Search

Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]

Reasonable road design can not only improve traffic efficiency, ensure driving safety, reduce construction costs, but also reduce the energy consumption of road transportation. However, existing road design research mainly focuses on safety, efficiency, and economic factors, with few studies considering the actual construction difficulty as well as energy saving and emission reduction of transportation. To fill the research gap, this paper proposes a longitudinal slope design method for highway sections that considers both vehicle fuel consumption minimization and construction feasibility. First, the passenger car trajectory data and fuel consumption data from the Beihuan Highway in Guangzhou, Guangdong Province, China were collected, and the optimal combination features of the random forest algorithm were extracted using parameters such as vehicle operating conditions and road grades, thus establishing a fuel consumption estimation model for passenger cars on highways. Second, by combining the fuel consumption estimation model with dynamic programming algorithm, an ecological speed planning model for passenger cars on highways was developed. Finally, for the scenario of ecological driving of intelligent vehicles, a fuel-efficient slope design (FSD) method was proposed based on the Nested Monte Carlo Tree Search algorithm, and the best fuel-efficient slope design was selected considering the balance of cut and fill factors. The results show that FSD can reduce the fuel consumption of passenger cars on the section by about 4.50%–6.52%; the fuel-efficient slope design considering the balance of cut and fill not only can reduce the fuel consumption of passenger cars on the section by about 5.21%, but also has the advantage of lower engineering volume. The research results conduce to establish a theory and standards for fuel-efficient slope design of highways suitable for ecological driving scenarios of intelligent vehicles, which can reduce the trip fuel consumption of highway traffic flow in the long run while considering traffic efficiency, and have great significance for reducing carbon emissions, saving travel costs, and protecting non-renewable resources. • The study proposes a fuel-efficient longitudinal slope design method for highway. • The method can reduce the fuel consumption of a passenger car by about 4.50%–6.52%. • The concave curve is effective in reducing fuel consumption for passenger cars. • Considering the balance of cut and fill makes the slope scheme more fuel-efficient. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

Highlights • A nonlinear relationship between industrial agglomeration and energy efficiency. • A threshold effect exists when industrial agglomeration affects energy efficiency. • When agglomeration degree is less 1.3865, it will promote energy efficiency. • Future policy for energy efficiency in China's textile industry is suggested. Abstract Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

National Development and Reform Commission and National Energy Administration have launched a series of policies on closing down small coal-power units, in order to reduce energy consumption and pollutant emissions. However, it is hard to change current situation in the short term since coal is still the domain source of power generation in China. Aiming at efficiently closing down the small power units, to create a power generation planning model with minimized costs needs to take both economic and technical aspects into account. In this paper, eight types of coal-fired generators are classified into three categories: Inefficient units; Efficient units; and Low-carbon units. This paper has developed a power generation planning model under multiple constraint conditions such as coal-power demand, total installed capacity, and carbon capture etc. Also, the model involves variable costs of CCS technology and contingency payments at the same time. This paper has applied the power generation planning model into China’s coal-fired power industry research during the period from 2016 to 2030. The results show that because the coal-power demand ends up with a drop following a rise, the total costs thereby shows a same trend. During the planning period, the fuel costs and the operation and maintenance costs decrease most obviously. Given the installed capacity, compared with the increase in the number of efficient units, the number of inefficient units shows a gradual decrease. The number of low-carbon units displays a slight increase. Since low-carbon units can capture and store 90% of their carbon emissions, the total carbon emissions from coal-fired power industry have significantly been reduced in their operation year. Thus, it is imperative to develop high efficiency and low-carbon units as they will be the major contributors to the sustainable development of the coal-fired power industry. [ABSTRACT FROM AUTHOR]

• Propose an integrated framework for the pipeline pricing strategy. • Utilize a logistics optimization model to guide multi-scheme analysis. • Contribute to increasing pipeline carriers' revenue by 11.41 million CNY per month. • Reduce energy consumption by 272 tce per month. • Provide recommendations to policymakers in China. Pipeline transportation is a low-energy and economical mode of transporting petroleum products in the downstream supply chain, however, there is almost no theoretical research on its pricing strategies. The unreasonable pricing strategy has resulted in low utilization of multi-product pipeline capacity as well as high energy consumption of petroleum products transportation. Therefore, this paper aims to improve pipeline turnover and promote the low-carbon transportation market from the perspective of pipeline pricing optimization. We propose an integrated framework for multi-product pipelines that couples pricing strategy and logistics optimization model. This framework simulates the pricing behavior of the pipeline carrier and the corresponding logistics planning behavior of the oil shipper. We apply the framwork to 10 pipeline pricing schemes for two regions in China with different logistics structures, and analyze the economic and environmental benefits of the new strategy. The results show that the well-performing scheme can increase pipeline carriers' revenue by 11.41 million CNY per month, significantly improve the competitive advantage of long-distance pipelines, and reduce energy consumption by 272 tce. Based on these findings, we provide recommendations for policymakers at four levels. In conclusion, the new pricing strategy will help reverse the disadvantageous situation of the pipeline in the competitive market and promote energy conservation in the petroleum products logistics industry. [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]

• We examine the energy implications of China's food system transformation. • The share of farming declined, whereas the share of processing increased. • Energy consumption tied to transportation grew rapidly. • Food-related energy intensity declined between 2002 and 2012. • Technological changes decreased energy intensity while diets changes increased it. Food systems in developing countries are undergoing a dramatic transformation from traditional to more modernized forms. This paper examines the energy implications of the transformation of China's food system based on a time-series multi-sectoral input–output model. Results reveal several trends. First, the estimated food-related energy consumption (excluding energy consumption related to in-home food preparation and storage) increased by 73% and 53% in total and per capita terms, respectively, between 2002 and 2012. Second, supply chain analysis shows changes in the distribution pattern of energy consumption along the supply chain; the share of farming declined, whereas the share of processing increased. Energy consumption tied to transportation grew rapidly. Third, food-related energy intensity declined during the study period. Decomposition analysis found that technological changes spurred a decline in energy intensity, whereas changes in diets increased energy intensity. Furthermore, the contribution of each supply chain stage was decomposed, and findings indicated that farming accounts for the largest contribution and packaging, storage, wholesale and retail and the food service sector (restaurants) contributed to decrease energy intensity. Processing and transportation increased energy intensity. Finally, the network analysis shows that electricity has been a major direct energy-consuming sector, of which the usage is induced by crop, fishing and aquaculture livestock, processed food and restaurant. [ABSTRACT FROM AUTHOR]

