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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]

As one of the most vital wood-based products, the carbon footprint of wooden doors has received increasing attention in recent years. The studies on the carbon footprints of different types of wooden doors are few. In this study, the cradle-to-grave carbon footprints of three types of wooden doors produced in China were comparatively analyzed. Models used for calculating the total life-cycle carbon footprint of a wood-based composite door, a solid wood composite door, and a solid wood door were developed. Considering the biogenic carbon storage and its delayed emissions, the wood-based composite door, solid wood composite door, and solid wood door featured carbon footprints of 0.379, 0.257, and −0.277 kg CO 2 e per kg product, respectively. The solid wood door exhibited a negative carbon footprint, indicating that its carbon storage was higher than the carbon emission during the life cycle stage. The raw material acquisition (38%–66%) and the transportation stages (23%–38%) of the three types of wooden doors were the main contributors to the total carbon footprints (excluding the biogenic carbon). The product manufacturing stage released relatively small emissions (11%–24%). The waste disposal treatment at the end-of-life phase, the degradation of wood materials in landfills, and their service life displayed significant impacts on the carbon footprints of the three types of wooden doors. Fiberboard, laminated veneer lumber, sawn lumber, finger-jointed lumber, and electricity were identified as the main emission hotspots. This study provides guidelines for low-carbon development in the wooden door industry. [Display omitted] • The full life cycle carbon footprint of three types of wooden door were compared. • Solid wood door showed the best profile, followed by the solid wood composite door and wood-based composite door. • The disposal at the end of life significantly impacted the carbon footprint of wooden door. [ABSTRACT FROM AUTHOR]

Abstract Industrial green development (IGD) is a critical response to the over-consumption of natural resources and pollution caused by modern industry. Innovation-driven IGD has generated great interest in recent years. However, relatively less attention has been paid to the various aspects of IGD and the moderating role of regional factors, including the developmental stage of IGD, government-scale, and enterprise-scale. The present study was conducted to fill these research gaps using panel data across 30 provinces in China from 2005 to 2015. The empirical results show that 1) innovation does promote IGD and is most effective in low-carbon production, followed by resource reduction, economic operation, and pollution abatement; 2) there is an inverted U-shaped relationship between the regional IGD level and the role of innovation in IGD; and 3) both government-scale and enterprise-scale contribute to the innovation-driven IGD. These findings provide new insights into the impact of innovation on IGD and may shed light on future decisions related to green development. Highlights • Innovation significantly contributes to industrial green development. • IGD level shows an inverted U-shaped relation with the role of innovation in IGD. • Large-government-scale enhances innovation-driven IGD. • Large enterprises have an advantage in promoting innovation-driven IGD. [ABSTRACT FROM AUTHOR]

Abstract The huge energy intensity gaps among Chinese prefectures make the overall reduction of energy intensity extremely difficult. Energy intensity convergence (EIC), i.e., catching-up of the high energy intensity prefectures with the low ones, is imperative for narrowing these gaps. As one of the most important channels for international technology diffusion, foreign direct investment (FDI) has great impact on energy intensity and may be beneficial for the convergence. Based on pollution haven Hypothesis, however, FDI may result in energy intensity divergence. To provide an in-depth understanding of the mechanisms of FDI-induced convergence/divergence, this paper employs Markov Chain approach to examine the impact of FDI on EIC for 256 Chinese prefectures in the period from 2005 to 2014. Moreover, we particularly emphasis on the influences of local specific characteristics, such as human capital, resource endowment, and environmental regulation, on FDI-induced convergence/divergence. Our study on 256 Chinese prefectures finds that FDI helps prefectures with higher energy intensity to catch up with lower ones, which strengthens EIC. The impact of FDI on EIC varies depending on different local characteristics. Among all characteristics mentioned above, human capital and environmental regulation benefit the convergence-driving effect of FDI, while this effect is hindered by resource endowment. More comprehensive and detailed policies are recommended to address energy intensity gaps in China. Graphical abstract Image 1 Highlights • The impact of FDI on energy intensity convergence is investigated. • The Markov chain approach is employed in this paper. • The results suggest FDI strengthens energy intensity convergence in China. • Human capital and environmental regulation promote FDI-induced convergence. • FDI-induced convergence is hindered by resource endowment. [ABSTRACT FROM AUTHOR]

