Search

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers. [ABSTRACT FROM AUTHOR]

The introduction of the new round of electricity reform policies has made the electricity sales companies' trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China's current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market. [ABSTRACT FROM AUTHOR]

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption. [ABSTRACT FROM AUTHOR]

As one of the most significant carbon emission departments in China, the power industry will gradually become the core hub of reducing carbon emissions in the process of undertaking carbon emissions transferred from other industries. Therefore, it is of vital importance to predict the power consumption in China's end energy consumption to achieve the carbon peak goal on time. This paper firstly uses the gray relational analysis model to study the relationship between power consumption indicators of the three major industries and some social and economic indicators and obtains the influencing factors with the greatest correlation with the power consumption of the three industries. Then, based on the analysis of socio-economic factors, considering different growth rates, the GMCN(1,N) model of electricity consumption in China's three major industries is established. Forecast data under different scenarios have important practical significance for formulating active and effective energy policies. The data indicate that the secondary and tertiary industries consume the greatest amount of electricity. It is estimated that the power consumption of China's three major industries will reach 10.15 trillion kWh (kilowatt hours) by 2030. [ABSTRACT FROM AUTHOR]

The household energy consumption has been a hot field in the study of household energy consumption in recent years. With the increase of residents' income level and the pushing of urbanization, there is a complex nonlinear relationship between energy price and energy consumption. The purpose of this paper is to investigate the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. To this direction, based on the regional characteristics of economic development in China, with the residents' disposable income and the urbanization level as the conversion variables and the electricity sales price as the core explanatory variable, the panel smooth transition regression (PSTR) model of electricity sales price and urban household electricity consumption from the perspectives of income level and urbanization has been constructed in this paper. The empirical results show the following: (1) Under the consideration of regional difference of residents' income level, with the increase of residents' disposable income level, there is a significant negative correlation between electricity sales price and urban household electricity consumption in the whole country, the eastern region, and the central region, while such correlation is significantly positive in the western region. (2) Under the consideration of the difference of urbanization development level, the national regional electricity sales price and the urbanization level are positively related to the urban household electricity consumption, and the urbanization level in the western region plays the biggest role in promoting the urban household electricity consumption, followed by the eastern region and then the central region which plays the smallest role. This paper discusses the effect of electricity sales price on urban household electricity consumption from the perspective of regional difference in income and urbanization, which provides the decision-making basis and empirical support for developing regional electricity price policy and household energy consumption policy. [ABSTRACT FROM AUTHOR]

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

In recent years, the issues related to carbon emissions and environment have attracted extensive attentions. Considering four scenarios (the energy conversion, energy capital savings and loans, energy exports and cement production carbon emissions), this paper adopts the energy consumption method and input-output method to analyze China's carbon emissions structure on the supply-side and demand-side of energy, and finally provides policy recommendations for China's structural emission reduction. The results show that, if the four influencing factors were not considered, the measurement of carbon emissions from the final demand was 44.91% higher than the baseline scenario, 12.36% lower than the baseline scenario from intermediate demand, and 10.23% lower than the baseline scenario from the total. For China's carbon emissions structure on the supply-side of energy, the carbon emissions from high-carbon energy, represented by raw coal, accounted for 66.805% of the total energy-related carbon emissions, while the carbon emissions from low-carbon energy, represented by natural gas, only accounted for 2.485%. For China's carbon emissions structure on the demand-side of energy, the carbon emissions from intermediate demand (enterprise production) accounted for more than 95% of total energy-related carbon emissions, while the carbon emissions from final demand (residents and government use) accounted for less than 5%. For each specific industry in intermediate demand for energy, the heavy industry, electric power, fossil energy, and chemical industry have high carbon emissions and low carbon emissions efficiency. However, the agriculture, construction, light industry, and service are the opposite. Finally, we provide policy recommendations for improving the accuracy of carbon emissions measurement and carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

At this critical junction of industrialization, China is facing an increasingly prominent imbalance between energy supply and demand. It is of great social significance to study the energy-saving of buildings that consume a huge amount of energy. For most heating systems, energy waste mainly arises from inefficient operations of boilers, low efficiency of pipe transmission, and poor quality of heat supply. This paper researches and evaluates the energy-saving reconstruction of intelligent community heating (ICH) system based on the Internet of things (IoT). Firstly, the energy-saving reconstruction of the ICH system was analyzed, and a hydraulic regime model was derived from the simplified model of the water supply and return pipe networks. Then, the system pipe network was investigated in three aspects, namely, node flow balance, pipeline pressure drop balance, and loop pressure drop balance. On this basis, the authors presented the balance equation and finalized the hydraulic regime model of the system. After that, the energy consumption of the system was evaluated before and after the reconstruction, and the energy-saving benefit was predicted. The effectiveness of the proposed method was proved through experiments. [ABSTRACT FROM AUTHOR]

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances. [ABSTRACT FROM AUTHOR]

• Carbon emissions from energy consumption of 282 prefecture-level cities in China were calculated. • A new method for calculating carbon emission factors for electricity is proposed. • Extending the Atkinson Index to the application of Carbon Fairness. • Measured the inequality and fairness indicators of carbon emissions from energy consumption at the prefecture-level city level in China. This paper focuses on the conceptualization and quantification of carbon emission inequality and fairness from energy consumption. In order to more accurately measure the carbon emissions generated by electricity consumption in each city, we firstly propose a new method for calculating the carbon emission factor for electricity, and calculates the carbon emissions from energy consumption in 282 prefecture-level cities in China from 2003 to 2019. Through the application of the Theil-Kaya decomposition mechanism and the improved Atkinson index, we conducted an in-depth study of the carbon emission inequality and fairness indicators from energy consumption. The results show that (1) Although China's overall inequality has declined, it remains significant in the western region, with its inequality indicator fluctuating from 0.548 in 2003 to 0.683 in 2019.(2) The decomposition of the drivers of inequality indicators shows that the contribution of GDP per capita to inequality is increasingly being replaced by energy intensity and the energy carbon emission factor, with energy intensity emerging as the main driver, contributing more than 40% to inequality in 2019, both overall and in the three regions.(3) The results for the fairness indicator, on the other hand, show a certain degree of divergence. Further Dynamic Time Warping fitting analysis shows that regardless of how the public's sensitivity to carbon emissions varies, the change trends of the fairness and inequality indicators for carbon emissions in the eastern region show a high degree of convergence, even in the two most extreme cases. The fit is poorest in the western region, but improves significantly if public's sensitivity to carbon emissions increases. (4)To sum up, some policies that are consistent with the context of the times, such as the East-West regional counterparts and the construction of a new type of power system, are worthy of priority consideration by the Government. [ABSTRACT FROM AUTHOR]

