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Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development. • The industrial structure rationalization has highest contribution to the economic growth of eastern China, followed by central and western China. • There exists unidirectional causality from industrial structure upgrading to structure rationalizing in western China and bidirectional causality between them in central China. • Effects of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption. • Unidirectional long-term causality from renewable energy to economic growth exist in each sub-region. [ABSTRACT FROM AUTHOR]

Abstract: Under the pressures of rapid industrialization and urbanization, many Asian countries are facing the challenge of reducing air pollution and CO2 emissions simultaneously while maintaining their economic growth. Under such a circumstance, a growing attention is focused on successful implementation of co-benefit policies that are designed to reduce both air pollutants and CO2 emissions. However, the concept of co-benefit policies must be developed further in order to identify its quantitative and qualitative validity, which can be assessed using a reliable methodology for the estimation of co-benefits and through clarification of the importance of this approach for stakeholders in urban environmental policy-making. Based on a systematic review of co-benefit research and air pollution control policies in Japan, we investigated the local air pollution control policy in Kawasaki City, one of the industrial centers of Japan, and confirmed that it has contributed both to the sharp decline of atmospheric sulfur dioxide (SO2) levels and to energy efficiency improvements in local industries. Our results suggest that preventive actions and local-scale compliance enforcement tools (that are uniquely designed and act as a form of agreement between government and individual firms) could contribute significantly to successful implementation of co-benefit policies in the long-term. We conclude our paper with a discussion of linkages between low-carbon technology innovations and policy designs that could further nurture co-benefits. [Copyright &y& Elsevier]

Industrial robots have been widely utilized in factories to replace or help employees performing tasks such as handling, welding, and assembly. With the increasing breadth of installation and deployment of industrial robots, it is critical to predict and optimize their energy consumption in order to assure their environmentally friendly characteristics. While the data-driven modeling method based on multi-layer perception has been demonstrated to be a viable way for revealing quantitative relationships between operating parameters and energy consumption for industrial robots regardless of physical process or case-specific information, there are still some limitations in terms of prediction range, modeling efficiency, and sample size requirement. To accelerate model rebuilding and improve model accuracy without collecting a large number of samples simultaneously, this paper proposes a transfer-learning-based model creation method for energy consumption of industrial robots, where the associated operating parameters are analyzed, and structure adjustment strategies for multi-layer perception are planned for various industrial robot systems, then, schemes for the reuse of parameters in well-trained networks on source domain and the fine-tunning of multi-layer perception model are formulated to efficiently build the accurate energy consumption model on target domains. Experiments are conducted on two distinct robot systems, the Epson C4 and the Siasun SR10C, with nonidentical operating parameter structures and sample sizes. The results demonstrate that transfer learning models with fine-tuning strategy outperform previous data-driven modeling methods in terms of model accuracy and modeling efficiency in both of these situations. [Display omitted] • The transfer-learning based model creation method for predicting the energy consumption of industrial robots. • Analyze related operating parameters and design MLP's structure adjustment strategies to help knowledge transfer among domains. • Create EC models for target domains by reusing and fine-tunning parameters on case-related transferred layers. • Boost the energy consumption modeling accuracy on small scale dataset and enhance the efficiency of modeling process. [ABSTRACT FROM AUTHOR]

Abstract: Increasing energy costs, new environmental legislation, and concerns over energy security are driving efforts to increase industrial energy efficiency across the European Union and the world. Manufacturers are keen to identify the most cost-effective techniques to increase energy efficiency in their factories. To achieve the desired efficiency improvements, energy use should be measured in more detail and in real-time, to derive an awareness of the energy use patterns of every part of the manufacturing system. In this paper, we propose a framework for energy monitoring and management in the factory. This will allow decision support systems and enterprise services to take into consideration the energy used by each individual productive asset and related energy using processes, to facilitate both global and local energy optimization. The proposed framework incorporates standards for energy data exchange, on-line energy data analysis, performance measurement and display of energy usage. [Copyright &y& Elsevier]

Abstract: Energy conservation is a key measure for reducing CO2 emissions. However, realising the emission reduction potential of an energy conservation investment depends on many factors, such as energy prices. The EU emissions trading scheme has made the investment analysis more complicated and increased the economic value of operational flexibility under fluctuating carbon prices. The different operational options in industrial energy production complicate the estimation of CO2 reduction potential in the investment phase. Increasing operational flexibility enables optimisation in the economic dimension, which may lead to less than optimum CO2 reduction. In our case study, which analysed the effects of an energy conservation investment made in the pulp and paper industry, the deviation from the expected emission reduction was around 30% over the period from 2000 to 2007. However, it seems that with high carbon prices, increasing operational flexibility has no significant effect on the carbon emissions. In policy-making, the freedom of action that is made possible by increasing operational flexibility should be taken into account when evaluating the contribution of an individual energy efficiency investment towards meeting the national targets for energy efficiency improvement and CO2 emission reduction. [Copyright &y& Elsevier]

