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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]

• A generalized integer linear programming model of the PMU placement is proposed. • Topology changes are considered to guarantee the observability with multiple topologies. • The effects of existing measurements and their relevance are covered. Distribution networks usually have a meshed structure but are operated radially to improve the operating efficiency and ensure the power supply under an emergency situation. It is of great significance to guarantee the observability of the entire system under the topology changes in operation. This paper proposes an optimal placement method of phasor measurement unit (PMU) for distribution networks. A generalized binary integer linear programming (ILP) model for PMU placement is proposed. The changes in topology are considered to guarantee the observability under any possible operation mode. The existence of zero injection nodes (ZINs), the existing measurements, and their relevance are also covered in the proposed method to reduce the installed PMU number. The objective of the ILP problem is weighted by the degree of the corresponding node to obtain a scheme with higher measurement redundancy. The correctness and effectiveness of the proposed method are verified via case studies on the IEEE 33-node test feeder, PG&E 69-node test feeder, and a medium voltage distribution network in Southern China. [ABSTRACT FROM AUTHOR]

• Proposed an improved two-stage optimization method for network and load recovery. • Decouples integer and continuous decision variables and solves them separately. • Proposed method significantly can improve the computational efficiency. • Verified the advantage in computational efficiency and plan optimality. • Demonstrated potential application through simulation in a practical system. Network and load recovery (NLR) during power system restoration is a multi-step mixed-integer nonlinear programming (MINP) problem. The NLR is difficult to be solved as it is NP-hard. Thus, NLR is commonly solved step by step, which is short-sighted and will result in longer restoration time. To obtain a far-sighted NLR plan, this paper proposes an improved two-stage optimization method for NLR. The first stage adopts a mixed-integer linear programming (MILP) model to obtain optimal solutions of integer variables in NLR, namely the load pick-up schedules and transmission line charging schedules. Then in the second stage, a continuous non-linear optimization method based on AC power flow with frequency constraints and load model is established to minimize the restoration duration of the plan generated in the first stage step by step. Case studies are undertaken on a 10-machine 39-bus system and Southeast Hubei Provincial power system of China. Simulation results indicate that the restoration plan obtained from the improved two-stage optimization method is highly effective, while the computational efficient meets the intensive need for restoration scheduling after blackouts. [ABSTRACT FROM AUTHOR]

• Multi-stage distributionally robust scheduling model for hydro–wind–solar system. • Multiple time-scale uncertainties are considered by ambiguity sets. • Stochastic dual dynamic integer programming is modified to solve the model. • Proposed scheduling model increases profits by 3.04% and lowers trading risks. Joint trading of hydro–wind–solar complementary systems (HWSCSs) in the electricity market (EM) helps to reduce the imbalance cost and increase profits. However, multiple energy resources and market price uncertainties affect the trading strategies. Existing medium-term (MT) scheduling approaches assume that the probability distribution of the random variable is perfectly known. Short-term variations were also ignored, which led to revenue loss and trading risk. To address the above issues, this paper proposes an MT multi-stage distributionally robust optimization (MDRO) scheduling approach for a price-taking HWSCS in the EM. Firstly, hourly unit commitment (HUC) constraints are incorporated into the MT scheduling model to accurately capture short-term variations. A novel ambiguity set is designed based on the modified chi-square distance to address probability distribution uncertainties at two different time scales. Subsequently, an MDRO scheduling model is proposed to optimize the trading strategy. Finally, the proposed MDRO model is converted to a large-scale multi-stage integer programming problem based on linearization and reformation. The stochastic dual dynamic integer programming algorithm is modified to ensure computational tractability. Xiluodu-Xiangjiaba HWSCS, located in the Jinsha River in China, was selected as a case study. The results show that: 1) the MDRO model is more robust to distributional uncertainties than the multi-stage stochastic programming (MSSP) model. When the probability distribution of the random variable changes, the MDRO model yields a higher expected revenue (+2.43%) and a lower standard deviation (-60.8%) of revenue, which illustrates lower trading risk. 2) Compared with MSSP, deterministic, two-stage stochastic programming, and distributionally robust optimization models, the MDRO model exhibits the best out-of-sample performance in terms of the highest expected revenue and lowest trading risk. 3) Incorporating HUC constraints into the MDRO model helps to increase the total revenue (+3.53%) and energy generation (+3.31%) at the expense of increasing the computational burden. [ABSTRACT FROM AUTHOR]

• A virtual real-time electricity price optimization model based on the credit mechanism is established. • Incorporate the balancing cost of electricity and the stability cost of the grid into the cost of electricity production. • A matching degree index is proposed. Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply–demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users' power load curve and distributed energy output curve. Taking a multi-microgrid system in a China's industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users' total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid. [ABSTRACT FROM AUTHOR]