Highlights • Renewable quotas incorporating equity and duty are allocated at regional level. • The entropy method coupled with Management by Objectives principles is adopted. • Quotas scheme is dominated by differences in resource endowment and infrastructure. • Nexus for renewable-rich and developed areas is vital to fulfill renewable quotas. • Renewable revolution needs a twin-track process of nearby and external consumption. Abstract Feed-in tariff and renewable portfolio standards are two major policies for renewable power generation and China is the first country to adopt both policies at the national level. The black box for regional allocation of renewable portfolio standards, however, may lead to the instability and inefficiency of this policy. This paper introduces the principles of Management by Objective to build a clear policy framework for renewable portfolio standards in China and employs the entropy method to equally and reasonably assign regional responsibility for renewable energy development. A comparison is made between our allocation and the goal-directed policy to determine whether historical responsibility and infrastructure construction should be further considered in crafting renewable portfolio standards. The trends for regional renewable quotas are influenced by the twin-track approach of nearby utilization and external transmission. Finally, based on our analysis, the policy implications behind the new incentive are presented to urge the establishments of supporting facilities. [ABSTRACT FROM AUTHOR]

Highlights • Parameters of 60 electricity-saving measures and 16 abatement measures are compiled. • Synergies of electricity use in China's chemical sector at grid level are assessed. • Efficient use of power can cut 154 Mt CO 2eq , 295 kt SO 2 , 322 kt NO x and 64 kt PM 2.5. • North and Northwest grids have the largest economic benefits on emissions reduction. • Grid with high electricity price is more sensitive to price change than other grids. Abstract Industry is a large electricity user. China's chemical industry (globally the largest based on sales) contributes 7% to China's GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO 2eq , 40% of SO 2 , 59% of NO x and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China's chemical industry. The emission factors per grid for GHG (i.e. CO 2 , CH 4 and N 2 O) and air pollutants (i.e. SO 2 , NO x and PM 2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO 2 , 33 kt CO 2eq of CH 4 , 571 kt CO 2eq of N 2 O, 235 kt of SO 2 , 275 kt of NO x and 52 kt of PM 2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored. [ABSTRACT FROM AUTHOR]

Highlights • A day-ahead scheduling strategy is proposed with detailed device modeling. • The effects of multiple thermal storage devices on flexibility are fully utilized. • The unit commitment constraints in practical operation are well considered. • The optimal scheduling model is transformed into a MILP formulation by linearization. Abstract The operation of community integrated energy systems (CIESs) is challenging because it is necessary to couple energy production, conversion and consumption on the end-user side. Multiple energy demands need to be fed simultaneously with the goal of economy and reliability. This paper formulates the optimal scheduling problem based on a real community integrated energy system in China. The system has multiple chillers (i.e. three ground source heat pumps, two conventional water-cooled chillers and two double-duty chillers) and two types of thermal storage devices (i.e. two cold water tanks and an ice-storage tank). Based on detailed device modeling, a community integrated energy system operation strategy considering unit commitment is proposed, and then is transformed into a mixed-integer linear programming model by linearization of nonlinear items. Case studies are conducted based on the data for a typical day in summer. The results show that the proposed strategy can utilize the flexibility of the energy storage devices and realize an economic and reliable operation of the community integrated energy system by coordinating various energy devices. The strategy can also reduce the startup/shutdown frequency of chillers significantly. [ABSTRACT FROM AUTHOR]

Highlights • The input–output model and ecological network analysis (ENA) are integrated. • The control and dependence conditions of economic sectors in China are analyzed. • The key regions and sectors for carbon mitigation of China are identified. Abstract In this paper, multi-regional input–output analysis (MRIO) and ecological network analysis (ENA) are combined to assess carbon flows within China and identify key regions and sectors in the context of spatial heterogeneity for effective carbon mitigation. An interregional carbon network model is established by articulating the directions and magnitudes of carbon flows based on MRIO. ENA is then used to unveil indirect carbon flows and mutual relationships among regions. The results show that the northwest is the largest controller for most regions in China. Most carbon emissions in the rest of China are induced by the east’s final demand and substantial consumption. In addition, at sectoral level, the control and dependence abilities vary by region in China. This study provides an integrated framework to investigate interregional carbon emission structure, identify efficient pathways for coordinated emission mitigation, and reduce global carbon inequality across regions. [ABSTRACT FROM AUTHOR]

