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Guiding industries to produce in an effective and environmentally sustainable manner has become a key issue for governments around the world. Over the past 15 years, series of regional and industrial development policies have been introduced by the Chinese government. This has influenced the geographical distribution and energy efficiency performance of China’s industries. This paper quantifies the influence of enterprises geographical distribution on energy efficiency improvement in the paper industry and aims at providing some helpful suggestions on industrial development to policy makers. The main results show that, firstly, the paper industry in eastern China shows obvious characteristic of agglomeration. The average location quotient is 1.2278. However, following the industrial and regional development policies, the characteristic of agglomeration is weakening. Secondly, only when agglomeration reaches a certain level (location quotient is above 0.5447) will industrial agglomeration positively impact on industrial energy efficiency improvement (a 1% increase in agglomeration will increase dynamic energy efficiency by at least 0.23%). Thirdly, the regional development policies carried out by the government slow the pace of energy efficiency improvement in China’s paper industry. The empirical results indicate that the government needs to consider regional characteristics and consciously guide industrial enterprises to concentrate in the dominant area. [ABSTRACT FROM AUTHOR]

China's paper industry accounts for a larger proportion of the global paper industry and consumes large amounts of energy. Energy conservation and pollution reduction of the industry have become the focus of China's national development strategy. Energy efficiency is an important index which directly determines the amounts of energy consumption and pollution emissions. To understand the energy efficiency evolution of China's paper industry, this paper establishes an input-output data of the paper industry for 29 provinces in China and applies DEA method and econometric models to estimate and comprehensively discuss the energy efficiency issues based on the framework of total factor efficiency. Our results indicate that China's paper industry has a large energy saving potential even though its energy efficiency has improved during the period 1990–2013. The saving potential is about 65% under the meta-frontier and 44% under the group-frontier. Moreover, there are significant differences between different regions in production technology and energy efficiency. The performance of eastern China is better than other regions. But the technology gaps between the different regions are shrinking and converging over time. Finally, some policy recommendations are proposed to improve the energy efficiency of the industry. [ABSTRACT FROM AUTHOR]

China’s paper industry consumes a lot of energy and emits huge amounts of pollutions. The Chinese government over the past decades has done a lot to promote energy efficiency of the industry. This paper studies the impact of industrial polices to improve energy efficiency. The energy efficiency change is firstly studied under a framework of total factor efficiency. On this basis, different econometric models are built to discuss the detailed energy efficiency characteristics of the paper industry. The main results are as follow: (i) ownership structure did influence the energy efficiency, but the influence is different in different conditions; (ii) energy efficiency of the paper industry can be improved by economies of scale; (iii) market competition can to some extent overcome the motivation and supervision problem that often occur in state-owned companies; (iv) energy price cannot play the role of resource allocation well in China. Finally, based on our findings, some suggestions are provided. [ABSTRACT FROM AUTHOR]

China has become the world largest paper and paperboard producer. However, pulp and paper industry has involved high resource consumption and severe pollution especially for water. This research has assessed the eco-efficiency of China's pulp and paper industry at the national level and provincial level. An overview regarding the cleaner production of China's pulp and paper industry was presented to reveal the measures for reducing environmental impact in the last two decades. Slacks-based measure was used to analyze the efficiency levels of 16 provinces' pulp and paper industries. To uncover the underlying causes of eco-efficiency performance, Malmquist–Luenberger index was calculated to discover the drivers of productivity growth of pulp and paper industries. Our results showed that the pollution treatment of China's pulp and paper industry has made progress in terms of water consumption and water pollution, although the absolute amount of pollution discharge is still large. Chemical oxygen demand emissions are still the first critical influencing factor of pulp and paper industry's inefficiency. Furthermore, efficiency progress was the dominating contribution of the industry's productivity growth between 2010 and 2013. The policies for adjusting the industrial structure of pulp and paper industry have resulted in the scale effects through eliminating backward production capacity and accelerating merger and acquisitions. Moreover, the productivity of pulp and paper industry was underestimated when the undesirable outputs were ignored. It indicates that the stricter environmental regulations have positive effects on paper companies to internalize environmental pressures in the production activities through environmental management. In the future, pulp and paper companies should further internalize the cost of pollution treatment through scale effects and technology improvement. [ABSTRACT FROM AUTHOR]

To reduce energy use and achieve a low carbon production in pulp and paper industry (PPI), it is essential to improve energy efficiency in pulp and paper production. In this study, the energy savings from the PPI were estimated based on a techno-economic analysis by EAGER tool for PPI at the factory level. The structure and implement procedure were introduced initially. Two typical pulp and paper mills from China were determined to estimate the impact of selected energy efficiency technologies (EETs) on energy savings as the cases in the base year. One Factory (A) only produced paper products (i.e. corrugated paper and Kraft linerboard) while the other Factory (B) also produced market pulp besides sack paper. In totally 22 selected EETs were applied to assess their saving potential in Factory A, and 29 measures were applied in Factory B. The results show that 2290 TJ and 1179 TJ of final energy could be saved annually in Factory A and B, corresponding to reduction of 29.4% and 13.6% total energy consumption respectively for the case factories. The CO 2 reduction potential was 32.3% for Factory A compared with that of 16.6% in Factory B. It suggests that the pulp and paper mills should encouraged to use this method estimating energy saving potential, and improve their energy efficiency with suggested measures in order to reduce energy consumption and related carbon emissions further in the coming years. • Energy savings of pulp and paper production were estimated at the factory level. • A techno-economic evaluation tool was applied to access the energy savings and carbon emission reductions. • Two typical pulp and paper factories from China were investigated to estimate their energy savings in the base year. • It is useful to assess energy savings and select feasible EETs in achieving energy saving and emission reduction targets. [ABSTRACT FROM AUTHOR]

Papermaking is a water intensive industry. The growth of paper demand will further intensify the need of fresh water resources. Papermaking involves complex processing routes. Previous investigations of water consumption in papermaking industry have focused primarily on key processes and pathways, ignoring the impacts of many intermediate and inter-related processes in paper production cycles and there underestimating the sustainability impacts. Herein, the results of a life cycle analysis of water consumption for papermaking industry in China using an extensive system boundary that includes the water embedded in intermediate processing are presented. The results show the life cycle water consumption and water saving potentials for different papermaking pathways. The advocacy of "forest pulp and paper integration" may have few practical contribution to reducing water consumption due to the low indirect water consumption of raw materials and energy for transportation. [ABSTRACT FROM AUTHOR]

