Search

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]

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]

The industrial sector is characterized by high energy consumption and high carbon emissions. It is of great significance to clarify the determinants of industrial CO 2 emissions for emissions abatement policy making. For this purpose, a comprehensive decomposition framework that can quantify the impacts of both oriented technologies and economic factors on CO 2 emissions is developed. By applying the proposed decomposition framework, this paper investigates the determinants of China's industrial CO 2 emissions. The main findings show that, during the study period, (1) investment scale expansion was the leading factor promoting emissions growth; (2) against our expectations, efforts for energy structure adjustment failed to curb emissions but had marginal promoting effects on emissions growth; (3) energy-oriented and output-oriented technologies were indispensable contributors to emissions mitigation, followed by decreases in investment efficiency, potential energy intensity, and the CO 2 emission coefficient; and (4) energy use efficiency and production efficiency changes usually exerted moderate influences on industrial CO 2 emissions, and their influencing directions were sensitive in different sub-industries and Five-Year Plan periods. In most situations, the inhibiting effects could not counteract the promoting effects. Based on these empirical findings, policy recommendations to further reduce industrial CO 2 emissions are provided. • Empirical analysis was conducted at sub-industries and different FYPs levels. • Investment scale was the leading factor promoting industrial CO 2 emissions. • The technology progress contributed most to emission mitigation. • Driving effects distinctly differed among sub-industries and FYP periods. [ABSTRACT FROM AUTHOR]

In recent years, the profit growth of wind power enterprises in China is generally weak, and the phenomenon of "wind curtailment" in some areas is relatively common. The comprehensive evaluation of wind power performance in various regions of China and the study of the driving forces of wind power performance differences in different regions are of great significance for scientific planning and layout of wind power investment. Based on the panel data, this paper combines Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to establish an evaluation model to evaluate the wind power performance of China's 29 provinces and cities from 2011 to 2018. Then uses the evaluation results as input data of Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model to predict the wind power performance of 2019 in various regions of China under the current policy and environment. Finally, the main factors affecting the performance of China's wind power generation are explored by establishing a regression model. The research results show that at present, the main factors affecting the performance of China's wind power generation are local power consumption capacity, economic development degree and the rate of wind abandonment. On the premise of solving the problem of wind curtailment, appropriately promoting power consumption and renewable energy policy reform, from Feed-in-Tariff (FiT) to a Renewable Portfolio Standard (RPS), are effective means to promote the development of wind power generation in China. Image 1067 • In wind-intensive areas, wind power performance levels are relatively low. • Power consumption capacity and wind curtailment rate are the main influencing factors. • Current policy cannot effectively solve the problem of wind curtailment. • Renewable energy policies with regional differences should be adopted. [ABSTRACT FROM AUTHOR]

Sectors tend to generate pressure on the environment due to its large amounts of emissions. The aim of this study is to identify critical betweenness sectors and transactions that play an important intermediate role in the supply chain, which can provide additional insights for CO 2 emissions. Based on the input-output and energy consumption data in China, the notion of network centrality and structural path analysis method are employed in this paper to identify critical betweenness sectors and transactions, and then the evolutionary trends of betweenness sectors and transactions are analyzed from 2005 to 2015. The results show that some critical betweenness sectors, such as Metal products sector and Electrical equipment sector, have huge potential for carbon emission reduction, which fail to receive sufficient attention from the perspective of production or consumption. Some critical transactions, such as the transaction from Nonmetal products sector to Construction sector, have a large amount of CO 2 emissions. The transaction from Metallurgy sector to Electrical equipment sector became increasingly important in China from 2005 to 2015. Additionally, the evolutionary trends of different transactions are significantly different. The findings reveal that special attention should be paid to these critical betweenness sectors and transactions and policy development should be dynamic to respond to changes in CO 2 emissions that are generated by different betweenness sectors and transactions. • Critical betweenness sectors and transactions for CO 2 emissions in China are identified. • Input-output relationships are converted into a supply chain network. • Edge betweenness and SPA are used to identify critical transactions in China. • The intermediate effect of Metal products sector is significant. [ABSTRACT FROM AUTHOR]

• Battery transport and logistics to increase renewable use and system flexibility. • Full train transport and carpooling to improve the economics of battery use. • Levelized cost of energy of battery transport & logistics is $0.045/kWh averagely. High share of variable renewable energy is challenging to the traditional power system technically and economically. This calls for a significant increase to the system flexibility, which might result in the costs associated with energy storage and costly upgrades to the traditional transmission and distribution system. This paper presents a multi-stage battery transportation and logistics optimization method to increase the renewable energy consumptions, economics, and mobilities of the battery utilization. A new approach is proposed in which the batteries are charged in the renewable power plants and transported back and forth by railways between the renewable power plants and cities. Based on the forecasts of battery supplies/demands, multiple optimization stages (full train transport and carpooling) are designed by the branch-and-bound algorithm and genetic algorithm respectively. The proposed battery transportation and logistics concept and model are performed using the Beijing-Tianjin-Hebei region in China as an example. The results show that the levelized cost of energy of the battery transportation and logistics model is $0.045/kWh averagely. Also, by the use of mobilized batteries, the proposed battery transportation and logistics model increases the system flexibilities and renewable energy deliveries to the end users without the reinforcement of transmission and distribution system and any constraint from a highly penetrated power system. [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]