As the largest carbon emissions source sector, electric power industry is undoubtedly a principal part of the carbon trading market in China. Aimed at researching the impact on power industry beyond establishing the national carbon trading market, a dynamic computable general equilibrium (CGE) model embedded with carbon trading block is introduced in this paper. Subsequently, we design 8 scenarios of which illustrate corresponding industry carbon emissions baselines and free quotas ratios. The main simulation results are as follows. The implementation of carbon emissions trading would bring a certain negative impact on the overall economy. Real GDP will be reduced by about 0.08%–0.52% in 2030. Though low free quotas ratio will cause a relatively high loss of GDP, this negative impact would be eliminated in the long run. In addition, carbon emissions trading would also promote the clean production of electricity. Indeed, the electric power industry would reach carbon emissions peak around 2020. Relative to scenarios without carbon trading, carbon emissions would reduce more than 1000 million tons in 2030 when industry carbon emissions baseline declines at an annual rate of 2%. Consequently, the carbon market can achieve a more significant reduction in carbon emissions with the influencing of quotas fully auctioned scenario. [ABSTRACT FROM AUTHOR]

Embodied carbon emissions have an important role in China's "dual carbon" target and carbon reduction policy. Our study applied a multiregional input–output (MRIO) model and structural decomposition analysis (SDA) to examine the spatiotemporal evolution and driving forces of embodied carbon emissions in the specific region of Shanxi Province, an energy-rich region in China, in the empirical study using MRIO data. First, we found that Shanxi had considerable net embodied carbon outflow in interprovincial trade, with outflow increasing from 216.18 Mt in 2012 to 221.60 Mt in 2017. Second, the main driving forces of embodied carbon outflow include technological level and trade demand. We then classified 29 sectors into six categories, including mainly developed, controlled, moderately guided, moderately controlled, cost guided, and maintained to describe the relationship between the net transfer of embodied carbon emissions and value added, and formulated proposed sectoral development policies. Third, the sectors with the highest net embodied carbon outflow include electricity and heat production and supply; metal smelting and rolling processing; and petroleum processing, coking, and nuclear fuel processing. We constructed a coupled relationship model to adjust the technology and scale of interprovincial trade to reduce carbon emissions with lower cost and higher equity. The results can be used as a reference for formulating carbon reduction policies in energy-rich regions. [Display omitted] • We calculate the amount of net embodied carbon transferred from Shanxi. • We estimate the sectors' embodied carbon emissions in inter-provincial trade. • Driving forces are decomposed of embodied carbon outflowed from Shanxi. • A classification model is constructed to identify sectoral characteristics. • The key sectors are identified and then trade relationships are adjusted. [ABSTRACT FROM AUTHOR]

Promoting low-carbon development in the iron and steel industry (ISI) is essential for China to achieve the carbon neutrality targets. This paper separately adopts the Logarithmic Mean Divisia Index (LMDI) technique and the Mean Impact Value (MIV) method to analyze the impact of driving factors on the CO 2 emissions of ISI in the historical and future dimensions. Furthermore, this paper establishes the extreme learning machine model optimized by the bat algorithm (BA-BP) to explore the carbon emission reduction pathways of ISI in the business-as-usual (BAU) scenario, the low-speed, medium-speed and high-speed development scenarios considering the constraints of carbon neutrality targets. The results reveal that: (1) Production capacity and energy efficiency are important drivers of CO 2 emissions in ISI; (2) The emission reduction situation is not optimistic under the BAU scenario, and it is difficult to accomplish the carbon neutrality goals by 2060; (3) Under the most ideal emission reduction pathway (corresponding to the high-speed development scenario), ISI will reach its peak in 2022 with the peak value of 2143.42 MtCO 2. Compared to the peak year, the CO 2 emissions will be reduced by 654.69 MtCO 2 and 1558.61 MtCO 2 in 2030 and 2050, respectively. Moreover, the achievement of short-term and long-term emission reduction targets depends on production capacity decline and technological progress, respectively. The optimal emission reduction pathway provides a reference for ISI to formulate periodic emission reduction targets. • The improved extreme learning machine is employed to predict the carbon emissions. • The emission reduction pathways are explored under the carbon neutrality target. • Controlling production capacity is a crucial reduction strategy. • The energy rebound effect is supposed to be weakened. • The application of advanced technologies will greatly promote emission reduction. [ABSTRACT FROM AUTHOR]