The allowance mechanism is one of the core and sensitive aspects in the design of a carbon emissions trading scheme and affects the compliance cost for each entity covered under the scheme. By examining China’s allowance mechanism from two aspects-allowance allocation and allowance distribution, this paper compares China’s carbon trading pilots with the EU Emissions Trading Scheme and California Cap-and-Trade Program. The comparison identifies the unique features in allowance mechanism and particular issues that affect the efficiency of the pilots. The paper also recommends courses of action to strengthen China’s existing pilots and to build valuable experiences for the establishment of the national cap-and-trade system in China. [ABSTRACT FROM AUTHOR]

Based on a Structural Vector Autoregression (SVAR) model, this paper decomposes oil price changes into four components: oil supply shocks, global demand shocks, domestic demand shocks and precautionary demand shocks. Then, this paper investigates the impacts of these oil price shocks on the stock returns of China’s listed companies in the oil industrial chain using data from 2009 to 2014. The empirical results show that the returns of the listed companies in the whole oil industrial chain benefit from appreciation in the oil price, the impacts of oil supply shocks and precautionary demand shocks are the most significant, and there is a structural change in the impacts of oil price shocks in 2012. Among the four oil price shocks, the impacts of oil supply shocks and precautionary demand shocks are the most significant. Moreover, there is a gradual increase in the aggregate contributions of oil price shocks to the changes in stock returns. A robustness check with different global crude oil prices and a different industry classification standard confirms that the above empirical results are robust. The empirical results of the paper imply that stock investors, oil-related companies and the government need pay close attention to sudden changes that may affect current and future oil availability and pay greater attention to the stocks at the two ends of the oil industrial chain. [ABSTRACT FROM AUTHOR]

With important implications for global climate change mitigation, the development of a cap-and-trade emission trading scheme (ETS) in China is embedded in distinctive political, economic, and institutional contexts and has been attracting increasing attention in recent years. To offer a clear perspective on current research progress, this paper systematically reviews the recent literature on China’s ETS from three main aspects: mechanism design, policy and regional linkages, as well as impact assessment. The current research consensuses are summarized based upon detailed literature analyses and commentaries, and together with observations of international experience and China’s actual condition, the paper provides policy proposals for the development of China’s national ETS during 2016–2020. Finally, several important controversies in the current literature are discussed with regard to the practical demands of China’s ETS development. Five pressing topics that need to be further studied in the coming years are highlighted, including cap setting, allowance allocation among sectors and enterprises, carbon pricing, policy package and unified carbon market construction, as well as ex-ante and ex-post impact assessments. [ABSTRACT FROM AUTHOR]

• More reliable modeling assumptions and methods are proposed. • Price regulation may increase industrial output in China. • The reform can lead to a cleaner power mix and better economic structure. • The reform can be of great help to alleviate the fiscal deficit. • Impact mechanisms through cost linkages in different situations are put forward. China promised to achieve carbon neutrality in 2060. However, electricity prices are still regulated, which may affect the effect of carbon neutrality, especially in China, because 70% of China's electricity comes from thermal power generation. In this paper, a dynamic recursive computable general equilibrium model (CEEEA/CGE model) is applied to simulate the electricity market-oriented reform under the target of carbon neutrality from 2020 to 2060. The results show that the target may increase the demand for labor and capital to cope with increasing energy costs, and the reform may reduce GDP. However, power reform can significantly optimize the energy and economic structures and alleviate the fiscal deficit. Moreover, this paper finds and concludes the mechanism causing these effects (such as factor substitution, price advantage, government revenue, and structure), which provides policymakers with a new perspective of power reform. [ABSTRACT FROM AUTHOR]

Schematic diagram of the research framework: A new network DEA method. [Display omitted] • A new network DEA allocation model considering cross-border transmission of SO 2. • The WRF-CMAQ is combined with the DEA allocation model for the first time. • The principles of optimal efficiency, GDP protection and fairness is considered. • The per capita relative deprivation coefficient was used to evaluate the fairness. • The new network DEA model is a powerful tool for SO 2 emission rights allocation. Currently SO 2 emission rights are allocated without considering the cross-border transmission characteristics of pollutants, and the allocation scheme cannot reflect the fact that air pollutant overflows in space. In this paper, the WRF (Weather Research and Forecasting Model)-CMAQ (Community Multiscale Air Quality) is combined with the data envelopment analysis (DEA) allocation model for the first time, and a two-stage (Stage-1: SO 2 formation, Stage-2: SO 2 treatment) network DEA allocation model is proposed with the SO 2 spatial transmission taken as the intermediate output or input index between decision-making units. Based on the principles of efficiency, economic protection and fairness, the model is applied to the study of SO 2 emission rights allocation in the Yangtze River Delta city agglomeration with high homogeneity in China. The results show that: (1) When the weight coefficient (α 1) in stage-1 is 0.7, 0.8 or 0.9, the per capita relative deprivation coefficient (PRDC) of each decision-making unit (DMU) is less than 0.5 in the allocation scheme, indicating that our scheme can be used as a reference to allocate emission rights in the Yangtze River Delta city agglomeration. (2) With great robustness and universality, our allocation model allows decision-makers to adjust the GDP constraint coefficient and the weight coefficients in the two stages according to their preferences, so as to obtain the optimal allocation results. The model proposed in this paper can help decision-makers to formulate effective policies to reduce pollutant emission, and promote energy conservation & emission reduction and coordinate environmental governance in regions of city agglomerations. [ABSTRACT FROM AUTHOR]

