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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

Promoting energy transformation in rural areas, building a Rural Integrated Energy System, and developing multi-energy complementary and comprehensive utilization of rural energy in accordance with local conditions are important paths to support global low-carbon transformation and rural revitalization. However, how to consider the coupling synergy of multiple devices and plan the capacity of multiple devices in rural energy systems in an economical, low-carbon, and reliable manner is an urgent issue facing the development of Rural Integrated Energy System. This paper proposes a bi-level programming optimization method of Rural Integrated Energy System based on coupling coordination degree of energy equipment. Firstly, a multi-dimensional rural user energy utilization framework is designed that comprehensively considers agriculture, animal husbandry, industry, and lifestyle. Secondly, the bi-level optimization model of the Rural Integrated Energy System based on the coupling coordination degree of energy equipment is constructed. The upper-level planning model puts forward the equipment coupling coordination degree, and determines the equipment selection scheme for different types of Rural Integrated Energy System planning. The lower-level planning model considers economy, low carbon, and reliability, and uses the improved multi-objective Harris Hawk optimization algorithm to solve the capacity of different energy equipment in the Rural Integrated Energy System. Finally, the effectiveness and scientific nature of the model and method proposed in this paper are verified by the Integrated Energy Systems of a rural area in the north of China. In addition, by comparing the equipment selection scheme with the traditional method, it can be found that the optimization scheme in this paper that considers the coupling coordination of energy equipment reduces the annualized total cost by 29.34 %, reduces the annual carbon emissions by 60.43 %, and increases the reliability by 15.85 %. The collaborative planning of energy economy, low carbon energy and reliable energy has been realized. • A framework for the utilization of energy by multiple users in rural areas was designed. • A bi-level programming optimization method of RIES based on coupling coordination degree of energy equipment was proposed. • An improved multi-objective Harris Hawk optimization was constructed. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in 'Type III' solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve "elite capture." • We analyze the changes of energy accessibility and affordability in rural China. • We quantify the energy services effect of solar PV projects (SPVPs). • SPVPs perform well in improving family energy consumption cleanliness. • The energy services effect of solar PV projects has spatial heterogeneity. • The implementation of China's rural SPVPs does not involve "elite capture.". [ABSTRACT FROM AUTHOR]

Prefabricated buildings (PB) are assembled using prefabricated components, known for their cost-effectiveness and high efficiency. They are seen as an effective means to achieving sustainable development goals and mitigating climate change. This paper examines the impact of prefabricated buildings on urban green total factor energy efficiency (GTFEE) using panel data from 278 cities in China. The analysis is conducted through the application of a super-efficiency slack-based measure (SBM) model and a difference-in-differences model. The results of the study indicate that the promotion of PB can significantly improve urban GTFEE; there is heterogeneity in the improvement of urban GTFEE by PB; the innovation effect, industrial effect and marketization effect of PB are the main ways to improve urban GTFEE. Specifically, PB exerts an innovation effect by promoting technological innovation. It influences consumer and investor demand, leading to increased activity in the secondary industry, thereby creating an industrial effect. Moreover, PB facilitates the marketization effect by phasing out outdated industries, encouraging autonomous enterprise innovation, and fostering the growth of new advanced enterprises. This study reveals the critical role of PB in urban GTFEE improvement. It clarifies its mechanism of action, which provides a reference for urban sustainable development strategies and energy efficiency. • Prefabricated buildings can improve green total-factor energy efficiency. • Urban green total-factor energy efficiency has been measured. • PB improves GTFEE through innovation, industry and marketization effect. [ABSTRACT FROM AUTHOR]

Government auditing plays a crucial role in enhancing energy efficiency and advancing green and low-carbon economic development. Given that audit oversight's quality and efficiency are influenced by the independence of auditing institutions, this study utilizes the vertical management reform of government auditing institutions as an exogenous shock, and empirically examines its impact on urban energy efficiency (UEE) using a Difference-in-Differences model (DID). We find that the audit reform improved UEE by 0.023 which is both economically and statistically significant. Concurrently, the audit reform and the development of the digital economy play substitutive roles in enhancing UEE. Finally, the results of the mediation analysis using the stepwise regression method indicate that the improvements in audit efficiency, green technology innovation, and energy structure optimization have mediated the promotion of UEE in the audit reform process, accounting for respective mediation effects of 4.35%, 4.35%, and 8.69%. • We investigate the audit independence from the level of audit management systems. • This paper finds a substitution effect of digital economic development. • The study discusses the heterogeneity in audit oversight from a global perspective. [ABSTRACT FROM AUTHOR]

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply. • Cluster analysis to extract steam user load characteristics. • Divide peak/valley times based on network storage and user traits. • Create a multi-objective Time-of-Use model for steam pricing optimization. • Carry out a case validation in an actual industrial park in Jiangsu. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

The essence of industrial chain structure optimization is to strengthen the synergy and division ability among sectors. It is a crucial factor in reducing energy intensity and achieving carbon neutrality goals. This paper completed the China's non-competitive input-output table from 2000 to 2020, constructed industrial synergy and division index using structural path analysis method and examined its development trend. Combining with a two-way fixed effects model, we explored the influence of industrial synergy and division on energy intensity. The research found that China's industrial synergy and division development level shows an upward trend. The proportion of the low-layer industrial chain decreases, and the peak contribution rate moves to the high-layer industrial chain. Secondly, industrial synergy and division significantly reduces sectoral energy intensity, the results are robust after endogeneity test. Finally, heterogeneity analysis showed that industrial synergy and division can reduce the energy consumption intensity of all sectors, and it is more obvious for sectors with high energy dependence. This research can provide targeted policy suggestions for China to optimize its industrial structure. It also has a reference significance for other countries. • The impact of industrial synergy and division on energy intensity is explored. • This paper extracts China's non-competitive IO table from the global MRIO table. • SPA is applied to construct industrial synergy and division index. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