Highlights • An energy-related PM 2.5 footprint inventory framework for households is constructed. • PM 2.5 footprint is decreasing from urban households (high income) to rural households (low income). • Sectors related to food, residence and health care are the main contributors of PM 2.5 footprint. Abstract Particulate matter 2.5 (PM 2.5 ) as a major hazardous constituent is strongly associated with household energy consumption. In this paper, we investigate the PM 2.5 footprint of household energy consumption in Beijing based on input–output analysis. An inventory of primary and secondary household energy consumption is developed to quantify the direct PM 2.5 emissions. The household PM 2.5 footprint is then traced through goods or services that ultimately consumed by households to unveil the indirect PM 2.5 emissions triggered by economic activities. PM 2.5 fingerprint is also proposed to describe the characteristic of household PM 2.5 footprint. Results show that PM 2.5 footprint of Beijing households in 2010 is 7831.36 kt, of which 92.61% is contributed by urban households. The source of direct PM 2.5 emissions in urban area is diversified, which is composed of coal (42.07%), heat and electricity (32.83%), gasoline (21.29%), natural gas (3.04%) and liquefied petroleum gas (0.77%), while in rural area, coal (98.09%) plays a dominant role. The indirect PM 2.5 accounts for 99.96% of the total footprint in urban area, about one third of which is contributed by sectors of “Food Processing and Production”, “Healthcare and Social Security”, and “Farming, Forestry, Animal Husbandry and Fishery”. The disparity between urban and rural households PM 2.5 footprints is further evaluated with income levels. The PM 2.5 footprint from living expenditures of urban households is found to be nearly twice as much as that of rural households. Such inventory of PM 2.5 footprint and examination of drivers for PM 2.5 emissions may be essential for urban pollution mitigation policy. [ABSTRACT FROM AUTHOR]

The objective of this study is to trace and quantify the sources of rebound effect for different energy sources in China. This paper decomposes the economy-wide rebound effect into 135 production sector-level rebound effects and five final demand components. The sector-level rebound is further decomposed into output effect and substitution effect. A two-stage decomposition method and a static computable general equilibrium model are developed to achieve this. Five types of energy-specific efficiency improvements are introduced, respectively, in all the production sectors. Results show that improving efficiency of using coal leads to the smallest macro-level rebound. For rebound decomposition, four findings are generalized. First, final consumption shows larger impact on the rebound of secondary energy sources than on primary energy sources. Second, production sectors that are big user of the efficiency-exposed energy source are expected to be key rebound contributor. Third, energy-producing sectors make negative rebound contribution. Results also show that, substitution effect is the predominant mechanism that triggers sector-level rebound. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

Coal mining not only produces, but also consumes a large amount of energy. Coal production has an extremely high energy efficiency potential, and benchmarking is critical to discover this potential. To address problems such as ambiguity of coal production (e.g. underground mining), benchmarking range and absence of both indicators and standards for energy efficiency benchmarking, this paper makes use of product-based and process-based benchmarking. These two techniques are used to construct a benchmarking system for coal production with high energy efficiency, and improve the standards used at the eight coal mines owned by the Yankuang Group. Comparison and analysis of benchmarking in enterprises indicates that energy use during raw coal production could be reduced by 22.77%. Across China energy savings from coal production could reach 15.98–32.98 Mtce, with an equivalent 42–87 Mt of CO 2 emissions saved. Lastly, this paper provides measures to improve coal production energy efficiency. [ABSTRACT FROM AUTHOR]

Listed energy companies play an important role in both the energy financial market and energy commodity market. As owners of listed energy companies, shareholders can influence the stability of the energy stock market by increasing or decreasing their holdings. To analyze the evolutionary features and stability of shareholders’ co-holding behavior, we constructed a derivative holding-based structural equivalence network of the shareholders (holding-based network) based on the primitive shareholding network of the listed companies and their shareholders. The main data used in this paper are the semi-annual reports from 2003 to 2012 that contain information about Chinese listed energy companies’ top 10 shareholders. The holding-based network takes the shareholders as nodes, whether holding the same listed energy companies’ stock at the same time as the edges, and the number of listed energy companies co-held as the weights. Due to the unique topological features of the holding-based network, we improved the Heuristic Algorithm to find the associated maximal connected sub-graph of the network, which is the main group of shareholders with shareholding consistency of the listed energy companies. This paper creates a new approach to analyze the relationships in the energy stock market, presenting a new method to analyze shareholder behavior. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

Household fuel-use (HFU) patterns are strongly associated with multiple effects, including air quality, human health, and regional climate change. This paper presents the results from the first Chinese Environmental Exposure-Related Human Activity Patterns Survey (CEERHAPS), carried out among 91,121 households located in 9108 villages, 636 towns, and 159 counties in 31 provinces. Face-to-face interviews were conducted with each participant to obtain information about the type of fuels used for cooking and heating. The main objectives of this paper were to investigate HFU for cooking and heating in China, to validate the World Health Organization (WHO) model, and to help inform local evaluation models. In China, gas and biomass fuels, the dominant energy fuels for cooking, are used by 44.8% and 32.1% of households, respectively. Approximately 34.1% of families have no household heating during the cold season, and 16.7%, 15.6% and 12.8% of households rely on coal, electricity, and biomass, respectively, for household heating when no central heating was available. The proportion of households using solid fuels has generally decreased during the last two decades. Considerable spatial variation was evident in the HFU patterns for both cooking and heating. The data revealed that the predominant fuel for cooking was biomass (47.6%) in rural populations, whereas urban households were more likely to cook with gas (65.8%). In terms of heating, coal (21.4%) and biomass (19.0%) were the main fuels used in rural households, while electricity (23.6%) and coal (10.5%) were more commonly used in urban areas. The overall HFU results for cooking from this study were comparable to those predicted using the WHO model. We also developed models at the provincial level to estimate HFU for cooking and heating in China; it is expected that the model, if confirmed by future studies, could be used for future research on household air pollution, domestic human exposure and burden of disease. [ABSTRACT FROM AUTHOR]

This paper estimated the cost curve of energy saving and CO 2 emissions reduction in China's iron and steel sector. Forty-one energy saving technologies which are widely used or popularized are selected, their investments, operation costs, energy savings and CO 2 abatement are collected and the data in 2010 are taken as a baseline. Then energy conservation supply curve and CO 2 conservation supply curve under two different discount rates are calculated in the paper. These 41 technologies result in a saving contribution of 4.63 GJ/t and a CO 2 abatement contribution of 443.21 kg/t. Cost-effectiveness of technologies was analyzed based on the fuel price and an estimated CO 2 price. When comparing the result with the promoted technologies during the 12 th five-year-plan, we found that some promoted technologies are not cost-effective in current situation. Three scenarios are set through changing the diffusion rate of technologies and the share of BOF and EAF, based on this energy saving potentials of technologies in 2020 and 2030 are forecasted. At the same time, we compared the change of the CSC depending on the year and the energy saving potentials in three scenarios of 2020 and 2030, respectively. [ABSTRACT FROM AUTHOR]