• Comprehensive energy base planning model integrating DLR and industrial DR. • Extends DLR to planning stage, linking RES generation with DLR calculations. • Process modeling of coal-to-methanol production and energy-consuming process. • Highlights potential for optimizing energy base operations with industrial loads. • Real case study on energy base validates effectiveness of proposed model. The integration of renewable energy sources (RES) into power systems during the low-carbon transformation has resulted in the emergence of energy bases due to geographical imbalance between electricity production and consumption. Existing research often assumes that the transmission capacity is fixed when optimizing energy base sizing, without considering the dynamic changes in the real environment. Additionally, energy base planning traditionally relies on allocating flexible resources to ensure flexibility and hedge against operational risks resulting from RES uncertainty. However, the approach neglects the inherent operational flexibility of local high-consumption loads and their potential for generation substitution. To address these issues, the paper proposes an optimal energy base planning method considering dynamic line rating (DLR) and integrated industry demand response (DR). Firstly, a planning-oriented approach is proposed to estimate transmission capacity by establishing the relationship between wind/solar power generation and DLR calculation. Furthermore, the study explores the operational flexibility and maximum generation substitution capacity of the chemical industry using the state-task network. Finally, the proposed model is validated using a modified energy base located in North China. Numerical results demonstrate that considering DLR and industrial DR improves the economy of the energy base and facilitates RES utilization. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

• A solar assisted ground source heat pump system with graded thermal energy storage (SAGSHP-GTES) was proposed. • Performance for two SAGSHP systems were simulated and compared in depth. • The SAGSHP system showed a poor η SC of around 19.4%, while the SAGSHP-GTES system showed excellent performance, with a high η SC of around 42.7%, and the problem of soil temperature reduction was solved. • The SAGSHP-GTES system was a feasible and efficient technology for hot water supply in all climate zones of China. The solar assisted ground source heat pump system (SAGSHP) is recognized as an efficient, clean and economical renewable energy technology for hot water supply. However, in SAGSHP systems with an all-day hot water supply, the solar collectors can only heat the water tank with intense solar radiation, which wastes moderate and weak solar resources. In addition, as the SAGSHP system draws heat from the soil for a long time, the soil temperature around the borehole decreases, causing the performance of the GSHP to deteriorate. To solve the above problems, a SAGSHP system with graded thermal energy storage (SAGSHP-GTES) was proposed. This paper investigated the performance and feasibility of the SAGSHP-GTES system, using a case study of hot water supply in a campus dormitory in Changsha, China. Firstly, the drawbacks of solar energy utilization in the conventional SAGSHP system were analyzed. Then, the SAGSHP-GTES system was modeled by TRNSYS, and its operational performance was evaluated and compared with the conventional SAGSHP system. Finally, the feasibility and performance of the SAGSHP-GTES system in different climate zones of China were analyzed. The results showed that the conventional SAGSHP system had a poor solar energy utilization efficiency, with an average thermal efficiency of the SC (η SC) of only around 19.4%. The long period of operation caused the soil temperature to drop from 18.1 °C to 2.78 °C, which led to a decrease in system efficiency and an increase in electricity consumption. By controlling the heat collection of the SC and the graded utilization of solar energy in the SAGSHP-GTES system, a high η SC of around 42.7% was obtained, which improved the system efficiency while eliminating the problem of soil temperature drop. After 15 years of operation, the soil temperature increased from 18.1 °C to 19.9 °C, which led to an increase in the coefficient of performance of the system (COP sys) from 3.77 kW/kW to 3.84 kW/kW and COP GSHP from 3.45 kW/kW to 3.5 kW/kW, and a reduction in electricity consumption from 51,746 kWh to 50,735 kWh. In addition, the conventional SAGSHP system was not feasible in severe cold zones (Harbin) and cold zones (Beijing) of China, where soil temperatures fell below 0 °C and freezing of the circulating fluid occurred, while the SAGSHP-GTES system showed excellent performance in all climatic zones of China and is a feasible and efficient technology for hot water supply. [ABSTRACT FROM AUTHOR]