Probabilistic load forecasting (PLF) aims to predict the future uncertainties of loads to reduce the potential risks in power system planning and operation. In the increasingly complex power market environment, exploring advanced approaches to obtain more accurate PLF is still a significant topic. Optimizing individual forecasting method is no longer the only direction to improve the accuracy of load forecasting in recent years. Researchers started to focus on combination methods because of their better accuracy in most cases than a single model. There are existing combination methods designed for parametric environment, where some results are based on the certain assumption (e.g., Gaussian distribution assumption of single prediction). Combining probabilistic forecasts in nonparametric environment is rarely investigated, because modeling the combination problem without assuming distributions of parameters is hard. This paper proposes a novel combined model for probabilistic forecasting tailored to nonparametric environments, which combines multiple quantile-based models by minimizing the overall loss function composed of continuous ranked probability score (CRPS) under kernel density estimation (KDE). We define a multilayer Gaussian mixture distribution, which is an extended form of Gaussian mixture distribution that can simulate any distribution type in nonparametric environment. Based on the multilayer Gaussian mixture distribution, the combined model is further formulated into a quadratic programming problem with linear restrictions that can be solved efficiently. Case studies are performed using benchmark and competition datasets from the United States and China. The results show that our proposed method outperforms the best individual model and other existing combination methods. In summary, this paper constructs a complete theoretical framework of nonparametric probabilistic combination forecasting and proves its effectiveness in practical application. • Exploit an ensemble framework to combine multiple quantile forecasts. • Design a novel combined model to determine the optimal weights of base models. • Cast the combined model to a quadratic programming problem with the help of KDE. • Overcome the limitation of distribution assumption on predicted results. • Conduct case studies on ten datasets to verify the performance of the combined model. [ABSTRACT FROM AUTHOR]

• A high spatial resolution CO 2 emissions inventory of 280 Chinese cities was compiled. • Three methods were compared to evaluate the carbon efficiency of Chinese cities. • The best and worst-performing cities were consistent regardless of different methods. • Top-ranking cities were located in the coastal regions of China. • Lagging-behind cities were located in the central and western regions of China. Comprehensive and accurate city-level carbon dioxide emissions inventory is key to success in China's pursuit of low-carbon development. Based on a high-resolution gridded data, this paper evaluates and benchmarks the carbon emissions efficiency of 280 Chinese cities using three different methods, i.e., composite index, data envelopment analysis and stochastic frontier analysis. The comparative analysis reveals that the average carbon emissions efficiency of Chinese cities is about 70–90%, with only 9 efficient cities (3%). Although the ranking of intermediate cities varies greatly according to the method employed, the most and least carbon-efficient cities remain consistent. While the best-performing cities are found in the coastal regions, the worst-performing cities are located in the central and western regions, which suggests an imbalanced low-carbon development in China. Guided by the results, local governments are encouraged to compete and learn from similar ranking benchmarks in a new regime of "Top Runner City". Finally, the scores of carbon emissions efficiency can provide a scientific basis for the bottom-up allocation of carbon emissions rights to boost the development of the nationwide carbon market in China. [ABSTRACT FROM AUTHOR]

• A PVT collector with a roll-bond absorber is designed for rural home application. • Thermo-laminating is a novel and convenient method for PV-absorber connection. • The validated PVT model can simulate the roll-bond PVT performance accurately. • The model is available for predicting the dynamic performance of PVT systems. This paper mainly investigated the performance of a low-cost roll-bond water-type photovoltaic thermal (PVT) collector from both experimental and modelling approaches under the climatic conditions of Sichuan Basin of China. The design and manufacturing process of the PVT prototype adopted the available commercial PV panels for convenience and for reducing the manufacturing cost. The experiment measured all the main properties of the developed PVT module. The excellent heat transfer performance of its roll-bond absorber plate was also validated by outdoor steady-state tests. Then, the steady-state numerical models were used to simulate the performance of the PVT prototype in TRNSYS. By comparing the simulation and experimental results, two robust models were selected which have their own advantages in charactering the thermal and electrical properties of the PVT collector. Through validating the numerical models with experimental data, the deviation between the simulated and the measured values was further reduced. In addition, a complete photovoltaic thermal–domestic hot water (PVT-DHW) system with such PVT collectors was developed in TRNSYS, by which the annual system output and efficiency were predicted and analysed. [ABSTRACT FROM AUTHOR]