After China launched its “Belt and Road” (BR) initiative, the international community became concerned that it may worsen the environmental performance of the BR countries. Due to a lack of data for empirical testing, this paper addresses this concern through an indirect method and draws the implications of the potential impacts of China’s BR initiative. This method empirically examines the effects of trade integration and regional cooperation, two major functions of the BR initiative, on energy efficiency (EE) convergence, a concept that describes the catching up process of EE across countries. A sample of 89 countries was selected to analyse the process of EE convergence from 2000 to 2014. The results indicate that although the gaps in EE among countries around the world become larger after 2010, regional cooperation may lead to a convergence process. It also finds that trade integration has a positive influence on convergence across the countries, especially among middle- and low-income countries. The results suggest that the BR initiative, through its roles in trade integration and regional cooperation, may promote EE convergence among countries. This is a desirable environmental outcome. This research also provides policy implications for both China and the other BR countries. [ABSTRACT FROM AUTHOR]

This paper explores the optimization scheme of carbon trading in China based on a novel energy-saving and emission-reduction (ESER) system with carbon price constraints. With the aid of nonlinear dynamics theory, the dynamics behavior of the novel system is discussed. Genetic algorithm and back propagation neural network is used to identify the quantitative coefficients according to the statistical data of the second period in European Union (EU). Taking the actual situation in EU for instance, the variables which are sensitive to carbon trading are detailedly researched. Enlightened by the EU’s experience, an optimal road of China ’ s carbon trading is put forward. The results show that carbon emissions could be controlled by carbon trading. The investment to carbon trading hampers economic growth in the near future, and ESER technical progress is negatively correlated with carbon trading in the long run. Demand and supply relationship is closely related to carbon price, both are the important issues in carbon trading system. Excessive government control and extortionate carbon price will deliver the opposite effect and even fatal influence on carbon trading system. [ABSTRACT FROM AUTHOR]

The key distinction between production-based and consumption-based accounting lies in the way in which trade-embodied emissions are allocated between producer and consumer regions. Yet, these two regions are not necessarily directly connected to each other in supply chains, due to spatial production fragmentation. To better understand the link from the production-based to the consumption-based regional carbon inventories, this paper defines the economic length of carbon transfer (ELCT) as the number of administrative border-crossing involved in the influence process of a region’s final demand on the carbon emissions of another region. Using a multi-regional input-output analysis framework for China, we propose an approach to calculating the ELCT related to a region’s production or demand for domestic consumption or international export. We find that the average ELCT in 2010 is 1.34 and that more than a quarter of interregional carbon transfer crosses provincial borders twice or more. The provincial and sectoral analysis of ELCT reveals that spatial production fragmentation is an important challenge to be conquered for successful implementation of consumption-based climate regulations. [ABSTRACT FROM AUTHOR]

Accurately understanding the real situation of China’s carbon emissions at the provincial level is not only crucial for realizing carbon mitigation in China, but also of great importance for tackling climate change on a global scale. This paper uses detailed energy consumption data subdivided within each fuel type and sector-specific emission factors, to calculate both direct and indirect energy-related CO 2 emissions for China’s provinces, and offers provincially-targeted policy proposals on emission reductions. The results uncover large divergences in calculation of provincial CO 2 emissions between production-based and consumption-based accounting approaches. Considering the large amount of interprovincial CO 2 emission transfers embodied in secondary energy deployment, the consumption-based accounting performs better in reflecting the actual features and responsibilities of carbon emissions in the provinces of China. From the consumption perspective, Shandong, Hebei, Jiangsu, Inner Mongolia, Guangdong, Henan, Liaoning and Shanxi contributed to the majority of China’s carbon emission growth during the period of 1996–2013, while Xinjiang, Sichuan, Anhui, Tianjin and Hunan were playing an increasingly important role. These two groups of provinces should be regarded as the key regions for national emissions abatement. Meanwhile, the energy industry was the top sectoral contributor to CO 2 emissions growth in most provinces, while service and household sectors made increasingly significant impacts on driving up provincial emissions. Together with ongoing efforts in the energy sector, the mitigation of CO 2 emissions in service and household sectors deserves much more attention, especially in the transportation and urban household sectors. Furthermore, the temporal trends of carbon emissions in various provinces, as well as their factor and sectoral driving forces, varied greatly with each other. Therefore, each of the provinces should formulate emission reduction policies according to their own situation, and target the key driving factors and sectors of carbon emissions at the current stage or in the near future. [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [ABSTRACT FROM AUTHOR]

• An energy-consumption permit trading scheme (ECPTS) was implemented in China in 2016. • This policy's contribution to reducing energy intensity is proved by a PSM-DID model. • The pilot ECPTS generated an average energy saving of about 43 Mtce in 2016–2019. • Different mediating effects of industrial structure and energy structure are proved. • A national ECPTS is suggested to promote linkage with the emissions trading scheme. The Chinese government implemented the energy-consumption permit trading scheme (ECPTS) pilot program in 2016, in order to achieve energy saving and carbon reduction. Based on the PSM-DID model, we attempt to test the effects of the ECPTS on energy consumption and energy intensity. With a counterfactual simulation, this paper also evaluates the potential gains and different mechanisms of this policy. The results show that, under the constraints of the established resources and policy efficiency, there is a positive relationship between the ECPTS and low-carbon economic transformation; the implementation of the above policy in the pilot provinces brought about an average energy saving of 43 Mtce during the period of 2016–2019. However, the potential gains of this policy need to be further increased to meet China's energy control targets. Additionally, a complete mediating effect of the energy structure indicates that the impact of the ECPTS on energy consumption and energy intensity depends significantly on energy structure adjustments. Finally, this paper highlights the key policy implications associated with the empirical results. [ABSTRACT FROM AUTHOR]