With the completion of the poverty alleviation task in China, the Chinese economy has made great breakthroughs. Meanwhile, the implementation of Chinese "dual carbon" goal has entered an important window period of both strategic opportunities and difficulties. The emission of carbon dioxide (CO2) leads to the greenhouse effect, which affects the sustainable development of society. Therefore, this paper proposes an improved analytic hierarchy process (AHP) with Slacks-based measure integrating data envelopment analysis (SBMDEA) (AHP-SBMDEA) based efficiency evaluation model for different provincial-level administrative regions in China, which is used to analyze and optimize the Chinese energy structure of different provincial-level administrative regions. With coal, gasoline, petroleum, fuel oil, diesel, crude oil and natural gas as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, after linear weighted calculation of the results of the AHP-SP model and the SBMDEA model, the energy efficiency evaluation model of different provincial administrative regions in China is established. The results show that there is a large difference in efficiency between Chinese northern and southern regions, and the overall efficiency of southern regions is higher than that of northern regions. In addition, it is easier to find out the optimal energy efficiency benchmark, which can provide suggestions and help to guide the energy consumption input structure improvement of other inefficient provincial-level administrative regions. Furthermore, the energy structure can also be adjusted through the redundant variable calculated by the SBMDEA model. For example, Sichuan Province can reduce the consumption of the coal, the gasoline, the diesel, the fuel oil, the crude oil and the natural gas by 5269.95 × 10000 tons (WT), 513.01 WT, 59.05 WT, 34.37 WT, 4,73.33 WT and 130.37 billion cubic meters (BCM), respectively. Its CO2 emissions can be reduced by 1925.4 WT, and raise the efficiency of energy utilization in the province. [Display omitted] • Novel economy and CO 2 emissions evolution model based on a AHP-SBMDEA is proposed. • The proposed model can identify the optimal energy efficiency benchmark. • GDP per capita can be increased and CO 2 emissions can be decreased. [ABSTRACT FROM AUTHOR]

Over the past decades, China's vast regional disparities in resource endowment, economic development, and industrial structure have driven large-scale relocations of high energy-consuming industries (RHEIs). Despite extensive research on broad-based industrial relocation and emission transfer, the trend, structure and environmental impact of China's RHEIs need further investigation. This paper employs the multi-regional input-output model (MRIO) to calculate the trend, scale, and structure of the RHEIs among China's 30 provinces. Additionally, we investigate the RHEIs' effect on green total factor energy efficiency (GEE) and its heterogeneity. Our results indicate that the net inflow of high energy-consuming industries decreases GEE, with a more noticeable effect observed in central provinces and after the execution of the Belt and Road Initiative (BRI). However, the positive effect of RHEIs on GEE in outflow areas is more substantial than their negative impact in inflow areas, suggesting that RHEIs can enhance overall efficiency. This study contributes to the understanding of China's industrial relocation by providing valuable insights into China's RHEIs. • China has a large-scale relocation of high energy-consuming industries. • The relocation of China's high energy-consuming industries is calculated. • The relocation's impact on green total factor energy efficiency is analyzed. • Regional heterogeneity is found among different provinces. [ABSTRACT FROM AUTHOR]

Under the background of "carbon neutrality" and "carbon peak" concepts, China desires to develop a new power system based on renewable energy sources (RES), which will be the primary energy support in prospective China. Thus, it is imperative to improve the peak shaving capability of power system to address the problem of random fluctuation and intermittency of RES by developing various energy storage systems. An innovative approach to enhance the flexibility of the conventional thermal power unit (TPU) through the utilization of flywheel energy storage array (FESA) is presented, simulated, and evaluated in this paper. Using the relatively practical and specific model of TPU and FESA, modified frequency modulation and automatic generation control (AGC) instruction tracing operation scenario models are constructed. A comprehensive control strategy of FESA and a well-designed operation coordinated control method for TPU with FESA are proposed and applied. The simulation results are distinctively processed and evaluated by the methods in "two documents", aiming to assess the real-world operation performance of the model proposed in this paper. The comprehensive analysis of the indicators demonstrates that the FESA can improve the flexibility and economy of the units. This research may guide the usage of FESA to improve the operational flexibility of TPU. • Composition and Control Methods of the flywheel energy storage array are provided. • A coordinated control scheme for the thermal power unit with flywheel energy storage array is proposed. • Frequency modulation and AGC instruction tracking scenario models are constructed and simulated. • AGC regulation indicators are conducted and analyzed to evaluate the unit's performance. [ABSTRACT FROM AUTHOR]

Abstract This paper explores the effects of enforced Chinese policies regarding environmental matters at the provincial level. The paper proposes several features and explores datasets from the last four Five-Year Plans, which extensively covered the period from 1995 to 2015. As the datasets are multidimensional, dimensional reduction is applied with a control of information loss described by data variance preservation. A data-based clustering algorithm is applied to identify the similarities, and characteristics are explored in relation to the specific features. The use of this method makes it possible to identify the changes in the relevance of the features in the most recent Five-Year Plan, in which decarbonization is relevant instead of productivity. This method also makes it possible to identify specific efforts several provinces have made to introduce cleaner energy production. Following the analysis, it is concluded that the proposed features enable the proposed method to be considered a tool for analyzing the real-world effects of the adopted policies. [ABSTRACT FROM AUTHOR]