• A novel strategy for integrated transmission of wind farms and pumped-storage hydropower plants. • Wind power forecast errors and hydraulic constraints of the pumped-storage hydropower plants are considered. • The total profits are significantly increased via integrated transmission. • The findings can provide effective references for owners of wind farms and policy makers. Long-distance and large-capacity wind power transmission from western and northern China to load centers through ultra-high voltage (UHV) transmission lines is an important and effective measure for large-scale wind power accommodation in China. This paper proposes a novel practical integrated transmission mode for a wind farm (WF) and a pumped-storage hydropower plant (PSHP). In order to cope with additional imbalance costs caused by deviations from the submitted day-ahead schedule and increase the total operational profit, an optimization model for the integrated transmission of a WF and a PSHP has been formulated. The forecast errors for wind power generation are modeled by a scenario analysis technique. Hydraulic constraints and the head effect on the performance of each pumped-storage hydropower unit (PSHU) in both generating and pumping modes have been considered. Through incorporating the nonlinearities, including constraints on the operation status of units and turbine performance curves, the model is converted into a mixed-integer linear programming (MILP) formulation using piecewise linear approximation techniques. Case studies demonstrate that the integrated transmission of the WF and PHSP can efficiently alleviate the negative effect of wind power fluctuations and significantly increase profit. Sensitivity analyses have been carried out to evaluate the effect of the penalty factor for energy imbalance and the electricity price mechanism in the receiving-end power grid on the overall performance of the WF-PSHP union. [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]

Graphical abstract Highlights • We incorporate a variety of principles and indicators into an improved ZSG-DEA model. • We conduct a multi-criteria allocation of China's CEA by 2030 to provincial shares. • We measure the total and per capita space for carbon emissions by province. • All provinces reach the DEA frontier with different CEA ranging from 4.21 to 16.77 Gt. • Differentiated provincial reduction policies are the key to achieving China's INDCs. Abstract Accelerating global warming has suggested the importance of controlling greenhouse gas emissions associated with human activities. In striving to fulfill the Paris Agreement, China has announced its Intended Nationally Determined Contributions (INDCs) aimed at reducing its carbon dioxide (CO 2) emission intensity by 60–65% in 2030 against the level of 2005. However, China's INDCs cannot be fulfilled without formulating appropriate schemes for the allocation of carbon emission allowance (CEA) at sub-national scales. To help close the gap in our knowledge, this paper starts with measuring the overall CEA of China by 2030, and then proposes a science-based scheme for CEA allocation by developing an improved zero sum gains-data envelopment analysis (ZSG-DEA) model. It demonstrates that the final CEA of some northern provinces can be cut down as compared to their initial shares and, conversely, most southern provinces experience an increase in their CEA. Comparing the final share of CEA by province with current carbon emissions, we observe that provinces with abundant energy reserves, such as Shanxi, Inner Mongolia and Shaanxi, tend to be operating in a state of overshoot in terms of space for carbon emissions (SCE). In contrast, there remains SCE when it comes to Guangdong, Hunan, Fujian, etc. The remaining provinces, such as Heilongjiang, Hebei and Ningxia, are close to the break-even point. In view of the differing SCE of individual provinces, common but differentiated policies for CO 2 emission control would be the key to achieving China's INDCs. The research findings lay a scientific basis for the Chinese government to make its INDCs come true through inter-provincial collaboration on emission reduction, but also serve as a reference for fueling further scientific discussions and development of schemes for the allocation of responsibility for carbon emissions at multiple scales within and beyond China. [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]

• An energy storage value estimation model in monopoly power markets is put forward. • Ancillary services model in the monopolized electricity market is proposed. • A novel value-drive policy is introduced and compared with two other mechanisms. • Energy storage value under different energy storage configurations is analyzed. Recent trend in increasing the penetration level of renewable energy challenges safety and stability of the power grid. Electrical energy storage (EES) has been deemed as one of the key elements needed to mitigate the fluctuant and intermittent nature of renewables, and is undergoing rapid development nowadays. However, there are still many questions regarding the benefits and drawbacks that EES technologies will present to society, as well as the economic feasibility of EES in monopoly power markets. Given this, this paper presents a grid-scale production cost model for monopoly power markets in which EES generates profits by offering both energy and ancillary services. The production costs of the power system with and without an EES power plant was compared in order to generate a value estimation. Results indicate that the value of EES is related to the unit fuel price and the start-up cost of thermal power units, the penetration level of wind power and size of the EES system. Based on these variables, a value-driven pricing mechanism for EES was put forward and compared with other policies. Economic viability analysis revealed that the pricing mechanism currently used in monopoly power markets are highly inaccurate in capturing the value generated by EES. [ABSTRACT FROM AUTHOR]