• Examine the decoupling relationship from provincial perspective. • Decompose decoupling index into five sub-decoupling indices and measure its contributions. The transport sector imposes enormous challenges for energy consumption and CO 2 emission reduction. Using data from 30 provinces in China, this paper adopted the Tapio decoupling index to examine the relationship between the development of the transport sector and its CO 2 emissions from provincial perspective. Additionally, we employed the logarithmic mean divisia index method to explore the effect of several factors on the state of decoupling. The results showed that the under-developed provinces were more likely to present a weak decoupling state than the developed and coastal provinces. Income level was the major influential factor limiting the development of decoupling in the transport sector. The population scale had a very small negative role in the development of decoupling. Moreover, the effects of CO 2 emissions efficiency, transport intensity and industry structure varied across provinces. By offering a provincial perspective on decoupling states and its driving factors, this study can provide a reference for governments in proposing carbon-reduction policies and promoting low carbon development of the transport sector. [ABSTRACT FROM AUTHOR]

Energy consumption is an international issue and plays an important role in the national energy security, especially for China of which the energy market is in transition. Accuracy and trustable forecasting of future energy consumption trends with nonlinear data sequences is very important for the decision makers of governments and energy companies. In this paper, a novel nonlinear grey Bernoulli model with fractional order accumulation, abbreviated as FANGBM(1,1) model, is proposed to forecast short-term renewable energy consumption of China during the 13th Five-Year Plan (2016–2020). The new model is discussed in details with the fractional accumulated generating matrix and the Bernoulli equation. Further, the Particle Swarm Optimization algorithm is used to search optimal system parameters. Based on the updated real-world data sets from 2011 to 2015, the FANGBM(1,1) model is established to forecast the total renewable energy consumption, hydroelectricity consumption, wind consumption, solar consumption, and consumption of other renewable energies, respectively. The FANGBM(1,1) model presents high accuracy in all cases and is also proved to be efficient to deal with nonlinear sequences. [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]

This paper investigates the effect of the implementation of smart city policy (SCP) on energy efficiency (EE) in China. For this purpose, we have developed a non-convex metafrontier data envelopment analysis (DEA) approach to measure EE, and empirically examine the impacts of SCP on China's EE using Difference-in-Differences (DID) analysis, using a dataset of 251 cities between 2003 and 2016. Results show that there exists significant regional heterogeneity across cities, with the SCP exerting a significant positive effect on EE, indicating that the SCP can effectively improve EE during the study period. These findings are robust to different sub-sample, energy efficiency alternative measures, estimation strategies, and Baidu Search Index. Based on empirical results, the implications of SCP are outlined. • Energy efficiency measures of 251 Chinese prefectural-level cities are estimated. • Eastern region performs the best, followed by the central and western regions. • The effects of smart city policy on energy efficiency is investigated using DID method. • The smart city policy can effectively improve energy efficiency during the study period. • Empirical results are robust to different sub-sample, energy efficiency alternative measures, etc. [ABSTRACT FROM AUTHOR]

For many years, China has invested effort into achieving resource conservation and environmentally friendly economic development. The measures implemented include promoting technological innovation, market-oriented reform and industrial structure adjustment. But there are open questions: What have the effects of these measures been? Have they helped China achieve win-win development in terms of its resources, environment, and economy? This paper aims to answer these questions by using a newly proposed global three-hierarchy meta-frontier Luenberger index that can quantify the effects of promoting technological innovation, industrial restructuring, etc., on economic growth, energy conservation and emissions reduction. The results show that (1) during 2001–2016, China's energy efficiency, economic efficiency, and emissions efficiency improved, indicating that China's efforts have brought about the win-win development of resources, the environment, and the economy; (2) the implemented measures have heterogeneous effects on economic growth, energy conservation, and emissions reduction. The implemented measures are generally beneficial to energy conservation and emissions reduction but potentially harmful to the economy; (3) the effects of these measures on economic growth, energy conservation and emissions reduction across China's regions and provinces are also quite different, indicating that local provincial governments should have a clear understanding of their own characteristics before implementing the relevant policies and measures. • A global three-hierarchy meta-frontier Luenberger index was proposed for the study. • It can quantify the effects of China's efforts on economy, energy, and emissions. • Results show that China's efforts have brought about a win-win development. • There are the heterogeneities of China's pathways to economic growth, energy conservations and emissions reductions. • The future strategies for China's win-win development should take the heterogeneities into account. [ABSTRACT FROM AUTHOR]