Under the dual policy targets of carbon emission peaking (CEP) and industrial green transformation (IGT) in China, where a top-down policy implementation is usually adopted, a more integrative planning mode needs to be constructed for greening industrial development. This paper proposes a technical framework to tackle the weakness faced when integrating IGT-associated issues with CEP factors during the development of IGT options, with an application in the Hohhot–Baotou–Ordos–Yulin (HBOY) city cluster. A CEP-IGT model was built to portray the effects of seven selected indicators on industrial carbon emissions, and help develop six options by defining the transformation strengths of HBOY's energy-intensive industrial system. Particularly, the CEP times and volumes were employed as the criteria for evaluating the IGT options. We found that the regional CEP goal will not be accomplished by even 2035 under the business-as-usual scenario. To halt the emission growth trend will rely much on the structural factors of energy mix and industrial output, which were also determinants of the IGT performance. Option 3, representing a moderate level transformation, was the preferred IGT plan, reaching a peak volume of 255.27 MtCO 2 eq·a−1 in 2026. The preferred plan will result in the replacement of at least 14% of the coal used in thermal power by renewable energy, and an increase in industrial coal-use efficiency by 32.02%. Policy measures need to be implemented to cap the total industrial coal consumption to no more than 2494 Mt and sustain an industrial growth by developing lower-energy or carbon-intensive industries. By 2030, the intensity and volume of industrial carbon emissions will reach 1.89 tCO 2 eq·10,000 CNY−1 and 224.85 MtCO 2 eq·a−1, respectively. This CEP-IGT progress will benefit the goal of being carbon neutral. As such, we concluded that this new framework will aid the IGT planning process, not only improving our understanding of the relationships between carbon emission control and industrial updating levels, but also presenting an integrative path for China's city clusters to promote green, low-carbon industrial development. • A technical framework is raised to tackle the weakness of integrating the CEP factors into IGT planning. • The peaking time and volumes of carbon emissions are used as the criteria for determining the preferred plan. • Both industrial updating and carbon emission control rely on the structural factors. • The preferred Option 3 can achieve the peaking goal in 2026 with a moderate transformation strength. • The new framework presents an integrative path to promote the transformation to green, low-carbon industrial growth. [ABSTRACT FROM AUTHOR]

As a kind of zero-carbon and high-efficiency energy, hydrogen plays a vital role in environment protection and industry application. However, large-scale green hydrogen production is difficult to be promoted because of its high production cost. China is rich in hydropower resources, but the utilization rate is low. It is an ideal solution to combine a hydrogen energy system with the current installed hydropower system to promote the use of hydropower and the application of hydrogen energy. To study the capacity planning problem of a hydropower–hydrogen system, a bi-level programming model is first developed to maximize the system profit through incorporating long-term investment decision and operation strategy within a framework. Then, the developed model is solved based on Particle Swarm Optimization (PSO) algorithm and Cplex. Finally, the model is verified through the empirical study of China's Hongru River basin over a five-year planning horizon. Multiple scenarios related to different seasonal and load demand characteristics are examined. Sensitivity analysis is discussed considering the impact of investment cost and hydrogen price. [ABSTRACT FROM AUTHOR]

"Carbon Peaking and Carbon Neutrality" is one of the important tasks of China's current and future economic construction and development, but the carbon emission reduction effect of High-speed rail (HSR) has not yet been fully studied. In this paper, based on the panel data of 274 cities in China from 2003 to 2016, we take the opening of HSR in the cities as a "Quasi-natural Experiment", and construct the Multi-period Difference-in-Difference Model to explore whether the opening of HSR can reduce carbon emissions of cities along the route, and use the mediating effect model and moderating effect model to further analyze its internal mechanisms. We find that:①The opening of HSR can significantly reduce the carbon emissions of cities along the route, and the opening of HSR in a city will reduce the carbon emissions of the city by about 2.4% on average, and this carbon emission reduction effect usually manifests significantly in the first year after the opening of HSR, and increases year by year thereafter.②The mechanism test shows that the traffic substitution effect, the optimization of the industrial structure and the promotion of economic agglomeration have partial mediating effect on the influence between the opening of HSR and urban carbon emissions. In addition, the increase in the intensity of government environmental regulations can strengthen the effect of the opening of HSR on reducing urban carbon emissions.③The regional heterogeneity research shows that the opening of HSR has a more significant impact on the carbon emissions of cities in the eastern coastal region and western region of China. Moreover, in the western region, the reduction effect of the opening of HSR on carbon emissions is only reflected in the key cities rather than ordinary prefecture-level cities. This paper provides certain reference significance for promoting low-carbon and green economy and helping China to achieve the "Carbon Neutrality" climate action goal. [ABSTRACT FROM AUTHOR]