With the goals of carbon peaking and carbon neutrality, China's renewable energy has entered a stage of rapid development. The government gives financial and power-rate subsidies to clean power while putting great support on its construction. However, there is currently a big gap in the government's subsidy on new energy power generation, which is challenging to meet the demand for renewable energy consumption and sustainable development. In this regard, participating in the market is inevitable for renewable energy development, which is significant for promoting renewable energy power consumption and alleviating the subsidy dilemma. Considering the market-based mechanism, this paper regards the electricity market, the tradable green certificate market, and the carbon emissions allowance trading market as an integrated energy platform system that couples carbon emissions with electricity, linking generators and retailers. We consider two scenarios, including green electricity–green certificate separation and unity, and introduce the intermittency of new energy generation to construct the optimal pricing decision for this platform supply chain. We explore the optimal price of tradable green certificate for renewable power and the equilibrium demand for green power and thermal power in two circumstances. The results show that: (1) there is an equilibrium solution in various conditions. Moreover, the optimal price of green certificate in condition two, namely green electricity–green certificate unity, is higher when the on-grid price of green electricity and thermal one and the substitution effect of these two powers satisfy a certain constraint, which can stimulate generator and retailer to generate and consume green electricity as much as possible. (2) The price of carbon emissions and the responsibility weight of renewable electricity consumption both have a positive influence on the optimal price of green certificate and the share of equilibrium demands for green power, whereas intermittency of new energy generation has a negative impact on them. Adjusting the carbon price and incentive responsibility weights and taking relevant measures to reduce intermittency in different situations help to reduce thermal power demand and promote green power consumption. (3) When the prices of green certificates in diverse circumstances are in a certain interval, the impact intensity of changes in green certificate price on electricity demand will arise. It is conducive to performing the facilitative role of green certificate price on the power market better and stimulates the generation and consumption of new energy power in the carbon-electricity coupling market. • Through the carbon-electricity coupling perspective, the optimal TGC price was explored. • Introducing substitution effects of green power and thermal power to analyze optimal TGC prices. • A higher incentive responsibility weight of renewable consumption in scenario two can promote the retailer to consume. • Impact intensity of changes in green certificate price on power demand will arise when the price is in a certain interval. [ABSTRACT FROM AUTHOR]

As an important path to realizing carbon neutrality in China, digital development may also lead to the growth and rebound of energy demand. Investigation of digital rebound effect is important to evaluate whether digital development can effectively conserve energy. Thus, this paper examines the impact of digital development on electricity consumption and its heterogeneity among Chinese cities, and further evaluates the sizes of energy rebound effect triggered by digital development. The results show that, first, electricity consumption in Chinese 285 cities increases by 7%–20% due to digital development, and this positive influence tends to increase with the increase in electricity consumption. Second, in the eastern, high-income, and high-tertiary industry share regions, digital development has a greater role in promoting electricity consumption. Third, the digital development of Chinese cities induces the rebound effect of electricity consumption, and even induces the backfire effect in the early stages of digital development. Finally, the energy rebound effect tended to decrease over time, with a partial rebound in electricity consumption, and the minimum rebound effect was 22%. The results highlight the necessary attention on digital rebound effect, and the importance of low-carbon digital development. • Electricity consumption increased 7%–20% due to digital development in China • The positive influence tends to increase with the increase in electricity consumption • Digital development in Chinese cities leads to energy rebound effect • The energy rebound effect tended to decrease over time, with a partial rebound [ABSTRACT FROM AUTHOR]

In the context of global climate change and energy security pressures, biomass power generation has been regarded as an effective way to supplement the energy supply, reduce fossil fuel consumption, and alleviate environmental pollutants. This paper presents an integrated modeling framework to address the regional biomass power generation planning to facilitate biomass utilization in developing countries. By combining the geographic information system, multi-criteria decision-making approach, as well as the type-2 fuzzy inexact programming method, the proposed integrated framework could assist the optimal decision-making for the specific site and size selection of biomass power plants in the whole region as well as the optimal operation strategies under deep uncertainties. The proposed modeling framework is applied to the particular context of Henan, an agricultural province in China with great biomass potential. The results suggested that biomass power plants with a total capacity of 2.4 GW could be established, which could satisfy 6% of local electricity consumption and avoid 18.51 Mt of carbon emissions. The sensitivity analysis of the main economic parameters indicated that lower investment and operation costs were essential to stimulate the utilization of local biomass energy. A higher feed-in tariff is not encouraged due to the limited incentive effect but is a heavy burden for the government. Furthermore, the optimal strategies for biomass power generation planning under uncertainties could be suggested for different decision makers with various risk preferences to achieve a tradeoff between system cost and risk. [ABSTRACT FROM AUTHOR]

The direct rebound effect offsets part of energy savings brought about by energy efficiency, causing academics and governments to re-examine the relationship between energy efficiency and conservation. Although suppressing the rebound effect contributes to energy conservation, how to implement it is debatable. This paper puts forward a framework to character the direct rebound effect, and develops non-linear models to explore the impacts of urbanization on the direct rebound effect based on a panel dataset of 30 Chinese provinces. The results show that, first, the magnitude of the direct rebound effect is much smaller in urban areas than that in rural areas. Second, urbanization could restrain the direct rebound effect in urban areas, where employment and environmental consciousness play important roles while the impact of agglomeration is relatively weak. The impacts of urbanization on direct rebound effect are not substantial in rural areas. In addition, our findings reject the saturation effect caused by income. Income promotes the direct rebound effect in rural areas while it could not restrain the direct rebound effect in urban areas. Therefore, the reason that the average direct rebound effect tends to decline with income growth seems to be the urbanization rather than the saturation effect. • This paper puts forward a framework to character the RE and explore its evolution. • A new urbanization that looks at its quality rather than its growth is conducted in this paper. • The relationship between income and the RE is re-examined and it has the urban-rural heterogeneity. • The RE of urban areas decreases with urbanization mainly in the employment and environment dimensions. [ABSTRACT FROM AUTHOR]

Researches revealed that there has been existed a relatively stable and positive relationship between economic growth and electricity consumption. However, the inconsistency between them appeared in 2008. Therefore, it is urgent to analyze whether the GDP in China is cheated and to seek the underlying reasons contributed to the deviation. Anhui province is selected to act our study area. In order to explore the underlying reasons of the deviation between economic growth and electricity consumption, a multivariable regression models is established in this paper. Empirical results are as follows: (1) real GDP, industrial structure (IS), heating degrees days (HDD), cooling degree days (CDD), investment in fixed assets (IF) are all positive factors for the consumption of electricity; (2) IS is the major contributor to raising the consumption of electricity, followed by IF, CDD, HDD, and GDP; (3) energy intensity (EI) and financial development (FD) are negative factors, when the value of EI increase 1.0%, the consumption of electricity will decrease 7.4%; (4) if the electricity consumption caused by other factors except GDP is eliminated, the deviation would almost shrink; (5) the real growth rate of GDP of Anhui province is authentic. [ABSTRACT FROM AUTHOR]