This paper estimates the linkages among total Sulphur dioxide (SO 2) emissions, total GDP and energy efficiency using China's provincial panel data from 2002 to 2015. We investigate total emissions rather than per capita emissions or ambient concentrations since it is total emissions that the environment cares about. Energy efficiency is estimated using stochastic frontier analysis and decomposed into both persistent and transient efficiency. We then investigate the long-run dynamics among SO 2 emissions, economic growth, and energy efficiency by employing the panel-based error correction model and taking the effects of cyclical variations into account. Our analysis shows that GDP has a positive impact on total SO 2 emissions in the short run and gains in energy efficiency have a significant negative effect on emissions in the long run. By controlling the effects of the business cycle, the effects of GDP on emissions remain positive in both the short and long run. The cross-sectional analysis provides similar insights. We argue that economic growth itself is an emission generator. Therefore, the government needs to establish a long-run strategy to curb emissions by improving energy efficiency. Image 1029 • We estimate the linkages among sulphur dioxide emissions, GDP and energy efficiency. • Energy efficiency is estimated by SFA and decomposed into persistent and transient efficiency. • We employ the panel-based error correction model with cyclical variations and structural break. • GDP has a positive impact on emissions in both the short and long run. • Gains in energy efficiency affects emissions negatively in the long run. [ABSTRACT FROM AUTHOR]

• A new spray cooling system is proposed based on PV/T and heat recovery in sow houses. • A simulation model is developed to dimension the new system. • The maximum error is less than 7.2% for the key equipment models in the new system. • The optimal operating ranges of new system are determined in five climate regions. • The operating control suggestions of new system are obtained in five climate regions. In this paper, a new spray cooling system (NSCS) is proposed based on PV/T and heat recovery in sow houses. It contributes to improving system applicability in high-humidity areas and reducing system energy consumption due to solar dehumidification and heat recovery. Meanwhile, the simulation model of NSCS is established and the reliability is verified for the models of its key equipment. The results show that the maximum error is less than 7.2% for the models. The optimal control parameter ranges are further determined for NSCS in five climate regions in China through simulation. The results show that in hot-summer and warm-winter (HSWW) region or hot-summer and cold-winter (HSCW) region, the optimal operating ranges are 40–45 kW and 7245–9245 kg/h for spray power and mass flow rate of circulating water, respectively. However, they are 25–30 kW and 13245–15245 kg/h in other three climate regions, respectively. Finally, the annual performance parameters are compared for NSCS in five typical cities. The regional applicability and operating control suggestions are further obtained for NSCS in five climate regions. The results show that the heat recovery coefficient and energy efficiency ratio are the maximum when NSCS is operated in HSWW and HSCW regions. In high-temperature and arid regions, removing desiccant wheel from NSCS may be a good method to reduce system investment and energy consumption. Meanwhile, the surplus electric and thermal energy can be used for regional energy utilization in NSCS. The above conclusions can determine the regional applicability of NSCS and guide its operating control in different climate regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

• The high-quality development index is introduced into STIRPAT model. • The average yield of ecological footprint is updated. • High-quality development can effectively reduce the increase of ecological footprint. • The improvement of population quality helps ease pressure on the environment. • Lower energy consumption intensity helps improve ecological quality. It is urgent to coordinate the development of economy and environment under the "dual carbon" goal. As China's new development stage and mode, high-quality development takes into account both the speed and quality of economic, providing the possibility of achieving a "win-win" situation for the economy and the environment. In order to test the practical impact of high-quality development on ecological, based on the STIRPAT model, this paper introduces high-quality development index instead of per capita GDP, so as to measure the current economic situation in China, and explore the relationship between high-quality development and ecological footprint, and demonstrates the feasibility from theory and practice. At the same time, it pays attention to the complex effects of population and technology on environmental quality. The results show that the contradiction between economy and ecology still exists in China. The ecological footprint index of 30 provinces in the past 21 years has increased as a whole, and the fworst ecological footprint has concentrated in the Yellow River basin. But since 2012, the growth of the footprint has slowed. The slow down time and low value region of ecological footprint were observed, and it was found that they overlapped with the proposed time and the high value area of high-quality development. The level of high-quality development in China has also continued to rise, with regional differences but gradually narrowing. Among them, green development and shared development contribute more to the improvement of high-quality development level. Compared with the traditional economic development mode, high-quality development can reduce the increase of ecological footprint, and the longer the time, the smaller the increase, more conducive to long-term sustainable development. In addition, population and technology factors are also profoundly affecting China's ecological pattern. The increase in population size and population density, as well as the increase in the proportion of coal-based energy consumption structure, will lead to increased environmental pressure. The improvement of population quality and the reduction of energy consumption intensity will help alleviate ecological pressure. The research is helpful to provide policy guidance and path combination reference for China's sustainable development, and also provide new ideas for the global coordination of the relationship between economy and environment. [ABSTRACT FROM AUTHOR]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50 % of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730 h to 1300 h when operating at 67 % of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44 % and 21.05 %. [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]