• Solves uncertain investment decisions in low-carbon transition toward renewable energy. • Develops a more complex model by using the real options method. • Considers more influencing factors and integrates their changes. • Indicate both the optimal investment strategy (time and value) and the effects of influencing factors on investment strategy. This paper proposed a model for solving the investment decisions amidst uncertainty in the low-carbon transition toward renewable energy. The real option method is used and numerous influencing factors are considered, including the market price of electricity, fossil fuel cost, carbon dioxide price, investment cost and long-term changes of feed-in tariff policy associated with renewable energy. The investment decisions related to the transition from a coal-fired power plant to a solar photovoltaic power plant in China have been analyzed in detail. The results indicate the optimal transition investment strategy and elucidate the effects of multiple factors on the investment strategy. This provides a clear suggestion for both the government and investor how to scientifically address uncertainty and make optimal investment decision to accelerate the low-carbon transition. [ABSTRACT FROM AUTHOR]

Highlights • Two-stage planning method of multi-energy system is proposed. • Novel energy pricing strategy is first proposed. • Load aggregator is used to conduct the integrated demand response program. • Model of district heating/cooling network under quantity regulation is built. Abstract This paper proposes a two-stage planning and design method for the multi-energy system (MES), which consists of multiple combined cooling, heating and power (CCHP) systems connected by the district electrical network and district heating/cooling network. In the first stage, the optimal objective is to minimize the annual capital and operating costs of the CCHP systems, as well as to obtain the optimal type and capacity of the equipment using the system parameters and load data. Then, a novel nodal energy pricing strategy is first proposed and calculated using the flow-tracing method. The nodal energy price comprises two parts: (i) nodal producing price and (ii) nodal transmission price. In the second stage, depending on the electrical, heating and cooling prices, a load aggregator (LA) is applied to manage the electrical, heating and cooling loads of the end users using an integrated demand response (IDR) program. The objective of the LA is to minimize the annual consumption expense of the end users and update the loads for the optimization in the first stage. In addition, the IDR program includes electricity-load shifting and flexible electrical, heating/cooling supply. Three cases are presented to demonstrate the effectiveness of the planning and design method and to show the influence of the nodal energy prices on the IDR program as well as the penetration of solar energy based on a reconstructive MES in northern China. [ABSTRACT FROM AUTHOR]

Highlights • A security-constrained co-planning of power lines and energy storage is proposed. • The corrective control with multi-energy storages and generator units is studied. • The large-scale formulation is solved using Benders decomposition algorithm. • Case studies on the real-world power system in Gansu, China are conducted. Abstract This paper presents a security-constrained co-planning of transmission line expansion and energy storage with high penetration of wind power. The energy storage can not only improve the accommodation of renewable generation but also help to mitigate the emergency overflow under the post-contingency state. Also, the adjustment capabilities of the wind farms are considered, and the risk of excessive wind curtailment under the post-contingency state is assessed quantitatively. The coordinated multistage corrective control strategy for the battery storage, the pumped storage, thermal generators, and wind farms is studied. The mixed-integer linear programming problem based on the proposed corrective control strategy has been formulated. The Benders decomposition algorithm is introduced to divide the co-planning problem into one master problem and two sub-problems. In the master problem, the system is operating under normal conditions, so the security constraints are not considered. It minimizes the sum of the investment cost of newly built facilities and the operational cost. In the sub-problems, the corrective control during different post-contingency stages is formulated, minimizing the risk costs under fault conditions. The two-step technique is developed to generate the Benders cuts from the mixed-integer linear programming sub-problems, and return to the master problem. The real-world power system in Gansu, China is studied to validate the applicability and scalability of the proposed model and solution technique. The results denote that the co-planning model with security constraints is profitable in the real power system, with savings of 25% for the newly built transmission lines and a 5.5% reduction in total cost. [ABSTRACT FROM AUTHOR]

Highlights • Yield per hectare of sweet sorghum are influence by temperature and precipitation. • Xinjiang and Inner Mongolia are suitable for planting sweet sorghum. • The 2020 and 2050 biofuel goal can be satisfied by certain scenarios in this paper. • Climate change and land-use change are critical to energy crop development plan. Abstract Biofuel from energy crops will play an important role in supplying carbon-neutral fuel for the transport sector. However, there are large uncertainties regarding energy crop development and the energy crop potential and distribution need to be evaluated more comprehensively and systematically. In this study, we propose an integrated approach using the AquaCrop model, a geographic information system approach, and a scenario analysis and we conduct a land availability assessment with multiple factors including climate change, land use, soil, topography, regional crop management, and technological improvement to shed light on the potential production, distribution, and temporal-spatial variation of land suitable for sweet sorghum production in China. We evaluate a full range of spatial scales from 1-km resolution to a national assessment in four representative concentration pathways from 2010 to 2100. The results show that most provinces can plant sweet sorghum. The most suitable provinces for sweet sorghum production are Xinjiang, Heilongjiang, Jilin, Inner Mongolia, Yunnan, and Sichuan. The biofuel potential of sweet sorghum in high-slope farmland (0.30–0.73 EJ) and marginal land (11.98–15.18 EJ) can satisfy the biofuel goal in 2020 (0.12 EJ) and 2050 (6.70–8.60 EJ). Climate change and land-use change are critical factors influencing for the current implementation and long-term planning of energy crops in China. [ABSTRACT FROM AUTHOR]