• A novel discrete GM(2,1) model with a polynomial term is proposed. • The generalization and adaptability of the proposed model were validated from the theoretical and practical perspectives. • The newly-designed model is applied to predict per capita living electricity consumption. The forecast of electrical energy demand has played an increasingly relevant role in sustainable electrical power system. This paper develops a new method for forecasting China's per capita living electricity consumption by grey modelling technique. Considering the multiple and mixed change patterns, a novel discrete grey model with polynomial term (abbreviated as DGM(2,1, kn)) is proposed in this study. Firstly, the polynomial term is introduced into the discrete DGM(2,1) model. Secondly, the Tikhonov regularization method is employed to solve the overfitting problem. Lastly, two published cases and China's per capita living electricity consumption are used to validate the generalization and adaptability of the newly-designed model. The numerical results of such experiments show that the proposed model outperforms other competitive models in terms of accuracy level. Therefore, the projections of China's per capita living electricity consumption in 2020 and 2025 have been made for providing a solid reference for the formulation of electrical power strategies. [ABSTRACT FROM AUTHOR]

The efficient lighting project, aiming at developing and promoting energy-efficient lighting products, has been initialized in China since 1996. The use of energy-efficient lighting products is one of the best and most cost-effective ways of reducing lighting energy consumption. Great attention has been put into this project and effects have been made nationwide. But no effective evaluation system has been established yet, which makes the summary of experiences and lessons less systematic and the improvement of future work quite difficult. Based on the Analytic Hierarchy Process, this paper established a new evaluation system of promotion effects after taking the public policies and practical situation in China into consideration. Aspects of environment, economy, adequacy, and technology were considered comprehensively in the evaluation system to score the promotional effects. The efficient lighting project during the 11th Five-Year period (2005-2010) in China was examined to prove the feasibility of the evaluation method. A 'proper' level was finally rated based on the evaluation results, which meant that the promotion of the study case was fruitful on the whole. Benefits such as the evident improvement of energy efficiency and the weak points of producing enterprises were both discovered. Relevant to the problems and experiences, suggestions for the future work were also proposed for the efficient lighting project in China. [ABSTRACT FROM AUTHOR]

China and India accounted for two-thirds of the world׳s rise in energy use between 2000 and 2012. This paper aimed to calculate the P (population)– A (affluence)– T (technology) effects of energy use in China and India – the world׳s two most populous and largest developing countries. To this end, a combination of the IPAT method and the logarithmic mean divisia index technique, and annual time-series data on population, energy consumption, and gross domestic product during 1970–2012 are used. In China, a 12.53-fold growth of energy use emissions from 1970 to 2012 is driven by a combination of rapid growth in individual income and slow growth in population, with offset by technological advancement since 1980. The accelerating rise in energy use since 2000 is a result of accelerating growth rates in individual income and a reversal of earlier declining in energy intensity (technological advancement). Unlike China, the long-term rise in energy use exceeded the long-term rise in individual income in India. In addition, a strong trend of decline in energy intensity has not yet occurred in India. Thus, a 7.39-folds growth of Indian energy use for 1970–2012 was a result of relatively rapid increase in population and relatively slow increase in income, without effective offset by technological advancement. It suggests that market-oriented economic and energy reforms need to send the correct price signal to promote energy-efficient technologies thus improving energy efficiency, which is the key to a sustainable energy future in China and India. [ABSTRACT FROM AUTHOR]

Regional energy development and approaches are significant for China's overall economic and social development. The GDP in Zhejiang province surpassed 6 trillion yuan in 2019, and its energy consumption reached 200 million tons of standard coal (tce), both of which are at the forefront of China. In order to explore the main factors of the increase in energy consumption in Zhejiang and provide essential references for energy saving and other provinces, this paper analyzes the total energy consumption and industrial sectors on the basis of the logarithmic mean divisia index (LMDI) model. Study results show that the economy's scale is the most crucial factor affecting Zhejiang's energy consumption, with a significant growth effect. In 2015, the scale effect increased energy consumption to the highest value of 14 million tce and then reduced it to 13 million tce in 2019. The impact of the population on energy consumption increased by 10 million tce from 2010 to 2019. Energy intensity reduces energy consumption by between 0.05 and 0.15 billion tce per year, which is the main factor in reducing energy consumption. The energy structure generally plays a weak positive role due to the different energy types. The decomposition of the energy consumption per unit of value added in the industrial sector showed that the intensity and structural effect primarily reduce energy consumption, for example, the metal smelting and rolling, textile printing and paper, electric power, heating, and other industries. According to the results, enterprises should enhance the intelligence and efficiency of dispatch management and emergency responses. Zhejiang should also accelerate an international oil and gas trading center and resource allocation base to reach its carbon-neutrality goal. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of space weather on China's electricity market. Based on data products provided by NOAA and the National Energy Administration in China, this paper uses solar wind velocity as a solar weather indicator and the disturbance storm time index as a magnetospheric weather indicator to match monthly Chinese electricity market data over 10 years. Based on a VAR model, we found that (1) space weather increases the demand for electricity in China, and solar wind speed and the geomagnetic index increase the electricity consumption of the whole of Chinese society, as space weather mainly increases the electricity consumption of the secondary and industrial sectors. (2) The geomagnetic index significantly promotes power station revenue. (3) Space weather is associated with increased energy consumption. The geomagnetic index significantly increases the coal consumption rate of fossil power plants in China, but the solar wind speed has nothing to do with the coal consumption rate of fossil power plants. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the world is experiencing many extreme climate events. The temperature fluctuation associated with climate change is often considered as a primary driving force of electricity consumption. To better explore the impact of temperature on electricity consumption, this paper aims to analyze the question from the perspective of income growth, i.e., the moderating effect of income growth on the response of urban residential electricity to temperature changes. Using the unbalanced panel data of 23 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2015, we construct the response model of residential electricity consumption to income growth, heating degree days, cooling degree days and their interactions. The results show that heating demand, cooling demand and income growth significantly increase residential energy consumption. However, urban residential disposable income growth weakens the positive effect of heating demand and cooling demand on residential electricity consumption. Because, urban residents have obvious demand for improving indoor heating comfort and cooling comfort, the moderating effect of income is shown as the crowding-out effect. Residents may adjust the structure of heating energy consumption (from electricity to natural gas) and upgrade their electrical equipment to high energy efficiency equipment, thereby reducing residential electricity consumption. This study also indicates that urban residential energy consumption is very sensitive to changes in permanent populations. The main contribution of this study is proposing the hypothesis of the negative moderating effect of income growth on temperature changes and expanding the response model of residential electricity consumption to temperature changes. Finally, this paper also proposes the adaptive strategies and policy implications for climate change. [ABSTRACT FROM AUTHOR]