PM 2.5 emissions has become a serious issue in China with enormous regional variation in economic development, resource endowment, and consumption patterns, which leads to great variations in regional PM 2.5 emissions and embodied PM 2.5 transfer among provinces. The spatial and temporal evolution pattern of PM 2.5 and its influencing factors are important issues in the study of air pollution emissions and control, which could help policy makers allocate emission responsibilities and reduction targets. Based on the perspective of final consumption, this paper uses a multiregional input-output model to estimate PM 2.5 emissions in 30 provinces in different years in China, applies a geographically weighted regression model to analyze different driving factors, and then analyzes the spatial heterogeneity of consumption-based PM 2.5 emissions. The results show that consumption-based PM 2.5 presents a significant reduction trend, although the separation of production and consumption areas of PM 2.5 emission is further intensified. The provinces with rich energy but relatively backward economies and manufacturing industries correspond to the primary net inflow of PM 2.5 , while the provinces with strong consumption ability and the most developed economies and industries present a net outflow of PM 2.5. Moreover, the slowdown of consumption growth and the improvement of consumption quality have a positive impact on PM 2.5 emission reductions. The optimization of industrial structure has a more significant impact on pollution control and haze reduction in the western region, while the improvement of energy utilization efficiency and rational use of construction land are more conducive to the reduction of consumption-based PM 2.5 emissions in the eastern region. From the perspective of temporal and spatial changes, significant spatial differences occur between regions. Energy consumption, environmental protection investment and construction land show the trend of " high value area diffusion and low value area reduction". • Explores the spatial distribution characteristics of PM 2.5 emissions in China from the perspective of consumption. • Consider and analyze the inter-annual variation of pollutant emission transfer. • Clarifies the spatial heterogeneity characteristics of influencing factors. • Emphasizes the importance of further optimizing influencing factors to reduce haze from the consumption end. [ABSTRACT FROM AUTHOR]

The application of aldehyde-assisted chemical looping separation technology in the separation of ethylene glycol (EG) and 1,2-butanediol (1,2-BDO) azeotropic mixture from coal-based and biomass-based crude ethylene glycol refining process can relax the operation conditions of the 1,2-BDO removal column and EG refining column, save energy, increase the yield of EG and guarantee the quality of EG product. Formaldehyde (FA), acetaldehyde (AA) and prolyl aldehyde (PA) are feasible reactants and assessment of the opportunities and challenges of different aldehyde-assisted processes before industrial application is necessary. In this paper, the mole composition of EG and 1,2-BDO in the distillate from the 1,2-BDO removal column was optimized. FA, AA, and PA-assisted chemical-looping separation processes (FA-ACLSP, AA-ACLSP and PA-ACLSP) were developed and simulated to provide fundamental data for the techno-economic and environmental evaluation. Results show that PA-ACLSP has significant economic and technical advantages (the yield of polyester-grade EG is 94.42% and the internal rate of return is 137.28%). However, it suffers from high potential environmental impacts (PEIs) and liquid discharge (the input and output PEIs and liquid discharge per kilogram of EG per hour are 4.87, 2.95 and 0.538). AA-ACLSP has zero liquid discharge and is truly environmentally friendly. FA-ACLSP faces a tough challenge in industrial application because of the large energy consumption and waste discharge (the internal rate of return is 4.63% and the liquid discharge per kilogram of EG per hour is 4.161). Finally, the discussion and analysis of different processes are illustrated to guide commercialization. • Chemical-looping separation technology is applied in non-oil-based EG purification. • Three aldehyde-ACLPs are assessed from techno-economic and environmental views. • AA-ACLP has zero waste discharge and is truly environmentally friendly. • PA-ACLP is the most competitive in the economy with an IRR of 137.28%. • PA-ACLP and FA-ACLP are technically favorable with EG yield over 94%. [ABSTRACT FROM AUTHOR]

The mitigation of climate change is the main issue for environmental policymakers. A large number of authors analysed the impact of various factors on greenhouse gas (GHG) emissions. In this paper, we proposed a multiplicative approach to use 2- and 3-way interactions simultaneously in order to analyse the effects of energy efficiency, renewable energy and sector mix as the moderators that shape the economic activity – environmental degradation nexus. The results encompassing global panel data show that the turning point increased from 1990 to 1995 to 2002–2007 and decreased afterwards. Considering the analysis of moderators, a high share of agriculture and industry sectors make the curve flat with a positive slope and a high level of economic activity was associated with more GHG emissions with no turning point. The marginal effect of energy efficiency to reduce the negative impact of agriculture and industry sectors was growing. Meanwhile, the marginal effect of renewables to reduce the negative impact of agriculture and industry sectors was diminishing. Service sector and energy efficiency, service sector and renewables worked as the substituting forces reducing GHG emissions with a diminishing marginal rate of substitution. Therefore, this study confirms that the economic activity – environmental degradation relationship is very complex and further studies should consider the role of the moderators of this nexus. [Display omitted] • Technique, composition effects should be considered as moderators of HGH emissions. • The multiplicative approach was applied to model interactions of GHG factors. • Agriculture, industry sectors made the economic activity GHG emissions curve flat. • Marginal effect of renewables to reduce GHG emissions was diminishing. • Service sector and renewables worked as substituting forces reducing emissions. [ABSTRACT FROM AUTHOR]

The importance of implementing energy efficiency methodologies in industrial environments has increased considerably in the last decade given the high energy costs and environmental impact (e.g., greenhouse gas emissions). This paper proposes a methodology to improve the energy efficiency of an industrial machine, without sacrificing either production or quality, using an adaptive predictive controller based on dynamic energy models that manages peripheral devices to activate/deactivate them at the proper times. The proposed adaptive mechanism aggregates robustness to the control system in industrial environments, which experiment constantly changes related to equipment degradation and that affect their energy consumption profile over time. Thus, this novel adaptive mechanism automatically updates the energy model to minimize the error between prediction and real energy consumption, including new energy behavior resulting from machine degradation. This methodology has been validated via a testbed and its performance was compared with rule-based control, which is the most widely used control strategy in industry. The energy efficiency of both approaches was evaluated using performance indicators, which show the effectiveness of the proposed control approach, highlighting remarkable improvements in reducing both energy consumption (about 2%) and sudden power peaks (more than 11%). • Optimization-based control to manage peripheral equipment maximizing their efficiency. • A continuous machine energy model adaptation scheme to react to its degradation. • Experimental validation of control in a real industrial application via testbed. • Energy efficiency performance comparison between adaptive and rule-based control. • Results show that the proper management of peripheral equipment reduces energy costs. [ABSTRACT FROM AUTHOR]

