of CCCUS over a 30-year period is estimated to be approximately 4.67 × 108 CNY under the base scenario. These findings, along with field monitoring and laboratory experiments, preliminarily present evidence supporting the potential techno-economic feasibility of CCCUS. This paper offers a fresh perspective on the development of the circular carbon economy. [ABSTRACT FROM AUTHOR]

Under the background of high-quality development, the e-commerce industry needs to seek more comprehensive development, and the logistics industry should not only maintain higher efficiency, but also meet the requirements of green and low-carbon transportation. This study aims to investigate the link between the logistic density and the proportion of e-commerce transaction scale. There are few studies on how logistics density affects the development of e-commerce quantitatively. In order to solve this gap, this study establishes an impact model of high-quality growth of e-commerce, and based on Solow production model, constructs a calculation method of logistics density, and analyzes the impact mechanism of logistics density on the scale ratio of e-commerce transactions. Using the bidirectional fixed effect model, this paper makes an empirical analysis of the panel data of 31 provinces consisting of 248 observations and autonomous regions in China from 2014 to 2020. The results show that logistic density has a positive and significant impact on the growth of e-commerce, thus promoting the optimization of economic structure. The acceleration of logistics density decreases the expansion of the ecommerce. Finally, there are obvious regional differences between the development of e-commerce and logistics infrastructure in China. Finally, based on the empirical result, this paper puts forward recommendations for government and companies. [ABSTRACT FROM AUTHOR]

Manufacturing is both an essential pillar of China's economy and a significant source of environmental pollution, and there is an urgent need for the manufacturing industry to use green innovation to accelerate its goal of sustainable development. From the intellectual property protection (IPP) perspective, this paper uses Chinese manufacturing and its 26 sub-sectors as a sample from 2003 to 2018, examining the impact of two-way foreign direct investment (FDI) coordinated development in manufacturing green innovation capability. The empirical results suggest that two-way FDI coordinated development enhances manufacturing green innovation, and this finding still holds after endogeneity and robustness tests. Under the influence of IPP, the relationship between two-way FDI coordinated development and manufacturing green innovation capability is nonlinear. Under the influence of macroscopic, mesoscopic, and microscopic dimensions IPP, the effect of two-way FDI coordinated development on manufacturing green innovation capability is U, Steps, and N shaped respectively. This paper's findings can provide empirical evidence for the government to formulate industrial development strategies and for manufacturing enterprises to improve their green innovation capabilities to achieve sustainable development. [ABSTRACT FROM AUTHOR]

Plastic pollution is currently one of the environmental pollution issues of global concern, and microplastics (plastics with a size ≤ 5 mm) have also attracted much attention due to their large specific area, higher pollutant adsorption capacity, and higher migration performance. As the population increases and more chemical products are manufactured, the level of microplastic pollution continues to increase. Microplastics discharged from sewage treatment systems will enter the ocean and rivers, so estuary ecosystems are vital to controlling marine microplastics. As the estuary of China's most significant river entering the East China Sea, the Changjiang River Estuary has become one of the typical areas for microplastic research in estuaries. This paper systematically reviews the sources, hazards, and impacts of microplastics in the Changjiang Estuary. Firstly, the research on microplastics in the Changjiang Estuary was analyzed, and we found that the bioaccumulation, removal, and interaction with microorganisms of microplastics have become a new research hotspot. At the same time, the current abundance of microplastics in the surface water of the Changjiang River Estuary is 1.71 ± 3.12 MP/L, which is lower than that of the Changjiang River area and the river farmland in Shanghai, but still higher than the 0.22 ± 0.27 MP/L in the area adjacent to the East China Sea. Microplastics smaller than 1000 μm accounted for 68% of the total, while transparent, white, blue, and black were the primary colors of microplastics in the Changjiang Estuary. In addition, polyethylene and polypropylene are the primary materials of microplastics in the central Changjiang River Estuary and related water bodies, and fibers are the main shape of microplastics. Industry, agriculture, domestic sewage, and air deposition may be the primary sources of microplastics in the Changjiang Estuary. The numerous lakes and water conservancy facilities in the Changjiang River Basin can reduce the number of microplastics reaching the Changjiang Estuary to a certain extent. At the same time, seasonal rainfall and ocean currents will affect the abundance of microplastics in the surface waters of the Changjiang River Estuary. Finally, this paper puts forward relevant suggestions based on the actual situation of the Changjiang River Estuary and the developed microplastic treatment technologies. This article will provide a structured reference for future research on microplastics in the Changjiang Estuary and assist in assessing microplastics' impact on the ecological environment. The Fate of Microplastic Pollution in the Changjiang River Estuary. [Display omitted] • Microplastics' sources, hazards, and impacts in the Yangtze Estuary were reviewed. • Bioaccumulation, removal, and interaction with microorganisms are the hotspots. • The microplastics in the water of the Changjiang mainly exists in the form of fibers. • Source control and establishment of MPs barriers will help to solve MPs pollution. [ABSTRACT FROM AUTHOR]

