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The trade conflict between China and the United States will have a complex and far-reaching impact on international trade patterns. The significant change in regional trade will not only impact the global transportation industry, but also have an important impact on international shipping carbon emissions. However, there are few studies that explore this issue. Based on the global multi-regional computable general equilibrium model, this study sets up three scenarios according to the development trend of the Sino–US trade conflict to measure the changes in global shipping carbon emissions under different trade conflict scenarios. The results show that, compared with the no trade conflict scenario, the tariff increase will reduce international trade by 0.329%–0.345%, but global international shipping carbon emissions will rebound by 0.25%–0.33%. The main reason for this result is that the trade conflict caused notable trade diversion. The direct trade between China and the United States became indirect trade that must be undertaken by "third countries", which will increase the global freight mileage by 0.23%–0.34%. This paper finds that Sino–US trade conflict will not only hinder international trade volume, but also increase the transportation distance of international trade of goods, which will have a negative impact on the control of carbon emissions from international shipping. This study will help to clarify the impact of the Sino–US trade conflict on international maritime carbon emissions and provide a new perspective to comprehensively identify the economic and environmental impacts of trade conflict. [Display omitted] • The newest carbon emission factors of different ship types in IMO 2020 are used. • A global multi-regional CGE model is used to simulate Sino-US trade war impacts. • The impacts on global trade patterns and shipping carbon emissions are analyzed. • Tariff hikes will increase global shipping carbon emissions through trade diversion. [ABSTRACT FROM AUTHOR]

Promoting environmental regulation and the reduction of carbon emissions is of great significance for sustainable development. China plans to achieve the following two major goals by 2030: to experience the peak of carbon dioxide emissions, and to be at the forefront of innovative countries in the world. The realization of these goals not only depends on the environmental regulation policy regarding carbon emissions, but also on the development imbalance among Chinese provinces. The Porter hypothesis theory holds that environmental regulation can produce the Porter effect and promote enterprise innovation; but few studies focused on the causal relationship between environmental regulation and the regional Porter effect. Under this framework, this paper uses propensity score matching - difference in difference (PSM-DID) to construct a quasi-experiment on China's carbon emissions trading pilot policy. Dynamic panel data models are used to double test whether environmental regulation can stimulate the regional Porter effect. The empirical results show that: (1) Under the premise of multiple robustness tests (whether quasi-experimental or dynamic panel data models), no evidence was found in support that environmental regulation can stimulate the regional Porter effect. (2) The reason why the regional Porter effect does not been stimulated is that the pollution heaven effect, induced by the carbon emissions trading pilot policy, counteracts the Porter effect to a certain extent. (3) There is no non-linear relationship between environmental regulation and the regional Porter effect. (4) The analysis of spatial heterogeneity shows that environmental regulation cannot stimulate the Porter effect in the eastern, central, and western regions of China. This suggests that it is very important to establish appropriate provincial environmental regulation objectives and to conduct appropriate environmental regulation in combination with local resource endowments. This study provides quantitative evidence toward an in-depth understanding of the relationship between environmental regulation and the regional Porter effect as represented by the pilot policy of carbon emissions trading. A certain degree of reference is provided for the practice of environmental regulation in other developing countries. • Environmental regulation cannot stimulate the regional Porter effect. • The pollution heaven effect explains why regional Porter effect is not stimulated. • No nonlinearity exists between environmental regulation and regional Porter effect. • Environmental regulation cannot stimulate the Porter effect in all Chinese regions. • The government should appropriately strengthen environmental regulation. [ABSTRACT FROM AUTHOR]

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene, and the methodology developed in this paper provides a research paradigm for enterprise survival analysis. [ABSTRACT FROM AUTHOR]

The development of ports is essential to the competitiveness of cities. Harbor cities in China have put much effort into expanding the business of their ports, ignoring the overlapping of hinterland markets with other ports. Excess resources expended by ports may intensify regional environmental issues, such as carbon emissions. Motivated by the emissions reduction targets proposed by the International Maritime Organization and the urgent need for the development of green and sustainable ports, the Chinese government is trying to draft a carbon constraint policy to prevent the disorderly development of ports. It is thus necessary to study competition strategies under the context of carbon emission constraints. In this paper, the improved Hotelling model is used to describe the competitive relationship between ports. Then, three competition scenarios of the seaports under no carbon emissions policy, a carbon emission cap policy, and a carbon cap-and-trade policy are examined to analyze the effects of carbon emissions policy on port service prices, demand for goods, maximum profits, and social welfare. The results show that (1) from the perspective of port managers, only when government-defined carbon emission limits are lower than the critical value, the carbon cap policy is binding on port competition; when it comes to the cap-and-trade policy, government-defined carbon emission limits only affect profits. (2) From the government's perspective, the cap-and-trade policy maximizes social welfare more than the other policies regardless of the value of government-defined carbon emission limits. [ABSTRACT FROM AUTHOR]