Hybrid hydro-photovoltaic (PV) operation is an innovative model for improving the overall efficiency of clean energy use. However, the uncertainty of large-scale PV output substantially exacerbates the difficulty of flexible regulation in hydropower systems and creates challenges for the safe, stable, and efficient operation of hydropower units. This paper proposes a real-time dispatch method for hybrid hydro-PV based on the dynamic reserve capacity, which targets the uncertainty distribution characteristics of PV output. First, a multi-scale nested joint operation model considering short-term and real-time operation strategies is proposed to simulate the operation process of the hybrid system. Then, different reserve capacity operation strategies are formulated according to the output deviation of PV. Based on the case study conducted in the Beipan River, China, it has been proven that taking into account PV uncertainty can lead to various positive outcomes. Firstly, the total monthly water consumption can be reduced by 4.29 %, which can ensure the economic performance of the system. Secondly, the total number of monthly passes through the vibration zone can be reduced by 53 times, which is a significant reduction that can minimize the negative effects of PV power generation fluctuations on the performance of hydro units. However, it is crucial to note that this may lead to spatial loss of hydropower units, especially during extreme weather conditions. • Propose a real-time scheduling method for the hydro-photovoltaic hybrid system based on dynamic load storage capacity. • Quantify the demand for PV reserve capacity. • Develop a reserve capacity setting and utilization strategy for hydropower units. • Evaluate the benefits and risks of real-time operation of the hydro-photovoltaic hybrid system. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the natural gas pipeline network is an efficient approach for storing and delivering renewable energy. However, the variation in gas quality and the diversity of delivery paths can lead to difficulties in pipeline operation management. In this paper, we propose a bilevel optimization model to understand the influence of hydrogen blending on pipeline capacity allocation and gas transmission tariffs. The lower-level model optimizes the best capacity booking of each shipper, and the upper-level model optimizes the best capacity allocation of the transmission system operator. Detailed transmission paths and gas calorific values at each delivery point are taken into account to calculate the actual gas transmission tariffs. The proposed model is solved by the alternating direction multiplier method and validated by a real-world pipeline network. Results show that: (1) the proposed method can facilitate shippers to meet the energy demand under different hydrogen blending cases, (2) a fair-cost sharing is achieved among all parties, (3) when the hydrogen blending ratio reaches 5 %, the pipeline capacity can be optimally utilized to achieve the maximum pipeline capacity allocation. This approach can guide efficient gas pipeline capacity allocation and facilitate the establishment of trading platforms. • Customize a bilevel optimization model for the operator and shipper. • Determine cost and capacity allocation when hydrogen blending. • Apply alternating direction multiplier method algorithm to solve the model. • Conduct a comprehensive analysis of a real case in China. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric effects of oil price shocks on coal and renewable energy consumption in China over long and short periods. The novelty of this study is to investigate whether the consumption of coal and renewable energy are asymmetrically affected by the oil price in a time–frequency framework by performing a Fourier nonlinear ARDL model which is robust to overcome the multiple smooth structural changes in data. The bounds test of the FNARDL specification provide strong evidence of cointegration among variables, including coal energy consumption, renewable energy consumption, oil price, and economic growth. Results reveal the following: First, the oil price-coal energy consumption and oil price-renewable energy consumption relationships are nonlinear. Second, oil price increases have significant short-run effects on coal energy consumption. However, oil price increases induce an increase in renewable energy consumption in China in both the short and the long term. Finally, the relationship between coal energy consumption and economic growth is U-shaped. Hence, increasing oil prices boost China's prospects for coal in the short term and its usage of renewable energy in the long run. • Effect of oil price on coal and renewable energy consumptions in China is tested. • Strong evidence of cointegration among variables is shown. • Oil price increases have significant short-run effects on coal energy consumption. • Oil price increases induce an increase in renewable energy consumption in China. • Relationship between coal energy consumption and economic growth follows U-shaped. [ABSTRACT FROM AUTHOR]

The global shift to renewable energy is conducive to carbon reduction, but its impact on the economy is still uncertain. Unlike existing research focusing on the effect of domestic renewable energy (RE), this paper considers the global RE connection among countries and examines the impact of global RE development on economic growth (EG) using China as a sample. In addition, we apply a novel wavelet-based quantile-on-quantile method, which can estimate the local influence of the α-quantile of global RE on the β-quantile of the EG in different periods. The finding shows that the upper quantiles of RE promote EG , and this effect is greater at the upper quantiles of EG in the medium- and long-term. This is mainly because China plays a leading role in RE development and has an advantage in exporting RE -related products with the development of global RE. The finding proves the growth effect, indicating that RE is a blessing for China's EG. However, in the short term, a higher level of global RE development impedes China's EG due to high upfront costs of RE production, fierce competition from other countries, and insufficient energy supply when significant investment shifts from non-renewable energy to RE. The empirical results provide important policy implications to balance RE 's short-term inhibitory effect and long-term promoting effect on EG in the process of the global energy transition. • The global RE connection and the impact of global RE development on economic EG. • Applying wavelet-based quantile-on-quantile method can estimate the local influence of global RE on EG in different periods. • Upper quantiles of RE promote EG is greater at the upper quantiles of EG in the medium- and long-term. • The growth effect indicating that RE is a blessing for China's EG. • Balance RE's short-term inhibitory effect and long-term promoting effect on EG in the process of energy transition. [ABSTRACT FROM AUTHOR]

Transferring energy-intensive industries to regions with abundant clean energy is crucial for achieving low-carbon transformation of energy-intensive sectors during carbon neutrality. By manually collecting the project data from energy-intensive enterprises in China, we construct a three-dimensional panel data with 1.86 million observations to empirically examines the impact of the carbon intensity constraint policy (CICP) on the transfer of high energy-intensive projects (HEP). Our findings indicate that the CICP accelerates the transfer of HEP. The transfer direction shows that the HEP transferred from the eastern region to the western region have increased, but the HEP transferred from the western region to the eastern region have not decreased. To explain the above phenomena, we further discuss the underlying mechanism from the perspective of energy structure and industrial agglomeration. From the standpoint of transfer-out regions, the higher the proportion of thermal power in the energy consumption structure, the more HEP will be transferred out. From the standpoint of transfer-in regions, the industrial agglomeration will attract the transfer of the HEP. However, we do not find evidence that wind power and photovoltaics can attract the HEP. This paper provides project-level empirical evidence for promoting the low-carbon transformation of energy-intensive industries. • The carbon intensity constraint accelerates the transfer of energy-intensive projects. • The dynamic effect shows a fluctuating and decreasing trend. • The transfer direction is the net transfer from the east to the west. • The more thermal power, the more energy-intensive projects will be transferred out. • We don't find the attraction of the clean energy for the energy-intensive projects. [ABSTRACT FROM AUTHOR]