The current approaches to study the coupling mechanism between economy and environment seem to fall into self-circulation, failed to reveal "economy–environment interdependence". In this context, an exogenous variable is introduced into these models for better understanding the relationship. Benzo[a]pyrene (Bap) environment inventories in different cities in China were estimated by fugacity model based on Bap concentration data collected from previous studies. An extended model, stochastic impacts by regression on population, affluence and technology (STIRPAT), generated from original IPAT (a model that expresses the idea that environmental impact (I) is the product of three factors: population (P), affluence (A) and technology (T)) was used to study the relationship between Bap environment inventory and anthropogenic factors including population, vehicle amount, affluence and energy consumption. A ridge regression has been applied to optimize the model parameters. Overall, there exist good relationships between Bap inventories and economic factors. Specifically, population and affluence are the most significant factors that influence Bap inventory. Rise of the scale of population and vehicle amount increases the Bap inventory, as growth of GDP per capita and the efficiency of energy utilization have inhibitory effect on Bap inventory. Apparently, the influences of anthropogenic factors on Bap inventory are different between north and south China. Bap inventory in north China is more sensitive to the change of population and energy consumption, while is highly correlated to GDP per capita in south China. Further, according to social-economic development prediction, with assist of the result from STIRPAT model, we find out that Bap inventory drop 31% from 2001 to 2020 in China. Our estimates are comparable with the published data and confirmed that Bap can be regarded as a chemical indicator of social-economic development in China. Image 1 • An extended STIRPAT model derived from original IPAT was developed. • Population and affluence are the key factors that influence Bap inventory. • Advance of affluence and technology level curbs Bap environmental exposure. • We predict Bap environmental inventory drop 31% from 2001 to 2020 in China. • Bap can be used as an indicator of social-economic development in China. [ABSTRACT FROM AUTHOR]

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

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

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

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

The application of a photovoltaic (PV) system for zero energy buildings is crucial to balance energy consumption. This paper investigated the potential of PV systems for buildings in different climate zones of China. The tilt angle, orientation, plot ratio, PV conversion efficiency and location were selected to study their influences on electricity production. The performance of the PV system was analyzed based on a residential building, and employed RETScreen in combination with TRNSYS. The results indicate that southwest China is the best place to develop zero energy buildings. Low-rise residential buildings can realize zero energy in China when the PV conversion efficiency is higher than 20%. However, an energy balance only occurs in the southwest when the PV conversion efficiency is 10%. For different building heights, with a high conversion efficiency (20%), the energy consumptions rates are less than 15 kW h/m2a all over China. In the southeast, the effect of the building shape on energy generation can be ignored due to the high solar elevation angle in the summer. Although the optimal tilt angle varies for different cities, the energy deviations are less than 10% when tilt angle is within 10° of the optimal angle. This study proposed a formula to evaluate the potential of a building PV system. The maximum difference between the simulated and calculated results is approximately 10%, which is reasonable and acceptable. This paper provides some references for the application of PV systems in zero energy buildings. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces in China from 2006 to 2016, the Gaussian kernel density estimation was initially used to describe the characteristics of the inter-provincial differences in household energy consumption (HEC), a dynamic panel data model was established secondly to study the main factors affecting HEC, and finally the counterfactual decomposition method was used to calculate the contribution of each factor to the inter-provincial differences in HEC. The research results are shown as follows: First, the inter-provincial HEC in China shows a phenomenon of agglomeration and growth, and the HEC level of different regions presents difference and imbalance. Second, the logarithms of consumption habits, total population and residents' income have significant effects on the logarithm of HEC, the influence coefficients are 0.191, 0.073 and 0.745, respectively; HEC not only has significant inertia characteristics, but also has certain population size effect and wealth effect. Third, the logarithms of technological progress, urbanization and household energy price have significant effects on the logarithm of HEC, the influence coefficients are 0.864, 0.195 and −0.346, respectively; the HEC saved by technological transformation is not sufficient to offset the increase in HEC demand, urbanization can improve HEC significantly, but the household energy price significantly inhibits HEC. Finally, from the perspective of contribution, the total population, residents' income and urbanization contribute a lot to the inter-provincial differences, totaling 73.02%. The possible innovations of this paper are as follows: First, the contribution of the influencing factors to the difference of inter-provincial HEC is studied, which is rarely seen in the previous literature. Second, compared with traditional decomposition method, counterfactual decomposition method is more convenient for economic interpretation. Third, the divergent conclusions of scholars are pointed out in this paper, and the actual impact of influencing factors on HEC are clarified. Image 10086681 • The level of inter-provincial HEC shows difference and imbalance. • HEC has significant inertia characteristics, population size effect and wealth effect. • HEC saved by technology is not sufficient to offset the increase in HEC demand. • Total population, residents' income and urbanization contribute 73.02%. • The statistical analysis tool used in this paper is Stata15. [ABSTRACT FROM AUTHOR]

The objective of the traditional dispatching optimization model is to find the most economical scheduling mode, while ignoring the efficient use of renewable energy. To properly arrange thermal power generation and renewable energy power generation, this paper constructs an optimization model of the combined wind–photovoltaic (PV)–thermal dispatching system under a carbon emissions trading (CET) mechanism. The contribution of this paper is twofold. First, a dispatching optimization model is proposed, which not only considers the total cost but also takes into account the impact of the renewable energy spillage rate on the dispatching system. As a factor affecting renewable energy consumption, a carbon emissions trading mechanism has also been introduced into the dispatching system. The second contribution is to develop a model for predicting the output of renewable energy generation. Then, the optimization model is applied to the auxiliary power dispatching system of the Tianzhong ±800 kV UHV DC transmission channel in Xinjiang. The results indicate that under a carbon emissions trading mechanism, thermal power generation will be limited by the carbon emissions cost, thereby reducing the total cost of the system. Optimizing the spillage rate increases the share of renewable energy in the dispatching system, resulting in a 3.5% reduction in the actual spillage rate. The proposed model alleviates the serious problem of electricity being abandoned in the existing scheduling mode and helps to realize economical low-carbon dispatching of the power system. [ABSTRACT FROM AUTHOR]