• Energy sustainability includes security, efficiency, cleanness, low carbon emissions. • Chinese energy sustainability has obvious spatial heterogeneity. • Cleanness and efficiency perform better than security, low carbon emissions in China. • Energy supply, consumption structure and pollution emissions are key impact factors. • Targeted measures are proposed to promote Chinese energy sustainability. Energy sustainability is one of the largest challenges for sustainable development. Energy sustainability in China varies by regions because of the differences in energy resources, technology and economic levels. Previous studies have assessed national energy sustainability, few to none have looked internally on a regional or provincial scale. This paper defined energy sustainability from the four dimensions of security, efficiency, cleanness and low carbon emissions, established an index system and evaluated the energy sustainability levels of 30 provinces in China by the Improved Entropy Weight-Cloud Model (IEW-CM). The results show that: (1) General energy sustainability in Chinese provinces is deficient, and energy cleanness and efficiency perform better than security and low carbon emissions. (2) Chinese energy sustainability has obvious spatial heterogeneity and within the same province, the level is unbalanced in four dimensions. The characteristics of energy sustainability exhibit significant differences in different regions. (3) Energy security and low carbon emissions play a crucial role in achieving energy sustainability. Natural gas deliverability, energy consumption elasticity coefficient, CO 2 emission per unit of energy and share of fossil energy consumption are the primary factors affecting energy sustainability differences among provinces. [ABSTRACT FROM AUTHOR]

The fundamental requirement of carbon abatement is to complete the carbon abatement task set, at the lowest cost. In this paper, the non-radial distance function method is used to calculate the marginal carbon abatement costs of each unit, and the statistical method is used to estimate the carbon abatement efficiency of each unit. Based on the sum of dynamic carbon abatement costs in each province, the carbon abatement task allocation is optimized, and the data before and after optimization is compared and analyzed. The main conclusions are as follows: (1) The function of carbon abatement efficiency can be estimated using the marginal cost of carbon abatement, by which, the carbon abatement cost of each province can be calculated. (2) The correlation between carbon emissions and carbon abatement efficiency of each province is weak, so the overall carbon abatement efficiency of China is low. (3) Optimizing the allocation of provincial carbon abatement tasks can significantly reduce the total carbon abatement cost and improve the carbon abatement efficiency. This study has significant theoretical and practical value for improving China's overall carbon abatement efficiency, and establishing the provincial initial carbon emission quota. [Display omitted] • We estimate carbon abatement efficiency of China and its provinces. • We improve the method for carbon abatement efficiency using the dynamic data. • We fill a gap in research on carbon abatement efficiency based task allocation method. [ABSTRACT FROM AUTHOR]

Sustainable development is crucial to rural regions in China. Although previous studies have investigated the carbon emissions and costs of single-family and rural houses, relatively few studies have compared the characteristics of different structural forms and verified the reliability of their results. In light of sustainable development of rural building structures in China, this study assessed process-based and subproject-based emissions, as well as the costs associated with the construction of a rural residential house, with consideration of three common structural forms. Among the schemes designed using a reinforced concrete framed (CF), block masonry (BM), and prefabricated masonry (PM) structure, the BM scheme afforded the lowest emissions and costs, whereas the CF scheme led to the highest emissions and costs. To identify the key processes for measuring embodied emissions and costs, contribution indicators were proposed and an uncertainty analysis was conducted according to variations in emission factors. The results indicate that the comparison and findings are reliable with respect to current technical levels. A further sensitivity analysis revealed that the CF and PM schemes had the potential to be low-carbon alternatives if emissions from steel and cement production can be significantly reduced in a future scenario. This paper proposes relevant suggestions from the perspectives of material selection, structural member design, and structural form decisions and its findings can help improve the sustainability of rural houses. • Comparison on the emissions and costs of different structures for a rural house. • Detailed emission and cost analyses from both process and subproject perspectives. • Contribution analysis finds the key processes for the embodied emissions and costs. • Sensitivity analysis indicates the potential of framed and prefabricated structures. [ABSTRACT FROM AUTHOR]

This paper investigates the time lag effect of carbon emissions using a novel temporal input-output approach (TIO). The sectoral average production time of various sectors is evaluated based on the turnover rate data of 3394 listed companies in China's A-share market. Then, a polynomial function is developed to fit the indirect carbon emissions and time lag at each consumption stage in the industrial chains. Finally, an empirical study is carried out to forecast the time lag effect of carbon emissions with final demand fluctuations based on the latest 2017 input-output table for China. The results show that indirect carbon emissions caused by sectoral economic activities decline stage by stage in the industrial chains, and the decline rate and time lag intensity vary significantly among sectors. The logistics industry has the highest spread efficiency for indirect carbon emissions in the industrial chains, and the construction industry is the first industry affected by the indirect carbon emissions caused by the manufacturing industry. The power/thermal industry's impact on China's carbon emissions is the fastest, but its duration is the shortest, while the impact of the manufacturing and mining industries are more stable and durable. However, considering the fluctuations in final demand, the manufacturing industry has replaced the power/thermal industry as the most important industry affecting the time lag effect of China's carbon emissions. This study provides important theoretical and practical value for indirect carbon emissions forecasting. • A novel temporal input-output approach is investigated. • Indirect carbon emissions and time lags in industrial chains are calculated. • A polynomial function is developed to fit the time lag functions of carbon emissions. • An empirical study is conducted based on the 2017 I–O table for China. [ABSTRACT FROM AUTHOR]