Balancing the ecological and economic benefits of energy consumption is an essential requirement and a key part of achieving the task of high-quality economic development and the goal of carbon neutrality. This paper analyses the energy efficiency and spatial and temporal evolution characteristics of thirty Chinese provinces and cities from 2006 to 2019 based on the DSBM model. The results show that, firstly, environmental factors have a significant impact on the evaluation of energy efficiency, with the national energy efficiency showing phase fluctuations and a decreasing trend from the east to the center and then to the west during the sample period. Secondly, the country and the three major regions did not show a σ convergence. Thirdly, there is significant absolute and conditional convergence in overall energy efficiency after considering spatial spillover effects, and with a turning point. Fourthly, energy efficiency in the three major regions also shows a clubbing phenomenon, with different regions tending to their own steady state of energy efficiency. Finally, we put forward suggestions for improving energy efficiency. The results of the study help to understand the changing trends and influencing factors of inter-regional energy efficiency in China, which can provide a reference for achieving the transformation of energy structure and has important practical significance. [Display omitted] • The DSBM model to evaluate energy efficiency relaxes the assumptions of DEA models. • The DSBM model includes environmental and relaxation variables in EE assessment. • The spatio-temporal characteristic of EE is analyzed from multifaceted perspectives. • We analyze the club characteristic and key turning points of EE convergence in China. [ABSTRACT FROM AUTHOR]

China is facing the challenge of selecting appropriate policies to implement tradable green certificate (TGC) under the renewable portfolio standards (RPS). This paper proposes a tripartite evolutionary game model among thermal power plants, green power plants and the grid under the RPS and TGC policy to analyze the evolutionary stability strategies (ESSs) of unilateral subjects and systems and use numerical simulation to discuss the influence of different types of TGC purchase ratios and different trading markets on the choice of tripartite strategy. The results are as follows. (1) When the renewable energy consumption quota ratio is 21%, thermal power plants are more and more active in choosing to trade TGC as the ratio of wind power TGC and photovoltaic TGC gradually increases from 1:5 to 5:1. (2) In the TGC market where the price of wind power and photovoltaic TGC is 50 yuan, green power plants are more inclined to hold TGC; in the subsidized TGC market, where the price of wind power TGC is 190 yuan and the price of photovoltaic TGC is 650 yuan, green power plants tend to sell TGC. (3) With the price of excess green electricity consumption gradually decreased from 700 yuan/MWh to 100 yuan/MWh, the activity of three parties participating in TGC trading gradually increased, effectively activating the TGC market. Finally, from the perspective of policy management, we provide suggestions for the long-term sustainable development of the TGC market. • The strategy selection of stakeholders involved in the implementation process of the RPS and TGC policy has been examined. • The evolutionary convergence of tripartite behaviors shows a high dependent degree. • In parity TGC trading markets, the government has the potential to lower the transaction price for excess consumption. • An important policy reference for promoting the balanced development of the RPS and TGC has been provided. [ABSTRACT FROM AUTHOR]

Integrated energy system is an important approach to promote large-scale utilization of renewable energy. Under the context of energy market reformation and technology advancement, the economic operation of integrated energy system confronts new challenges, in terms of multiple uncertainties, multi-timescale characteristics of heterogeneous energy, and coordinated operation of hybrid energy storage system. To this end, this paper investigates the multi-timescale rolling optimization of integrated energy system with hybrid energy storage system considering the above challenges. Firstly, a basic framework of an integrated energy system with hybrid energy storage system (consisting of battery and hydrogen storage) is proposed, and the typical devices are modeled in detail. Secondly, the parameters and variables are divided into fast/slow timescale according to dispatch needs, and the multi-timescale problem of heterogeneous energy and the coordinated operation of the hybrid energy storage system can be solved simultaneously through two-stage optimization. Thirdly, the dispatch model is incorporated into the framework of model predictive control, the uncertainty of price, renewable energy and load can be effectively handled. Finally, a case study is conducted using an industrial park in the southern coastal region of China, the comparison with the rule-based method shows 60.65% reduction in the operating cost of hybrid energy storage system, the comparison with robust optimization shows 7.01% reduction in the total cost of the system, thus the feasibility and effectiveness of the proposed method are verified. • The application of HESS in IES is considered and modeled in a more detailed way. • Based on the two-stage approach, the heterogeneous energy multi-timescale scheduling problem and the HESS coordinated operation problem of IES can be solved simultaneously. • The dispatch model is incorporated into MPC for rolling optimization, and multiple uncertainties are effectively handled. • Using an industrial park in the southern coastal region of China as case study, the feasibility and effectiveness of the proposed method are verified. [ABSTRACT FROM AUTHOR]

As a comprehensive energy transition reform strategy, China's new energy demonstration city (NEDC) pilot policy aims to accelerate the transformation from a fossil fuel-based energy system to a more sustainable energy system. Employing unique data on firms' energy consumption from National Tax Survey Database, this paper investigates how NEDC policy affects firms' energy utilization efficiency based on the difference-in-differences model. We observe that NEDC construction reduces firms' energy consumption intensity (ECI) in the pilot cities. After using the instrumental variable approach to deal with endogeneity problems, the conclusions still hold. The mechanism analysis reveals that the decrease in firms' ECI is driven by offering more tax incentives to enterprises and prompting them to pursue technological innovation. Heterogeneity analysis shows that this negative impact is more prominent for SOEs and high-energy-consuming enterprises; we also observe that this effect is more pronounced for firms in resource-based cities and old-industrial-based cities. Our results provide policy implications for initiating policies in other countries to improve firms' energy utilization performance. • NEDC pilot policy affects firms' energy utilization efficiency. • NEDC construction reduces firms' energy consumption intensity (ECI). • Tax incentives and technological innovation are the channels that cut firms' ECI. • This negative impact is more prominent for SOEs and high-energy-consuming firms. • This effect is more pronounced in resource-based and old-industrial-based cities. [ABSTRACT FROM AUTHOR]