Abstract Currently, wind and solar energy have not been well utilized in China, and the rate of abandoned wind and solar facilities in China is increasing annually. Renewable portfolio standards (RPS) are an effective system for promoting large-scale renewable energy integration and consumption and realizing the goal of renewable energy development. To clarify the interactive relationship between the different levels and multiple actors in RPS, this paper uses policy network theory to analyze the interactive relationship of stakeholders from the three aspects of network subject, network structure and network interaction. The policy network of the RPS consists of the policy community, professional network, intergovernmental network, producer network and issue network. Under the RPS, to meet their own needs, participants affect the formulation and implementation of the RPS through the actions of confrontation, alliance, competition and cooperation. In different stages of developing the RPS, the openness and integration of policy networks change accordingly. This study overcomes the limitations of past simple analyses of the actor relationship in the policy process and thereby offers certain reference significance. Highlights • The actors of RPS policy networks are analyzed. • This paper analyze the game relationship of the stakeholders from three aspects. • This research reveals the network relationship between multiple stakeholders. • This study breaks through the limitations of past analysis of actor relationship. [ABSTRACT FROM AUTHOR]

Abstract China has encountered the environmental issues and energy pressures in the middle-income stage. Strengthening environmental regulation is a crucial measure to prevent environment deterioration and energy dilemma. This paper investigates the three types of environmental regulations on energy consumption using feasible generalized least squares method during 1997–2015 in direct and indirect path. The main conclusions are as follows. In the direct path, the economical and supervised types of environmental regulations have inhibited energy consumption whilst legal environmental regulation has no significant impact. As for indirect path, the "rebound effect" of energy is greater than the energy conservation generated by technological innovation, which is embodied as an inhibitory effect. The effects of environmental regulation display regional difference. The legal and supervised types of environmental regulations have significant "cost effect" and "rebound effect" on energy consumption in the entire samples. On the contrary, the economical environmental regulation shows the opposite sigh in eastern regions. The counterfactual simulation results show that the net effects of environmental regulations on energy consumption are negative, which is beneficial for relieving energy pressures. Based on the findings, this paper provides pertinent policy implications. Graphical abstract Image 1 Highlights • Environmental regulation can be divided into three types. • Environmental regulation can affect energy consumption in direct and indirect path. • Differernt types of environmental regulation have distinct impacts on energy consumption. • The effect of environmental regulation displays regional difference. • Strengthening environmental regulation can relieve energy pressure. [ABSTRACT FROM AUTHOR]

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

The operation of tidal pumping station has an energetic impact on energy course of massive water pumping for a long time. Thus, a multi-objective optimal operation model was developed to optimize the operation scheme and reduce the energy consumption of the tidal pumping station. In the model, a day is divided into several sub-periods according to the regulation of water level in tidal reach. The partition of sub-periods, the number and blade angle of operating units in each sub-period are adjusted to minimize daily operation energy consumption. And the less switching times, the better the reliability and durability of the unit, the lower the failure rate. Therefore, the reduction of wearing parts' service life is quantified for each switching time in the form of cost. A case study is performed with the tidal pumping station of the South-to-North Water Transfer Project in China. In this paper, energy consumption and switching times are quantified as costs respectively, and the result shows that the daily energy consumption could be saved about 2.28%–6.56%, and the switching times reduced at least 60%. Therefore, this paper could provide an effective optimal operation scheme for the tidal pumping station whose operation is affected by tides. [ABSTRACT FROM AUTHOR]

Addressing energy poverty and promoting energy equity are crucial for China to eradicate poverty comprehensively and achieve shared prosperity. Through a combined dynamic evaluation approach to accurately measure the extent of energy poverty, this paper analyzes the staged time series, spatial distribution characteristics of energy poverty applies the Dagum Gini coefficient and variance decomposition to observe the imbalance characteristics of energy poverty and identify the sources of variation. The objective of this paper is to mitigate the influence of spatial disparities on coordinating efforts to eradicate energy poverty across different regions. Consequently, provides valuable insights for decision-making to effectively eradicate energy poverty and expedite the achievement of energy equity. The results indicate: The reduction process of energy poverty in China exhibit phased characteristics from 2000 to 2020, with significant variations in reduction levels among provinces. Different sources of energy poverty exhibits imbalanced spatial distribution and evolution. The overall disparity in energy poverty widens as the phases progress, with characteristic nodes experiencing a temporal delay of 2–3 years compared to the policy nodes. Inter-regional disparities are the primary spatial source of energy poverty differences, while energy use capacity is the main factor contributing to variation. [ABSTRACT FROM AUTHOR]

The five major power generation corporations dominate the power industry in China, and play vital roles in China's carbon trading scheme. Under this circumstance, this paper studies the allocation of carbon emission quotas to China's five major power generation corporations based on the fairness and efficiency principles, which proves the primary prerequisite for setting up the national carbon market. Specifically, a bi-level programming model is developed to optimally allocate carbon emission quotas to the corporations, and then a zero sum gains data envelopment analysis (ZSG-DEA) model is adopted to evaluate the efficiency of the allocation so as to adjust towards the optimal solution. The results indicate that power generation has significantly positive influence on the allocation of carbon emission quotas. Moreover, the difference between the sum of optimal carbon emissions and the sum of carbon quotas allocated is 1.431 billion tons, which implies that the emission reduction potential of the five major power generation corporations is tremendous. Finally, the bi-level model in this paper can well capture the context of China's power industry and provide effective results for the allocation of carbon quotas. [ABSTRACT FROM AUTHOR]

Due to the ever-increasing quantity of produced and sold automobiles in China, many side effects such as resource depletion, energy shortages and air pollution have emerged. Remanufacturing, as the highest form of the recycling economy, has been promoted by the Chinese government as a major measure to save resources and energy. The remanufactured products, however, get very low market recognition from Chinese consumers. Thus, understanding how to cultivate consumers potential behavior of purchasing remanufactured products is the first step and also very important for developing the remanufacturing industry. This paper, focusing on heavy truck engines due to their short life expectancy and repair frequency, aims to identify the factors influencing remanufactured heavy truck engine (RHTE) potential purchase and analyze the relationship among those factors and potential purchasing behavior. Based on 301 questionnaires from Chinese heavy truck drivers, this paper develops a conceptual model to hypothesize how the influencing factors affect RHTE potential purchasing behavior. Hierarchical regression results show that individual factors (including product knowledge, product trust and environmental values) mediate the relationship between environmental factors (including product attribute and advertisement) and RHTE purchasing intention. Contextual factors (including incentive policy and purchasing convenience) moderate the relationship between RHTE purchasing intention and potential purchasing behavior. Our results indicate that individual factors are essential in cultivating RHTE purchasing intention and environmental factors are helpful for improving individual factors. Contextual factors can motivate the transformation from RHTE purchasing intention to potential purchasing behavior. [ABSTRACT FROM AUTHOR]