Energy Internet (EI) is typically characterized by digitalization and clean energy that seeks to revolutionize the energy system and reduce carbon emissions. Even though several scholars conclude that EI accelerates the clean energy transition of energy system, since the controversial relationship between digitalization and energy consumption in academia, current studies on the carbon emission reduction advantages of the EI are still in dispute. This paper employs the effect of EI's construction on the carbon emission peak goal of China's energy system based on the system dynamics model and different scenarios. The simulation results find that: (1) the construction of the EI could enable China's energy system to meet the carbon reduction commitment in 2028, with a carbon emission peak of 11.61 billion tons; (2) As digitalization and clean energy become more extensively employed, rising energy demand is satisfied by the manner of consuming low-carbon electricity energy. This is thought to be the primary factor that could promote China to achieve the carbon peak goal earlier than expected; (3) The advancement of digitalization has further promoted the decoupling of China's economic growth from carbon emissions. It is projected that China's energy system will still achieve the carbon peak goal by 2030 when the expected annual GDP growth rate is increased to 6.32%, 5.75%, and 4.6% for the years 2020–2025, 2026–2030, and 2031–2035, respectively. These findings might provide a new perspective for quantitative research on the environmental outcomes of the EI and the relationship between environmental improvement and economic growth. [ABSTRACT FROM AUTHOR]

Glued Laminated Bamboo (GluBam) has been innovated under the support of modern industrial technology, and this renewable material can drive the development of building structural system in a fashion of low carbon footprint. From the perspective of carbon emission, energy consumption and cost-based economy, this paper presents a comprehensive life cycle assessment (LCA) of steel-GluBam hybrid truss and virtually designed steel truss for a comparison. A steel-GluBam hybrid truss constructed at the Nanjing Tech University is used to carry on this study. The system boundary (i.e., production phase, transportation phase, construction phase, operation phase and disposal phase) of life cycle are fist determined. The mechanical properties of the truss structure are considered as the functional equivalence. The inventory of construction materials and the elements in each phase in the life cycle is compiled from the SimaPro and the available literature/specification. Based on the inventory analysis, the Monte Carlo method is employed to conduct the uncertainty analysis of carbon emission of the truss. The assessment results show that the life cycle carbon emission, energy consumption and cost of the steel-GluBam hybrid truss are equal to 4572.5 kgCO 2eq , 71.15 GJ and 15626.6 CNY, respectively. The production phase is the largest contributor to the carbon emission, energy consumption and cost, and the following one is the operation phase. Whereas the transportation phase, the construction phase and the disposal phase together contribute to only the small parts of emissions and cost. GluBam has the function of carbon sequestration leading to the reduction of carbon emission and energy consumption in the production phase, and other phases have no advantage on the emissions and cost of GluBam. The 95% confidence interval of life cycle carbon emission based on the Monte Carlo simulation is estimated as [4290,6370] kgCO 2eq for the steel-GluBam hybrid truss. With respect to the whole life cycle, using GluBam to replace the steel pipe in the upper chord pole and the diagonal web pole of the truss has a little larger cost but its environmental impact and energy consumption reduce a lot. The railway transportation of GluBam reduces 36.5 kgCO 2eq carbon emission, 0.68 GJ energy consumption and 83.7 CNY cost in the life cycle with reference to the road transportation. The steel-GluBam hybrid truss optimized based on the functional unit occurs in the case of reducing the cross-section of steel pipe from 42 mm × 4 mm to 32 mm × 2.5 mm. [ABSTRACT FROM AUTHOR]

• China has implemented a huge CO 2 capture project, the 150,000 tons/year CO 2 capture and purification project of Shaanxi Guohua Jinjie Power Plant, which is the largest flue gas capture project in China. • The energy consumption level of regeneration reaches the leading level of flue gas CO 2 capture demonstration project. New energy-saving process of "interstage cooling + MVR heat pump + desorption heat energy recovery" is applied, and comprehensive regeneration energy consumption is less than 2.4GJ/tCO 2. • The final research results show that the project has technical feasibility and economic feasibility. The results show that the regeneration energy consumption per unit product is 2.32GJ/tCO 2 , and the loss of absorbent is less than or equal to 1.0 kg/tCO 2. There was little research and demonstration experience in large flue gas carbon projects in China. 150 kt/a flue gas CO 2 capture project in the Guohua Jinjie power plant, which is the largest-scale flue gas carbon capture project in China, effectively solved this technical bottleneck. The performance of the used compound amine absorbent, phase change absorbent, and the ionic liquid was described, and the compatibility of performance was considered in the project; new energy-saving processes integrated mechanical vapor recompression heat pump, interstage cooling, and split flow desorption. The plane layout of the project was put forward, and the utility consumption of the capture system was analyzed. The paper explained the health, safety, and environmental protection instructions, presented test category tables, and developed energy consumption and economic analyses. The results showed that the renewable energy consumption per unit product was 2.32 GJ/t CO 2 , the loss of absorbent was less than or equal to 1.0 kg/t CO 2 , and the total investment (excluding tax) of the project was 13.8398 million yuan. When the CO 2 sales price (excluding tax) was 203.43 Yuan/t, the yield was 0 %, and when the CO 2 sales price (excluding tax) was 260.949 Yuan/t, the yield was 8 %. The feasibility study research conclusions indicated that the project was technically and economically feasible. [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]