Renewable energy is one of the fastest growing segments of energy consumption and wind energy is one of the most widely used sources of renewable energy. There is, however, much less known about the main drivers of wind energy consumption at the country level. This paper uses the logarithmic mean Divisia index (LMDI) method to study the driving factors in wind energy consumption for a group of 17 countries (Australia, Canada, China, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom, and the United States) that are major consumers of wind energy. The renewable energy share component has the largest impact on wind energy consumption. Improvements in energy intensity are the largest driver of reductions in wind energy consumption. Wind energy consumption forecasts for each country in the business as usual (BAU) scenario for the years 2018–2025 show that compound annual growth rates (CAGRs) for wind energy consumption are highest for Canada, Sweden, China, and Germany. Countries that have high shares of renewable energy like Spain, Portugal, and Denmark have low forecast values of CAGRs. Wind energy forecasts are also calculated for a high growth rate scenario and a low growth rate scenario. Future increases in wind energy consumption are going to depend upon the continued increase in renewable energy share which in turn is affected by energy policy designed to promote fuel switching from fossil fuels to renewables. [ABSTRACT FROM AUTHOR]

Green production and pollution control matter a great deal for resource conservation and sustainable growth. Today, Chinese firms are facing the challenge of upgrading the modes of production of industries with high energy consumption and high pollution to align with green initiatives. Financial systems can be designed to intervene in modes of production to control firms' pollution emissions, but the degree of impact and design mechanism are not clearly understood up to now. Thus, evaluating the policy impact of such transactions related to firm production is indeed an important contribution to existing knowledge about these topics. Using novel firm-level data with pollution and financial variables, we exploit the causal effect of bank loans on firms' abatement performance by using a natural experimental of a green loan policy shock in China. The results show that green loan policy has the positive effect of pollution abatement by state-owned and private-owned firms. This paper also investigates how green loan policy may promote green production through strengthening financial constraint and decreasing fixed and intangible investments. Furthermore, we find that political ties transfer financial burdens from firms with strong political ties to firms with weak political ties. In closing, we articulate the implications of our study of a green financing system for sustainable growth and cleaner production. Image 1 • Green loan policy significantly decreases high polluting manufacturing firms' pollution emission. • Green loan policy significantly facilitates high polluting manufacturing firms' pollution abatement. • Green loan policy performs well in state-owned and private owned firms, but not for foreign firms. • The policy increases the financial constraints and substitutes the fixed and intangible investments. • Political tie transits financial burden to those firms with less political tie. [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]

With the increasingly energy consumption and environmental degradation, the recovery of low-grade industrial waste heat and the development and utilisation of renewables have become urgent needs. At present, the recovery of waste heat is mainly at high temperature, and the recovery rate of waste heat at medium and low temperatures is low. Adsorption thermochemical energy storage (TCES) is a pivotal technology applied for long-term thermal energy storage based on the reversible gas-solid reaction at mid-to low-temperature. LiOH·H 2 O is a potential thermochemical material (TCM) candidate because of the high energy storage density (ESD). In this paper, the expanded graphite (EG) is used as supporting matrix to impregnate LiOH to further improve its heat and mass transfer. The water vapour adsorption properties such as sorption capacity and sorption isotherms of the consolidated composite sorbents with EG contents of 0, 5, 8, 12, and 15 wt% were tested. It was found that the samples with 5% and 8% wt% EG have superior sorption capacity and kinetics at various relative humidities (RHs), and the maximum water uptake substantially decreased with increasing temperature. The cyclability results showed 90% and 92% of the original ESDs are retained after 12 cycles for the samples with 8% and 15% wt% EG, suggesting good stability of composite sorbent. The ad/desorption isobars processes also proved the existence of the sorption hysteresis loop, and this phenomenon is more obvious at low pressure. The data fitting results revealed that the kinetics coefficient of the promising composite sorbent containing 8 wt% EG is 0.01671 s – 1 at 30 °C with 58% RH. Compared with the sensible or latent energy storage, the heating temperature produced from the sorbent is adjustable for different purposes. Considering the low emissions and pollution of LiOH-based heat production compared to the coal-fire system, i.e., 1262 kg pollutants are theoretically reduced for a household per year, it is a clean alternative for the residential heat supply. Image 1 • Sorption capacity of a mixture is 32% higher compared to pure salt at 58% relative humidity. • Over 90% of the original storage density is maintained after 12 cycles. • The adsorption hysteresis loop is more obvious at low pressure. • Sorption kinetics coefficient 0.01671 s – 1 at 30 °C with 58% relative humidity. • 1262 kg/y emissions of pollutants avoided for a household. [ABSTRACT FROM AUTHOR]

With strong reliability and maneuverability, industrial robots play an essential role in the transformation and upgrading of the manufacturing industry, known as industry 4.0. Due to their mass application, it is essential to optimize the energy consumption of industrial robots considering the rising cost of energy resources and deteriorating eco-environment. State of the art solutions have mainly focused on hardware improvement and trajectory-planning-oriented software approaches, implementation of which is, nevertheless, restricted by the commercial feature of control algorithms of industrial robots. In order to explore the potential on energy saving, a data-driven approach for practical energy consumption optimization is proposed in this paper. Different from traditional energy consumption modelling where kinematic and dynamic behaviors of industrial robots are the major concern, this study use an artificial neural network to accurately reveal the quantitative relations between operating parameters and energy consumption, then genetic algorithm to optimize the adjustable parameters to minimize the consumed energy. Experiments are designed and performed on an Epson C4 6-DoF industrial robot. The results demonstrate the applicability and effectiveness of the proposed method in assisting the operating parameter selection and energy saving. Image 1 • The data-driven method on the operating parameter optimizationfor reducing EC of IR. • Reveal the quantitative relations between operating parameters and EC of IR through the way of back propagation neural network. • Optimize the adjustable parameters of IR to minimized the EC using genetic algorithm. • Revolute the empirical or simulation assisted robot teaching which are not able to obtain the accurate EC model and find energy-efficient operating parameters. [ABSTRACT FROM AUTHOR]