Highlights • Novel formulation of unit commitment model for large-scale power system simulation. • Validated with standard test system and applied to real regional systems in China. • Increase the computational speed by more than 20,000 times with ∼1% error. • Can be applied to planning issues, e.g. optimal sizing of wind, solar and hydro. Abstract Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments. [ABSTRACT FROM AUTHOR]

Highlights • A Markov chain prediction method is embodied in dynamic CGE model. • Energy consumption intensity target shall be achieved under scenario shock. • Carbon emission intensity declined within scenario implement. • Electricity generation used gas plays a pivotal role in optimizing structure. Abstract The global 2030 agenda for promoting clean energy development explicitly calls for the implementation of official policy related to sustainable development. China pledged to formulate an energy structure that focuses on non-fossil energy and natural gas towards 2030. Natural gas is therefore entering a new critical and potentially advantageous development stage. Optimizing the utilization structure in a scientific manner can be regarded as the significant means to increase natural gas consumption in order to achieve the targets upon 2020 and 2030. In this study, what ultimately matters from a sustainable lens is not whether the future natural gas consumption increases but rather whether the restricted and limited proven reserves should be arranged and utilized to reach the best effectiveness on economy-energy-environment system to further ensure sustainable development. Herein, with aims to offering operationally actionable-related insights on the optimal natural gas consumption pathways for policy governors, this paper examined the multiple effectiveness of a spectrum of representing indicators selected from the economy-energy-environment system in China under natural gas consumption structure constraint policy scenarios using a dynamic CGE approach and Markov Chains forecasting technique. Consequently, this study recommends one optimal natural gas consumption structure scenario to policy governors given its comprehensive effectiveness in promoting sustainable development in China according to horizontal and vertical comparative analyses. [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 • We examined photovoltaic water lifting/pumping to supplement an urban water system. • One photovoltaic station lifts the sewage plant tail water for the river water supply. • Two photovoltaic stations improve hydrodynamic conditions in the dry season. • Applying renewable energy in urban ecological restoration produces benefits. • Photovoltaic water-pumping system can reduce energy supply and demand issues. Abstract As one of many potential applications for new developments in energy, there is a need to further explore renewable energy in urban ecological construction. In this regard, the construction of urban water systems that utilize renewable energy is important for safeguarding urban ecological environments while improving the aesthetic quality of residential areas. In this study, we examine a photovoltaic water lifting and pumping method to supplement an urban water system in Xiamen, China, with reclaimed water from a nearby sewage plant, which might serve as a water source during the dry season. We analyzed the current conditions for the planned water system in the Xiang'an District, Xiamen, as well as the requirements for maintaining a constant water supply to this system. This analysis illustrated the necessity for a water system that maintains the ecological balance in the area, and the necessary conditions for adopting the photovoltaic water-supply scheme as part of the Xiang'an water system. The sewage treatment plant in the Xiang'an District requires upgrades so that the standard of its reclaimed water complies with the Class IV water quality standard for post-treatment surface water. Furthermore, we analyzed water supply scenarios for the water system in the Xiang'an District based on the conditions in 2011, when the highest number of non-rainy days in the past ten years were recorded. Based on this analysis, we propose a photovoltaic water-supply scheme and describe the structure of a compatible photovoltaic water-pumping system scheme. The pump system includes three photovoltaic pump stations: one is the main pump station that operates throughout the year for water lifting, which utilizes a reclaimed water supply, while the other two are supplementary, and serve to increase the recirculation of water in the river, and operate when necessary during the dry season. The design of the photovoltaic panels in each pump station complies with the relevant water quality standards. This paper further describes the application, ecological effects, and economic feasibility of the proposed photovoltaic water-supply scheme. This study therefore provides a potential reference for future studies related to sustainable urban development and ecological remediation while highlighting a new application of renewable energy. [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]

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]