Reform of energy prices in China has stepped into a crucial stage. It has become a controversial issue after the reform of increasing block tariffs (IBTs) for household electricity, followed by a time-of-use (TOU) pricing policy. Using ELES model, this paper estimates the price elasticity of residential electricity demand; based on perceived price and price elasticity, a power structure model demand response for nonlinear electricity price is proposed; taking Anhui province as a case, this paper analyzes the attainment situation of such policy objectives as electricity saving, consumer welfare, equity improvement and peak shaving, with the introduction of TOU pricing into IBTs for household electricity. The results show that IBTs helps reduce electricity consumption by 0.74%–0.93%, with minor energy effect；IBTs may lead to a maximum 1.57CNY increase of electricity expense per household per month, which is acceptable for residents. It also may reduce cross-subsidy to some extent. After introducing TOU pricing, the efficiency of peak shaving is only 2.25%–2.78%, and the load decrement at peak periods is far lower than the increase at valley periods; while the effect of electricity conservation and cross-subsidy reduction have been alleviated. In other words, peak-valley price is not only difficult to achieve its own objective effectively, but also prohibits the effect of IBTs. Based on the above conclusions, some policy recommendations are provided to improve the price reform of residential electricity, natural gas and other public resources in various regions of China and foreign regions with similar electricity market. [ABSTRACT FROM AUTHOR]

Considering both regional heterogeneity and industrial heterogeneity, this paper proposes a three-hierarchy meta-frontier data envelopment analysis (DEA) to decompose energy efficiency and energy-saving potential into three components, i.e., industrial structure, regional balance development, and management. Based on this approach, the paper then analyses the historical evolution and current status of energy efficiency in China. The results show the following. (1) From 2001 to 2015, Mainland China's energy efficiency increased by a factor of 2.48, primarily because of technological progress and the optimization of its industrial structure. (2) Nonetheless, the current energy efficiency in Mainland China remains very low and energy inefficiency is as high as 0.5922, indicating that 59.22% of total energy consumption is invalidly used. Severe industrial structure inefficiency and regional balance development inefficiency are the two main factors responsible for the very low energy efficiency. (3) By promoting industrial restructuring, regional balance development, and management improvement, Mainland China is expected to save, respectively, 13.67%, 24.54%, and 61.73% of its current total invalid energy consumption. Based on the estimation results, this paper formulates strategies to improve energy efficiency and save energy for China's provinces based on their specific situations, considering not only the direction of industrial restructuring in both the short and long terms but also the path to improved energy efficiency. In addition, a brief discussion about the applicability and the preconditions for an application of the multi-hierarchy meta-frontier approach is given at the end of the paper. [ABSTRACT FROM AUTHOR]

The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry. [ABSTRACT FROM AUTHOR]

The time-varying dynamics of the transition to renewable energy reflect the effectiveness and efficiency of energy policy. This paper investigates renewable energy transition behavior in China using Markov regime-switching models. Two distinct phases, expansion and contraction, are identified based on the growth rates of renewable energy consumption share. We find that since China started its new-energy national strategy in 2005, there has been an extended period in which it has been in the expansion state, although it occasionally reverts to a contraction state. Intriguingly, we uncover that the timing of transition state changes can be explained by the changes in policy-related, market-based instruments and risk-related indexes. These findings present implications for policymakers in developing more precise and effective policies, that drives investment choices to align with the goal of a sustainable energy system. [Display omitted] [ABSTRACT FROM AUTHOR]

Establishing an intuitive link between driving factors of household energy consumption activities and inequalities is important for the understanding of household heterogeneity in energy consumption behaviours. This paper proposes a novel typology framework based on machine learning approaches and data from 3637 Chinese households in 2014 from 85 cities. Activity-based energy consumption was measured, highlighting inequalities across activities, regions and household types. The results showed significant energy consumption disparities between urban/rural and north/south households, especially in cooking, space heating and vehicle activities. By identifying driving factors of energy consumption, a new household typology classified samples into 6 (all), 6 (urban) and 7 (rural) types. Within these types, households with similar demographic structures, lifestyles and energy consumption habits were clustered. Demographic structure, region, and primary energy demand were used as the basis for the typology. The findings demonstrated how household lifestyle differences explained the cause and underlying driving factors of urban-rural energy consumption inequalities and provided suggestions for city-by-city and type-by-type measurements to support effective low-carbon transformation in cities. • Activity-based household energy consumption (HEC) is measured and calibrated. • Household energy consumption inequalities exist in urban/rural and the north/south groups. • A four-step machine-learning approach well classifies households into reasonable clusters. • Activity-based variables are driving factors for HECs in a LASSO model. • Inequality is narrowed in household clusters and type-by-type policies are needed. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

The battery electric vehicle (BEV) industry, commanding over half of the global market share in China, has garnered significant attention in response to escalating concerns about climate change. NCM (LiNi x Co y Mn z O 2) batteries, prized for their high energy density, are pivotal in this industry. However, their production relies on critical metals, posing environmental challenges. Recycling processes aim to address this issue by recovering critical metals from end-of-life batteries. The industry is updating from NCM 111 to NCM 811 batteries to enhance energy density and reduce costs, but the key factors influencing the material flows and environmental impacts remain uncertain. This paper aims to identify the key factors contributing to the potential differences in material flows and environmental impacts introduced by the NCM battery upgrading in China. Following the new energy vehicle sales projections in China until 2035, a dynamic material flow analysis was employed to calculate material demands, recovered amounts, and substitution rates for various upgrading scenarios. The study reveals that the majority of retired batteries until 2035 were sold between 2022 and 2029. Material substitution rates prove to be more sensitive to the upgrading scenario than the lifetime and growth rate of BEVs. Faster NCM 811 penetration results in over 2.5 times more saved cobalt demand compared to additional nickel demand. A life cycle assessment of NCM batteries reveals a 39% lower global warming impact for NCM 811 compared to NCM 111, highlighting environmental benefits from increased energy density and reduced cobalt consumption. The energy density improvement contributed 85%, while the alteration in material composition contributed 15% to the global warming reduction in NCM 811. Recycling emerges as a crucial strategy in mitigating environmental impacts across all categories, particularly addressing eutrophication, previously deemed challenging due to the difficulty in recovering Li 2 CO 3. [ABSTRACT FROM AUTHOR]