The energy demand of body shop manufacturing lines is primarily determined by decisions that are made in the early planning phase. However, energy related data which is needed in order to make well founded decisions is mostly unavailable in that phase. The lack of information leads to overdimensioned equipment and inefficient processes. Therefore, in this paper energy related data is acquired and analyzed from existing manufacturing lines. With an established energy and equipment database, the prediction of the future manufacturing lines energy demand is possible. In combination with a rule-based decision support, improvements are already possible in the early planning process. In comparison with existing approaches, the developed holistic methodology addresses all levels in the planning process and is exemplified by a use case in the automotive industry. [ABSTRACT FROM AUTHOR]

The manufacturing of large marine power component including cutting, welding, shrink fitting, heat treatment is a typical industry with high-material consumption, high-energy consumption and high-environmental pollution. It is noted that green manufacturing is gradually emerging. Comprehensive optimization of energy consumption for multiple processes is an effective way to promote a cleaner production industry. To reduce carbon emission from the plant-wide production processes, a new integrated sustainable scheduling approach of mixed-line production with heterogeneous manufacturing technologies is presented in this paper. In this approach, three carbon emission formulations related to cutting, welding and heat treatment are established respectively by analyzing energy consumption and its composition. An optimization mathematical model with the goal of minimizing the total emission of carbon dioxide and the total makespan is then established. In order to solve the model, the genetic algorithm is adaptively improved. The niche and elite retention strategy are introduced in the selection operator, and the dynamic crossover operator is set to adjust the genetic parameters with its iteration and adaptive function values to improve the efficiency of the algorithm. Finally, the effectiveness of the proposed approach is validated by comparing with two types of models considering the makespan and carbon emission respectively. The results indicate that the proposed method satisfactorily tackles the mixed-line production scheduling problem of large marine power component manufacturing. [ABSTRACT FROM AUTHOR]

China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei's industrial carbon dioxide (CO 2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO 2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO 2 emissions, gross domestic product (GDP), energy consumption intensity, and CO 2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS. [ABSTRACT FROM AUTHOR]

Environmental problems caused by the rapid popularization of agricultural mechanization and the increase of energy input in China's agricultural production have caused increasing concern. Based on the study sample of China's 30 provinces from 2000 to 2017, this paper estimates the energy-environment performance of agricultural sector using the global DEA method. Furthermore, it applied a panel TOBIT model based on censored data to empirically analyze the impact of popularization of mechanization on energy-environment performance in China's agricultural sector. The main findings are as follows: Firstly, the mean value of energy-environment performance is 0.1842, and the values of national and regional performance showed a downward trend in the sample period, indicating that most regions in China's agricultural sector did not perform well in terms of energy saving and pollution control. Secondly, the development of agricultural mechanization has a significant negative effect on energy-environment performance. This result confirms the fact that the regions with high penetration rates of agricultural mechanization usually have a low energy-environment performance. Finally, in regard to the control variables, energy price, reservoir infrastructure construction and agricultural industrial structure negatively affect energy-environment performance, while the agricultural labor force and agricultural mechanization service organizations have a positive effect on China's energy-environment performance. [ABSTRACT FROM AUTHOR]

Currently, China's industrial sector is a primary source of both energy consumption and environmental pollution. This paper proposes a two-stage, network-based super data envelopment analysis (DEA) approach to investigate the overall efficiency and eco-efficiency of the sub-stages in China's industrial system, including the production and three pollutant treatment stages, from 2004 to 2015. The results reveal that: (1) the industrial system's overall efficiency improved during the sample period. Specifically, efficiency has significantly improved in the production and wastewater stages, while no such improvements have been observed in the solid waste and waste gas treatment stages. (2) Both absolute and conditional β-convergences exist in the overall efficiency and sub-stage efficiencies. Over time, these types of efficiency gradually reached higher levels. (3) Although China has initially achieved positive results in improving its industrial system's overall efficiency, room for further improvement still exists. Among the industrial system's sub-stages, the waste gas treatment stage exhibits the highest efficiency, followed by the solid waste treatment stage, while the production and wastewater treatment stages have relatively low efficiency. • A two-stage network super DEA approach was proposed for industrial eco-efficiency. • The industrial system includes a production and three environmental treatment stages. • The industrial eco-efficiency of China has been improved during 2004–2015. • The performances of the industrial sub-stages are quite different. • Industrial eco-efficiency of China show distinct spatial characteristics. [ABSTRACT FROM AUTHOR]

In order to better achieve the goal of carbon emissions, this paper combined the theory of gravity center and Logarithmic Mean Divisia Index (LMDI) model to analyze the regional emission differences in China. Researches have shown that the difference in economic development levels between the eastern and western regions is an important factor that causes the shift of the center of gravity of carbon emission and its intensity to the west. In decomposition effects, the economic output effect has the largest contribution (56%) to emissions, and the energy intensity effect has the best effect in suppressing emissions. Except for some industries in the East and Central South of China, energy structure in other regions is still dominated by high-carbon energy. In the energy intensity effect, the suppression effects in the East and Central South of China have reached −83,440 Mt and −78,850 Mt respectively, while the Northeast and Northwest of China areas are not significant. Vigorously developing low-carbon technologies and promoting clean energy can effectively reduce carbon intensity. It is recommended to implement the policy of adapting to local conditions according to the current situation of the region, and gradually phase out high energy-consuming industries under the guidance of the government, so as to strive to implement the emission reduction target. • Industrial energy consumption difference between regions were analyzed for China. • Economic output has the largest effect to emission. • Center of gravity theory is used to analyze emission. • The adjustment of industrial structure has a lagging effect on carbon emissions. [ABSTRACT FROM AUTHOR]