The dependence of the economic development of a region on its energy consumption is a major challenge for developing countries. The aggregate embodied energy intensity (AEI) based on an input-output framework reflects the interaction between regional economy and energy consumption driven by final demand. Studying the change in AEI from the perspective of sector production and final demand can effectively control the source and trend of energy intensity change in order to control the growth of energy intensity. However, there is a relative lack of studies determining the causes of AEI changes from the perspective of sectors and final demands. This study attempted to identify the influencing factors of AEI changes and establish an AEI-based environmental policy in China. A multiplicative structural decomposition analysis and a structural path analysis were used to study the AEI under conditions of different final demands, sectors, and transmission layers, along with the factors influencing AEI changes in China from 2012 to 2017. Our results indicate that the final demand category of gross fixed capital formation made the largest contribution to AEI changes in 2012 and 2017, and the multiplicative intensity factor significantly inhibited the decrease in AEI. Among the sectors studied, only a few were found to significantly affect the change in AEI, with "S11-Processing of petroleum, coking, and nuclear fuel", "S12-Chemical industry", and "S14-Smelting and rolling of metals" being the key sectors that input high-energy-consumption products to other sectors. Image 1 • Multiplicative SDA and Structural Path Analysis are applied to I-O model. • Key sectors for the decrease of aggregate embodied energy intensity are identified. • Aggregate embodied energy intensity changes of transmission layers are decomposed. • Key transmission paths of embodied energy flows among sectors are identified. [ABSTRACT FROM AUTHOR]

Manufacturing companies encounter pressure related to not only cost and service level but also certain limitations based on the constraints of energy saving and emission reduction. This study investigates production planning for a sustainable supply chain, for which factors such as the limits for CO 2 emission, stochastic demands, service level, and inventory capacities are considered. We build a mixed-integer programming model and propose a Lagrangian relaxation (LR) algorithm to solve the large-scale production planning problem in a cost-efficient way. Based on the historical data of HY Automobile Co. in China and 120 randomly generated instances, we find that the LR algorithm can generate near-optimal solutions with less than a 1% difference as compared to CPLEX solutions. The algorithm can find a solution within 5 min for a case with a large problem size, whereas CPLEX cannot find the solution in an hour. At the same service level, the cost decreases with the initial inventory capacity and CO 2 emission limit and increases with the demand and number of the types of products. The cost gap that is caused by increasing these parameters becomes larger when the service level is higher. This finding indicates that the parameters' values are important to the company when a high service level is required. Image 1 ∙A production planning model considering emission, stochastic demands, and service level is formulated for a sustainable supply chain ∙A Lagrangian relaxation algorithm is proposed to solve the problem in a cost-efficient way ∙A real case study is conducted to demonstrate the effectiveness of the proposed algorithm ∙The solutions generated by the proposed algorithm and CPLEX are compared ∙Sensitivity analysis is conducted to show the trade-off between the cost and service level [ABSTRACT FROM AUTHOR]