The accumulation of waste biomass and plastic and the inefficient combustion of low-quality coal is a major cause of energy loss and air pollution in rural China. This paper reports for the first time an experimental and multi-perspective analysis on an industrial scale co-pyrolysis of waste biomass and plastic. The results show that the resulting biochar product had a low calorific value of ≥21.0 MJ/kg, which meets the national standard for commercial solid fuel (GB/T 31862–2015). The calorific value of the produced gas was over 10 MJ/m3 and met the requirements of the national standard for residential heating and cooking (GB/T 13612–2006). The products of the co-pyrolysis can replace 1100 t of standard coal per annum, and reduce carbon dioxide emissions, sulfur dioxide emissions, smoke, and other contaminants at the rate of 1720 t, 5–6 t, and 320 kg, respectively, as well as reducing smoke pollution. In addition, through the recycling of 750 t of plastic waste per annum for use in the co-pyrolysis, this method can reduce the white pollution of 50–66.7 km2 of farmland in the local area. The present study can guide the optimization of such systems and lead to adequate flexibility in the co-pyrolysis of different wastes to generate economic and socioenvironmental benefits. Image 1 • Industrial co-pyrolysis of biomass, waste agricultural film, and low-quality coal. • Systematic analysis of energy flow and economic and socioenvironmental benefits. • Biochar and fuel gas calorific values reached 21.0 MJ/kg and 10 MJ/m3, respectively. • The project annually reduces 1100 t of coal combustion and 1720 t of CO 2 emissions. • Recycling agricultural film reduces white pollution on 50–66.7 km2 land annually. [ABSTRACT FROM AUTHOR]

An integrated approach is proposed in this paper to incorporate the group heterogeneity into the framework of natural disposability and managerial disposability, which allows evaluating the CO 2 emission performance under different mitigation strategies (scale-oriented strategy and innovation-oriented strategy). It is able to deal with the intertemporal incomparability when adopting a global technology. In the empirical study, we categorized China's 27 manufacturing sectors into five groups and evaluated their emission performance under different mitigation strategies during 2003–2016. According to the empirical results, we found that there is still much potential (about 70%–80%) for improving the CO 2 emission performance considering the entire manufacturing industry. From the perspective of different groups, the global technologies/innovators group shows the highest emission efficiency, while the energy-/resource-intensive commodities' group performed the worst. Besides, different sectors showed significant technology heterogeneity. Finally, the desirable CO 2 mitigation strategies varied with the different groups. For the labor-intensive tradables' group, we found that the desirable strategy is to decrease the input of energy and other production factors, i.e. scale-oriented strategy. For the regional processing group, the innovation-oriented strategy is more efficient. • Modified global meta-frontier non-radial DDF approach is proposed. • Group heterogeneity is incorporated in natural/managerial disposability framework. • CO 2 emissions performance for China's manufacturing sectors is measured. • Global technologies/innovators group performs best, while energy-/resource-intensive commodities group performs worst. • Desirable mitigation strategies varies with respect to technology heterogeneity of different groups. [ABSTRACT FROM AUTHOR]

Global climate change caused by human activities has posed a huge threat to the environmental governance and sustainability of human-being. Fortunately, the carbon emission trading scheme, a powerful tool for reducing carbon emissions, has been established in many countries to slow catastrophic climate change thus promoting human well-being. The nexuses between energy and European carbon markets have been extensively studied due to the grim reality of global climate change. However, no literature to date has examined the multi-scale associations between China's energy and regional CET markets. This paper aims to fill this gap by utilizing a proposed wavelet-based multi-scale investigation framework to explore the information spillovers and dynamic dependence over different time horizons. The original energy and carbon return series are decomposed by the MODWT method. The TVP-VAR-based connectedness and the DECO-FIGARCH model are then built at each selected wavelet component. The wavelet coherence method is also applied to depict the time-frequency dependence. The empirical results demonstrate that the information spillovers at multivariate time-scales are heterogeneous. Generally, GDEA and the crude oil markets act as the information spillover net-transmitters at the raw data level and all wavelet scales. The dynamic spillovers present significant time-varying features and a similar evaluation trajectory. Besides, we offer strong evidence of a low dynamic equicorrelation between China's energy and regional CET markets, suggesting the portfolio diversification benefits. Furthermore, the information spillover and dependence are proved to be more significant at longer time horizons. Finally, the energy-carbon portfolios offer diversification opportunities, and the risk reduction effectiveness varies with wavelet scales. Moreover, the optimal-weighted portfolio exhibits the best risk management performance. Image 1 • Wavelet-based multi-scale spillover and dependence analysis framework is proposed. • MODWT is applied to decompose the original data into various wavelet components. • TVP-VAR-based connectedness is utilized to quantify the information spillovers. • Wavelet-based-DECO-FIGARCH model is employed to examine the dynamic dependence. • Wavelet coherence method is used to depict the time-frequency dependence structure. [ABSTRACT FROM AUTHOR]

• Build the load uncertainty model of incorporating the annual load growth rate. • Integrate the grid integration level into multi-objective optimization. • Propose the multi-objective two-stage adaptive robust planning method. • Implement the planning method into an industrial park. • Analyze the influence of quantitative parameters on the evaluation indicator. An integrated energy system (IES) is considered as an effective approach to reduce carbon emissions, lower energy supply costs and increase system flexibility. As numerous energy conversion and storage devices with various features have been developed, determining their types and capacity size and optimizing the synergic action of all selected energy devices under load uncertainty have become challenging issues during the planning and designing of a new IES. To address these issues, a load uncertainty model is constructed by integrating the regional equivalent standard building-integrated load prediction method, the robust uncertainty set method and the fuzzy C-means clustering algorithm. Based on this uncertainty model, this paper proposes a planning method that incorporates the fuzzy multi-objective decision and two-stage adaptive robust optimization methods for handling these challenging issues. The multi-objective decision method enables comprehensive optimization based on the system's economic performance, carbon emissions and grid integration level. The two-stage adaptive robust optimization method aims to optimize the single-objective problem transformed by the multi-objective decision method. The planning method is implemented in an industrial park in the Baoding City of China. The influence of load budget uncertainty, annual load growth rate and energy purchase price is then investigated. The results show that the load budget uncertainty and annual load growth rate only affect the optimal capacity size of the selected devices, whereas the energy purchase price influences both the optimal device types and their capacity size. The optimal capacity size remains constant when the load budget uncertainty exceeds 3. This study provides a methodology to select the suitable device types and determine their capacity size for IES under load uncertainty, which will benefit the design of a new IES. [ABSTRACT FROM AUTHOR]