Global warming and climate change have become a common concern to all countries in recent years. As an industrial sector with high energy consumption, the construction sector is facing enormous pressure to reduce carbon emissions. Given the continuous integration of the world economy, the construction sector in each country has extensive linkages with other sectors and countries. By using a multi-regional input–output (MRIO) model and hypothetical extraction method (HEM), this paper aims to further decompose the CO 2 emission linkages of the global construction sector from consumption-based and production-based perspectives in order to identify the key sectors and regions related to global trade. Based on data from the World Input–Output Database from 1995 to 2009, the results indicate that the average internationalization ratios of the net backward linkage and the net forward linkage of the global construction sector increased from 37% to 46% and from 16% to 20%, respectively. The global construction sector mainly imported CO 2 emissions from secondary industry and the transportation industry. The construction sector exported small amounts of CO 2 emissions to other sectors both domestically and internationally. The CO 2 emission flows embodied in the international trade of the construction sector mainly occurred between the USA, China, Russia, Germany, and the Rest of the World (ROW). The construction sectors of the ROW imported the largest amount of CO 2 emissions from China (32.72 Mt). The largest exported CO 2 emission flow was between the construction sectors of the USA and the ROW, with a value of 0.091 Mt. Based on these findings, some corresponding countermeasures are suggested, such as optimizing international trade structures, establishing a mechanism for both consumers and producers to share responsibility for carbon emission reduction, applying new environmental materials, and upgrading industrial structures by reducing dependence on high-pollution sectors. [ABSTRACT FROM AUTHOR]

Remanufacturing can bring substantial environmental benefits by reducing process consumption and resource waste. It has been proved to play an essential role in solving environmental pollution and waste of resources. In the machinery industry with excessive resource consumption, a large number of retired products will be produced during industrial transformation and upgrading. Therefore, remanufacturing and reuse are vital to clean production. Current remanufacturing studies mainly aim at structural recovery and performance improvement, which fail to systematically study the growth laws and generalized reuse features of retired mechanical products. The growth law of retired products is analyzed based on structural and failure features. Therefore, this study will focus on how to establish an appropriate generalized growth decision scheme based on product structure and failure features. Firstly, based on the theory of polychromatic sets, this paper analyzes the failure features of various parts of retired mechanical products and establishes a cascaded failure network according to the product hierarchy. Secondly, through the combination of three-way decision and the technologies for order preference by similarity to an ideal solution (TOPSIS), the generalized growth mode of each component is determined step by step from the bottom, and the generalized growth decision is made from the top. Finally, through the research on the generalized remanufacturing of the first stage gearbox, the generalized growth level of the gearbox is the top-level component level. It reduces the waste of resources and energy consumption caused by the traditional unified part level material recycling, and conforms to the actual production, and verifies the feasibility of the generalized growth features and remanufacturing decision-making scheme, which has excellent guiding significance. [ABSTRACT FROM AUTHOR]

Excessive CO 2 emissions from energy combustion are the main cause of the global greenhouse effect, while the energy consumption in China's heating and power industries account for more than a half of national energy consumption. Based on this, to more accurately implement carbon emission reduction in heating and power industries, we studied the evolution and driving factors of the carbon emission structure in these industries between 2007 and 2015 from supply-side and demand-side dual perspectives. The results indicated that: (1) From the view of supply-side, the change of energy consumption structure led to the decline driving force of high-carbon energy (washed coal, crude oil, diesel oil and raw coal) and the rising driving role of low-carbon energy (coke oven gas, other gas and natural gas) for CO 2 emissions growth. Moreover, seen from the entire sample interval, 2007–2010 and 2010–2012 were the best period and the worst period for energy consumption optimization in China's heating and power industries, respectively. (2) From the view of demand-side, the expansion of industrial scale was the responsibility of the carbon emissions growth in China's heating and power industries, and the driving force of the second industry on carbon emissions growth was stronger than that of tertiary industry and primary industry. (3) However, since the direct consumption coefficient of heating and power energies in the tertiary industry and primary industry was lower than that of the secondary industry, the optimization of industrial structure was conducive to slowing the growth of CO 2 emissions in the China's heating and power industries on the demand-side. At last, this paper provides a theoretical analysis for the carbon emission structure of the heating and power industries, and a practical guidance for the industries to carry out more accurate and efficient emission reduction from both sides of supply and demand subsequently. [ABSTRACT FROM AUTHOR]

The Environmental Kuznets Curve (EKC) is an important tool to analyze the relationship between environment and per capital income. But two significant issues have been overlooked: why some points are above the EKC while others are below it? And what factors caused this phenomenon? Based on the data of 30 provinces in China over 2005–2016, this paper aimed to solve the above two questions. For the first question, we take the point on EKC as the theoretical pollution emission value of the different economic development stages; and use the difference between the theoretical pollution emission and the actual pollution emission under the same per capita income as the pollution emission efficiency. The difference value is used to reflect whether the pollution emission of the corresponding economic development stage of each region exceeds the standard and by how much. The result shows that most of the provinces with low pollution emissions concentrated in the north of the Yangtze River. Great progress has been made in China's pollution reduction work during 2015–2016. Then, for the second question, through the analysis of multiple mediating effects, we find that Environmental regulation, industrial transfer, proportion of output value of heavy pollution industry, energy consumption structure and energy using efficiency are important determinants to the pollution emission efficiency in Chinese province. [ABSTRACT FROM AUTHOR]