Rapid industrial growth has significantly contributed to the prosperity of Chinese cities over the past decades. Current studies on environmental sustainability (ES) focused on industrial systems, which are the main source of pollution emission, water use, and energy consumption in cities. In light of the weakness of conventional methods in correlating relevant environmental management with ES evaluation, a methodology was developed primarily to improve the evaluation indicators and procedure by integrating the latest three-line environmental governance (TLEG) policy and the ES evaluation of urban industry in China. Specifically, a four-dimensional indicator system was built in a hierarchical structure based on the requirements of TLEG on aspects of ecology, environment, and resource; further, a problem-solving procedure was developed to continuously determine the progress of the industrial ES of the Nansha industrial base (NSIB) in Guangzhou, China. An ad hoc retrospective evaluation was developed to investigate the variations of ES performance of the NSIB from 2005 to 2017 and further to identify a set of restraining indicators exhibiting negative effects on ES. Five alternatives of the industrial upgrading plan up to 2025 were assessed based on the accessibility of ES targets, and the best one was selected against the restraining indicators. The results indicated that the general trend of ES of NSIB was progressive from 2005 to 2017, 13 restraining indicators out of 33 were deemed as key, and Alternative IV was screened out as the best one. This study indicated that such integration with environmental management policies may set up a goal-oriented, problem-solving process for ES evaluation. Further, it may increase the potential to deliver more relevant information for decision-making on the greening industrial systems of cities. Image 1 • Greening industrial system is crucial to sustainable development of Chinese cities. • The indicators and procedure are improved by linking the policy with ES evaluation. • A restraining indicator set is obtained through a retrospective ES evaluation. • The three-line based ES evaluation is a goal-oriented, problem-solving process. • Industrial upgrading alternatives are evaluated by accessibility of the ES targets. [ABSTRACT FROM AUTHOR]

Unprecedented huge mitigation task should be associated with profound efforts in facilitating emission reduction process over the globe. As the largest CO 2 emitting country, China has been strenuously promoting the mitigation resilient development pathways in conjunction with the 2030 carbon emission peak commitment from Nationally Determined Contribution document which submitted under the Paris Agreement. Based on the peaking objective, carbon emission in heavy chemical industry should be received most attentions in terms of its large proportion with respect to the emission sources from other sectors. Particle swarm optimization (PSO) algorithm optimized back propagation neural network (BP) model is employed to predict future carbon emission for heavy chemical industry with the timeframe of 2017–2035 on the basis of the previous data. The significant magnitude of each carbon emission driving force is acquired in terms of the absolute influence coefficient method. The results indicated that, carbon emission in heavy chemical industry and its corresponding sub-sectors could be achieved peak under the implementation of the predetermined mitigation scenarios. The proportion of carbon emission in energy processing industry, steel industry, and building material industry is accounted for a larger fraction over the cumulative carbon emission in heavy chemical industry during the simulation period upon 2035. • IPSO-BP model can be provided a new path for predicting carbon emissions. • Carbon emissions would be peaked at 9.5 billion tons in heavy chemical industry. • The largest emitting subsectors are energy processing sector and steel sector. • The best ideal carbon emission peaking year for heavy chemical industry is 2021. • Heavy chemical rate is the most significant driving force on carbon emissions. [ABSTRACT FROM AUTHOR]

Abstracts Social impacts have become increasingly important for urban development in China. In addition, unlike general construction projects, construction projects in high-density urban areas have high levels of complexity and uncertainty. Thus, from a network perspective, an improved social risk analysis theory and a method for these projects were generated in this paper. A case study involving 66 cases of social conflicts in construction projects in China was performed, and 16 social risk factors were summarized. Eight stakeholders related to these social risk factors were then identified based on a literature review and the case database. A theoretical model for social risk network analysis considering the interrelationships between stakeholders and risks was developed by using NetMiner, a social network analysis (SNA) tool. A 2-mode network and two 1-mode networks were used to analyze the relationships among the identified 16 social risk factors and 8 stakeholders. In terms of three-level challenges such as complex environments, the unreasonable behavior of individuals or organizations, and diversified interaction between behaviors and environments, the most important social risks of construction projects in high-density urban areas were explored to help the government implement effective social risk management measures. In addition, multiple, complicated relationships among residents, contractors, governmental authorities, and owners represented a major factor leading to social risk; therefore, more attention should be focused on this topic. The proposed social risk analysis method can increase the attention paid by project managers, governmental authorities, and project owners to social risk factors in high-density areas and provide a structured framework to analyze such social risks, as well as put forth possible solutions to reduce the social impacts on construction projects in high-density urban areas. Highlights • 16 social risks for construction projects in high-density urban areas are identified. • A model for social risk and interrelationships network analysis is developed. • Social risks is highly related to complex environments and unreasonable behavior. • Multiple and complicated relationships among stakeholders lead to social risk. [ABSTRACT FROM AUTHOR]