Given the evident spatial disparity in China's energy consumption and economic development, achieving Pareto optimization of regional energy allocation efficiency proves to be a formidable challenge. Based on this, this paper ingeniously proposed an extended "labor-capital-energy-environment" resource mismatch mode, using 2004–2018 provincial panel data to measure and analyze the spatiotemporal pattern of China's energy mismatch and efficiency loss, while further investigating influencing factor. The results show that: (1) Although the degree of resource misallocation has decreased, the reduction of the number of provinces with excessive energy allocation and the serious shortage of energy allocation are the only effects of the optimization of energy resource misallocation, which has not significantly improved the situation overall. (2) Energy resource misallocation has grown to be a significant factor in China's losses in green total factor productivity (GTFP) brought on by resource misallocation. In addition, the extent of energy mismatch's loss in GTFP demonstrates the spatial heterogeneity of the "eastern > central > northeast > west" region. (3) Environmental regulation can significantly reduce energy mismatch and it is more successful at addressing the issue of excess energy than inadequate energy allocation. Also, the "rebound effect" of technological progress is the main factor aggravating the shortage of energy allocation. • We innovatively proposed an expanded "labor-capital-energy-environment" HK model to construct a theoretical framework for the impact of factor price distortions on GTFP. • The optimization of energy resource misallocation is only reflected in the serious shortage of energy allocation and the reduction of the number of provinces with excessive energy allocation. • The degree of GTFP loss of energy mismatch shows the regional heterogeneity of "eastern > central > northeast > west" spatially. • Environmental regulation can significantly improve the problems of energy mismatch and surplus. [ABSTRACT FROM AUTHOR]

Hydrogen is a key raw material for many chemical processes and a clean fuel for various power generation strategies. Coal-to-hydrogen (CTH) conversion is an alternative way to produce hydrogen that is applicable to the abundant coal reserves in China. The use of fossil energy has contributed to severe environmental problems, which drives the development of potential hydrogen production processes. Biomass is a promising renewable energy as well as an attractive resource for producing hydrogen, and it may address some of these environmental problems. In this paper, the simulation results of the biomass-to-hydrogen (BTH) and CTH processes were validated using available experimental data from the literature. Based on the simulation results, a techno-economic analysis was conducted from the viewpoints of the first and second laws of thermodynamics. The techno-economic analysis included determination of the energy efficiency, material consumption, total capital investment, production cost and carbon tax. The energy efficiencies of BTH and CTH were 37.88% and 37.82%, respectively. The BTH process had a larger raw material consumption and total capital investment (TCI) than the CTH process. However, the BTH process had a lower production cost and GHG emissions than the CTH process. The results of energy analyses of the BTH and CTH processes showed that the energy efficiency can be improved from a thermodynamic perspective. The combination of thermodynamic analysis and techno-economic performance evaluation provides insights into the improvement and operation of clean hydrogen production. • Biomass-to-hydrogen (BTH) shows better performances on production cost. • Coal-to-hydrogen (CTH) shows better performances on raw material consumption and TCI. • Techno-economic performance of CTH and BTH is conducted and compared. • BTH has a superior performance in terms of its resistance to price risk than CTH process. [ABSTRACT FROM AUTHOR]

It is highly important to clarify the determinants of China's energy consumption for the smooth implementation of energy conservation. For this purpose, this paper discusses the contribution of China's different economic sectors to decoupling progress. Then, it uses an extended logarithmic mean Divisia index (LMDI) method to decompose China's energy consumption into seven factors. The major findings indicate that during the 2000–2016 period, (1) the decoupling status between China's economy and energy consumption were determined largely by the secondary industry; (2) the positive contribution of three sectors to the decoupling process was ranked as follows: primary industry>tertiary industry>secondary industry; (3) China's energy consumption showed a growing trend, and the expansion of per capita fixed asset investment was the main factor responsible for the energy consumption increase, especially in some provinces whose investment structure was skewed towards energy-intensive industries. Additionally, population growth had mild positive impacts on the energy consumption increase; (4) declines in energy intensity and investment efficiency were two major contributors to energy-saving. However, economic structure, energy structure, and spatial structure adjustment failed to reduce energy consumption but had overall positive impacts on the energy consumption increase. Finally, several policy recommendations related to reducing energy consumption are proposed. • The empirical analysis was conducted at both national and provincial levels. • Decoupling status was determined largely by the secondary industry. • Per capita fixed asset investment was dominant for energy consumption increase. • The decline in energy intensity and investment efficiency contributed to energy-saving. [ABSTRACT FROM AUTHOR]

Windows integrated with PV can provide electrical energy and control the energy flow (heat and light) through the windows. With the objective of evaluate the potential application of window integrated PV in southwest China, the performance of silicon PV windows was comprehensively studied. A test unit was developed to monitor the performance of window integrated PV, then a validated comprehensive approach integrated with heat transfer, optical, electrical and building simulation model was used to comprehensively evaluate the performance of the window integrated PV under various climatic conditions in southwest China. The effects of the air gap on window thermal performance, window orientation, different window-to-wall ratio and coverage ratio of PV cells are evaluated. It was found that a maximum energy saving could be achieved, when the PV window is installed on the south facing façade with a PV cell coverage ratio of 0,87, air gap of 9 mm between the two glazing panes. It was found that a maximum energy saving of 983 kWh was achieved in Lhasa. A highest energy saving ratio of 83% was found in Kunming. The window integrated PV shows good energy saving potential under different climatic conditions in Southwest China. The highlights of this paper could be collected as below: • C-Si PV window with reasonable c-Si cells arrangement was designed. • The energy consumption of the building with PV windows was tested. • The design of STPV façade was optimized by the test and simulation results. • The energy saving potential of c-Si PV window in Southwest China is studied. [ABSTRACT FROM AUTHOR]

Market-oriented reforms on production factors in China are far lagged behind that of the commodity market, remaining the problem of price distortion in production factor markets. In this paper, we measure the degree of relative price distortion among capital, labor and energy focusing on China's secondary industry. Then, we take the own-price and cross-price elasticities as the link to estimate the demand changes of the three production factors in three cases. At last, we estimate the influence of relative price distortion elimination on the total factor energy efficiency. The main findings are: relative price distortions among the three factors exist. No matter taking the prices of capital, energy or labor as the benchmark, the prices of the other two kinds of production factors should be changed. Second, the own-price elasticities of the factors are negative, while the cross-price elasticities between the factors are positive, indicating the substitutability among the factors. Finally, correcting the relative price distortion can serve for the improvement of total factor energy efficiency of China's secondary industry. Therefore, if taking improvement of total factor energy efficiency as the goal, eliminating the price distortion in the factor market is necessary. • The degree of relative price distortion among capital, labor and energy is estimated. • The changes of demands for the three production factors are calculated in three cases. • The influence of relative price distortion elimination on the total factor energy efficiency is discussed. [ABSTRACT FROM AUTHOR]