Abstract: In March 2011, China’s central government set a new challenging target of reducing its energy intensity by 16% during 2011–2015, after it had achieved a reduction of 19.1% during 2006–2010. And this new target was assigned to provincial authorities in August 2011. However, China’s provincial energy-economic developments are unbalanced and different provinces have different key sectors for energy conservation. Most previous studies focused on provincial energy efficiency at the aggregate level, or the three-industry level (or one-digit level). However, whether for policy decision or academic research, it is necessary to further subdivide the sectors. In this paper, we use three indicators (Gini Coefficient, energy consumption share and energy intensity) to compare provincial energy conservation potentials at the two-digit sector level. To our knowledge, this paper is the first one to identify the keys for energy conversation across the 31 provinces×65 sectors. And the results are shown in visualized maps and matrix tables to help identify the key province×sectors for energy conservation easier. This also helps the central and provincial governments to distinguish key sectors when they monitor the energy conservation progress. [Copyright &y& Elsevier]

This paper employs the global Malmquist–Luenberger productivity index to measure and decompose green productivity growth for three major urban agglomerations in China over the period 2003–2013. As the first study known to focus on the green productivity of emerging cities in developing countries, the results show that technical progress, rather than efficiency improvements, is the main contributor to green productivity growth. Using the criterion of eco-friendliness, we categorize the cities into ‘green’ and ‘yellow’ city groups and identify 10 green innovators for the sample cities. The analysis also discusses the determinants of the drivers of green productivity growth and provides some useful policy implications. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the effect of energy construction adjustment on the energy-saving and emission-reduction (ESER) dynamical evolution system. Based on the nonlinear dynamics theory, the dynamic behavior of the novel system is discussed. The quantitative coefficients of the actual system are identified with the aid of genetic algorithm-back propagation neural network. Scenario analysis results show that, energy construction adjustment could effectively control energy intensity. To clarify this further, an example of 4D ESER system with new energy constraints is demonstrated. Investigation results show that, government control can effectively control energy intensity, while brings inhibiting impact on economic growth and people’s livelihood. Economic investment is the key variable affecting energy construction adjustment and ESER, the ESER system will crash when the investment is too low. Energy construction adjustment could effectively reduce energy intensity. However, the ESER system will also crash if the development of energy construction adjustment is too fast. The interesting thing is that the ESER system should be pulled back to steady state as the investment getting bigger. Energy intensity could be controlled in expected range by taking adequate measures. Full use of the role of energy structure adjustment should be made to promote the development of new energy, while government control is used only when necessary. [ABSTRACT FROM AUTHOR]

Effectively analyzing and then treating energy-related air pollution requires examining every factor, from the pollution source to the end of treatment. This paper applies index decomposition analysis and a whole process treatment perspective to identify the factors facilitating air pollution reduction across three stages: source prevention, process control, and end-of-pipe treatment. Empirical research using data from China’s Jiangsu Province and its 13 cities reveals differences in local approaches to pollution prevention. At the provincial level, end-of-pipe treatment remains the primary approach to control air pollution emissions, indicating that the pattern of “pollute first, govern later” has not yet been fundamentally reversed. At the city level, 13 cities can be divided into four types, based on their approach to air pollution treatment: the leading type, process-dependent type, end-dependent type, and lagging type. Of these, 7 cities are using multiple control approaches, reflecting the comprehensive effect of whole process treatment. The Jiangsu Province should consider further strengthening effective whole process air pollution treatment models, by transitioning to pollution control, adjusting industrial structure, promoting technological progress, and consuming clean energy. [ABSTRACT FROM AUTHOR]

There is a large amount of water consumption embodied in energy production. Driven by a growing population and economy, the energy sector will continue to expand, increasing more pressure on the fresh water demand of countries. This paper analyzes the energy–water nexus embodied in international energy trade via a combination of input–output analysis (IOA) and ecological network analysis (ENA). The energy–water element nexus is depicted based on a mixed-unit IOA to investigate the parallel relationship between energy and water. ENA is used to uncover energy–water pathways intertwined in global energy trade and identify the pair-wise relationship between countries according to the energy–water pathway nexus. Then, a case study of the international energy–water nexus of China is conducted. The results of energy–water nexus element analysis show that water use per unit of energy products in the country is much greater than those of other countries. Moreover, indirect water use during energy production deserves more attention, which is often neglected in water resource management. The results of energy–water nexus pathway analysis demonstrate that international energy trade has a positive effect on China’s water condition, mitigating serious water shortage problems but increasing dependency on other countries and regions. Mutual relationships among trading countries are described using utility and control analyses. The results show that system integrality is arguably more critical in determining system behavior than direct effects alone. Europe changes from a beneficiary in direct mutual relationships to a contributor in integral utility mutual relationships, whereas Australia has an opposite change. Although China has a dominant position in direct relationships, it becomes an equal competitor with the USA in utility mutual relationships. China depends heavily on most countries, especially those in South and Central America, the Middle East, Africa, and Australia. The network mutualism index (=0.56) and synergism index (=9.187) indicate that the current global energy trade network has negative relationships within a competitive environment. Finally, policy and economic suggestions are put forward to alleviate the potential impacts of international energy trade on water scarcity in China. [ABSTRACT FROM AUTHOR]