Existing studies have primarily examined the impact of farmland fragmentation on agricultural production from the perspective of crop yield per unit of land. However, they generally overlook the effects of farmland fragmentation on the productivity of both labor and capital. Drawing upon micro-level data from rice, wheat, and maize farmers across 14 provinces in China for the years 2019 and 2020, this paper measures the effects of farmland fragmentation on the labor productivity, land productivity, and capital productivity of these three different crops, within the same temporal and spatial dimensions. We utilize the internal instrumental variable method (KV-IV) to address potential endogeneity issues in the model. The research findings indicate: (1) Farmland fragmentation hampers the use of machinery, resulting in a significant decrease in the labor productivity of farmers. On average, for every 1% increase in the level of farmland fragmentation, labor productivity for rice, wheat, and maize farmers drops by 21.3%, 19.9%, and 21.9% respectively. (2) Farmland fragmentation has significantly increased the land productivity of rice. With every 1% increase in farmland fragmentation, the land productivity of rice farmers increases on average by 3.4%. However, the impact of farmland fragmentation on the land productivity of wheat and maize is not significant. (3) The capital productivity of rice, influenced by farmland fragmentation, mirrors the changes seen in land productivity. Rice farmers with higher levels of farmland fragmentation exhibit greater capital productivity. On average, with every 1% increase in the level of farmland fragmentation, the capital productivity of rice farmers increases by 6.7%. Conversely, farmland fragmentation has a significant negative impact on the capital productivity of both wheat and maize farmers. On average, for every 1% increase in farmland fragmentation, the capital productivity of wheat and maize farmers drops by 9.1% and 7.0% respectively. Based on the empirical results of this paper, we propose corresponding policy recommendations. In the conclusion, we delve into the underlying causes of farmland fragmentation in China and ascertain that, to address the issue of farmland fragmentation in the country, the government must take a leading role. The significance of this paper lies in analyzing the varying degrees and directions of the impact of farmland fragmentation on different crops, providing a basis for the government to implement differentiated farmland fragmentation management policies in different crop-producing regions in the future. [ABSTRACT FROM AUTHOR]