Energy production has become an important concern because excessive coal and oil use for electricity generation and consumption in developing economies threaten the global environment. Past research mainly focuses on energy consumption, while there is a scarcity of models on the energy and electricity production-environmental pollution nexus in the Environmental Kuznets Curve literature. As an initial attempt, the paper examined the simultaneous impact of electricity production and electricity consumption on carbon dioxide emissions in one of the top energy-consuming, energy-producing, and carbon-emitting nations (China). The study applied the Zivot and Andrew tests to check the stationarity of the series with structural breaks. The autoregressive distributed lag (ARDL) method was to compute long-run coefficients using two separate models: Model 1 (total electricity production and environmental pollution) and Model 2 (total electricity consumption and environmental pollution) were considered. The results showed that electricity production and electricity consumption significantly contributed to environmental pollution in the short- and long-run. Apart from validating the EKC hypothesis (inverted-U-shaped curve) in the short- and long-run, the estimation demonstrated that agriculture value-added mitigated the environmental pollution. The paper offers important policy implications. [Display omitted] • Environmental effects of electricity (production and consumption) were examined for China. • Simultaneous equation modeling framework was used. • Electricity production increased CO 2 emissions in China. • Electricity consumption increased CO 2 emissions in China. • Agriculture value added decreased CO 2 emissions in China. [ABSTRACT FROM AUTHOR]

In order to scientifically formulate electricity saving policies, it is necessary to study the industry heterogeneity and spatial correlation of electricity consumption. This paper used spatial autocorrelation test and dynamic spatial econometric model to identify the spatial correlation characteristics, main driving factors and spatial spillover effects of electricity consumption in different industries. The results are shown as follows. Firstly, except for agriculture, forestry, animal husbandry and fishery, electricity consumption of other industries has the spatial autocorrelation of economic distance, and electricity consumption of different industries has the significant time inertia effect, space contagion effect and space-time warning effect. Secondly, the total population, economic growth, urbanization, fixed asset investment and energy consumption structure can promote electricity consumption in this area. Thirdly, the total population, economic growth and urbanization of the region can restrain electricity consumption in the adjacent area. Finally, The driving factors and dynamic spatial spillover effects of electricity consumption are heterogeneous among different industries, and different factors have a more profound long-term impact on electricity consumption of different industries in China. Therefore, it is necessary to establish a regional coordination mechanism for electricity saving with industry differentiation and overall consideration, and formulate electricity saving policies for joint control. • EC has the spatial autocorrelation of economic distance. • EC has temporal inertia, spatial contagion and spatio-temporal warning effects. • Electricity saving factors did not play a good role. • Dynamic spatial spillover effects are heterogeneous among different industries. • Factors have a more profound long-term impact on EC of different industries. [ABSTRACT FROM AUTHOR]

China has pledged to reduce its carbon intensity defined as carbon dioxide emissions per unit of GDP by 40-45% by 2020 and by 60-65% by 2030 compared to the 2005 level. Tofulfill the pledges, China's government has made energy efficiency its de facto climate policy. This article raises the question to what extent energy efficiency will be an efficient mitigation measure for reaching the targets as pledged by China to the UNFCCC. In this context, two issues blur the picture. One is the potential rebound effect, generally causing one percent improvement in energy efficiency to generate less than one percent reduction in energy-related emissions since users adapt to the direct and indirect productivity gains and cost reductions in energy use. Further, there is the impact on energy use in China from interaction with global markets, in which China has emerged as a dominant player. In the present paper, we study the net implications of energy efficiency improvement in China within alternative global climate policy regimes. Our results show that a one percent energy efficiency improvement in China reduces energy use by 0.38-0.59 percent per year depending on alternative international contexts. Hence, policy makers should consider climate policies adopted by the other regions such as carbon trading system when assessing the implications of energy efficiency for energy consumption and climate mitigation. Policy makers should also consider overlapping effects of alternative energy policies, as energy efficiency improvement might have no effect on energy and emission reduction if there is global carbon trade. However, policy makers can expect more reduction in energy use and emissions due to energy efficiency improvement in the new mechanism announced in the Paris Agreement at the COP21. [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]