The abatement of carbon emissions is an important way to alleviate global warming. It is therefore crucial to explore the factors behind the corporate proactivity of carbon emissions abatement (CPCEA). However, existing research mainly focused on the factors that promote national, provincial, city-level, and industrial carbon emissions reduction, without looking at the micro-company level. In response to the research gap, this study therefore explored the drivers of CPCEA using panel data from 736 listed companies in China. This study used the logit regression to examine the effects of pre-identified factors on the CPCEA. The results indicated that political ties, corporate scale, industry category, and regional disparity have significant impacts on the CPCEA. Contrary to expectations, environmental regulation was not found to affect CPCEA. Based on the findings of this study, five policy implications to improve CPCEA in China were proposed. This paper presented empirical evidence to promote CPCEA. Additionally, the results have important practical implications and priority guide for carbon emissions reduction in China and around the world. Image 1 F09F • Political ties are negatively related to the corporate proactivity of carbon emissions abatement (CPCEA). F09F • Corporate scale is positively related to the CPCEA. F09F • Companies in the higher carbon industries and companies in the more developed region have stronger CPCEA. F09F • Environmental regulation has no significant impact on promoting CPCEA. [ABSTRACT FROM AUTHOR]

In China's low-carbon development progress, industrial sectors play a crucial role in reducing CO 2 emissions and maintaining economic growth targets. Because the Chinese government is promoting industrial structure transformation for "high-quality economic development," the dominant CO 2 emitters have changed in recent years. It is important to identify the trend evolution of industrial CO 2 emitters and implement targeted measures and policies for CO 2 emission control at the sector level. In this paper, a hybrid input–output model is adopted to assess the situation and trend of sectoral CO 2 emissions from 2002 to 2017 in China. Demand-side drivers of sectorial embodied CO 2 emissions are discussed. Based on the embodied CO 2 emission decomposition of 27 sectors, a CO 2 emission evaluation index is defined for CO 2 emission ranking and key sectors for embodied CO 2 emissions are identified. For the key CO 2 emitters, we provide targeted policy suggestions for CO 2 emission mitigation from the perspective of trend evolution and index ranking. The results indicate the following. (1) 2015 is a turning point for China's CO 2 emission mitigation progress, because the previously increasing embodied CO 2 begins to decrease from 2015 to 2017. (2) Although the Construction sector stimulates a large amount of CO 2 emissions at present, the major driving forces of CO 2 emissions have gradually changed from infrastructure construction to the increasing demand of consumer goods, service industry, and export of technology-intensive products. (3) The CO 2 emission intensity of the Electric Power and Heat Supply sector has increased, and promotion of renewable energy consumption is urgently needed. (4) From the demand-driven aspect, tertiary industry has greater CO 2 emission potential than heavy industry. • A turning point of embodied carbon dioxide of China appears in 2015. • Most sectors have reached the peak of embodied carbon dioxide by 2015. • Construction, Other Service Industry and Manufacture of Machinery are the top three embodied carbon dioxide emitters. • Electric Power and Heat Supply sector should be concerned for high embodied carbon dioxide intensity. • Sectors with great carbon dioxide emission potential change towards tertiary and high-tech industries. [ABSTRACT FROM AUTHOR]

Copper-based mixed wastes include copper-containing wastes from the chemical, metallurgy, copper processing, printing, electronic component manufacturing, and other industries. Copper-based mixed wastes have high humidity and a significantly lower copper content than general waste copper, pose serious environmental hazards, and are very difficult to recover. The recycling of copper-based mixed wastes has important economic and environmental significance. Chinese companies have started extracting copper and other metals from copper-based waste mixtures and have achieved a high economic efficiency. This paper assesses the environmental impacts of copper-based mixed waste recycling and suggests possible optimization measures for the production process with respect to environmental impacts. The life cycle assessment (LCA) method is adopted to evaluate the effects of different production processes and to compare the environmental impacts of copper recovery from copper-based mixed waste recycling to the environmental impacts of primary and secondary copper production. Sensitivity analysis is conducted to estimate and evaluate the effects of possible production process optimization measures on environmental impacts. The results show the following: 1) The environmental impact of the copper-based mixed waste recovery process is generally superior to that of primary copper production in China on average, while it is inferior to that of secondary copper production. For example, the fossil energy consumption in primary copper production accounts for 74.9% of the impact of this process. 2) Smelting is the production process with the greatest environmental impact throughout the life cycle of copper-based mixed waste, accounting for 64.5% of the normalized index of the total environmental impact; and electrolysis and zinc recovery reach 19.8% and 12.1% of the total normalized index, respectively. 3) The raw materials and emission components with the largest environmental impacts are the carbon emissions from coal use and carbon dioxide emissions during smelting, the use of electricity during electrolysis, and the use of heavy oil and lime as raw materials during refining; these five items account for 69.7% of the total environmental impact. Thus, efforts at process improvement should focus on the full life cycle of energy utilization in the smelting process and on energy and material depletion in the refining and electrolysis processes. Specific process improvements and simulation analyses are important directions for further research. • The impact of the copper-based mixed waste recovery process is generally superior to that of primary copper production in China on average. • Smelting is the production process with the greatest environmental impact. • Five input-output items account for 69.7% of the total environmental impact. [ABSTRACT FROM AUTHOR]