As a non-Arctic country and a major energy consumer, China is increasingly interested in participating in Arctic energy development. This study aims to investigate potential strategies to promote China's participation in Arctic energy development. Accordingly, this study analyzes the strengths, weaknesses, opportunities, and threats (SWOT) of China's participation in Arctic energy development based on a discussion of the current status of Arctic energy development and the Arctic policies of major countries. In addition, this work develops strategies in accordance with the above analysis. The results show that China has strengths in terms of capital, energy demand, policy support, and international cooperation when participating in Arctic energy development, while its weaknesses include the lack of core technology and equipment, high carbon intensity of energy development, and geographical disadvantage. China faces opportunities stemming from conflicts among Arctic states, the advancement of global carbon neutrality, and the development of Arctic waterway while facing threats such as increased competition, international public opinion pressure, high development costs, environmental risks, and pandemic instability. Under an integrated framework, this work offers four strategies: Strength-Opportunity (SO), Strength-Threat (ST), Weakness-Opportunity (WO), and Weakness-Threat (WT). Finally, this study proposes specific measures in four areas, including strengthening policy support, promoting technical research and development, enhancing international cooperation, and improving enterprise competitiveness. [Display omitted] • This study examines China's participation in Arctic energy development. • This paper reviews the status of fossil and non-fossil energy in the Arctic. • This study compares the Arctic energy development strategies of major countries. • This study proposes four strategies, namely SO, ST, WO, and WT. [ABSTRACT FROM AUTHOR]

• A dynamic time-delay calculation algorithm based on MI and adaptive sliding window (MIDTA) is proposed. • MIDTA algorithm is used to solve the problem of long time-delay of cement kiln. • An algorithm of residual network and bidirectional novel gated recurrent unit (ResNet-BiNGRU) is proposed. • A Gaussian-weighted multi-model fusion method is proposed. • MIDTA-ResNet-BiNGRU multi-model fusion algorithm can be used to measure the temperature of cement rotary kiln with high precision. China's cement production energy consumption accounts for more than 10% of the country's total energy consumption, and its emissions account for about 60% of the country's industrial emissions. The temperature of a cement rotary kiln directly affects the quality of the final product, the energy consumption of a plant, and emissions. Therefore, it is necessary to measure, to a high degree of accuracy, the temperature of a cement rotary kiln to reduce energy consumption, emissions, and improve product quality. However, the conditions in cement kilns are complex and variable; and there are large time-delays, while temperatures are affected by historical working conditions, making temperature measurement extremely difficult. In this paper, a modelling algorithm with a residual network and a bidirectional novel gated cycle unit is used to predict the clinker exit temperature of the rotary kiln. In addition, to solve the problem of large time-delays in cement kilns, a dynamic time-delay calculation algorithm based on Mutual information and adaptive sliding windows is proposed for time series data reconstruction, and finally a Gaussian-weighted multi-model fusion is applied to the prediction results for cement rotary kilns that produce a wide range of working conditions. To verify the advantages and feasibility of the proposed method, experimental validation is carried out using original production data from a large cement manufacturing enterprise in South China. The results demonstrate that the single step prediction time of the presented approach is 0.66 s, which can predict cement kiln temperatures quickly and accurately, compared to a single conventional forecasting algorithm, the average temperature difference dropped from 11.76 °C to 3.51 °C, R2 increased from 0.43 to 0.89, 94% of the depicted temperature errors are at ± 10 °C This will provide an effective solution for online decision-making during actual cement production. [ABSTRACT FROM AUTHOR]

With the explosive growth of electric vehicles (EVs), it is an urgent task to incorporate low-carbon EVs with advanced optimization strategies to achieve orderly charging–discharging and economical operations for EVs. Nevertheless, current charging–discharging optimization strategies for EVs may be impractical since their energy consumption uncertainty and the driving trip cost are generally not considered. Therefore, a collaborative optimization model for large-scale EV charging–discharging with energy consumption uncertainty in this paper is proposed to simultaneously maximize passenger revenue and reduce the costs of the driving, charging–discharging, and battery depletion. Subsequently, a data-driven approach is developed to tackle the model. In this approach, an uncertainty predictor based on wavelet transform, deep deterministic policy gradient, and quantile regression is first applied to estimate the energy consumption uncertainty. Then, an adaptive learning rate firefly algorithm is presented to identify the most satisfactory solution for the optimization model. Finally, taking the actual data of 300 EVs in a city in China as case studies, the simulation results reveal that the proposed method is effective and has high application significance. • A new collaborative optimization model for EV charging–discharging is formulated. • A deep reinforcement learning method for energy consumption prediction is proposed. • The impact of driving behavior on energy consumption prediction is studied. • A new adaptive learning rate Firefly algorithm is presented and applied. • The effectiveness of the proposed methodology is verified by simulations of 300 EVs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calculate carbon emissions of energy consumption by using annual emission factors of indirect energy. • Put forward quantitative indicators of mine intelligence. • Impact of intelligence on carbon emissions of energy consumption in mining industry. • Prediction of carbon emissions in mining industry based on intelligent scenario analysis. The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China's mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China's mining industry is proposed. [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]