Global warming has aroused the attention all over the world, because not only can it directly damage the living environment, but it can also impact on the social and economic development, of a region in an obvious and far-reaching way. In this paper, we builded a trans-log production function model incorporating variables, such as rainfall, labor force, technology and temperature to simulate how climate changes impacted on the regional economics. Time series data of the domestic economy of Nanjing, a city in the southeast China, between 1996 and 2017, were used to verify the production theory of trans-log production frontier. The results showed that: with the rainfall increased and the temperature declined, the climate impact on the economy and the absolute value of the output elasticity of the frontier diminished. In addtion, the climate change was also positively related to the capital investment. The larger variation of temperature negatively impacted on the economic development. • This paper builds a trans-log production function model. • Time series data are used to verify the production theory of trans-log production frontiers. • Analyzing the effect of climate change on the regional economy. • The larger variation of temperature negatively impacted on the economic development. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2 ) emissions have become a rising concern in China. Few studies have considered spatial correlation and agglomeration effect of CO 2 emissions for adjacent regions and provinces. This paper employs Geographical Weighted Regression (GWR) model to examine the impact of urbanization, industrial structure and energy intensity on CO 2 emissions and reveals the spatial correlation in different provinces in 2005, 2008, 2011 and 2015. The results indicate that there is an obvious spatial effect on CO 2 emissions of each province based on the GWR results. Urbanization is the most significant factor in the increase of CO 2 emissions for all provinces in each year. For the neighboring provinces, a coordinated low-carbon urban construction plan should be carried out based on the urbanization development level. Energy intensity has a positive effect on CO 2 emissions, but the effect on the emission reduction is relatively weak and unstable. It should strengthen exchanges and cooperation between provinces and regions by jointly exploring and promoting technologies to improve the efficiency of resource use and reduce CO 2 emissions. The influence of industrial structure on CO 2 emissions is positive, indicating that the industrial structure adjustment plays an important role in carbon emission reduction. [ABSTRACT FROM AUTHOR]

Improvement of coal consumption efficiency is very important both for China in solving its problems of energy security and environmental pollution and for the world in addressing the issue of greenhouse gas emissions. Using a total-factor framework, this paper employs a data envelopment analysis (DEA) approach to evaluate the coal consumption efficiency of six energy intensive sub-industries in China in 2015. Coal consumption efficiency is factorized into economic efficiency and environmental efficiency, with sulfur dioxide, nitrogen oxide, and industrial smoke, dust and soot emissions treated as undesirable outputs of energy consumptions. The results show the following. (1) Of the six energy intensive sub-industries studied, two exhibit both DEA-effective coal economic efficiency and coal environmental efficiency. Coal economic efficiency is found to be greater than coal environmental efficiency, indicating that the benefits of economic efficiency have masked the negative effects of the environmental impact. China should therefore pay more attention to the clean utilization of coal. (2) Low environmental efficiency mainly resulted from the joint effects of pure technical efficiency and scale efficiency; therefore, enterprises in these industries must engage in management efforts to improve their ability to manage large enterprises. (3) The energy intensive industries characterized by coal environmental efficiency that failed to achieve pure technological effectiveness all exhibited input redundancy or output inadequacy of varying degrees while showing a potential for energy conservation and emissions reductions. [ABSTRACT FROM AUTHOR]

The oil-bearing drilling cuttings (OBDC) produced in the petrochemical industry contain a large number of hydrocarbons, and their direct discharge will not only fail to recover the organic matter, but will also seriously harm the environment. In the process of treating OBDC, the consumption of resources and energy and pollution problems may arise, and the current clean production focusing on reduction, non-hazardous and resourcefulness has become the focus of achieving sustainable development. However, there is a lack of studies that consider treatment technology, resource consumption and utilization, and environmental impact under the perspective of cleaner production. In this paper, by studying the indicators related to cleaner production and the Driving Force-Pressure-State-Impact-Response (DPSIR) model, an evaluation index system for OBDC processing technology in the petrochemical industry under cleaner production is constructed. The evaluation results can provide reference for the selection of OBDC treatment process, and provide direction for the development and promotion of the treatment process. In the evaluation system, the DPSIR model can evaluate and manage the OBDC process from different levels and perspectives. The combination of the G1 method and the C-OWA operator method was used to calculate and analyze the index weights, and the improved matter-element analysis method was used to comprehensively evaluate the treatment of OBDC to determine the treatment of technical methods. The evaluation system is applied to actual cases in China. The results show that the low-temperature extraction technique was the best and the chemical washing method was the lowest compared with the three treatment techniques. The oil removal rate of OBDC, recovery of organic matter, secondary waste gas pollution and other indicators have a great impact on the evaluation of the OBDC treatment process under clean production. In order to contribute to the optimal recovery and utilization of resources, it is recommended to develop improvements to the treatment pair technology treatment process based on the results of the evaluation. • Analysis of oil-bearing drilling cuttings hazardous waste from the perspective of clean production. • Established the evaluation index system of oily drill cuttings treatment process under cleaner production. • A new set of oil-bearing drilling cuttings evaluation model under cleaner production was constructed. • Apply the improved matter-element analysis method to evaluate and analyze indicators at different levels. • Analyze and evaluate actual cases and propose areas for improvement. [ABSTRACT FROM AUTHOR]