The power sector accounted for almost 40% of China's carbon emissions in 2017 and is the first industry that is included in the national carbon emission trading system, so the current paper carries out a study on allocating carbon emission allowances for the power sector. Different from previous studies, this article assigns carbon emission allowances to 30 provinces according to the principles of equality, efficiency, and sustainability rather than just using the principles of equality and efficiency. We first apply the entropy method to initially distribute emission allowances to 30 provinces. Then, a zero-sum gains data envelopment analysis (ZSG-DEA) model is used to test the efficiency of quota allocation and redistribute the carbon quotas across 30 provinces continuously until the quotas are optimally distributed. The results show that in 2030, Guangxi, Hainan and Shandong have the most emission quotas, while Heilongjiang, Jilin and Qinghai have the least emission quotas. By comparing the quota share of each province in 2017 with 2030, it is found that Hainan, Guangxi and Qinghai located in western China have small emission reduction challenges, while there are great difficulties for provinces Jiangsu, Shanxi and Shandong situated in the central and eastern China to cut down the emissions. • Multiple criteria were used to allocate carbon quotas of China's electricity sector. • We used the entropy method to initially allocate emission quotas to 30 provinces. • A ZSG-DEA model was used to redistribute the carbon quotas across 30 provinces. • Provinces located in central and eastern China have more challenges in decarbonizing. [ABSTRACT FROM AUTHOR]

Carbon emission efficiency directly determines the overall carbon emissions level. Previous studies mainly investigated carbon emission efficiency from the perspective of direct carbon emissions without considering carbon emissions associated with intermediate production and consumption, resulting in deviations in the measurement results of carbon emission efficiency. This study distinguishes the differences between direct carbon emissions and embodied carbon emissions, including intermediate production and consumption, and constructs a noncompetitive input-output model to measure the embodied carbon emissions from 28 industry sectors in China between 2005 and 2017. The super-efficiency slack-based measure model was used to analyze the carbon emission efficiency of 28 industry sectors in embodied carbon emissions and direct carbon emissions scenarios. The results are as follows. First, China's embodied carbon emissions and direct carbon emissions are generally increasing, with large differences in the total amount, growth rate, and rate of change. Second, China's overall efficiency of direct carbon emissions and embodied carbon emissions has improved. However, there was significant heterogeneity among various industry sectors. Third, in the 28 industry sectors, the change in embodied carbon emissions efficiency was generally gradual, while its average value was significantly lower than that of direct carbon emissions efficiency, indicating that indirect carbon emission efficiency is lower than direct carbon emissions efficiency. Fourth, the reduction potential of embodied carbon emissions for the 28 industry sectors was less than that of direct carbon emissions, although both have decreased, with a greater magnitude of decrease in the former. Finally, this paper proposes countermeasures to promote low-carbon development of China's industries based on improving intermediate production technology and implementing dynamic supervision. Image 1 • The input-output analysis and data envelopment analysis were combined. • The factor of trade openness was introduced when measuring embodied carbon emissions (ECEs). • The carbon emission efficiency and emission reduction potential of industries were analyzed. • China's overall efficiency of ECEs and direct carbon emissions (DCEs) improved. • The reduction potential of ECEs was less than that of DCEs, but both have decreased. [ABSTRACT FROM AUTHOR]

This paper proposed a model-based framework to optimize the energy-water-emissions nexus (EWEN) for regional electric power system (EPS) planning. The framework integrated the superiority of bi-level programming (BLP), two-stage stochastic programming (TSP), mix-integer programming (MIP), interval parameter programming (IPP) models and carbon emission trading (CET) mechanism, which could effectively deal with the planning problems in the EWEN optimization process. BLP helped decision makers make leader-follower choices when they have conflicting preferences. TSP with IPP could validly address the uncertain information expressed as interval values with unknown distribution. MIP was introduced as an efficacious tool to evaluate the capacity expansion potential in EPS. CET mechanism was applied to reinforce the CO 2 mitigation effect during the EWEN optimization process. Then the proposed framework was applied into a case study in Shanxi Province, a main thermal power base in China to optimize the EWEN. Results indicated that the power supply structure was inclined to show a low-carbon transition trend in the planning periods. Meanwhile, coupled with the strict water conservation policy, every 5% reduction in carbon emission density would lead to an average decreasing rate of 8% for SO 2 , 3% for NOx and 12% for PM. This study may help provide an effective way to deal with planning problems with conflicting and sequential goals under uncertainty and make better tradeoff strategies in EWEN optimization in EPS. • Propose a stochastic leader-follower framework for energy-water-emission nexus. • Control uncertainty by the combination of TSP and with IPP. • Interactions of CO 2 emissions and air pollution emissions are revealed. • Introduce CET mechanism to help improve the CO 2 mitigation effect. • The empirical results demonstrate the excellent performance of the framework. [ABSTRACT FROM AUTHOR]