China has been the world’s largest cement producer and the cement production has led to increased energy consumption and CO 2 emissions, which greatly affect the country’s social and economic development. In this paper, an integrated assessment framework was developed by combining the Stock-based model and the Integrated MARKAL–EFOM System model of China (China TIMES), and it was used in China’s cement sector to simulate the trends of energy consumption and CO 2 emissions during 2010–2050, under a reference scenario and three carbon tax scenarios. The modeling results show that: (1) the cement production in China will increase from 1.8 billion tons in 2010, to a peak of 2.5 billion tons in around 2017, and then gradually decrease to 1.5 billion tons in 2050; (2) Through the adoption of three alternative abatement measures (i.e., switching the fuel types, implementing the energy-efficient measures and CCS), China’s cement sector could potentially achieve a great reduction in CO 2 emissions; (3) In the near future, the cement sector’s decarbonisation will mainly rely on energy efficiency improvement; while the use of alternative fuel use and CCS will be of great significance from a long-term perspective. Additionally, based on the modeling results, we build the energy-saving and emission-reduction technologies roadmap for China’s cement sector. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has been recognized as the most effective way to reduce the greenhouse effect and achieve sustainable development. From the perspective of air emissions, this paper adopts data envelopment analysis approach to evaluate the energy efficiency in China’s coastal areas over the period of 2000–2012. Carbon dioxide, sulfur dioxide and nitrogen oxide are treated as undesirable outputs of energy consumptions. The proposed global Epsilon-based measure is used to estimate the static energy efficiency with an annual cross-section of data. The weights of the three undesirable outputs are determined according to their treatment costs. A global Malmquist-Luenberger productivity index based on directional distance function is employed to dynamically evaluate the energy efficiency. The results indicate the following in China’s coastal areas: (1) the level of economic development is positively related to energy efficiency scores; (2) energy efficiency scores decrease when considering undesirable outputs except Beijing and Hainan; (3) the Circum-Bohai Sea Economic Region greatly improves energy efficiency and has great potential of air emission; (4) the annual growth rate of Malmquist-Luenberger productivity index change is overestimated; (5) energy efficiency improvement is mainly driven by technological improvement, and scale efficiency and management level are the main obstacles. [ABSTRACT FROM AUTHOR]

Abstract: Recently, energy production in China fell behind energy consumption. This poses important challenges for the rapidly growing Chinese economy. As a consequence, the causal relationship between energy consumption and GDP is an important empirical issue. This paper examines Granger causality between energy consumption and GDP in China using province-level data. The current paper extends the Granger causality analysis employed in previous studies by taking into account panel heterogeneity. Specifically, four different causal relationships are examined: homogeneous non-causality (HNC), homogeneous causality (HC), heterogeneous non-causality (HENC), and heterogeneous causality (HEC). HC and HNC hypotheses are rejected for causality in either direction, from GDP to energy or from energy to GDP, which implies that the panel made up of Chinese provinces is not homogeneous. Then, heterogeneous causality tests (HEC ad HENC) are conducted for each province. For the causality running from GDP to energy, 19 provinces exhibit HEC and 11 provinces exhibit HENC. For the causality running from energy to GDP, 14 provinces exhibit HEC and 16 provinces exhibit HENC. The results suggest that the Chinese government should incorporate a regional perspective while formulating and implementing energy policies. [Copyright &y& Elsevier]

Improving energy efficiency sustainability is a target of the Chinese government. However, the effectiveness of energy conservation policy is affected by the energy rebound effect under which energy efficiency improvement reduces the effective price of energy services, thereby completely or partially offsetting the energy saved by efficiency improvement. Based on the output distance function, this paper develops an improved estimation model of the energy rebound effect, which is logically consistent with the quantities of energy savings and energy rebounds induced by technological progress. Results show that the aggregate energy rebound effect of 36 industrial sectors in China over 1998–2011 is 88.42%, which implies that most of the expected energy savings are mitigated. Investment-driven economic growth is not conducive to energy-saving and results in a strong energy rebound effect in the following year. The equipment and high-end manufacturing sectors have low levels of rebound effect, indicating that increasing the proportion of such firms in the total manufacturing sector can improve the performance of energy conservation. The high level and heterogeneity in rebound effects strongly suggest that varies strategies are necessary for energy conservation among China’s industrial sectors. [ABSTRACT FROM AUTHOR]

Governments worldwide are attempting to reduce energy consumption and environmental pollution by confronting environmental problems and adopting more energy-efficient products. However, because of the rebound effect, energy-saving targets cannot always be fully achieved, and sometimes greater energy consumption is generated. Research on the rebound effect from the perspective of industrial ecology considers not only direct energy consumption but also its life cycle negative impacts on the environment with China’s rapid economic development and simultaneously improving quality of life, the ownership of room air conditioners (RACs) has increased more than three hundred times, and air conditioners’ energy consumption has increased one thousand times over the last twenty years. The Air Conditioner Energy Efficiency Standard is one of the most important measures in China for reducing the amount of energy consumed by RACs. This paper introduces a life cycle based method to estimate the rebound effect of Chinese RACs consumption. This model provides a product’s life-cycle view to assess the rebound effect, considering the contribution of both producer and consumer. Based on the established life cycle rebound effect model, we compared urban household RAC consumption behaviour before and after the launch of the Air Conditioner Energy Efficiency Standard. A rebound effect in RAC consumption was found that there was a longer daily usage period in the household as air conditioner efficiency levels improved. The life cycle rebound effect of household air-conditioner consumption was calculated to be 67%. The main conclusion obtained from this study is that policies and regulations should consider the rebound effect when encouraging households to alter their energy consumption patterns. [ABSTRACT FROM AUTHOR]