• Spatial pattern and evolution trends of four kinds of residential energy consumption in China are investigated. • Spatial difference in the distribution of gravity centers of residential energy consumption is depicted. • Standard deviational ellipse demonstrates the dynamic evolution patterns of residential energy consumption. • Coupling coordination degree between residential energy consumption and the economic indicator is investigated. • Electricity should be taken into further consideration in the development of residential energy improvement. The residential energy consumption in China shows diversity and complexity in space. This paper aims to investigate the spatial pattern and evolution trends of four kinds of residential energy consumption (electricity, coal, oil, and natural gas) through gravity center modeling and standard deviational ellipse modeling. Further, their relations to economic growth are probed by using the coupling and coordination degree model. Panel data at the provincial level in China are collected from 2000 to 2019. Our results show that the most drastic movement trend of gravity centers is coal, natural gas, followed by oil, whereas electricity moved slightly in space. Further, the standard deviational ellipse of electricity exhibits relatively stable patterns than the other three residential energy consumption during the study period. In addition, the coupling coordination degree between residential energy consumption and the economic growth is examined, and the results indicate that the total residential electricity consumption shows the highest coupling coordination degree, followed by residential gas, oil, and coal. It demonstrates lower coupling coordination degrees in rural areas than in urban areas in the total and sectoral residential energy consumption. In sum, several policy implications can be demonstrated based on our findings, i.e., the development of electricity consumption in the residential energy structure may improve the energy stability in space in China, and ensuring access to electricity should be taken into further consideration in the development of residential energy improvement to achieve a better coordination level to economic development. [ABSTRACT FROM AUTHOR]

• A net zero energy wooden structure building in severe cold zone was designed in severe cold zone. • A new type of water-passing wall was designed and applied in the wooden structure building. • Renewable energy systems are adopted to pursue net zero energy consumption for wooden structured buildings in severe cold areas. • Study on the operational performance of wooden structure building. • Evaluated the energy criteria and economy of the wooden structure building. At present, there is no lack of research on net zero energy buildings (nZEB) in China, and most of the research on "nZEB" focuses on buildings using traditional building materials like rebar concrete, brick, etc. In this paper, we take a wooden structure residential building in the severe cold zone as the research object, design a new type of water-passing wooden structure wall, and explore how to achieve net zero energy consumption through appropriate methods in the design stage. During the operation of the wooden structure building throughout the year, the solar photovoltaic (PV) system provides electricity to the building. In the heating season, we will supply groundwater or shallow geothermal water into the water pipes in the wall, and the solar thermal system provides hot water for heating. According to the whole year operation of the wooden structure building, the indoor environment meets the requirements of comfort standards, and the designed envelope structure of the wooden structure building has an excellent thermal performance. The energy consumption was reduced by 62.9% compared with energy-saving buildings without water-passing walls. The electricity generation capacity of the solar PV system is more than the actual electricity consumption of the building, the energy criteria are met the relevant requirements of the net zero energy building, and its economy is also evaluated. [ABSTRACT FROM AUTHOR]

Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development. • The industrial structure rationalization has highest contribution to the economic growth of eastern China, followed by central and western China. • There exists unidirectional causality from industrial structure upgrading to structure rationalizing in western China and bidirectional causality between them in central China. • Effects of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption. • Unidirectional long-term causality from renewable energy to economic growth exist in each sub-region. [ABSTRACT FROM AUTHOR]

Abstract: Though improving energy efficiency is an important approach to decrease the energy consumption, the rebound effect caused by technology progress negatively affects the effectiveness of energy efficiency policies. This paper empirically investigates direct rebound effect of urban residential electricity use in China. Using China's 30 provincial government panel data from1996 to 2010, we build a co-integration equation and a panel error correction model to analyze the direct rebound effect. The results indicate that an obvious rebound effect in the Chinese urban residential electricity consumption does exist. Specifically, the long-term rebound effect is 0.74, while the short-term rebound effect is 0.72. Therefore, the rebound effect significantly impairs functions of energy efficiency policies. For this reason, Chinese government should take the rebound effect into consideration when formulating energy policies. [Copyright &y& Elsevier]

Different from many studies on Energy Kuznets Curve, this paper does not directly consider income factor but conducts a nonlinear study on per capita primary energy consumption and electricity consumption in China, whose economic development is considered investment-driven, in which government macroeconomic policies, especially government expenditure plays a very important role. In particular, this paper attempts to compare the different impacts of government expenditures and the other influencing factors on energy consumption. Based on the results of the threshold regression, in the future, with the further development of the economy, the driving force of labor population growth will slow down. Comparing with primary energy, the relationship between electricity consumption and its influencing factors will change later, and the difference in the effects of the influencing factors is mainly reflected in the formation of capital stock (investment) and the development of urbanization. Government expenditure on private goods may not be always as inefficient in energy consumption and cleaner production as previously thought in the literature. Meanwhile, the growth of government expenditure on public goods will become a powerful driving factor which indicates that the government needs to assess its overall impact on energy consumption and energy efficiency when participating in the provision of public goods. The paper also found that when studying the issue of energy consumption and regional differences in China, the geographic classification of eastern, central and western regions may not be appropriate. Grouping according to energy consumption levels may be one of the better options. And the distribution of energy consumption levels in China's provinces is consistent with the distribution of government expenditure levels. The estimation results also provide us with important policy implications: the government expenditure in education is of great importance, and it can produce a strong energy-saving effect through technological progress. An important feature of China's economic development is investment-driven and demographic dividend, and government expenditure plays a very important role. Different parts of income have a non-linear effect on energy consumption. Government may not be always inefficient in terms of energy consumption or cleaner production. The growth of government expenditure will become a powerful driving factor on energy consumption.When China's energy consumption is studied, regional grouping according to energy consumption levels may be one of the better options.The distribution of energy consumption levels in China's provinces is consistent with the distribution of government expenditure levels. Image 1 • A comparative study on per capita primary energy and electricity consumption. • The increase influence of labor population growth on energy consumption will slow down. • Urbanization has different influence on different energy consumption.. • Considering energy consumption, government expenditure may not be always inefficient. • The government expenditure in education is of great significance. [ABSTRACT FROM AUTHOR]