Remanufacturing is a critical tool for solving resource depletion and environmental deterioration by reducing raw material and embedded energy consumption. The Chinese government, like many other countries, has been keenly interested in remanufacturing during the past ten years, because of its potential material and energy usage reductions. However, the development of the remanufacturing industry has been unsatisfactory because of little knowledge of its key industry indices. Many potential customers of remanufactured products do not trust the quality of remanufactured products. The major objectives of this paper are to classify the key industrial indices of the remanufacturing industry and to suggest future directions for improvements. To achieve these objectives, this paper used a combined method of grey incidence analysis and grey clustering, which are suitable for distinguishing critical industry indices that can measure the status of development situation of the remanufacturing industry. This approach was successfully applied to a case in Jiangsu Province, China, and consequently developed useful governmental and industrial policy recommendations for remanufacturing enterprises to promote that industry. The research results identified the “total number of employees with a master's degree or above”, the “total number of engineering and technical staff” and the “number of intermediate technicians” as the most appropriate industry indices to reflect the development situation of the remanufacturing industry in China. There are two more important findings. The first is that remanufacturing talents are the most important internal impetus of the remanufacturing industry and should be paid more attention to. The second is that investment in the remanufacturing industry does not have a strong positive correlation with industry characteristics. The “annual revenue of the remanufacturing industry” and huge investment have not played an effective role in promoting this industry so far. The main contribution of this paper is to provide an effective decision-making tool for defining key industry indices that have an important effect on the remanufacturing industry. The tool provides decision-makers a scientific basis for developing and implementing appropriate industry policies and improvement measures to expand the size and scope of the remanufacturing industry in the future. [ABSTRACT FROM AUTHOR]

Whether industrial agglomeration can promote an increase in energy efficiency is of great importance to not only China’s energy conservation and emissions reduction plans, but also China’s future sustainable development. This paper uses statistical data from 285 cities, from 2004 to 2013, to analyze the effect of industrial agglomeration on energy efficiency. The spatial autocorrelation tests show that there is significant spatial autocorrelation and spatial heterogeneity in urban energy efficiency. Although dynamic spatial econometric results show that industrial agglomeration can promote an increase in energy efficiency from a country level, there are significant differences at a regional level. In the east, the effect of industrial agglomeration on energy efficiency has a threshold effect, but in central and western areas, industrial agglomeration can promote the increase of energy efficiency, with the positive effect in western areas greater than that in central areas. Finally, based on the research, this paper puts forward concrete suggestions to enhance energy efficiency. [ABSTRACT FROM AUTHOR]

Sustainable consumption (SC) is key for sustainable development, but only limited studies have examined how to motivate SC among consumers in developing countries. Younger consumers tend to more sustainable consumption while whether they really understand the SC concept is not clear. To explore the role of SC understanding for motivating SC behaviors, this paper develops a concept model with hypotheses to propose that SC understanding mediates relationships between influencing factors and SC behaviors. Based on 623 usable questionnaires collected from junior high schools and senior high schools in three eastern cities of China, five influencing factors for SC behaviors are identified, they are, education, awareness & attitude, social influence, effectiveness of energy saving and emission reduction for sustainable products, and differentiation of sustainable products from regular products. Statistic results through hierarchical regression analysis demonstrate that SC understanding is necessary to motivate SC behaviors with certain influencing factors. Chinese adolescents can understand the SC concept through education and then purchase sustainable products. Only if sustainable products can show benefits of energy saving and emission reduction, Chinese adolescent would like to use these products. Environmental awareness and attitude can bring environmental treatment and disposal of products, but SC understanding is necessary. This paper contributes to extend the TPB to develop a conceptual model including education as one influencing factor for SC behaviors. It also develops three factors for SC behaviors from the product life cycle perspective. Besides, it also contributes to extend the previous studies about motivation of SC consumption to adolescents in an emergent economy of China to examine the role of SC understanding. Statistic results reveal the low implementation level of SC behaviors, and the government should highlight sustainable education to improve sustainable awareness and SC understanding. It is also shown that adolescents would like to purchase sustainable products if environmental performance of such products can be demonstrated, which also provide insights for enterprises to achieve win-win opportunities. [ABSTRACT FROM AUTHOR]

Under the trend of global low-carbon development, reasonable and accurate prediction of electricity consumption plays an essential role in vigorously adjusting power system structure, promoting electrification, and other energy-saving and emission reduction measures. Considering the development trend of energy consumption, this paper introduces the Logistic model of energy structure into the system structure, and establishes a novel grey prediction model with system structure. According to the division of energy factors with similar attributes, this model seeks the internal relationship of the development of electricity consumption and describes the interaction between related factors and multiple main factors in the form of equations, which makes the model have better applicability and stability. In the validation part, the ten types of energy are divided according to their attributes, and the main factor group and the related factor group are distinguished. The model proposed in this paper is used for simulation and prediction, and is compared with the three types of models (six models). In the two cases, the simulation error of the new model is as low as 3.9790%, and the prediction error is 0.5645%. Compared with other models, the new model has shown good performance in the case of electricity consumption forecasting in China. The effectiveness of the optimization of the model in structure, background, and application is verified. At the same time, based on the analysis and prediction of China's consumption data, this paper gives relevant policy suggestions for developing China's power structure. • A novel model based on energy background (EPMGM(1,M,N)) is established. • The relationship of factors in the system are characterized by the equations. • A Logistic model based on energy structure is introduced to the new model. • China's electricity consumption is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Based on 196 prefecture-level cities in China from 2006 to 2021, this paper constructs a weight matrix of urban economic geography and uses the spatial differential model to verify the impact of smart city policies on urban green energy efficiency. The results show that the efficiency value of green energy fluctuates in Chinese cities. Different test results show that smart city policies can effectively improve urban green energy efficiency, but mainly from the time effect rather than the individual effect. It means the improvement in urban green energy efficiency comes more from the fast-growing of the Chinese economy and urban construction in this period than from the country's smart city pilot policy. In addition, from the spatial spillover effect result, China needs to design a better urban spatial development plan to improve the capacity of smart city energy services and vigorously develop intelligent technology represented by information and communication science. [ABSTRACT FROM AUTHOR]