When talking about energy conservation, "Rebound Effect" (RE) is always concerned, which defined as the increasing energy consumption relative to the counterfactual predicted by technological progress because of efficiency induced decrease in the real price of energy services. In this paper, we try to find a way to figure out the RE through calculating the substitution relations accord with the definition. Based on the trans-log cost function and considering the asymmetric impact on energy-cost share equation, the paper applies Allen-Uzawa substitution elasticity to establish the price-oriented analysis diagram of fossil-energy consumption RE. Using time series data, applying the joint method of the dynamic OLS (DOLS) and the seemingly unrelated regressions (SUR), the RE in China's electricity generation sector is estimated. The results showed that the RE is 11.6% in China's electricity generation sector if allowing for asymmetric price effects, which indicates that China's power generation sector generally displays a feature of energy saving. [ABSTRACT FROM AUTHOR]

Abstract: With increasing attention on energy shortage and global warming, bioenergy is a promising alternative energy to mitigate CO 2 emissions. Despite the environmental friendly potential, a certain quantity of bioenergy-related CO 2 is still emitted in China. This paper investigates the temporal dynamics of bioenergy-related CO 2 emissions in 2000–2010 in China, and explores the underlying influence factors based on logarithmic mean Divisia index (LMDI). Then the core results are verified with a GIS-based approach from a spacial perspective. The results show that four impact factors: emission intensity, energy structure, energy efficiency and economic development have different impacts on bioenergy-related CO 2 emissions, of which economic development is the major factor contributing to its increase, while energy efficiency is the major determinant for the decline. Per capita bioenergy-related CO 2 emissions in different areas are verified in accordance with either economic development factor or energy efficiency factor to some extent. To achieve the dual purpose of sustainable development and CO 2 emissions reduction, this paper provides a practical policy framework for rural bioenergy and economic development. [ABSTRACT FROM AUTHOR]

The iron and steel sector is energy-intensive, and it consumed 18% of the world's total industry final energy consumption in 2013. At present, according to the statistical data of International Energy Agency (IEA) published in 2012, the iron and steel industry has the technical potential to reduce its current total energy consumption by approximately 20% by applying the best available technology (BAT), and more than half of this technical potential may be obtained in China, where more energy-efficiency technologies/measures should be implemented in the steel plants. So a review of energy-efficiency technologies/measures in steel industry could be helpful for steel plants to improve their energy efficiency. Therefore, this paper presents a list of energy-efficiency technologies and practices applicable to the steel industry, which includes case studies around the world and information of energy savings and cost when available. Also, a brief overview of the steel industry around the world, details and energy use conditions of different steel manufacturing processes, types of energy use and specific energy consumption of steel industry and details of secondary energy are also included in this paper to give readers a clear understanding of the energy use situation of the steel industry. [ABSTRACT FROM AUTHOR]

China's wind power installed capacity is the largest in the world, but the utilization of wind power equipment is not very good, far behind USA. In this paper, the development of China's wind power is reviewed, and the present wind power curtailment restricts the sound development of China's wind power industry. The characteristics of China's wind farm are summered. With the insatiability and intermittence, wind power is not welcome to China's electric grid, and large-scale wind power construction does not match with the existing power grid, therefore, wind power curtailment is serious and the level of wind power utilization is very low. To solve the wind power curtailment rationing problem, in this paper, combined with the characteristics of China's wind farm operation, the wind power utilization level evaluation index system has been built, reflecting the wind resource characteristics, wind power equipment type, wind power output, wind power curtailment, grid technology, operation management and so on. Taking Hami wind farm in Xinjiang province as an example, wind power utilization level is evaluated comprehensively, combined the improved analytic hierarchy process (IAHP) analysis and fuzzy comprehensive evaluation method (FCEM). The results show that the establishment of wind power utilization level comprehensive evaluation index system is helpful to find the main factors which effect the level of wind power utilization and improve the wind power field operation, which can provide reference for the planning and design of wind farm, and the results have certain value on theoretical significance and engineering application. [ABSTRACT FROM AUTHOR]

This paper combines the concept of Ecological Footprint (EF) with the framework of total-factor energy efficiency to develop a new index of total-factor ecology efficiency ( TFEcE ), which is constructed as the ratio of the target EF input obtained from SBM (slack-based measure) model to the actual ecology input under the consideration of labor and capital inputs. This paper computes the TFEcE of 28 provinces in China for the period 2000–2012. Findings show that China’s TFEcE remains a low level of 0.5, which urgently needs to be improved. China’s regional TFEcE is extremely unbalanced and the eastern area ranks first with the highest score. Compared with total-factor energy efficiency and traditional single-factor ecological efficiency, The TFEcE index evaluates ecology efficiency more comprehensively through taking EF in conjunction with the total-factor framework. [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]

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]

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]

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]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