Air pollution is increasingly a focus of concern worldwide due to its adverse impacts on human health and profound influences on global ecosystem. Although the existing studies have paid much attention to the causes of pollutant emissions, they fail to distinguish between direct and indirect factors, yielding the mixed results. Direct causes denote energy-related factors, as air pollutants are mainly produced by energy utilization directly, while indirect elements refer to socio-economic factors, as these factors act on pollutant emissions through affecting energy-related aspects. This paper investigated the impacts of three dominant direct factors: total energy consumption (EC), energy structure (ES) and treatment technology (TT) on sulfur dioxide (SO 2 ) emissions in China during 1995–2014 using the logarithmic mean Divisia Index method. Distinguished from the previous studies which took particular interest in SO 2 emissions from the industrial sector, this study put the total amount of SO 2 emissions as the target. The results show that increased EC was the main reason for SO 2 enhancement, while increasingly advanced TT played a dominant role in inhibiting the emissions throughout the study period. In contrast, ES had an unusually slight effect on SO 2 emissions due to its minor variation in the meantime. On regional scale, the differences in relative contribution rates (RCRs) of EC, ES and TT among the eastern, central and western regions all gradually decreased over time; EC in central region had the largest improved effect, ES in eastern region held the greatest reduction effect, and TT in western region got the biggest inhibitory effect. At provincial level, most provinces (60%) had relatively quick EC growth and slow ES adjustment (i.e., reducing the coal consumption rate); only Beijing, Tianjin, Shanghai and Sichuan had a relatively slow growth of EC and quick decrease in the percentage of coal consumption. Further, the projection of SO 2 emissions in four scenarios from 2015 to 2020 based on a grey projection model indicated that controlling both EC and ES would be the most efficient approach to SO 2 abatement followed by individually controlling EC and ES. [ABSTRACT FROM AUTHOR]

This paper investigates the temporal and spatial changes of embodied energy transfers via China’s domestic trade over 2002–2007 based on the multi-regional input–output models. Interregional trade of total embodied energy uses approximately tripled between 2002 and 2007, and the total trade volumes in it were equivalent to 38.2% of the national total direct primary energy input in 2002 and 62.9% of that in 2007, respectively. Among all the eight regions, Northwest , Central , Northeast and Southwest were the interregional net exporters and deficit receivers of embodied energy in contrast to East Coast , South Coast , North Coast and Beijing–Tianjin as interregional net importers and surplus receivers. Significant growth of net embodied energy transfers can be identified from central and western inland regions to eastern coastal regions, and the Central region partly served as a “transmission channel”. By considering the interregional embodied energy transfers, regional energy use inventories changed largely, and the spatial and temporal differences between 2002 and 2007 were expanding. Industrial positions in domestic and global supply chains and inherent economic driving factors such as increasing regional consumption level, accelerated investment in fixed assets and rapidly expanding export were the major driving forces for the embodied energy transfers among regions. To form a set of useful tool for controlling energy consumption and achieving the goals for energy saving and emission reduction, China’s governors at all levels deserve to understand the relationships between energy producers and users from the view of demand-driven energy requirements. [ABSTRACT FROM AUTHOR]

The use of data envelopment analysis (DEA) in China’s regional energy efficiency and carbon emission efficiency (EE&CE) assessment has received increasing attention in recent years. This paper conducted a comprehensive survey of empirical studies published in 2006–2015 on China’s regional EE&CE assessment using DEA-type models. The main features used in previous studies were identified, and then the methodological framework for deriving the EE&CE indicators as well as six widely used DEA models were introduced. These DEA models were compared and applied to measure China’s regional EE&CE in 30 provinces/regions between 1995 and 2012. The empirical study indicates that China’s regional EE&CE remained stable in the 9th Five Year Plan (1996–2000), then decreased in the 10th Five Year Plan (2000–2005), and increased a bit in the 11th Five Year Plan (2006–2010). The east region of China had the highest EE&CE while the central area had the lowest. By way of conclusion, some useful points relating to model selection are summarized from both methodological and empirical aspects. [ABSTRACT FROM AUTHOR]

• Statistical analysis of hourly wind speed series. • Finding out the optimal distribution to characterize the hourly wind speed series. • Guiding wind farm operation with the hourly expected wind power. Accurate wind speed characterization is important for wind farms to efficiently utilize wind energy which plays a significant role in reducing the dependence on fossil fuels and promoting global carbon peak and carbon neutrality. Since hourly wind speed density exhibit a variety of shapes, their characteristic features change substantially within a day, influenced by meteorological factors, therefore, the hourly wind speed analysis is considered to improve the accuracy of wind speed characterization. In this paper, 4 commonly used wind speed probability distributions (Weibull, Rayleigh, Gamma, Lognormal distribution) are applied for hourly wind speed analysis, and the best fitting distribution of each hourly wind speed series is identified by the goodness-of-fit test. Then the hourly wind power distribution of wind farms is determined by the corresponding optimal hourly wind speed distribution and the relation between wind power and wind speed. Finally, the hourly expected wind power calculated by the hourly wind power distribution is used to guide the hourly operation of the wind farm. The measured wind speed and wind power data of Dashuikeng wind farm in the Ningxia Autonomous Region of China is taken as an example to illustrate the results, 1) the hourly wind speed analysis performs a more accurate characterization of wind speed than annual wind speed series analysis. 2) The range of the average value of hourly wind speed series is 5.7 m/s–6.9 m/s, and the average wind speed, overall, during the daytime is smaller than that during the nighttime. 3) Gamma distribution shows a better performance than the other three distributions in characterizing the hourly wind speed series. 4) Wind energy resources are not fully consumed during the daytime, especially the period of 11:00–18:00. The proposed method improves the characterization of wind speed and guidance of the hourly operation for the wind farm, which has an important significance in raising the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]

A new hybrid methodology is introduced which is a combination of multiple regression model and generalised autoregressive conditional heteroskedasticity (GARCH) model. Comparing the new approach and the vector auto-regression (VAR) model, this paper analyses the short-term dynamics of the energy efficiency index (EEI) in response to change in the five indicator variables for thermal power plants in China. The result indicates that: (i) The new hybrid model can directly calculate the EEIs of thermal power plants without artificial intervention. (ii) It can eliminate the disturbance of residual superposition. (iii) The new method will offer more direct information on the degree of volatility among determinants and operating inefficiency. [ABSTRACT FROM AUTHOR]