At present, the energy and environmental crisis is increasingly prominent, and how to solve the global energy and environmental problems is one of the challenges facing the world today. China is a major producer and consumer of organosilicon, so it is necessary to explore the stage of high energy consumption and high pollution in the organosilicon industrial chain. In 2022, the proportion of submerged arc furnaces of 25000 kVA and higher in China reached 40%. Further expansion of these furnaces in the future is inevitable. However, research on the life cycle assessment of organosilicon manufactured using large-capacity submerged arc furnaces is still blank.Therefore,in this paper, the life cycle assessment method is used to evaluate the synthesis process of chemical grade silicon-organosilicon monomer prepared by large capacity (33000 kVA) submerged arc furnace, and the dominant process in various environmental impacts is defined.At the same time, the measures to improve the production process of organosilicon monomer were put forward. In the production process of 1000 kg organosilicon monomer, global warming potential (GWP) contributed the most (73.43%) to the comprehensive environmental impact, followed by chronic public health impact (HTI, 10.55%). Energy consumption (EN) accounted for 8.50%, while resource consumption (RU) accounted for 7.43%. The contributions of other environmental factors were relatively small (0.09%).The energy consumption and environmental pollution in the production of organosilicon monomer can be reduced by strictly controlling the quality of raw materials into the furnace, using heat pump technology in the distillation unit, and comprehensively utilizing the heat of reaction by-products and steam condensate. [Display omitted] • This study is the first one in China to conduct LCA for the preparation of chemical grade silicon-organosilicon monomer synthesis process in 33000 kVA submerged arc furnace. It provides the best improvement suggestions for reducing the environmental load of the product life cycle process and achieving cleaner production. • In response to the national development policy of large-scale submerged arc furnace, the dominant process of preparing organosilicon monomer based on 33000 kva submerged arc furnace in various environmental impacts was clarified. • The main technical improvement directions are: Strictly control the quality of raw materials for submerged arc furnace; Strictly control the sulfur content and nitrogen content of carbonaceous reductant, and adopt the most suitable flue gas treatment technology route; Smelt with large submerged arc furnace; Use heat pump technology for distillation unit; Optimize the monomer synthesis device. [ABSTRACT FROM AUTHOR]

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

With the comprehensive progression of China's electric power reform, the participation of hydropower in the spot market has become inevitable. For hydropower-dominated power grids, the tight hydraulic and electric coupling relationship between cascade hydropower plants and the policy requirement of clean energy consumption greatly increase the challenge of optimal scheduling. Based on the market characteristics and actual demand of the hydropower-dominated power grids, this paper discusses the day-ahead spot market trading mechanism to promote the guaranteed consumption of renewable energy. On this basis, a day-ahead optimal scheduling model is established which aims to minimize the comprehensive power purchase cost of the power system. The nonlinear factors in the scheduling model, such as the relationship between the water level and storage capacity of hydropower plants, the relationship between the water level and water consumption rate, and the constraints on the start-up and shutdown durations of thermal power units, are linearized through multiple linearization techniques. The original nonlinear model is transformed into a mixed integer linear programming (MILP) model and is then solved using a CPLEX solver. The developed model is verified by taking 10 thermal power plants and 22 hydropower plants in Yunnan Power Grid as a case study. The calculation time is 183.6s, the comprehensive power purchase cost of the system is 155.35·million CNY, and no abandoned water is generated in the three basins. The simulation results show that the proposed model has high solution accuracy and efficiency, and maximizes the hydropower consumption under the premise of meeting the minimum comprehensive power purchase cost. The findings of this study can provide a scientific basis for the development of the day-ahead spot market of hydropower-dominated power grids. [Display omitted] • A day-ahead spot market trading mechanism to promote consumption of renewable energy. • Day-ahead optimal scheduling of hydropower-dominated power grids under a spot market environment. • A novel MILP approach is employed to solve the model. • Proposed model has high solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%. • Energy efficient speed planning methods for electric industrial vehicles are proposed. • The speed planning method uses data-driven vehicle energy consumption models. • The energy consumption models are developed using random forest and LSTM. • The speed planning problem is formulated as a Markov decision process. • The speed planning problem is solved by Dynamic Programming and Q-learning. [ABSTRACT FROM AUTHOR]

Recognizing the industrial sector's high energy use and vast potential for energy saving, many national governments have established voluntary or negotiated agreements that set quantitative energy-saving targets for individual industrial enterprises. The most commonly used energy-saving targets worldwide are volume and energy intensity targets. Since 2006, the Chinese government has set mandatory, enterprise-level energy-saving targets in terms of the “amount of energy saved”; this represents a unique category that differs from both volume and energy intensity targets. Based on first-hand data collected at ten case enterprises, this paper represents one of the first attempts to verify the reliability of the amount of energy saved data reported by the Chinese government and to rigorously and systematically investigate the inherent weaknesses of the amount of energy saved targets through a comparison with the mainstream categories of energy-saving targets. The ten case enterprises reported four different types of amount of energy saved. While all of the case enterprises claimed full achievement of their amount of energy saved targets, four enterprises exaggerated their performance by violating the National Standard for Calculating the Amount of Energy Saved of Enterprises. The paper thus hypothesizes that the National Top-1000 Enterprise Program's alleged achievement of performance 65% higher relative to its amount of energy saved target is likely an overestimation. Although amount of energy saved targets constitute an innovative energy-saving indicator, they do not represent a step forward from conventional volume and intensity targets because of the following four weaknesses: 1) amount of energy saved targets provide limited potential for comparison given the different types of amount of energy saved and the different calculation methods, 2) they generate uncertain environmental outcomes as it is possible to meet a portion of an amount of energy saved target through production volume expansion, 3) they are difficult to enforce due to the complexity of data verification on the part of local government agencies, and 4) they are poorly correlated with the national target for reducing energy intensity. Amount of energy saved targets should therefore be replaced with “double-control” targets that impose both volume and intensity targets on industrial enterprises. [ABSTRACT FROM AUTHOR]

The Chinese government committed to reduce its national CO 2 emissions intensity by 40–45% until 2020 in response to climate change. This paper quantitatively evaluates the performance of CO 2 emissions reduction over the period 2005–2020 from the historical and prospective perspectives by using a combination of decomposition analysis and scenario analysis, aiming to provide suggestions about how to achieve the target in key emissions reduction fields. Unlike traditional index decomposition methods, this paper incorporates the total energy conversion efficiency effect into the model, and further decomposes it into a final energy mix effect and a final energy conversion efficiency effect by a multilevel decomposition procedure, allowing to measure the contributions of primary energy structure low-carbonization, the final energy mix electrification, and the energy conversion efficiency improvement. The results show that the carbon intensity could decline over the period 2005–2020 by 47.8%, 50.9%, 48.0%, 44.5% and 47.5% in the Business as Usual, S1, S2, S3 and S4 scenarios, respectively. This shows that the 40–45% target is very likely to be achieved. The final energy intensity effect is always the most important driving factor, causing carbon intensity to decrease by 24.0% and 22.1%, again respectively, from 2005–2012 to 2012–2020. Moreover, the energy conversion effect was another major driver during 2005–2012. The Chinese government needs to make more efforts to adjust the industrial structure, which could cause carbon intensity to decrease by 3.6% during 2012–2020, and to adjust the primary energy mix, which could cause carbon intensity to decrease by 7.1% during 2012–2020. [ABSTRACT FROM AUTHOR]