For a developing country with a large rural population, to understand the rural households' energy consumption characteristic and energy consumption determinants for further public policy design is of increasing importance. Therefore, this paper provides a detailed analysis of the characteristics of rural households' energy consumption. Simultaneously, based on the data of 1472 rural households from the Chinese General Social Survey of 2015, the energy consumption determinants of rural households are estimated by Tobit model. Results reveal that rural households with a healthy and old household head reduce the share of coal consumption, and household labors with an off-farm job and high level of education, and a large household size increase the share of LPG and electricity consumption. The good economic condition of rural households contributes to the reduction of biomass consumption. Given the results, the government should work for poverty reduction, subsidies for modern equipment purchase, policies of effective and renewable energy technologies, and educational investment in rural areas, which may help for a positive transition in energy consumption for rural households. • Use a national survey data of 2015 on rural household energy consumption of China. • Display comprehensive characteristics of rural household energy consumption. • Comprehensively examine the factors affecting rural household energy consumption. [ABSTRACT FROM AUTHOR]

Under the carbon peak and neutrality targets, the power generation industry in China is facing an urgent demand for a low carbon transition and participation in the electricity-carbon market. This paper novelty proposes a near-zero carbon emission power plant (NZCEP) integrating gas turbines, wind turbines, power-to-gas and the carbon capture, utilization and storage system. And a two-stage Data-driven Set based robust optimization (DSRO) model, including a day-ahead dispatching phase and a real-time adjustment phase, is conducted to ensure the consumption of renewable energy resources and to develop the optimal operating strategy for NZCEP participation under the electricity-carbon market. The results demonstrated the following outcomes: (1) NZCEP shows better environmental benefits by reducing carbon emissions and consuming renewable energy resources. (2) the DSRO model can resist the interference of wind power output uncertainties and address the issue of traditional robust models being overly conservative. (3) under the electricity-carbon market, NZCEP shows better economic benefits, which can generate additional revenue from selling surplus carbon emission allowances (CEA). Moreover, when the annual average CEA price reaches 355 CNY/t, the NZCEP will achieve full capital cost recovery. • Optimal operation of near-zero carbon emission power plant (NZCEP) is studied. • A Data-driven robust optimization model is proposed for NZCEP operating. • Carbon emissions reduction and wind power accommodation of NZCEP are verified. • The economy of NZCEP participation in the electricity-carbon market is analyzed. [ABSTRACT FROM AUTHOR]

Improving energy efficiency helps achieve energy conservation and emission reduction. Examining whether China's SO2 trading emission system (TES) reform can enhance energy efficiency will provide empirical evidence for the institutional benefit of market-oriented environmental regulation in environmental governance. This paper employs the difference-in-differences (DID) method to examine the effect of SO2 TES on energy efficiency using panel data from 285 Chinese cities from 2003 to 2018. The empirical results indicate that: (1) SO2 TES significantly promoted green total factor energy efficiency and reduced energy intensity. This conclusion is still valid after the prerequisite assumption test and robustness test. (2) SO2 TES significantly promotes local energy efficiency and has significant spatial spillover effects on surrounding cities. (3) Promoting green technological progress, resource allocation, and curbing energy consumption, are the primary mechanisms for SO2 TES to improve energy efficiency. (4) SO2 TES's improvement in energy efficiency comes more from eastern, developed, small, resource-based, and non-old industrial cities. • SO2 trading emissions system (TES) improves energy efficiency. • TES has a spatial spillover effect on nearby cities. • Green innovation, resource allocation and energy saving effects are the channels. • The effect of the TES on energy efficiency is heterogeneous. [ABSTRACT FROM AUTHOR]

Urbanization is the primary driver of changes in total energy consumption and the transformation of energy consumption structure. Fossil fuel combustion in energy consumption is the main contributor to carbon emissions. Therefore, urbanization is closely related to energy consumption and carbon emission. China's urbanization level has reached a tipping point, meanwhile, China has accelerated the pace of energy consumption and structural transformation in response to climate change. Understanding the interactions among energy consumption, carbon emissions and urbanization is crucial for promoting coordinated development of energy environment and urbanization in China. Based on panel data of 30 provinces in China from 2003 to 2019, this paper investigates the effects of urbanization on the aggregate effect, composition effect of energy consumption and carbon emissions using econometric models such as fixed-effects model and panel threshold model. The empirical results show that: (1) The relationship between urbanization and total energy consumption exhibits an inverted U-shaped curve relationship. The decarbonization of energy consumption structure is significantly negatively impacted by urbanization, although the effect is significantly weakened when the urbanization rate exceeds 60.35%. In contrast, urbanization has a significant positive effect on total carbon emissions, although this effect is significantly attenuated when the urbanization rate exceeds 66.20%. (2) As of 2020, 17 provinces in China still have urbanization rates below 60.35%, indicating that they are at a stage where urbanization is having a significant negative impact on the decarbonization of energy consumption structure. Similarly, 20 provinces have urbanization rates below 60.30%, indicating that they are at a stage where urbanization is still significantly contributing to carbon emissions. Therefore, continued urbanization will still have a significant impact on decarbonization of energy consumption structure and carbon emission reduction. (3) Heterogeneity analysis revealed that when the urbanization rate exceeds 51.54%, the role of total energy consumption on carbon emission decreases, while the role of energy consumption structure increases. Additionally, beyond a threshold of per capital regional GDP and education social fixed asset investment, decoupling of urbanization and carbon emission can be promoted. Furthermore, urbanization can have a significant negative impact on carbon emissions after local fiscal resource tax increases to a certain level. • We constructed an index for decarbonizing the energy mix. • We applied the panel threshold regression method to our analysis. • We found that urbanization has a negative effect on the decarbonization of the energy mix. • We discovered that urbanization has a positive effect on carbon emissions. [ABSTRACT FROM AUTHOR]