• Province-level grid parity potential of wind and solar PV power plants in China. • Techno-economic feasibility of baseload supply in hybrid renewable energy system. • Correlation analysis of system cost and resource potential and complementarity. • Projected Grid parity potential of renewable energy system for baseload supply. To promote the large-scale deployment and grid integration of renewable-based power system, this paper investigates the province-level techno-economic feasibility of wind-photovoltaic-battery hybrid renewable energy system (HRES) for baseload supply application around China. Firstly, the power generation cost and grid parity potential of province-level standalone wind and photovoltaic power plants are assessed. Then, the optimal sizing of province-level HRESs aims to minimize the net present cost and meet the reliability constraint for baseload supply, which is solved by teaching and learning-based optimization. Finally, the techno-economic comparison between different system configurations, the correlation between HRES cost-effectiveness and resource potential as well as resources complementarity, and the grid parity potential of HRES for baseload supply are quantitatively analyzed. The results reveal that: (i) 84.4% of regions in China can achieve solar photovoltaic plant-side grid parity in 2022, while only 15.6% of regions can achieve wind power plant-side grid parity; (ii) HRESs in all regions are technically feasible but not cost-effective for baseload supply; (iii) wind-photovoltaic-battery HRES is the optimal configuration for most regions, indicating that the wind-solar hybridization is more reliable and cost-effective than standalone power plants; (iv) the economic performance of HRES depends mainly on the wind-solar joint resource potentials and the supply-demand relative deviation; (v) investment cost reduction and utilization of energy curtailment are promising to help HRES achieve plant-side grid parity for baseload supply. The findings of this paper can help policy-makers and investors optimally determine the national pathway of renewable energy transition. [ABSTRACT FROM AUTHOR]

• Analyze the synergy of CO 2 emission factors of energy-intensive industries via EMD. • Synergy of CO 2 emission factors: long-term trend, short-term volatility. • Elastic coefficient model is used to reveal the heterogeneity of the 10 factors. • Six industries are divided into "stable and balanced" and "sensitive and uneven". Carbon emission is a global problem that countries around the world are paying attention to and urgently need to be resolved. Under the requirements of China's high-quality economic development and the carbon peaking and carbon neutrality goals, energy-intensive industries, as a key link in the industrial chain and the main source of China's CO 2 emissions, have attracted much attention. This paper made a statistical analysis of the scale development of six energy-intensive industries, and explored the fluctuation characteristics of their carbon emissions. The empirical mode decomposition (EMD) model was further introduced to analyze the and synergistic effect among the driving factors of carbon emission, revealing the synergistic mechanism of long-term trend and short-term volatility of factors. Then, the elastic sensitivity analysis model of carbon emissions in energy-intensive industries was constructed to study the heterogeneity of driving effects of factor indicators. From the perspective of factors driving, this article reveals the heterogeneity of the driving effects of ten key factor indicators including economic level (GDP), urbanization level (UP), industrial structure (IS), technological innovation (TI), and environmental regulation (ER); from the perspective of industry development, this paper summarized the "stable and balanced" and "sensitive and uneven" trends presented by the heterogeneity of the six energy-intensive industries. Combining the characteristics of industry emission fluctuations, driving factors' synergies and heterogeneity, from the perspective of short-term and long-term development, four targeted emission reduction control strategies were proposed to provide reference for the low-carbon development of energy-intensive industries. [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]

• Residential energy consumption has become the second-largest energy sector after the industrial sector. • The impact of urbanization on residential energy consumption is heterogeneous across income stages. • The impact of urbanization on residential energy consumption is heterogeneous across urbanization stages. • Urbanization has a positive effect on residential electricity consumption and shows heterogeneity across regions. • Urbanization contributes more to increase of rural residential electricity consumption than that of urban's. Against the background of the continuous development of urbanization, the urbanization process can lead to substantial changes in residential energy consumption and structures. Residential energy consumption has become the second-largest energy sector after the industrial sector. The paper studies the impact of urbanization on residential energy consumption using annual Chinese provincial data from 1996 to 2014. The results show the following. First, the impact of urbanization on residential energy consumption is heterogeneous across different per-capital income stages and urbanization stages. Second, urbanization has a positive effect on residential electricity consumption with a coefficient of 1.1 and shows heterogeneity across regions. Third, a higher urbanization rate leads to an increase in both urban and rural residential electricity consumption, with a more substantial impact on the latter. As a result, the increase in the urbanization rate contributes to shrinking the gap between urban and rural residential electricity consumption. Finally, based on the conclusions set forth above, policy implications for sustainable urbanization development under the energy constraints are given. [ABSTRACT FROM AUTHOR]

• A novel vector field-based SVR for building energy consumption prediction (ECP) is proposed. • Nonlinearity of the ECP models is transformed into linearity via vector field-based SVR. • Two high precision, generalization capability, and robustness energy consumption prediction models are established. • A real case study demonstrates the superiority of this study compared with previous studies. Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Data-driven approaches, such as artificial neural networks, support vector regression, gradient boosting regression and extreme learning machine are the most advanced methods for building energy prediction. However, owing to the high nonlinearity between inputs and outputs of building energy consumption prediction models, the aforementioned approaches require improvement with regard to the prediction accuracy, robustness, and generalization ability. To counter these shortcomings, a novel vector field-based support vector regression method is proposed in this paper. Through multi-distortions in the sample data space or high-dimensional feature space mapped by a vector field, the optimal feature space is found, in which the high nonlinearity between inputs and outputs is approximated by linearity. Hence, the proposed method ensures a high accuracy, a generalization ability, and robustness for building energy consumption prediction. A large office building in a coastal town of China is used for a case study, and its summer hourly cooling load data are used as energy consumption data. The results indicate that the proposed method achieves better performance than commonly used methods with regard to the accuracy, robustness, and generalization ability. [ABSTRACT FROM AUTHOR]