Emerging economies like China, India, and Mexico are becoming the world's manufacturing leaders through the engagement in offshoring production activities. In achieving this status, however, they have met with difficult environmental issues. This paper features an analysis of industry-level panel data to examine the link between different offshoring tasks in China and carbon dioxide emissions. Through this analysis, the environmental effects of offshored production are addressed. Specifically an input–output model is presented, thereby quantifying offshored production tasks in China. The results show that iron and steel, nonferrous metals (IS) and chemicals (CH) generate the most carbon dioxide during production of processed goods. Additionally, electrical machinery (EM) and general machinery (GM) generate a higher amount of carbon dioxide during production of parts and components. The results also show that those industries characterized by higher energy consumption have higher carbon dioxide (CO 2 ) emissions induced by offshoring. Given these results, it seems that any solutions geared towards reducing the energy consumption of entire manufacturing industries would be useful. Furthermore, the results of the analyses presented here indicate that China would be responsible for working with developed countries to reduce carbon dioxide emissions through cooperative environmental regulation. A globalization perspective is adopted to offer unique insight into the environmental issues faced by China. The results of this paper would provide quantified, actionable information for formulating practical industrial policies geared towards reducing carbon dioxide leakage. [ABSTRACT FROM AUTHOR]

Environmental efficiency improvement has played a crucial role in the theory and practice of stimulating clean production. This paper analyzes the interaction between environmental efficiency and output efficiency, particularly whether they reinforce each other or compete with each other, on the basis of a data set of 137 firms in the textile industry in China's Jiangsu Province. In the first stage, generalized data envelopment analysis is applied to calculate efficiency measures of energy, waste water, waste gas, soot, and output efficiency taking capital, labor, water, and energy as inputs, industrial output value as desirable output, and waste water discharges, waste gas and soot emissions as undesirable outputs. In the second stage analysis, a structural equation model with latent variables is applied to analyze the interaction between the latent variable environmental efficiency, measured by the four observed environmental indicators, and output efficiency, taking also into account the endogenous variable profit. The main outcomes of the structural equation model are the following. Firstly, environmental efficiency negatively impacts on profit while profit positively impacts on environmental efficiency. In a similar vein, output efficiency is found to depress profit while profit increases output efficiency. Thirdly, environmental efficiency has a positive impact on output efficiency while there is no effect of output efficiency on environmental efficiency. Fourthly, taxes impair a firm's output efficiency. From the findings it follows that a swap of general taxes for an energy tax is likely to improve both output efficiency and energy efficiency. The latter outcome implies a win–win situation which will facilitate the further implementation and adoption of environmental policy. Finally, the paper illustrates the applicability of structural equation modeling in efficiency analysis. [ABSTRACT FROM AUTHOR]

This paper chooses 98 mining cities in China from 2007 to 2021 as research samples. It investigates the policy practice of green development in mining cities according to the point of view of policy combination. We establish a super-efficiency model considering undesirable output to measure the green development level of mining cities and further construct a dynamic panel model based on the Gaussian mixture model (GMM) to test the transmission mechanism and heterogeneous effect of different types of environmental regulation to promote the green development of mining cities. The findings demonstrate that environmental regulations about command control, market incentives, and public participation can all help mining cities develop more sustainably. Additionally, there is a nonlinear relationship of an inverted "U" shape between the degree of green development in mining cities and environmental regulations related to command control. Combining policies can encourage the green development of mining cities, but there are crowding-out and substitution effects when environmental regulations are combined with market forces. Environmental regulation promotes urban green development by improving the energy consumption structure of mining cities. In this process, the level of digital development positively regulates the impact of environmental regulation on urban green development. Therefore, the government should choose environmental regulation tools and combinations according to local needs to achieve a multi-pronged effect of energy structure improvement and urban green development and use digital technology to support the green development of mining cities. • Command-controlled, market-motivated, and public-participating environmental regulations can significantly promote urban green development by promoting the improvement of energy structure. • There is an inverted' U' -shaped nonlinear relationship between the intensity of command and control environmental regulation and the green level of mining cities. • The level of digitization can positively regulate the relationship between environmental regulation and urban green development. • The combination of environmental regulations have a significant positive impact on urban green development. • The combination of environmental regulations has heterogeneity in urban green development due to factors such as regional resource endowment. [ABSTRACT FROM AUTHOR]

This study aims to elucidate the profound implications of industrial transformation and upgrading in mitigating dependence on fossil fuels, which holds paramount significance in shaping energy transition strategies—an aspect that is currently missing in existing studies. Leveraging data spanning from 2000 to 2020, encompassing fossil energy consumption and economic development, this paper developed a second-order classification model as a new measure of energy elasticity. Comparing regional variations in fossil energy resilience, an in-depth analysis was conducted on four representative provinces to understand the evolution of their fossil energy consumption structures over time. Correlation analysis was employed to reveal the impact of industrial structures on the reduction of fossil energy dependence. The results indicate distinct spatial variations in the energy transition effect, with the southwest inland region retaining a pronounced reliance on fossil energy sources. However, approximately 80% of the provinces have made substantial progress in reducing fossil energy dependence from 2000 to 2020. Notably, the financial sector and the accommodation and catering industry contribute positively to mitigating fossil energy dependence, while the industrial sector exhibits the opposite effect. Urban agglomeration planning attributes the uneven regional energy transition to the delineation of urban industrial functions. Suggestions for narrowing the energy transition gap include measuring the sustainability of the industrial chain from a life-cycle perspective, avoiding the singular functional division of cities, and developing systematic policies. In the catering industry, adopting alternative cooking energy and implementing the conversion and recycling of catering waste can help prevent risks. This study underscores the positive role of the accommodation and catering industry in the energy transition, along with its potential risks. The study also makes efforts toward advancing the application of the widely used energy elasticity assessment method. [Display omitted] • A second-order classification model is developed as a widely used energy elasticity assessment method. • 80% of China's provinces have successfully reduced their reliance on fossil fuels. • The financial sector and the accommodation and catering sector play pivotal roles in diminishing reliance on fossil fuels. • In the forthcoming years, the catering industry has the potential to engender a reliance on fossil fuels. [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