The industrial sector is a pillar for China's economy, improving the energy productivity is of great significance to mitigate the discrepancy between energy use and energy supply, as well as the economic development and environmental friendliness. This paper extended the previous decomposition method by introducing six new components: energy/output-oriented regional efficiency, energy/output-oriented scale economy, energy/output-oriented pure technical efficiency. These new factors can uncover the impacts of regional balance development, scale economy optimization, and resource allocation efficiency change on energy productivity. The main research results indicate that during the 2000–2016 period: (1) industrial energy productivity yield progressively increased, and the discrepancies of energy productivity in different regions were obvious: eastern region represented the best energy productivity performance, followed by the central and western regions. (2) Technological progress in both energy-saving and production were two principal contributors to energy productivity growth. Overall, the pure technical efficiency also promoted energy productivity, suggesting that resource allocation efficiencies were performed well in both the energy market and the output market. (3) By contrast, both the energy and output-oriented scale economy effect failed to promote energy productivity growth. Besides, the energy-oriented and output-oriented regional efficiency had opposite impacts on energy productivity: the former one had the inhibiting effects while the last one had the promoting effects. Finally, several relevant suggestions for policymakers were provided. • The impacts of policy-factors on industrial energy productivity was investigated. • The traditional decomposition method was extended by introducing six new factors. • The discrepancies of energy productivity were existed in China's different regions. • Technology progress contributed the most in energy productivity growth. • Pure technical efficiency was another promotor in facilitating energy productivity. [ABSTRACT FROM AUTHOR]

Governments design energy-efficiency programs (EEPs) to reduce the energy consumption of the energy-intensive sectors. In this paper, we propose a comprehensive EEP for the industrial and household sectors as a multi-agent problem. A sustainable supply chain consisting of an energy supplier, an energy-efficient manufacturer, and a conventional manufacturer is considered on this matter in a duopoly. Some energy policies under direct, indirect, and combined intervention are applied by the government to improve the cost-effectiveness of the program. In addition, the authors investigate the rebound effects of energy consumption in both sectors, under the consumer behavioral intentions i.e., the efficiency-related awareness, loyalty, and income level as a novel contribution. A multi-stage model is extended based on Cournot model to formulate the problem. The scenarios' comparison and sensitivity analysis of the equilibrium decisions indicate that energy financial policies (e.g., energy tariff) encourage the manufacturers to follow the energy-efficiency path upon their high-energy-consuming products and processes. The combined intervention is more effective than the unilateral policies to reduce energy intensity and increase the energy-saving and social welfare of the industrial and household sectors, respectively. The industrial tariff also seems to be more powerful policy than consumer tariff and energy-efficiency awareness to satisfy the investigated energy-related targets. Finally, the present study provides a management point of view of a sustainable supply chain for energy policy-makers. • A Cournot model with consumer loyalty is proposed for an energy-efficiency program. • Consumer features and energy intensity as a novel utility function are considered. • Government policies encourage manufacturers to follow efficiency programs. • Combined energy policies are the best ways to promote energy efficiency approach. • Industrial tariff is more powerful than the other tools to satisfy energy targets. [ABSTRACT FROM AUTHOR]

The industrial sector, accounting for 30% of global greenhouse gas (GHG) emissions, is among the key drivers of human-induced climate change impacts. Given this volume of emissions, the sector could play a critical role in mitigating climate change. Canada is among the top 10 global GHG emitters and its industrial sector is responsible for more than 39% of national emissions. GHG emissions from the sector increased by almost 32% between 1990 and 2017 and are expected to almost double by 2050 if no measures are taken. This paper aims to evaluate how different technical and economic factors have contributed to the increasing GHG emission trends in Canada's industrial sector between 1990 and 2014. The Logarithmic Mean Divisia Index was developed and implemented as the decomposition method. Results from the analysis suggest that activity level is the biggest contributor to the increase in industrial GHG emissions between 1990 and 2014, which ranged from 5 Mt CO 2 eq in 1994 to 57 Mt CO 2 eq in 2014 compared to the 1990 baseline. Increased shares of low carbon-intensive industries and fuel switching contribute to GHG emission mitigation from the sector. The structural change is responsible for GHG reductions of 1.1 and 23.4 Mt CO 2 eq in 2000 and 2014, respectively. The impacts from fuel mixs and increased shares of low-carbon fuel ranged from −1 Mt CO 2 eq in 1994 to 16 Mt CO 2 eq in 2011. Despite improvements in process-energy-intensity (i.e., the energy consumed per unit of product) in several industries, changes in economic energy intensity (energy consumed per unit of economic output) have resulted in increased GHG emissions. These changes are mainly driven by changes in the process-energy intensity of the oil sands mining sector, the largest contributor to industrial GHG emissions in Canada. Image 107490 • Historical GHG emissions trends from Canada's industrial sector were analyzed. • GHG emissions decomposition analysis was done for the years 1990–2014. • The impacts of driving factors on increasing GHG emission trends were examined. • Activity level was found to be the main driver of GHG emissions. • Fuel switching and electricity decarbonization mitigate industrial GHGs. [ABSTRACT FROM AUTHOR]

The burnishing process is an efficient finishing operation which is widely used to enhance the surface properties of the machined components. The published works mainly focused on the parameters optimization of the burnishing process in which objective functions are relative to burnished surface qualities. Because of natural resource exhaustion and the rising energy prices, the reduction in energy consumption is an urgent demand in the manufacturing industry. This paper presented an efficient optimization to simultaneously decrease energy consumption as well as the mean roughness depth and improve the Brinell hardness for the burnished surface of H13 steel. The burnishing speed, feed rate, depth of penetration, and the number of rollers were the input parameters. The burnishing processes were carried out on a CNC milling machine. The mathematical relations between inputs and outputs were developed using the radius basis function models. The multi-objective particle swarm optimization and the technique for order of preference by similarity to ideal solution were used to generate the Pareto fronts and to determine the best solution. The results show that energy consumption and surface roughness are reduced by 39.50% and 7.83%, respectively. The Brinell hardness is improved by 29.61% compared to the initial values. The radial basis function models can be effectively used to render the approximations and to predict the response's values. The proposed method can be considered as a sufficient approach for modeling and optimizing the burnishing process. Image 1 • Optimizing processing conditions for the roller burnishing of H13 steel.. • Considering energy consumed, mean roughness depth, and Brinell hardness. • Development of the correlations using radius basic function (RBF) models. • Evaluating alternative solutions using the multi-performance index (MPI). • Determination of optimal factors using RBF-MOPSO-TOPSIS. [ABSTRACT FROM AUTHOR]