Prefabricated buildings (PBs) have been actively promoted worldwide with the advantages of low labor costs, rapid construction and resource conservation. PBs have been proven to be an effective solution for reducing carbon emissions in the construction industry. To realize the carbon-reducing potential of PBs, this paper explores the key factors affecting carbon emissions and the influencing relationships from the perspective of the supply chain. Applying structural equation modeling, a hypothesis model composed of 'social and governmental factors', 'market factors', 'technical factors' and 'supply chain coordination factors' is evaluated. The results confirm that 'technical factors' have the strongest effects on reducing carbon emissions in prefabricated building supply chains (PBSCs), and 'supply chain coordination factors' have the weakest. The results also reveal that the indirect influence of 'market factors' is the most significant. Moreover, 'level of low-carbon design' and 'low-carbon awareness of companies' are suggested as key factors for reducing carbon emissions in PBSCs. This research provides guidance for the adoption of low-carbon measures by participants in PBSCs. Against the background of construction industrialization, consideration of the carbon emission-reducing mechanism of PBSCs can also help the government effectively formulate low-carbon strategies for the construction industry. • A model of factors influencing carbon emissions in prefabricated building supply chains (PBSCs) is developed. • The influencing relationships among the critical factors influencing carbon emissions in PBSCs are analyzed. • The influence of 'technical factors' on reducing carbon emissions of PBSCs is very significant. • Market factors have the strongest indirect effects on reducing carbon emissions in PBSCs, mainly through technical factors. [ABSTRACT FROM AUTHOR]

• Energy rebound effect leads to "green paradox". • The effect of short-term energy rebound effect is greater than the long-term. • Investment bias and factor market distortion important for policy development. • Government decentralization can suppress the energy rebound effect. Analyzing the influence of energy rebound effect on carbon emissions at different urbanization stages is of great significance to improve the effectiveness of emission reduction polices. In this paper, the energy rebound effect is classified into the short-term energy rebound effect and the long-term energy rebound effect. Based on the environmental Kuznets curve, the nonlinear programming model of urbanization, energy rebound effect and carbon emissions is established to analyzed the influence mechanism of energy rebound effect on carbon emissions at different stages of urbanization. We further discuss how to avoid the impact of energy rebound effect on carbon emission reduction. The results show that: (1) The influence of short-term energy rebound effect on carbon emissions is greater than long-term energy rebound effect, and both of them decrease with the increase of urbanization rate. (2) The "discretionary" and "hitchhiking" behavior of local governments in formulating environmental policies will lead to the "Matthew Effect" of energy rebound effect. (3) Investment bias and factor market distortion could increase energy rebound effect, and they have obvious substitution effect. (4) The decentralization of power by local governments is an effective measure to suppress energy rebound effect. The analysis above could help local governments to formulate policies, suppressing the influence of energy rebound effect and promoting carbon emission reduction. [ABSTRACT FROM AUTHOR]

Understanding the coupling mechanisms between carbon emissions and the eco-environment is essential for building sustainable economies. Based on the coupling coordination model, this paper investigates the coordination degree of carbon emissions and the eco-environment in China from 2009 to 2015. The Logarithmic Mean Divisia Index decomposition method was used to identify the key factors affecting the coordination degree. Furthermore, different scenarios were simulated to show the impact of changes in government priorities on the coordination degree. The results show that the dynamic relationship between carbon emissions and the eco-environment in China has reached a barely balanced stage, but a few provinces are still struggling with imbalances. The coordination level of carbon emissions and the eco-environment becomes the key factor affecting coordinated development, while the interaction intensity has a limited effect. Further analysis indicated that although the carbon emission evaluation value has a positive effect on promoting an increase in the coordination degree, the eco-environment evaluation value determines the trend of coordinated regional development. Moreover, the priorities of government policymakers have a substantial impact on coordinated development, but these impacts gradually diminish as the differences in evaluation values decrease. These findings contribute to the formulation of effective regional sustainable development strategies. [ABSTRACT FROM AUTHOR]

China has a large population and limited land resources. The dearth of available land poses considerable challenges for economic development and environmental protection. In order to realize maximum benefits through the optimization of land use structure, land use structure efficiency (LUSE) becomes an important planning guideline for the sustainable development of a region. This paper conducts a case study in the Middle Reaches of the Yangtze River (MRYR) and investigates the LUSE of the region considering the effect of carbon emissions from 1995 to 2018 by using the slack-based measure with undesirable output (SBM-Undesirable) model and Markov transition probability matrix with spatial effect. The results show that: (1) the average LUSE in the study area is 0.652 when the carbon emissions are not considered, compared with that of 0.569 when the carbon emissions are factored in. The comparative result indicates that carbon emissions have a great impact on the LUSE; (2) the LUSE has a rising trend during the study period (except for 2010) starting with a value of 0.463 in 1995 and ending with a value of 0.671 in 2018. Furthermore, the spatial pattern of the LUSE in the study area has been through a drastic change, with the high-value areas clustered at a single center in 1995 evolving to a multiple-center pattern in 2018; (3) High LUSE areas have a positive spillover effect on its neighboring areas, while low LUSE areas have negative spillover effects. The results can provide insights into the optimization of land use structure and the efficient utilization of land resources. This study also suggests that policy makers should optimize the industrial structure and propose policy initiatives oriented towards goals of energy-saving and emission reduction to facilitate regional planning and sustainable development. • LUSE was measured with the SBM-Undesirable model. • The effect of carbon emissions had a great impact on the LUSE. • LUSE had a rising trend in the MRYR from 1995 to 2018. • Markov transition probability matrix with spatial effect was used to reveal spatial association characteristics of LUSE. • LUSE had a significant spatial spillover effect. [ABSTRACT FROM AUTHOR]