One of the critical challenges facing China is environmental pollution caused by insufficient use of clean energy. This paper empirically investigates the impact of rural labor mobility on household clean energy use using data from the China Family Panel Studies (CFPS). The results indicate that labor mobility significantly promotes the adoption of clean energy by rural households, with income effects and information channel effects serving as the underlying mechanisms. The study also finds that health risks, as an important consideration for household energy use choices, enhance this positive effect. Furthermore, heterogeneity analysis reveals that labor mobility has a greater impact on households with lower levels of education, lack of social security, and those located in central and western regions. Therefore, the government should take measures to increase the income of the rural migrant population and continuously widen the information channels for rural households to promote clean energy use. The findings of this study provide a new perspective and evidence for understanding household energy use behavior in China, offering valuable insights for policymakers aiming to expand the use of clean energy. • Labour mobility significantly contributes to clean energy use in rural households. • Income effects and information channel effects are the main mechanisms of action. • Health risks faced by households play a moderating role. • Heterogeneity in region, educational attainment, social security status will influence the effect of labour mobility. [ABSTRACT FROM AUTHOR]

The increasing contribution of refrigeration systems to global warming has led to widespread demand for low global warming potential (GWP) refrigerants as alternatives. This paper proposed a comprehensive environmental assessment method for evaluating the carbon footprint of units charged with low-GWP refrigeration. Experimental and numerical studies were used to investigate the carbon footprint impact of using alternative refrigerants at different ambient temperatures in China. Initially, the model was validated through experimental case studies on the use stage of cold storage units. Subsequently, a comparison was made between the cooling capacity (CC) and energy efficiency ratio (EER) of cold storage units charged with different refrigerants. Additionally, the impact of alternative refrigerants in the use stage was analyzed by numerical calculations, and the adaptability of the cold storage unit to the ambient temperature and the national energy structure was also studied. The results indicate that among the four stages of the cold storage unit, the use stage is the hotspot of carbon footprint due to the large amount of energy consumption and the existence of refrigerant leakage and other behaviors. Utilizing low-GWP alternative refrigerants could significantly reduce the carbon footprint of the cold storage units' use stage by 10.56%–16.39%, thereby lowering the whole stage carbon footprint. Furthermore, the ambient temperature and national energy structure could also effectively reduce the carbon footprint of cold storage units' whole stage by 1%–27.17%. Furthermore, after adopting low GWP refrigerants, the life-cycle cost (LCC) of the units have also decreased by 6.1%–12%. Taking into account both environmental impact and economic considerations, it is recommended to directly replace R404A with R448A in the units in regions where the average ambient temperature is above 15 °C. • A carbon footprint evaluation model for the units was established. • Influencing factors of carbon footprint are studied numerically and experimentally. • By GHG assessments, the optimal refrigerant and external conditions were determined. • Alternative refrigerants reduce carbon emissions for cold storage units by 8.8%–24.92%. [ABSTRACT FROM AUTHOR]

Alleviating energy poverty is a significant issue in many countries, for which effective energy poverty measurement is the prerequisite. This paper seeks to make a comprehensive and systematic assessment of the energy poverty performance of 30 provinces in China for 11-year period (2007–2017) by emphasizing their spatial-temporal changes. The paper firstly constructed energy poverty measurement conceptual framework based on overviewing previous studies. Secondly, a technique based on the integrated fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and Data Envelopment Analysis - Assurance Region (DEA-AR) for performance assessment was proposed. Then, the energy poverty performance was assessed, and clustering analysis was conducted to investigate the spatial-temporal changes of energy poverty in China. Finally, sensitivity analysis was applied for verifying the robustness of the proposed method. It is found that the energy poverty condition in China tends to be similar among most regions, but the gap between the best and worst performers gets wider. In addition, provinces with better economic conditions, have higher performance and are classified into the progressive group for alleviating energy poverty. On the contrary, provinces, whose economy lags, have regressed the most and are also the worst performers in reducing energy poverty. Thus, policies to improve household energy cleanability in high-income provinces and increase energy affordability in low-income regions would help reduce energy poverty in China. • A comprehensive energy poverty assessment framework was proposed. • An integrated Fuzzy DEMATEL/DEA-AR was proposed to evaluate the performance. • The comparison between the proposed Fuzzy DEMATEL/DEA-AR model and conventional DEA was conducted. • Spatial and temporal analysis of energy poverty performance in China was conducted. [ABSTRACT FROM AUTHOR]

Abstract: Due to increasing energy scarcity and pressures from climate change, Chinese iron & steel (IS) companies have struggled to implement practices to reduce their energy consumption and CO2 emissions. However, it is not clear whether these practices can improve the environmental or economic performance of these companies. The determinants that drive or hinder the implementation of these practices are also not known. Therefore, this paper describes an empirical analysis of a total of 85 questionnaires of various IS companies across China. A regression analysis was applied in this paper to verify the relationship among CO2 reduction practices, determinants and performance as reported by the IS companies. The results show that although some CO2 reduction practices can result in significant environmental performance, their impacts on the improvement of economic performance are less clear. The paper discusses some implications regarding strategies for prompting CO2 reduction practices that have been considered during the discussion about the determinants that impact on the practices. [Copyright &y& Elsevier]

Existing studies pay little attention to when or under which conditions foreign direct investment (FDI) can spill energy-saving technologies. From a perspective of energy-biased technical change and using a two-layer nested constant elasticity of substitution (CES) production function, this paper investigates the energy-saving spillover effect of FDI on technical change (i.e., energy-saving spillovers) in China between 2002 and 2015. In particular, we consider the conditions of marketization, industry, technology, and labor mobility to examine whether and when FDI has energy-saving spillovers. The results indicate no income inequality effect, i.e., there is no evidence supporting that FDI flowing into low- and middle-income regions increases energy consumption, while FDI flowing into high-income regions conserves energy. However, there is a condition effect: FDI can improve (support the halo effect) or deteriorate (contradict the halo effect) the environmental performance under different conditions. Moreover, there is a threshold effect: the direction of FDI spillovers varies with the different levels of the threshold variables. An increasing marketization motivates enterprises to select energy-biased technologies. It is more likely to generate energy-saving spillovers in the regions with a lower specialized agglomeration level. FDI will have energy-biased spillovers when domestic technological level is relatively high with an evident energy-biased technology. In addition, a moderate labor mobility is beneficial to the energy-saving spillovers of FDI. [ABSTRACT FROM AUTHOR]