The rapid growth of energy consumption in China has led to increased emissions of air pollutants. As a response, in its 12th Five Year Plan the Chinese government proposed mitigation targets for SO 2 and NO x emissions. In this paper, we have investigated mitigation measures taken in different sectors and their corresponding impacts on the energy system. Additionally, as non-fossil energy development has gained attention in addressing energy and environmental challenges in China, we investigated the impact of non-fossil energy development on air pollutant emissions, and explored interactions and co-benefits between these two types of policies. An extended Global Change Assessment Model (GCAM) was used in this study. The extended version of GCAM includes an additional air pollutant emissions control module coupling multiple end-of-pipe (EOP) control technologies with energy technologies, as well as more detailed end-use sectors in China. We find that implementing EOP control technologies would reduce air pollution in the near future, but with little room left to implement these EOP technologies, other cleaner and more efficient technologies are also effective. These technologies would reduce final energy consumption, increase electricity’s share in final energy, and increase the share of non-fossil fuels in primary energy and electricity consumption. Increasing non-fossil energy usage at China’s proposed adoption rate would in turn also reduce SO 2 and NO x emissions, however, the reductions from this policy alone still lag behind the targets for air pollutant reduction. A combination of air pollutant controls and non-fossil energy development could synergistically help realize the respective individual targets, and would result in lower costs than would addressing these issues separately. [ABSTRACT FROM AUTHOR]

The rapid growth of energy consumption in China has led to increased emissions of CO 2 . As a response, the Chinese government has proposed a series of carbon mitigation strategies, such as switching the production structure and implementing energy-saving and emission–reduction technologies in energy-intensive sectors. In this paper, an integrated assessment approach was developed by combining the Stock-based model, the China-TIMES model and the co-benefits analysis model. The approach was applied to China’s steel sector to simulate the trends of energy consumption and air emissions (CO 2 , SO 2 , NO x , and PM 10 ) during 2010–2050, under a reference scenario and three alternative carbon mitigation scenarios. The model quantified the air emission reductions, water conservation, and associated co-benefits during the study period. The modeling results show that by switching the production structure and implementing energy-saving and emission–reduction technologies, China’s steel sector can achieve significant co-benefits of carbon mitigation, water conservation, averted mortality and averted morbidity. Despite certain limitations and uncertainties associated with the elements of the integrated assessment approach, the results demonstrate that substantial social benefits and health effects could be realized through carbon mitigation strategies in China’s steel sector. [ABSTRACT FROM AUTHOR]

The growth of China’s road transport sector has driven huge increases in China’s oil demand and CO 2 emissions over the last two decades, and these trends are likely to continue in the absence of specific measures to reduce the average carbon intensity of road vehicles. This paper describes a model, provided in full online, to undertake scenario analysis on the cost and CO 2 emissions impact of substituting current vehicle drivetrain types with alternatives during the period 2010–2050. A detailed decomposition of the additional costs and CO 2 emissions savings of each low-carbon vehicle type into their component parts is undertaken to calculate the marginal abatement cost of each vehicle and drivetrain type in 2050. The results indicate that passenger cars and heavy-duty trucks constitute the majority of future CO 2 emissions savings potential, but that, using the central cost assumptions, alternative vehicle drivetrains are significantly more cost-effective for trucks than passenger cars. The low-carbon scenario sees demand for oil products (gasoline and diesel) more than 40% below the business-as-usual scenario in 2050. The total mitigation cost in 2050 is (US2010)$64 billion per year, or 1.3% of the total annual expenditure on road transport in China in 2050, using a discount rate of 5% to annualise vehicle purchase costs, although this cost increases with higher discount rates. A sensitivity analysis demonstrates that measures in addition to those assumed in the low-carbon scenario could achieve further emissions reductions, in some cases at negative costs. The availability and transparency of the model allows testing and development of a range of further scenarios and sensitivities, to aid in planning an optimal decarbonisation strategy for this highly carbon-intensive sector. [ABSTRACT FROM AUTHOR]

To achieve the goal of drawing up optimal plans for power generation, decision makers need an appropriate methodology to effectively identify the pivotal aspects of electricity consumption fluctuation and anticipate the future trend. The parameter identification of conventional statistical approach mainly relies on distributional assumptions and functional form restrictions, which might be problematic for the real application. This paper addresses these issues by implementing a novel semi-parametric modeling approach, which is suitable for investigating the uncertainties in the mid-long term forecast and estimating the probability distributions of future demand. To identify the significant impact factors of the electricity consumption, a new Kullback–Liebler (K–L) divergence based similarity measure strategy is designed. A case study concerning the electricity demand forecasting in China demonstrates the applicability of the proposed approach and verifies the feasibility of establishing explicit functional dependency between external variables and electricity consumption. Despite the complexity, notable reductions in the number of forecasting error are obtained due to the adoption of three indicators: deposits in financial institutions, exports, and imports. [ABSTRACT FROM AUTHOR]

• The new power supply structure resulting from the new policy has been considered. • The new models project the inefficient points onto a more realistic efficient frontier. • The new models have the power to allocate the shared resources. • Results show that the power production stage is allocated less shared resources. • The power sale stage's efficiency is lower than the one of power production stage. To relieve the problems of energy shortage and environmental pollution, many countries have implemented a variety of policies to improve the energy efficiency of the power industry. To understand the impacts of such policies on energy efficiency, this paper constructs novel network data envelopment analysis models to recognize the policy-induced structural changes in the power system. Compared with previous studies, the new models project the inefficient points onto a more realistic efficient production frontier, considering how the distribution proportion of shared resources will change when the power system, modeled as a power generation stage and a power sale stage, is led by the latter stage. Our empirical study on China's power system shows that the province of Guangdong and the eastern region of China perform best, and all power production stages perform better than the corresponding power sale stages. The development of the two power subsystems in the four regions is unbalanced. More than half of the power supply systems choose to allocate more proportion of the shared resources to the sale stage rather than use a more balanced distribution between the two stages. [ABSTRACT FROM AUTHOR]