The potential relocation of various industrial sectors from China to India and countries of the SE Asian region presents low cost opportunities for manufacturers, but also risks rising for energy demand and CO 2 emissions. A cross-country shift of industrial output would present challenges for controlling emissions since India and SE Asian countries present higher industrial emissions intensity than China. We find that although there is a convergence in emissions intensity in the machinery manufacturing and paper and pulp industries, there are significant variations in all other industrial sectors. Indian emissions intensity is double that of China in the iron and steel and textile and leather industries and almost triple in the cement industry; Indonesian emissions intensity is almost double that of China in the non-metallic minerals and textile and leather industries and 50% higher in the chemical and petrochemical industry. We demonstrate that the expected higher emissions are driven by both a higher carbon fuel mix intensity in the recipient countries and higher energy intensity in their industrial activities. While industrial relocation could benefit certain countries financially, it would impose considerable threats to their energy supply security and capacity to comply with their Paris Agreement commitments. [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]

China will enact a national emissions trading system in 2017. The rules for the initial allocation of emission allowances have not been settled yet. This paper assesses the effect of auctioning as an initial emission allowance allocation methods of the China electricity power generation sector. This study employs a combined model with modified Trans-log production function, dynamic simulation model and multi-objective linear programming to provide more realistic results. After estimating the optimal electric power sources structure and technological structure under different auctioning rates, this paper calculates the abatement cost of the China’s electric power generation sector. When the allowance is free, the carbon shadow price will be 206.12 Yuan/ton. When 5% allowance is auctioned, the price will be 216.91 Yuan/ton. When the auctioning rate increases, the carbon emission abatement cost increases accordingly, as does the growth rate. The results also show that when the allowance auction rate is 5%, the marginal abatement costs for coal-fired power generation and clean power generation are 0.123 Yuan/kW h and 0.121 Yuan/kW h respectively, and thus, the total additional cost of the electric power generation sector will increase by 0.244 Yuan/kW h, which would be a heavy burden to China’s electric power generation sector and the forward progress of auctioning in the near future may be hindered. [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]

After almost three years of preparation, the tiered pricing for household electricity (TPHE) was implemented across China in July 2012. The feedbacks of household electricity consumption to the new pricing policy are investigated in this paper based on the micro household-level data from China’s Residential Energy Consumption Survey (CRECS). We document that electricity consumption of 82% surveyed households did not affected by the TPHE, which is in conformity with the principle that the TPHE policy based on. More importantly, factors that influence elasticity of household electricity consumption are examined by empirical model in this paper. Results indicate that factors such as the energy price, household income, and demographic attributes have significant impacts on residential electricity consumption. Further discussion outcomes show that the publicity of the TPHE, energy price and some other factors are strongly correlated with the probability that households change the inefficient and lavish pattern of electricity consumption. Future directions for the design and improvement of residential electricity pricing reform are thus suggested. [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]

• 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]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

• A novel two-stage SMILP investment decision-making framework to enhance distribution network resilience is proposed. • Multiple uncertainties introduced by typhoon, distribution line damage, and load fluctuations are taken into account. • The real-world data of typhoon "Mangkhut" in Guangdong, China is collected to model wind field uncertainty. • Intentional islanding constraint is notably added in the second stage. • The proposed model combines distribution network long-term resilience planning with short-term post-event recovery. The reliability of power supply in distribution network is vulnerable to extreme weather events such as typhoon. Pre-event preparation can effectively mitigate the deterioration of system resilience. Therefore, we propose a distribution network resilience enhancement decision-making framework which is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) model. The first stage invests coordinately in four strategies, including hardening lines, installing distributed generators (DG), allocating mobile emergency generators (MEG), and deploying switches, etc. And the goal at the first stage is to minimize the investment cost of resilience enhancement strategies. The objective at the second stage is to ensure the minimum expected recourse operation cost of the comprehensive strategies for all typical scenarios. The proposed model can combine distribution network long-term resilience planning with short-term post-event recovery. Furthermore, in order to address the uncertain problems of wind field, line damage and load fluctuations under typhoon disaster, this paper proposes the following solutions. For wind field uncertainty, a detailed wind field model considering time transition, sea-land transition and extreme value distribution is established to deal with wind speed prediction. For line damage uncertainty, an improved stress-strength interference model is set up. And for the load fluctuation uncertainty, scenario generation using load random multipliers is applied to address load uncertainty. The proposed framework is tested in IEEE 33-bus distribution system using historical data from 2018 super typhoon "Mangkhut" in China and demonstrates the SMILP model can significantly reduce the post-event expected recourse operation cost and meanwhile improve the distribution network resilience. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

As part of its energy transition strategy, China has set ambitious targets for increasing the contribution of renewable energy and, in particular, of wind power. However, the Chinese power sector has not undergone the necessary reforms to facilitate the integration and absorption of a larger share of variable renewable energy. This is evident from the difficulties in absorbing wind power from already commissioned wind farms and the resultant curtailment of wind power. Pumped storage hydroelectricity (PSH) is a flexible power source that can facilitate higher penetration levels of wind power as well as complement China’s growing nuclear power capacity. However, regulatory policy constraints have restricted the effective utilization of existing PSH capacity and discouraged investment in new PSH capacity. This paper examines these constraints and assesses the likely impact of new policies designed to address them. Finally, policy recommendations and concluding remarks are provided. This paper contributes to the literature on renewable energy integration from a new perspective. The lessons from China are relevant to other countries going through the energy transition. [ABSTRACT FROM AUTHOR]