Technology evaluation of vehicle powertrains is the basis for policymakers and automotive manufacturers to make technical decisions. However, there is a research gap in comparative studies on different powertrain technologies under uniform assumptions (dynamic performance and test-driving cycles) for light-duty vehicles in China. To fill these gaps, this paper proposes an innovative technology evaluation model of the powertrains for the energy-saving cost-effectiveness of powertrains. The results are as follows: as the electrification degree of the powertrain technology increases, its curb mass and direct manufacturing cost increase, while its comprehensive fuel consumption gradually decreases. Changes in curb mass and direct manufacturing cost are the combined effect of powertrain electrification upgrades and engine simplification. ICEV has limited fuel-saving potential and poor cost-effectiveness. HEV can achieve more significant fuel savings with minor cost increases and has the highest cost-effectiveness. PHEV50, REEV180, and BEV400 have high fuel economies, but their cost-effectiveness is poor due to the high cost of the battery system. In the short term, the Chinese government should encourage automotive manufacturers to increase their research and development investment in HEVs to replace ICEVs. In the long term, the government should promote battery technologies to reduce the direct manufacturing cost of NEVs. [Display omitted] • A technology evaluation model of the powertrains (TEMP) is built. • The vehicle characteristics of different powertrain technologies with a detailed breakdown of components are calculated. • The energy-saving potential and cost-effectiveness of different powertrains are evaluated. • Some policy suggestions for promoting the development of powertrains are proposed. [ABSTRACT FROM AUTHOR]

Demand Response (DR) has been extensively developed and implemented in the US and Europe. However, DR hardly exists in many developing countries for similar problems such as rigid power market and state monopoly. With the increasing imbalance between supply and demand in China's power industry, the government has issued new policies on DR and approved the first batch of pilot cities. China is setting a good example of how to encourage DR under monopolistic electric market and open up the market to aggregators and DR suppliers. This paper summarizes the current DR status, feasible DR market schemes and DR pilot projects in China. First, electric power system reform, renewable energy policies and power industry development are reviewed, highlighting the problems associated with the current dispatch mechanisms of DR policies and markets. New DR programs and DR-related policies are also introduced. On this basis, the driving forces and challenges associated with DR in China are analyzed. The major challenge is the lack of a suitable market mechanism for the current Chinese power industry. Hence, this paper presents six feasible strategies that fully utilize the existing policies. Additionally, the latest DR applications in different pilot cities are summarized and analyzed. [ABSTRACT FROM AUTHOR]

China's economy has developed rapidly relying on a large amount of production factors input. It has caused excessive consumption of fossil energy which led to a series of social problems. Although Chinese government adopted various measures to reduce energy use, the existence of the energy rebound effect affects the results of energy saving. Therefore, it is necessary to study and carry out the energy rebound effect of various industries in China. This paper works out ten hybrid energy input-output tables (value type) based on the ten input-output tables and energy consumption of the sub-sector from 1987 to 2012. Relying on the supply-driven Ghosh input-output model, this paper calculates the energy rebound effect of industries from 1987 to 2012 in China, and then analyzes the factors which may affect the energy rebound effect value. The results show that: The energy indirect rebound effect is much higher than direct energy rebound effect in most sectors; the higher energy efficiency, the lower direct energy rebound effect; the higher forward linkage degree of industry, the greater indirect rebound effect of energy. • Independent preparation of a hybrid energy input-output table. • Explore the energy rebound effect values of sectors and influencing factors. • The indirect rebound effect is higher than direct rebound effect in most sectors. • The higher the energy efficiency, the smaller the direct rebound effect. • Industry "forward linkage" degree increases the indirect energy rebound effect. [ABSTRACT FROM AUTHOR]

The energy conversion efficiency of coal utilization is a focal point in modern energy management practices and in academic literature. This paper explores life cycle energy efficiency of coal (LEEC), which encompasses six distinct stages from the design of coal mines to the comprehensive reuse of resources. First, the study analyzes improvements to the evaluation of efficiencies in energy systems. Secondly, it proposes a multi-stage energy efficiency analysis and constructs an innovative LEEC model for coal exploitation and utilization. Thirdly, the energy efficiency of coal exploitation and utilization was evaluated, and found that total efficiency in China increased from 33.11% in 2010 to 37.69% in 2014, saving 126 Mtce of energy. Finally, this study found that mine design is the most important stage in improving energy efficiency, followed by the coal processing stage. Moreover, we found that coal processing has an indirect contribution to other stages in the life cycle utilization of coal, such as reducing transportation capacity. The primary research contribution is the development of a comprehensive LEEC model for the entire life cycle of coal, rather than just single-stage evaluation. This lays the foundation for further studies on energy efficiency of industrial chains and across entire systems. • Exploration of energy efficiency across the entire coal life cycle. • The energy efficiency of coal exploitation and utilization increased annually. • Analysis shows the implicit contribution of coal processing to life cycle. • Mine design is a crucial stage for overall life cycle energy efficiency. [ABSTRACT FROM AUTHOR]

Abstract Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock. Highlights • There are significant time-varying correlations and long-run persistence of shocks to the DCCs between three markets. • There is a bi-directional volatility spillover between the coal market and the new energy stock market. • The optimal weight is 82.65% for new energy stock and 17.35% for coal. • The optimal hedge ratio is 17.34% for new energy stock and 49.98% for coal. [ABSTRACT FROM AUTHOR]

Abstract Industrial sector is responsible for the majority of China's carbon dioxide (CO 2) emissions and contributed most to the rapid increment in the first decade of this century. However, CO 2 emissions in the industrial sector stagnated due to China's economic slowdown since 2012. This paper aims to estimate the CO 2 emissions in China's industrial sector during 2010–2016 to deliver sectoral and regional details and uncover the reasons for the emissions stagnancy. We find that the emission stagnancy is a result of decreasing energy intensity and economic slowdown. Industrial restructuring within the entire industrial sector and emission intensity (emissions per unit of energy consumption) had limited effect on the emissions reduction. From the sectoral perspective, iron & steel and nonmetal sectors accounted for most reduced emissions in China's industrial sectors during 2014–2016. From the regional perspective, the emissions decrease mainly occurred in western and northeastern provinces, rather than more developed coastal provinces. Mitigation policies are raised, including the industrial structure shift from energy-intensive sectors to other sectors and addressing the emissions mitigation in rapid industrializing provinces, such as Xinjiang, Inner Mongolia and Ningxia, so that an earlier and lower emission peak of China's industrial sectors can be achieved. Highlights • Iron & steel and nonmetal sectors accounted for most emissions reduction. • Decreasing energy intensity and economic slowdown are the main reasons. • Emissions in most energy-intensive sectors have peaked or approach the peak. • Emissions decrease occurred mainly in the western and northeastern provinces. [ABSTRACT FROM AUTHOR]