Based on panel data from 2007 to 2016, this paper investigates the carbon abatement effect of China's pilot Emission Trading Scheme (ETS) and the influencing paths of the policy at both national and regional level by using Difference in Differences model and parallel multiple mediator model. The results reveal that the policy has reduced the total carbon emission reduction by 13.39%, with an upward abatement trend annually. Secondly, a systematic framework containing technique effect, composition effect and allocation effect is proposed to further investigate the influencing mechanisms of the achieved emission curbing effect. It reflects that the policy has not yet promoted the low-carbon innovation completely, only innovation of high-quality played an effective mediating role. Meantime, two other influencing channels of industrial upgrading and optimized resource allocation are verified. Thirdly, policy's heterogeneous impacts are detected by classifying China into three representative regions according to economic and geographical differences, where western China experiences the strongest emission abatement by 21.57%, whereas central China and eastern China is 9.83% and 6.65% respectively. Fourthly, assessments of the regional influencing mechanisms show that the mediating role of high-quality low-carbon technology innovation is only significant in the western region, while the other two regions tend to have rebound effect. Besides, the promotion of industrial upgrading is positively related to the development levels of regions, with the improvement of 18.01% in the eastern region, followed by 17.34% and 12.46% in the central and western region. Finally, the optimization of the resource allocation has not been promoted regardless of the regions. In general, although the current policy has a significant carbon reduction effect, there is still much space for potential abatement to be developed. • China's pilot ETS has significant carbon reduction effects at both national and regional level. • Mediating effects of high-quality innovation, industrial upgrading and allocation optimization are significant nationally. • Only high-quality innovation is effective in western China, while other mediating roles are not significant regionally. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the realizability of China's "13th Five-Year plan" objective for energy-saving and emission-reducing (ESER). Based on the internal logic relationship between new energy, carbon emissions, economic growth and carbon trading (NCEC), the 4D NCEC (acronyms) system is constructed with the aid of nonlinear dynamics theory. And then, the NCEC system for China is given after parameter acquisition. Taking energy intensity and the proportion of new energy as the measure indexes, a scenario study is carried out. The scenario analysis results show that, the target of decreasing energy intensity can be easily met with various constraints. While the objective of increasing the proportion of new energy is hard to achieve, and this objective depends on some thresholds. Only the certain speed of new energy can guarantee the increasing of new energy proportion, and the certain speed was found. The implementation of carbon trading can promote the process of ESER and realize the objectives during the "13th Five-Year plan". However, it's not the faster carbon trading speed, the better. The appropriate speed interval is narrow. The driving function of carbon trading can help new energy to work better in ESER system. [ABSTRACT FROM AUTHOR]

This paper constructs a spatial association network of carbon emissions from the electricity sector in Chinese provinces by using an improved gravity model and the data on carbon emissions in 2005–2016 from the electricity sector. Social network analysis and the quadratic assignment procedure were used to analyse spatial association characteristics and influential factors. It is found that the overall structure of the carbon emission spatial network in China's electricity sector was relatively stable and provinces were closely linked. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang all played a leading role in the network centre position. Spatial adjacency relationships and per capita GDP were positively correlated with carbon emissions from the electricity sector, but electricity intensity and industrial structure were negatively correlated. Urbanisation level does not affect the spatial correlation network of carbon emissions from electricity sector. Based on the results, some policy recommendations are finally made for reducing the carbon emissions from the electricity sector in China. [ABSTRACT FROM AUTHOR]

This paper provides an insight into the structure of CO 2 emissions in China through transitions in economic and environmental policies for the period of 1957–2017 by using input-output and network analysis with newly compiled environmentally-extended historical input-output tables of China. These tables, which include firm-level, industry-level, and macro-level statistical data, are the first environmentally-extended input-output tables covering the years in the early stage of the People's Republic of China. The results present the following two main findings. The first is due to the stable economic structure associated with the independent economic policy, China's emission structure has been stable for more than 60 years. Heavy industry has contributed about 80% of CO 2 emissions since the 1950s. On a second account, although the emission structure was stable, stricter environmental controls and regulations led to a decrease of the growth rate of CO 2 emissions after 2010. Against this background, as a policy choice of China, instead of carbon leakage that could break up the stable economic structure, balanced technology upgrading across all sectors induced by stricter environmental controls is a realistic way for China to achieve increased energy efficiency, emission mitigation and economic development, so that the country can keep the promise of reducing CO 2 emissions after they peak around 2030. • Newly complied historical IO tables are used to study emission structure 1957–2012. • Heavy industry is the main contributors of CO2 emission for six decades. • China's CO2 emission structure has maintained a relatively stable status for more than 60 years. • Because of the "independence" ideology, China could not adopt carbon leakage policy. • Technology upgrading is a realistic way for China to achieve emission mitigation and economic development. [ABSTRACT FROM AUTHOR]