Chinese nonmetallic mineral products industry is one of the largest electricity consumer in China and has a large impact on overall electricity consumption in China. This paper examines the impact of economic variables on the demand for electricity in Chinese nonmetallic mineral products industry where economic growth, sectoral value added per worker, sectoral R&D intensity, and energy price were utilized to analyze sectoral electricity consumption, with an application of quantile model. The paper's estimates unveiled the varying effects of economic variables across the spectrum of sectoral electricity consumption where the coefficient of economic growth was positive and significant across all quantiles, while sectoral value-added per worker had both positive and negative significant coefficients. An increase in income level and sectoral value added per worker will increase the electricity consumed by the industry, however, the latter will also enhance a decreasing trend in few regions across China and did not influence high energy efficiency but rather enhanced the production of additional outputs, while sectoral R&D intensity and energy price will ensure a substantial decrease in sectoral electricity consumption with the latter having a greater impact. Increase in investment for sectoral R&D intensity will boost technological advancement and is recommended as a cornerstone to ensuring decline in sectoral energy consumption that will buttress sectoral value added per worker coupled with a robust energy price. [ABSTRACT FROM AUTHOR]

• We estimate the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. • The average value of efficiency is 0.491, and the average rebound effect is 48%. • We find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. • The rebound effect varies across before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China's household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the influence of different regulation strategies on wind energy integration into the existing energy system of Jiangsu. The ability of wind integration is defined in terms of the ability to avoid excess electricity production, to conserve primary energy consumption and to reduce CO2 emissions in the system. Firstly, a reference model of Jiangsu''s energy system is built using the energy system analysis tool EnergyPLAN based on the year 2009. The model results are then compared to actual values from 2009 to validate their accuracy. Based on the reference model, different regulations of Jiangsu''s energy system are compared and analyzed in the range of a wind input from 0% to 42% of the total electricity demand. It is concluded that operating power plants in a flexible way facilitates the promotion of more intermittent wind integration. [Copyright &y& Elsevier]

Abstract: China''s total installed electrical power capacity reached 700GW by the end of 2007 and is predicted to surpass 900GW in 2010. The rapid increase in energy demand and increasing global warming have both pushed China to change its current electrical power structure where coal power accounts for nearly 75% of the total electric power generation. China has already become the world''s largest solar water heater producer and user. However, there is still much to be done in the solar thermal power field before its commercialization. Solar thermal power technologies including solar power towers, solar parabolic trough concentrators, solar dish/stirling systems, linear Fresnel reflectors, and solar chimneys have been studied in China since the 1980s. A 10kW dish/stirling project was funded by the Ministry of Science and Technology (MOST) during 2000–2005 with a 1MW solar power tower and research of trough concentrator metal–glass evacuated tubes supported during 2006–2010. This paper describes a continued solar thermal power development roadmap in China in 5-year intervals between 2006 and 2025. [Copyright &y& Elsevier]

[Display omitted] • A novel structure-adaptative grey model with a time power is proposed. • The Cultural Algorithm is employed for determining the optimal time power item. • Monte-Carlo Simulation and Probability Density Analysis are originally introduced to enhance the model's robustness. • Empirical results comprehensively verify the efficacy of our novel method. • The future demands for nuclear energy are predicted by using the promising model from 2019 to 2023. Accurate estimations of nuclear energy consumption are an essential process for formulating appropriate policies and plans in the energy sector and associated companies. This paper presents a novel structure-adaptive grey model with an adjustable time power based on the nonlinear and complicated characteristics of nuclear energy consumption, in which three core innovations are summarized below. Initially, the generalized time response function for projections is theoretically deduced, which overcomes the fundamental flaws in the conventional grey model. Subsequently, the Cultural Algorithm is employed to determine the optimum values of the time power item to improve the adaptability and flexibility to confront diverse forecasting issues. Further, Monte-Carlo Simulation and Probability Density Analysis (PDA) are originally introduced to enhance the robustness of the proposed model. For illustration and verification purposes, experiments on predicting nuclear energy consumption in China and America are conducted in comparison with a range of benchmark models, including other prevalent grey models, conventional econometric technology, and artificial intelligences. The performance of the novel technique is evaluated from two different perspectives of PDA and level accuracy, confirming that this model is a very promising and powerful tool for predicting nuclear energy demands in China and America from 2019 to 2023. [ABSTRACT FROM AUTHOR]

• Two-year monitoring campaign of IEQ parameters of a nearly zero energy building was firstly presented in China. • The nearly zero energy building can provide the indoor with a comfortable thermal environment and good air quality with less energy demand. • The overheating problem in the whole year would be especially paid attention during design and operation. • The occupant behavior has a significant impact on energy consumption and thermal comfort. The paper presents a two-year field measurement and assessment of a nearly zero-energy Building in cold zones of China. These parameters of indoor environment quality (IEQ) and electricity consumption for HVAC are recorded. According to the measurement data, the most of indoor temperature was controlled in the range of 23 °C ~ 26 °C and relative humidity (RH) mainly fluctuates within the range of 30% ~ 60%. The concentrations of carbon dioxide (CO 2) and particulate matter (PM 2.5) are lower than the relevant standard requirements in most of the time. The average electricity consumption for HVAC was 33.05kWh/(m2·a) and the heating and cooling demands could be reduced by 55.2% and 54.0%, respectively, compared with existing buildings. In general, the NZEB can provide indoor comfortable thermal environment and indoor air quality with less energy demand, but the overheating problem in summer and effect of occupant behavior should be especially paid attention during operation. Overall, the experience from the actual operation of NZEBs will help China to reach its goals of reducing carbon emissions and addressing climate change through realizing the energy-saving potential of NZEBs. [ABSTRACT FROM AUTHOR]