Highlights • The average compliance cost for China's fuel economy regulation by 2020 is $688. • The market share of New Energy Vehicles will be 16.9% and 34.2% by 2025 and 2030. • China's fuel saving target cannot be fully achieved using current schemes. • Vehicles will grow heavier due to the regulation's curb weight based feature. • The favorable test method will make PHEV the most promising fuel-saving technology. Abstract Under the annually strengthened fuel economy regulations for light-duty vehicles, a technology pathway provides automakers with forward-looking guidance to strategically manage their vehicle assortment and fuel-efficient technologies. Because the regulations worldwide are very different in terms of their various schemes and designs, it is difficult to calculate the optimal pathway or to evaluate a regulation's impact on the vehicle fleet without an assessment model specifically designed to do so. To date, China has issued four Phases of Corporate Average Fuel Consumption regulations; however, a bottom-up optimized technology pathway for the regulation compliance of domestic automakers is still missing. In this paper, real-world data on each vehicle model's market and technology parameters are used to describe China's passenger vehicle fleet. Employing a combinational optimization assessment model for China's regulation, this study calculates bottom-up fuel-efficient technology pathways for complying with the 2020 regulation and the potential 2025–2030 regulations. Under scenarios that do not consider the subsidy or super-credit preferential policies for New Energy Vehicles (Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles in China), the average compliance costs for the three future phases of the regulations will be $687.5, $2344.4 and $3111.2. The average fuel consumption rate of passenger vehicle fleet will reach 5.28, 4.07 and 3.33 L/100 km, and the average vehicle curb weight will increase by 4.7%, 6.3% and 9.2%. These results mean that if current schemes are followed, the regulations will fail to achieve their fuel saving targets and to promote lightweighting vehicles. The technology pathway of powertrains and specific fuel-efficient technologies is analyzed in great detail. The results show that the market share of New Energy Vehicles will increase to 16.9% and 34.2% by 2025 and 2030, and that Plug-in Hybrid Electric Vehicle will be the most promising fuel saving technology due to the preferential test method in China. The sensitivity of the compliance cost and the market share of New Energy Vehicles to regulation stringency is further analyzed. For every change in regulation stringency of 0.05 L/100 km in the 2025 and 2030 regulations, the compliance cost will change by 3.03% and 2.31%, while the market share of New Energy Vehicle will change by 0.58% and 0.73%. These results indicate that if the various schemes for regulation compliance are not taken into account, only making adjustment to the regulations' stringency may have little effect on reducing automakers' compliance cost or promoting the market share of New Energy Vehicles. [ABSTRACT FROM AUTHOR]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

Abstract In the last two decades, a remarkable socio-economic transition has been witnessed in China. Existing studies have only shown that energy consumption of residents is influenced by economic growth and urbanization. It is unclear, however, how socio-economic transition influences the inequality of energy consumption among urban and rural residents. Focusing on the empirical investigation of two different regions (the eastern developed provinces with little energy resources and the western underdeveloped provinces with rich energy resources), this paper examined the major socioeconomic determinants of energy consumption inequality between urban and rural residents. The results of panel data analyses showed that there was a long-term relationship between socio-economic transition and changes in the inequality of energy consumption among residents. Meanwhile, the results revealed that the influence of socio-economic transition varied from regions to regions. Generally, the influence of urbanization on the inequality of both quantity and diversity was found to be the most profound and significant in all regions. The industrialization transition also exerted far-reaching effects, although its influence was determined by the growth of industry sectors. Moreover, the influence of economic transition was only significant in underdeveloped regions and such an influence fitted to a Kuznet's curve. For the transition of technology and education, its influence was significant in the developed provinces but was only effective in affecting the diversity of inequality. In summary, the findings of this research present policymakers a better understanding of the relationship between the socio-economic transition and inequality in energy consumption. [ABSTRACT FROM AUTHOR]

Abstract Since the beginning of China's reform and opening up in late 1970s, the contradiction between environmental quality and economic development has become increasingly acute. As the main driving force of development, China's fixed asset investment (FAI) has grown rapidly. In the meanwhile, China's energy consumption surged and the environmental pressures mounted. The surge in FAI may have great influences on the environmental quality. On the one hand, the rapid increase in FAI facilitated the development of the secondary industry especially energy- and pollution-intensive industries, which brought about significant amount of energy-related pollution. On the other hand, growing investment on environmental investment is expected to slow environmental deterioration. Moreover, the environmental quality and domestic investment may have potential spatial correlations. In this paper, after fully accounting for the spatial dependence, the relationship between domestic investment and environmental quality in China is quantitatively investigated. Using CO 2 and SO 2 as two representative pollutants, the estimation results indicate that the spatial dependence indeed exists, and total effects of FAI on pollutant emissions are positive. However, there is no evidence that environmental protection investment had expected effects to effectively curb pollutant emissions in China. [ABSTRACT FROM AUTHOR]