Rational assessment of the regional sustainable development level is an important prerequisite for formulating development strategies and achieving the UN Sustainable Development Goals (SDGs). Currently, little literature provides nuanced understanding for national sustainable development from the perspective of county sustainability. Such studies can provide more accurate regional heterogeneity characteristics and policy decision support. Thus, this paper applies the entropy weight method and Chinese county data from 2000 to 2017 to reassess the sustainability of Chinese counties. The results show that China's county sustainability (Sustainable Development Index, SDI) is slowly improving. County sustainability performance is poorer in ethnic minority agglomerations, poor counties, old revolutionary counties, and central and western regions, better in county-level cities and eastern regions. The decomposition results show that economic sustainability and environmental sustainability are the main supporters of SDI growth, and livelihood sustainability is in urgent need of improvement. The regional inequality of SDI in Chinese counties gradually decreases, and inter-group differences are the main contributors to regional inequality in SDI. This paper provides support for China to develop county-level sustainable development measures and practice the SDGs. The findings could be useful for policymakers. [Display omitted] • Based on the entropy weight method and the Chinese county data, the sustainable development levels of 1533 counties in China from 2000 to 2017 are estimated. • Analyses of spatial-temporal dynamic heterogeneity, Linear decomposition, and Dagum decomposition are conducted to study the features of sustainable development in Chinese counties. •The influence of natural features and national concern preferences on county sustainable development are discussed. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for assessing contaminant emissions, including the dominant contaminant reduction effects, recessive contaminant reduction effects, and contaminant reduction efficiency indexes. The system is used to evaluate the linkage changes in contaminant emissions reduction across different sectors, the ease of regulation, and the returns on emissions reduction efforts. The study's results demonstrate that firstly, COVID-19 initially reduced contaminant emissions across almost all sectors, although this effect waned over time. Secondly, applying conventional regulatory measures to sectors with high reductions in contaminant emissions may not yield the expected results. Thirdly, while some sectors may be more difficult to regulate, they may offer a higher return on investment in terms of emissions reduction. Based on the above results, the paper advocates for timely measures to consolidate environmental gains, prevent rebounding of contaminant emissions, prioritize supervision and management of easily regulated sectors, prioritize upgrading of cleaner production in sectors with high reduction efficiency indices, and increase investment in environmental technology. • Created a new comprehensive system for assessing contaminant emission changes. • Quantified the unique linkage changes of contaminant emissions during COVID-19. • Developed new models to reveal contaminants' regulatory difficulty and return. • The innovative reduction effects of four key contaminant indicators were measured. • Applied WHEM firstly in research across multiple areas of contaminant emissions. [ABSTRACT FROM AUTHOR]

The paper focuses on the spatial distribution and influencing factors of China's newborn digital enterprises (NDEs). Based on digital enterprises in 287 cities across China from 2005 to 2020, the paper uses the negative binomial model coupled with Geographic Information System (GIS) maps methods. The results reveal that NDEs are concentrated in the east of the country and are more sparsely distributed in the center and west. The number of NDEs in different Chinese regions varies significantly with location choice featuring agglomeration characteristics. The empirical analysis confirms that new location factors are of growing importance to the location choice of NDEs while the impact of traditional factors is waning. We also report that NDEs prefer developed eastern cities, while location factors are less important in the center and west. [ABSTRACT FROM AUTHOR]

With the increasing demand for chemical products in China, how to manage the risks associated with hazardous freight transportation has attracted arising attentions from both government and industry practitioners. From the perspective of carrier companies, transportation modal choice and routing for hazardous freight transportation need to be carefully designed, which motivates current research. This paper aims to optimize the service mode and routing for hazardous freight transportation in a rail-truck network with bi-objectives optimization approach. Several practical concerns are addressed, including the coordination of rail transportation which has fixed train running plan and road transportation which is flexible and mobile, and the consideration of alternative path when the expressway is temporally blocked for hazardous freight due to the traffic restriction measurements on inter-city road segments. Furthermore, the time-varying transportation risks and costs associated with accidents of hazardous freight transportation are investigated. The Max-Min Ant-colony Algorithm (MMAS) algorithm is adopted for solution. For model validations and conclusion generalization, this paper implements a real-life case study in Beijing-Tianjin-Hebei region in China with the objective of minimizing transportation risks and costs. The solutions show that traffic restrictions have an adverse impact on the carrier and alternative paths are able to help the carriers further optimize their routing strategy. A careful consideration and appropriate processing of differences in operational characteristics of two transportation modes, is able to provide a better and more realistic understanding of rail-truck hazardous freight transportation problem. [ABSTRACT FROM AUTHOR]