• A joint decomposition model based on meta-frontier was proposed. • The effect of scale efficiency was captured based on variable return to scale model. • The catch up effect of carbon emission and energy usage technology was captured. Production-theoretical decomposition analysis (PDA) is widely used to explore the factors related to changes in carbon emissions. However, traditional PDA mainly focuses on scenarios of decision making units (DMUs) with constant returns to scale, and neglects technology heterogeneity among DMUs. Therefore, this paper proposes a joint approach combined PDA and index decomposition analysis (IDA) based on the variable returns to scale (VRS) and the meta-frontier model, and further decomposed the change of carbon emissions into 9 driving factors. Based on above, we investigated the effects of the nine factors on the change of carbon emissions at national, regional and provincial levels in China during the period of 2007–2016. The results indicated that the increase of carbon emissions in China have slowed down since 2012, but the overall situation is not optimistic, especially in the western region. Economic activity played a dominant role in increasing carbon emissions for each region. Carbon emission efficiency and potential carbon factor were also the two important factors related to increase carbon emissions. Conversely, energy intensity, the catch-up effect of pure technical efficiency and scale efficiency on carbon emissions played a positive role in decreasing CO 2 emissions, especially for the eastern region. However, the driving factors related to energy usage, such as the catch-up effect of energy usage scale efficiency and the pure energy usage technical efficiency had trifling and unstable effects on the change of carbon emissions. Based on the above analysis, we propose suggestions to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

• We explore whether each of the 30 Chinese provinces will peak emissions by 2030. • We combine STIRPAT models and scenario analyses to predict emissions trajectories. • 26 provinces are likely to peak emissions under at least one scenario, but 4 are not. • Peaking time and cumulative emissions are considered in seeking optimal peaking path. • Provincial results in aggregate show China may peak in 2028–2040 and 2030 is optimal. China has pledged in its Nationally Determined Contributions (NDCs) that its carbon emissions will peak no later than 2030. However, the varying carbon emissions trajectories of individual provinces make it difficult to determine whether China can fulfill this ambitious goal on time. To bring transparency and clarity to this core issue, this paper explores for the first time whether and how all the 30 Chinese provinces will peak their energy-related emissions in future by developing a set of extended STIRPAT models that investigate the driving forces behind each province and integrating the estimates into scenario analysis for peaking simulation. We observe that, despite continued economic growth, 26 provinces are highly likely to reach peak emissions under at least one scenario, whereas the remaining are not. We find it interesting that the earliest peaking scenarios may not be the best option for those provinces whose cumulative carbon emissions are estimated to be considerably large. To ensure a viable reaching of China's peak emissions, much more attention should be paid to seeking for the most appropriate emissions trajectory to peak at the subnational level with relatively low cumulative value. Provincial results in aggregate show that China may peak emissions in the period 2028–2040, with cumulative emissions ranging from 403,607 Mt CO 2 (corresponding to peaking in 2030) to 456,191 Mt CO 2 (corresponding to peaking in 2038), while 2030 would be the optimal year for the country to peak because of the lowest cumulative carbon emissions. Our findings only provide a deeper understanding of China's potential peaking paths by province, but also assist policy makers in better prediction and evaluation of possible emissions trajectory for other nations and the whole world from a bottom-up perspective, and therefore could be of global significance. [ABSTRACT FROM AUTHOR]

The aluminium industry, with its traditionally high energy consumption, high emissions and high pollution, is facing increasing pressure to reduce carbon emissions in China. Implementing effective policies and measures to promote carbon emissions reduction is of great importance. This study deploys the life cycle assessment (LCA) method to explore carbon emissions and CO 2 sources from aluminium manufacturing in China as well as a scenario analysis to evaluate the reduction potential and driving forces of carbon emissions in the industry. The evaluated scenarios include a new capacity allocation scheme, technological progress and energy structure optimization. The carbon emissions from the aluminium chain are mostly contributed by indirect emissions (more than 68%) from electricity generation. Significant regional discrepancies are observed in the total emissions and intensity, and a reasonable industry layout tends to transfer to the south and southwest areas from the perspective of the environmental pillar. Production output remains the dominant factors associated with total carbon emissions from manufacturing firms, such as the aluminium industry. Technological progress and the energy structure contribute to total reductions and carbon efficiency, although the former has a relatively limited scope for aluminium firms. This paper also shows that marginal carbon efficiency improvements are not very effective in lowering the total CO 2 emissions under the context of high output growth. [ABSTRACT FROM AUTHOR]

Urbanization results in a considerable economic disparity between urban and rural areas in developing countries, which has had a consequent significant impact on CO 2 emissions. To accommodate this directly, this paper presents a modified version of the Kaya Identity formula, U-Kaya, that includes a direct urbanization factor to determine the relationship between energy consumption intensity, population growth, urbanization rates, urban and rural GDP per capita and the volume of carbon emissions, for application in the dynamic forecasting of carbon emissions by Monte Carlo simulation. The method is demonstrated in forecasting China’s likely carbon emissions in 2020 by three different modes of urbanization policy: the government-dominant mode, market-dominant mode and a hybrid government market-dominant mode. The results indicate that a higher urbanization rate, energy carbon emission coefficient and energy intensity will lead to increased carbon emissions. Finally, three policy implications are identified, comprising the narrowing of the economic gap between urban and rural areas, adjustments to the energy structure and technical innovation, which will provide a valuable reference for developing countries in their efforts to reduce carbon emissions. [ABSTRACT FROM AUTHOR]