Carbon emissions and energy consumption have serious impacts on humans and ecosystems. This paper investigates the effects of industrial reconstructuring on energy conservation and emission reduction. A multi-objective optimization model was established. The model classifies industrial sectors into four groups according to their carbon emission levels and contributions to economic growth, then non dominated sorting genetic algorithm is applied to solve the model and the Pareto frontier is obtained. The best solution is selected from the Pareto frontier by using a super data envelopment analysis model which measures the degree of coordination between economy and environment. The model is used to analyze the effects of industrial reconstructuring in Beijing during 2018–2020. The results show that industrial reconstructuring enabled economic growth to reach the government's planned rate while the carbon intensity and energy intensity surpassed the goal of the 13th Five-Year Plan. This case can provide decision-making basis for the sustainable development of ecology and economy in other regions. Fig. 6. Pareto front in Beijing region in 2020 The optimal Pareto solution for carbon emissions, energy consumptions and GDP targets after industrial structure reconstructuring. Image 1 • Classifying industrial sectors into four groups according to carbon emission levels and economic growth contributions. • A multi-objective optimization model was established and solved by the genetic algorithm. • A super DEA model evaluates the final plan which reflects the coordination of the economy and environment. • The analysis of each group can help achieving maximum benefits of economic and environment. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is important to ensure economic growth while conserving energy and reducing emissions. Based on the total factor energy efficiency of China's textile industry, this paper constructs a simultaneous equation model that includes the Tobit model, and empirically analyzes whether foreign trade affects energy efficiency in the textile industry as well as the mechanism of any such influence. The empirical results demonstrate positive feedback between foreign trade and energy efficiency in the textile industry, with imports impacting energy efficiency more than do exports. The foreign trade of textile industry is divided into import trade and export trade, which have different impacts on energy efficiency. Simultaneously, the results also confirm that R&D input, ownership structure and energy price significantly impact the energy efficiency of China's textile industry. • The foreign trade of textile industry significantly affects its energy efficiency. • The import trade impacts energy efficiency more than does the export trade. • Ownership structure significantly impact energy efficiency of textile industry. • Energy efficiency of textile industry has significant positive effect on foreign trade. [ABSTRACT FROM AUTHOR]

This paper presents an energy efficient optimization method for ecological 3D printing based on Adaptive Multi-Layer Customization (AMC). With the widespread application in end-use fields, the 3D printing (3DP) has already attracted great ecological attentions in energy efficiency, material saving and emissions reduction for better sustainability. Firstly, the energy and material consumption model of 3DP is built by decomposing 3DP into three sub-systems: thermal, mechanical, and auxiliary. The potential impacts on the ecological environment can be further evaluated by correlated degree using covariance analysis. The customized parameters of 3DP such as adaptive layer thickness, infill patterns, infill trajectories are sequentially and deeply investigated to form customization schemes for higher success rate by parameter combination. Considering the implicit non-linear relationships between energy efficiency and the customized parameters, adaptive Generative Adversarial Network (GAN) is built to improve calculation accuracy and prevent premature convergence inspired by Game Theory. To determine the best customization scheme according to the requirements, the mathematical model of AMC is built using multi-objective optimization (MOO). The physical experiment of energy efficiency is implemented by testing energy consumption, temperature, emissions and scanning electron micrograph (SEM). The energy efficiency is improved by maximum ratio of 11.51% and the maximum total carbon emission is reduced by 49.91% for ecological 3DP. The experiment proves that the AMC method can improve energy efficiency of complex functional specimens in highly customized fields such as medical healthcare and astronautics manufacturing industry. • Energy efficiency optimization is proposed for ecological 3D printing (3DP) via adaptive multi-layer customization (AMC). • AMC slices various 3D objects into multi-layer using customized parameters such as infill patterns, trajectories planning. • Represent the relationships between energy efficiency and customized parameters via Generative Adversarial Network (GAN). • Energy efficiency is improved by maximum ratio of 11.51% and total carbon emission is reduced by 49.91% for ecological 3DP. • Physical experiment involves energy consumption, temperature, emissions and scanning electron micrograph (SEM). [ABSTRACT FROM AUTHOR]

Water and energy resource are inextricably linked. Rapid industrialization has led to severe water and energy shortage as well as environmental issues in China. In industrial sector, industrial production and wastewater treatment are two interdependent subsystems. Accordingly, understanding the internal operation performance of the two subsystems as well as assessing their coordination level are of great importance for sustainable water and energy usage. From the perspective of production efficiency, this paper aims to explore industrial water-energy nexus in China by proposing an interactive meta-frontier network DEA approach. The proposed approach is applied to assess the industrial water and energy utilization efficiency in the processes of industrial production and wastewater treatment from 2011 to 2015 in China. The results show that although eastern and central regions have high industrial production efficiency, most regions have relatively low wastewater treatment efficiency. In addition, the nexus degree of eastern region maintains a relatively high level and shows a stable improvement, while the rest two regions perform relatively worse. Comparing the central and western region, the industrial production technology of central region is better yet the wastewater treatment efficiencies of western region are higher, which results in higher nexus degree of western region. Based on the empirical results, policy implications are given in terms of national level, regional level and provincial level, respectively. [ABSTRACT FROM AUTHOR]