Rapid industrialization and urbanization have caused serious conflict between humans and nature due to the spatial variability of natural environmental conditions. Integrated environmental zoning (IEZ) is an efficient tool to regulate sustainable development in developing countries such as China. Previous studies explore application of IEZ based on assessment of resource and environmental carrying capacity (RECC) and evaluation of urban development land-use suitability (UDLS). However, existing IEZ oversimplify the complexity of UDLS evaluation, and have low spatial resolution. In addition, it is difficult to express overlay results of RECC and UDLS from a multi-dimensional perspective. Therefore, this study sets forth an improved IEZ by a logic scoring of preference (LSP) and a comparative advantage evaluation. The LSP method and normalized revealed comparative advantage (NRCA) index is applied to assess UDLS and the comparative advantage of RECC (CARECC). The results of IEZ are integrated by overlaying the CARECC zones with numerically processed UDLS maps. A small-scale, rapid development area, Liangjiang New Area (LJNA), serves as the study area. The highly suitable, suitable, moderately suitable, marginally suitable, and not suitable land of LJNA covers 276.60, 368.40, 183.45, 143.22, and 228.33 km2, respectively. Furthermore, the spatial distribution of RECC in LJNA clearly differ. Based on the results of UDLS and RECC, ten types of integrated environmental zones in the form of "category + intensity" are delineated. The improved IEZ is consistent with the LJNA Master Plan, reflects the logic of human reasoning more accurately, increases the spatial resolution, achieves zoning from multi-dimensional perspectives, restricts the category and determines the sequence of urban development, which can serve as a reference for optimization of land space in LJNA and is also significant to other areas and places. [Display omitted] • An improved IEZ is proposed based on UDLS and RECC maps. • The advantages of LSP and comparative evaluation are combined. • The method enable more accurate and multi-dimensional zoning results. • UDLS determines the sequence and scale of urban development. • CARECC restricts the category of urban development. [ABSTRACT FROM AUTHOR]

Given the important economic role played by local governments in the development of industries in China, this paper incorporates the competition behaviors of local governments into the analytical framework of environmental externalities of industrial agglomeration; accordingly, it theoretically explores the environmental externalities that may arise from industrial agglomeration induced by local governments' competition. On the basis of panel data covering 282 Chinese prefecture-level cities from 2007 to 2016, this paper uses spatial econometric methods to test the impact of the interaction between industrial agglomeration and local governments' competition on environmental pollution and spatial spillover effects. The empirical test results show that, compared with simple industrial agglomeration, the combination of industrial agglomeration and local governments' competition is the key factor explaining the current changes in the environmental pollution level and that the interaction between industrial agglomeration mode and local government competitive behaviors aggravates environmental pollution. Among them, the interaction between specialized agglomeration and local governments' tax competition exerts negative externalities on the environment through the overcrowding effect, while the interaction between diversified agglomeration and local governments' investment competition has negative externalities on the environment through the centralized emission effect. Both have significant spatial spillover effects. Adjacent regions will form a larger-scale overcrowding effect and concentrated emission effect under the joint influence of the "race to the bottom" competitive strategy interaction and related industries. Lastly, based on our research conclusion, this paper proposes some policy suggestions to optimize local governments' competitive behaviors and industrial agglomeration and other market forces to jointly control environmental pollution. [ABSTRACT FROM AUTHOR]