Abstract With the increase in energy consumption in China, the influential factors and contributions to such increase have received more attention. In this paper, the decomposition of energy consumption and energy intensity in China from 1995 to 2015 was conducted by using a complete decomposition model. This study focuses on regional difference, which is not in line with the existing studies. The decomposition results indicate that the economic growth has a significant driving effect on the energy consumption, while the energy intensity effect reduces the growth of the total energy consumption. The structural effect plays a negative role but has less effect on the growth of the energy consumption. The contribution rate on the growth of the energy consumption in the eastern region gradually decreases. However, in the western region it grows fast and even exceeds that of the eastern region to become the highest energy consumption area in China in 2010–2015. The energy intensity declines substantially in China. It is mainly due to the improvement of regional energy efficiency and negligible contribution from the structural effect. In conclusion, the energy consumption and energy intensity in China follow different forms in different regions. Therefore, the application of differentiated regional energy policies is preferable over a single national energy policy. Highlights • The changes in energy consumption at the regional level were decomposed. • The influential factors of energy consumption variation and energy intensity variation in China was studied. • The coupling relationship between China's economic growth and energy consumption growth has gradually weakened. • We find that the western region become the area of China with the highest energy consumption. [ABSTRACT FROM AUTHOR]

Abstract A full understand of the influential mechanism concerning electricity consumption has a crucial political reference significance for the implementation of energy conservation and consumption reduction policies. To solve the lack of correlation between the empirical results of the research regions in present researches, this paper takes China's electricity consumption from 1995 to 2016 as the research subject, uses Multi-period ST-LMDI model by setting a unified reference province as a benchmark for different regions in China to completely decompose the changes in China's electricity consumption, and further analyzes the influence mechanism of China's electricity consumption changes by combining the characteristics of the departments and regions. As the result, it's found that the economic growth has a strong impetus for power consumption while the technological progress can effectively curb it. Meanwhile, results of studying electricity consumption from the perspective of industry and region show that the economic structure and consumption intensity have obviously different influences in the eastern, central and western regions. So when it comes to formulate the policy for electricity development and reform, the characteristics of industrial structure as well as the regional differences must be taken into consideration. Highlights • We apply Multi-period ST-LMDI to decompose China's electricity consumption. • The expansion of the economy is the main factor for the growth of electricity consumption. • Decline in electricity consumption intensity restrain the growth of power consumption. • All industrial sectors basically had positive effects on electricity consumption. [ABSTRACT FROM AUTHOR]

Abstract Using the trans -log cost function and elasticity model, this paper pioneered the analysis of the elasticity and characteristics of the substitution between energy consumption and capital in China's manufacturing industry between 1995 and 2014. Firstly, the research revealed significant differences in the magnitude and direction of the substitution between coal, electricity, petroleum, and capital, thus demonstrating heterogeneity in the substitution. Secondly, the analysis, based on the Morishima Elasticity of Substitution (MES) model showed that: 1) depending on what triggers changes in factor prices, the manufacturing sector shows a reverse asymmetry whereby capital substituted energy and energy complemented capital 2) a significant asymmetry exists in the direction and magnitude of substitution in capital- and labor-intensive sectors. This asymmetric effect indicates that China can attain overall energy conservation and apply energy-saving technologies in the manufacturing industry by implementing the substitution of capital for energy. However, the differences with regards to the forms of energy consumption among sectors, as well as the heterogeneity and asymmetry in capital–energy substitution should be considered in research on energy policies for the manufacturing industry. This should also involve the integration of energy sources to ensure that such policies are targeted and effective. Highlights • It evaluates the substitution between energy and capital in China's manufacturing industry. • MES model and translog cost function are used to investigate the heterogeneity and asymmetry of substitution. • It shows a reverse asymmetry whereby capital substitutes energy and energy complements capital. • It demonstrates the heterogeneity of substitution result from the difference of energy use. [ABSTRACT FROM AUTHOR]

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country. Highlights • We examine the relationship between hydropower consumption and economic growth in China. • We control for fossil fuel consumption, financial development, capital, institutions and globalization. • We employ the Bayer and Hanck, (2013) cointegration test and ARDL bounds-testing method with structural breaks. • The findings demonstrate a long-run relationship between those variables. • The explanatory and control variables have positive influence on GDP in China. [ABSTRACT FROM AUTHOR]

Abstract At present, the energy structure of China is shifting towards cleaner and lower amounts of carbon fuel, driven by environmental needs and technological advances. Nuclear energy, which is one of the major low-carbon resources, plays a key role in China's clean energy development. To formulate appropriate energy policies, it is necessary to conduct reliable forecasts. This paper discusses the nuclear energy consumption of China by means of a novel fractional grey model FAGMO(1,1, k). The fractional accumulated generating matrix is introduced to analyse the fractional grey model properties. Thereafter, the modelling procedures of the FAGMO(1,1, k) are presented in detail, along with the transforms of its optimal parameters. A stochastic testing scheme is provided to validate the accuracy and properties of the optimal parameters of the FAGMO(1,1, k). Finally, this model is used to forecast China's nuclear energy consumption and the results demonstrate that the FAGMO(1,1, k) model provides accurate prediction, outperforming other grey models. Highlights • A novel model FAGMO(1,1,k) is proposed to forecast nuclear energy consumption. • The fractional grey model can enhance the forecasting accuracy significantly. • Comparison results are presented to show the accuracy of the FAGMO(1,1,k) model. • The FAGMO(1,1,k) is more flexible and efficient than the existing grey models. [ABSTRACT FROM AUTHOR]