• Pyramidal approach for the analysis and decomposition of energy intensity. • Definition of structural and efficiency indicators for the whole energy system. • Impact of transformation processes and fuel types on supply side energy efficiency. • Emerging nations needed to worsen the efficiency of the supply side to thrive. • Renewable electrification simultaneously benefits energy and carbon intensities. Energy efficiency remains as the main mitigation factor to slow down the growth of energy consumption and related CO 2 emissions, undoubtedly the major responsible for climate change. Gaining insights into the driving forces that make efficiency change is a keystone to define energy policies and examine pathways to sustainable development. To this aim, this paper proposes a pyramidal approach for the analysis and decomposition of energy intensity, the main global efficiency indicator, using the LMDI method. First, the effects related to supply and demand sides of the energy system are separated in Primary Energy Factor and final energy intensity, respectively. Then, supply side is further decomposed to progressively reveal structural effects associated to transformation processes and fuel types. The approach is applied to the most emitting and consuming nations (China, United States, European Union, India, Russia, Japan) to provide a meaningful cross-country analysis over the period 1995–2017. Results show that energy intensity gains have been mainly driven by widespread demand side efficiency improvements from 25% to 61%. Regarding the supply side, unfavourable structural changes due to electrification, up to 12% in China, have only been offset by transformation efficiency gains about 6% in developed countries. Consequently, emerging economies have worsened their energy sector efficiency as they thrive. Changes in fuel mixes have generally contributed to energy intensity reductions (up to 4%) mainly due to shifts from coal and nuclear power towards gas and renewables plants. The proposed methodology could help stakeholders to effectively analyse the energy system and to develop policies to reduce its environmental impact. [ABSTRACT FROM AUTHOR]

• A data envelopment analysis model was built to assess the operation energy efficiency. • Wastewater treatment plants' distances from effective level were compared. • Disparities of results among regions and treatment processes adopted are evident. The energy use intensity of wastewater treatment facilities has been increasing in recent decades in China. In this paper, data envelopment analysis is used to evaluate the energy efficiency of municipal wastewater treatment plants (WWTPs) in China. In the DEA model, the six input indicators were load rate, design scale, unit electricity consumption, sludge generation, actual treatment volume and accumulated electricity consumption, and the six outputs were COD, BOD, SS, NH 3 -N, TN and TP pollutant removals per amount of electricity consumption. A total of 4100 plants were assessed in China in 2018. The relative efficiencies of 14 plants at 1.0, with an average electricity consumption of 0.091 kWh/m3 compared to the national average consumption of 0.38 kWh/m3, served as a benchmark for the remaining inefficient plants. According to the Malmquist index results, during 2014–2018, on average, 40.54% of the WWTPs experienced productivity improvements, 71.24% of the plants increased their technical efficiency relative to the frontier, and 27.21% of the plants showed progress on the production frontier. Western areas in China consume a large proportion of renewable energy with higher energy efficiency levels, while central and eastern areas had lower energy efficiency trends and high pollutant emissions levels. The oxidation pond method has the highest comprehensive technical efficiency of 0.446, nearly eight times the average efficiency level. It is suggested that wastewater treatment industries should not only consider technical efficiency increases but also match their pollutant burdens and implementation of discharge standards with energy-saving targets. [ABSTRACT FROM AUTHOR]

• The energy consumption of 38 Chinese district heating systems was investigated and compared. • Method to break down the consumption and evaluate the operational problems was presented. • Field measurements were conducted to evaluate problems leading to high energy consumption. • Overheating was responsible for 23% of total heat consumption. • Resistance, operation flow, and pump efficiency were three major problems in distribution networks. Worldwide, China has the largest district heating (DH) area and is also the fastest growing country. This paper presents field measurement results for operational energy consumption from 38 DHSs to fill in the gaps in understanding of the current operational issues of the district heating system (DHS). The average heat consumption during the heating period was 98.6 kWh/m2 and the monthly average electricity consumption for circulating pumps in the primary and secondary networks was 0.27 kWh/(m2 × month) and 0.29 kWh/(m2 × month), respectively. Thereafter, a systematic method for breaking down the energy consumption and quantitatively evaluating the underlying problems is presented. Based on several detailed measurements conducted in typical heat-exchange substations (HESs), it was found that overheating was the most prominent problem in energy-efficient buildings, constituting 23% of the total heat consumption. Another problem encountered was heat loss through pipes, which constituted approximately 5%–10% of the total heat consumption and was highly dependent on the actual conditions. In distribution networks, problems such as high resistance, high operational flow, and low pump efficiency were also identified and analyzed. Finally, practical energy-efficiency suggestions and future potential targets for the Chinese DHS are discussed from both a design and operational perspective. [ABSTRACT FROM AUTHOR]

A simple model was built to predict the national and regional electricity demand by sectors under China's new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggregate the national economic growth rate into regional levels and forecast each region's contribution rate to the national economic growth and regional industrial structure. Then, a bottom–up accounting model that considered the impacts of regional industrial structure transformation, regional energy efficiency, and regional household electric consumption was built to predict national and regional electric demand. Based on the predicted values, this paper analyzed the spatial changes in electric demand, and our results indicate the following. Firstly, the proposed model has high accuracy in national electricity demand prediction: the relative error in 2017 and 2018 was 2.90% and 2.60%, respectively. Secondly, China's electric demand will not peak before 2025, and it is estimated to be between 7772.16 and 8458.85 billion kW·h in 2025, which is an increase of 31.28–42.88% compared with the total electricity consumption in 2016. The proportion of electricity demand in the mid-west regions will increase, while the eastern region will continue to be the country's load center. Thirdly, under China's new normal, households and the tertiary industry will be the main driving forces behind the increases in electric demand. Lastly, the drop in China's economy under the new normal will lead to a decline in the total electricity demand, but it will not evidently change the electricity consumption share of the primary industry, secondary industry, tertiary industry, and household sector. [ABSTRACT FROM AUTHOR]