Retrofitting existing residential buildings has been claimed as one crucial way to reduce energy consumption and greenhouse gas emissions within the Chinese residential sector. In China’s government-dominated retrofitting projects, the participation of residents is often neglected. The objective of this paper is to assess the influence level of public participation (before, during and after retrofit) on energy saving by comparing three Beijing neighborhoods with different retrofitting models: a central government-led model, a local government-led model, and an old neighborhood retrofit model. In the three cases data were collected through interviews with neighborhood workers and residents. The results show that residents’ involvement in pre-retrofit activities, in technology selection and in the use of technology differs greatly among the three cases. This study concludes that in order to improve the effectiveness of energy saving interventions, the motives, intentions and living habits of residents need to be given more consideration when designing and implementing retrofitting. By highlighting the importance of public participation this paper contributes to energy saving policy development in China. [ABSTRACT FROM AUTHOR]

Chinese government has committed to reduce its carbon intensity by 40–45% over the period 2005–2020 at the 2009 Copenhagen Summit. To achieve the target in a cost-effective way, China is signaling strong intentions to establish emissions trading scheme, and presently seven pilots have been established. This paper focuses on the cost-saving effects of carbon emissions trading in China for the 2020 target. First, an interprovincial emissions trading model is constructed. Then, three kinds of policy scenarios, including no carbon emissions trading among provinces (NETS), the carbon emissions trading only covering the pilots (PETS), and the unified carbon emissions trading market (CETS), have been designed. The results show that China needs to reduce its emissions by 819 MtCO 2 for achieving the 42.5% reduction in carbon intensity over the period 2005–2020. The PETS and the CETS, which may result in a carbon price of 99 yuan/tCO 2 and 53 yuan/tCO 2 , could reduce the total abatement costs by 4.50% and 23.67%, respectively. This paper also finds that the carbon emissions trading could yield different impacts on different provinces, and the cost-saving effects of the eastern and western provinces are more pronounced than the central provinces. Necessary sensitivity analysis is also provided at the end of the research. These findings may be useful for promoting the development of carbon emissions trading in China. [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 builds a system dynamics model to simulate the effect of China’s strategic petroleum reserve (SPR) on stabilizing the domestic oil price. By setting the sensitivity of the market to the domestic oil price and the increase in international oil price, this paper discusses three different SPR sizes, namely, 30 days, 60 days and 90 days SPR, and analyzes the effects of releasing the three different sizes of SPR on domestic oil price in 2015 at three international oil price scenarios: low, baseline and high. The results show that the suppress effect of SPR release on domestic oil price is not obvious under a low oil price scenario. Only when the increase in the international oil price is greater than 81% in an insensitive domestic market and 94% in a highly sensitive domestic market, 90 days SPR release can suppress the rising domestic oil price. In the baseline scenario, the SPR fails to suppress rising oil prices when the increase in the international oil price is less than 25%. However, in the high international oil price scenario with a notably small fluctuation in the international oil price, the SPR can arrest the rising oil price. In addition, the suppressing effect of SPR is related to the sensitivity of the domestic market to oil price. The greater the sensitivity of the domestic markets to oil price, the weaker the effect of the SPR on suppressing oil price. Finally, the SPR size should reach to 90 day in China. [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]

The contribution of mid-temperature solar thermal power to improve the performance of coal-fired power plant is analyzed in the present paper. In the solar aided coal-fired power plant, solar heat at <300 °C is used to replace the extracted steam from the steam turbine to heat the feed water. In this way, the steam that was to be extracted could consequently expand in the steam turbine to boost output power. The advantages of a solar aided coal-fired power plant in design condition have been discussed by several researchers. However, thermodynamic performances on off-design operation have not been well discussed until now. In this paper, a typical 330 MW coal-fired power plant in Sinkiang Province of China is selected as the case study to demonstrate the advantages of the solar aided coal-fired power plant under off-design conditions. Hourly thermodynamic performances are analyzed on typical days under partial load. The effects of several operational parameters, such as solar irradiation intensity, incident angle, flow rate of thermal oil, on the performance of solar field efficiency and net solar-to-electricity efficiency were examined. Possible schemes have been proposed for improving the solar aided coal-fired power plant on off-design operation. The results obtained in the current study could provide a promising approach to solve the poor thermodynamic performance of solar thermal power plant and also offer a basis for the practical operation of MW-scale solar aided coal-fired power plant. [ABSTRACT FROM AUTHOR]