The metallurgy industry that was crucial in developing Chinese economy also enhanced energy consumption and carbon emissions. This paper examines the impact of economic variables on carbon emissions from the metallurgy industry of China, where energy structure, energy intensity, carbon intensity, industrial structure, and labor productivity were utilized in analyzing carbon dioxide emissions within a quantile models framework. Quantile estimates unveiled varying effects of variables across spectrum of carbon emissions and averagely, a unit increase in the above economic variables will influence carbon emissions by 97.2 percent, 100.3 percent, 118.6 percent, 98.4 percent and 100.2 percent approximately. Across all quantiles, results showed that carbon intensity had the greatest impact on carbon dioxide emissions, then energy intensity, labor productivity, industrial structure, and energy structure, most but not all the industry was plagued by carbon intensity, while industrial scale should be minimized optimally, labor productivity should be improved too. Energy intensity is the most influencing factor, prompting an urgent need for technology advancement The uniqueness of the metallurgy industry must be considered when administering economic policies across the industry in China. The enhancement of clean energy technology and optimizing energy structure are crucial for carbon emissions reduction and a review of energy consumption by the industry to accommodate renewable energy is recommended. [ABSTRACT FROM AUTHOR]

Excessive CO 2 emissions from energy consumption has become a key factor that results in environmental degradation and restriction on sustainable development of Chinese industries. In view of this, using the deposit and loan of energy capital, and energy exportation into consideration, we employed an energy consumption model and input-output analysis to study the structural carbon emissions from energy systems on the energy supply-side and from Chinese industries on the energy demand-side during 2002–2015. The results showed that on the supply-side, the China's majorization in energy consumption structure would result in a decline of proportion of carbon emissions from high-carbon energy (raw coal, diesel oil, combustion oil, etc.) and a rise in proportion of carbon emissions from low-carbon energy (natural gas, other gas, etc.) in energy systems. Moreover, the structure optimization of low-carbon energy was generally more robust than that of high-carbon energy, and 2012–2015 was the optimal period for energy consumption structure in the study interval. On the demand-side, intermediate use (enterprises) was still the driving factor for energy carbon emissions, although end-users (residents and government) had a limited driving effect on energy carbon emissions. Specifically, for industries with intermediate use of energy, the contributions of light industry, agriculture, construction, transportation, the service industry, and the power sector to carbon emissions exhibited a downward, fluctuating trend; The use of energy by heavy industry barely changed, and the use of energy by the chemical industry and fossil energy sector showed a rising, fluctuating trend. These data suggested that the change in industrial structure was closely related to carbon emissions from use of energy. Finally, this paper provides theoretical analysis and practical guidance for promoting the green and clean development of energy systems and for realizing the transformation and upgrading of low-carbon industry systems in China. [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]

This paper builds an input-output model to analyze the energy and economic impacts of the 4.7 trillion-CNY investment projects of the " Three-Year Action Plan for Major Construction Projects of Transportation Infrastructure ", which was launched by the Chinese government in 2016. The results show that, from 2016 to 2018, the investment plan could increase China's GDP by 2.39%, 1.30%, and 1.24%, respectively, and the country's energy consumption by 3.98%, 2.35%, and 2.40%, respectively. The top five production sectors could contribute 55% of the GDP growth induced by the plan, and the top three sectors could contribute 83% of the energy consumption growth, indicating that the impact of the investment plan centers mainly on a limited number of sectors. From the view of supply chains, the 20 paths with the largest GDP contribution cover approximately 47% of the GDP growth induced by the plan, and the 20 paths that consume the most energy cover approximately 32% of the energy consumption growth. In these paths, the construction industry is the dominant demand sector, whereas the industry of nonmetallic mineral products, the industry of production and supply of electricity and steam, and the industry of smelting and pressing of metals are the primary supply sectors. In the future, the Chinese government should pay more attention to indirect energy consumption related to the construction industry, and policymakers will also need to set up more effective energy conservation policies for the upstream sectors of the construction industry, such as the sectors providing cement, glass, and steel. • Estimate the energy and economic impacts of China's three-year investment plan for transportation infrastructure. • Identify critical supply chains for the energy consumption and economic changes induced by the investment plan. • Propose several policy recommendations on reducing energy consumption in the construction industry. [ABSTRACT FROM AUTHOR]

The industrial sector in China is energy-intensive and accounts for more than 60% of the nation's total energy consumption (demand). Narrating the story behind the growth of China's industrial energy consumption could help us understand the past and prepare for the future. This paper aims to investigate the factors affecting China's industrial energy consumption in the 21st century by using an extended production-theoretical decomposition approach (PDA) with considering the technological gap and scale of the economy effects. The new proposed decomposition framework includes four new factors, namely, the energy technological gap, the output technological gap, energy scale efficiency, and output scale efficiency. The results show that in the 21st century, the energy consumption of China's industry increased by more than one billion tons of standard coal. The expansion of the industrial economy is a dominant driver of China's industrial energy consumption, while potential energy intensity, energy technology, and output technology are the three largest energy-saving factors. In addition to the conventional factors, the newly proposed factors also have important effects on China's industrial energy consumption. Specifically, output scale efficiency is an energy-saving factor, while the energy technological gap, output technological gap, and energy scale efficiency are drivers of energy consumption. This suggests that China's industry is at the stage of returns to scale that is beneficial to output performance but harmful to energy performance. Meanwhile, the widening of the industrial technological gaps has negative impacts on China's industrial energy conservation. The proposed decomposition approach can provide information that cannot be obtained from traditional approaches and it can also be applied to many other cases for investigating feasible paths of energy-saving and emissions reduction. • A newly extended decomposition approach was proposed in this study. • The approach can quantify the effects of technological gap and scale economy. • Drivers of China's industrial energy consumption were investigated as a case. • The newly provided factors do have impacts on China's industrial consumptions. • It can provide information that cannot be obtained from traditional approaches. [ABSTRACT FROM AUTHOR]