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From a spatial perspective, this paper constructs a spatial Durbin model based on the adjacent weight matrix to analyze the impact of environmental regulation on the development of renewable energy in 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2007 to 2020. The results show that (1) both environmental regulation and renewable energy development have positive spatial autocorrelation and form relatively similar spatial agglomeration areas, specifically showing the characteristics of "high-high" agglomeration in resource-rich areas and "low-low" agglomeration in resource-scarce areas. (2) Environmental regulation significantly promotes the development of local renewable energy and has a significant spatial spillover effect on the development of renewable energy in neighboring provinces. (3) The moderating effect results show that renewable energy installed capacity and electricity demand play an inverse moderating role between environmental regulation and renewable energy development. (4) From the perspective of different regions, the impact of environmental regulation on the development of renewable energy is heterogeneous. Based on the research conclusions, this paper finally provides corresponding policy suggestions from three aspects: regional cooperation, technical support, and differentiation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper aims to identify the structural and technological determinants of provincial carbon intensity in the electricity generation sector (CIE) using the multiplicative LMDI-II method. Results demonstrate that (1) China's overall CIE decreases by 7.3% in 2001-2015, and the research period can be divided into four stages according to CIE changes (i.e., rapid growth, rapid decline, slow growth, and transition). The CIE in the 12th FYP estimated in this paper, 24.9% lower than that using the emission factors from IPCC, is closer to China's actual situation. (2) There exists huge heterogeneity in the determinants of provincial CIE changes in four stages. CIE growth in the Northwest and Northeast is caused by the coal-dominated energy structure. CIE growth in the Southwest is attributed to the electricity structure effect, while that of the Coast region is caused by the geographic distribution effect. The electricity efficiency effect is attributed to the CIE growth for these regions and the Southwest should also place focus on the electricity trade effect. The impact of electricity trade-related factors depends on the region being a net exporter or importer of electricity. (3) To achieve carbon intensity reduction targets, 30 provinces are categorized into four types based on various combinations of structural and technological determinants. The findings provide insights into capturing future emission-mitigating focus as well as defining the emission-mitigating responsibilities between electricity exporters and importers in China.

Malan loess is a common engineering geological material in Northwest China, which is often used in many engineering constructions. In this paper, the electrical properties of loess under high temperature environment are studied, which provides theoretical basis for engineering construction. The remolded loess samples are heated by different temperatures between 25 and 900 °C under laboratory conditions. Then, the surface features and electrical properties are measured and analyzed. The major findings are as follows: (1) The effect of high temperature on different electrical parameters of loess is different. As temperature increases, a series of physicochemical reactions occur in the loess; as a result, the resistance and impedance of loess samples initially increase and then decrease. The temperature of change trend changes between 500 and 600 °C, which fits the normal distribution. The capacitance of loess decreases first and then slowly increases with increased temperature. The temperature of change trend changes between 200 and 300 °C. (2) The experimental results also show that frequency has an important effect on the measurement of electrical parameters. As the frequency increases, the resistance, capacitance, and impedance decrease. In addition, at high frequencies, the electrical properties of loess are basically not affected by temperature. Therefore, using a frequency of 100 Hz is more advantageous to study the variation law of the electrical properties of loess.

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China's power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system. [ABSTRACT FROM AUTHOR]

The world faces several problems related to natural gas resource rents and energy production from renewable sources. One of the main problems is the influence of energy imports, manufacturing exports, and alternative energy sources on natural gas and electricity production from renewable sources. Energy imports, manufacturing exports, and alternative energy sources can impact natural gas and electricity production. This paper examines natural gas resource rents and electricity production from renewable sources nexus from 1971 to 2021, using energy imports, manufacturer's exports, and alternative energy sources in China. Electricity production from renewable sources and manufacturing exports are negatively associated with natural gas resource rents. Energy imports and alternative energy sources positively relate to natural gas resource rents in China. These results suggest that the energy sector in China is highly interconnected and that policies that seek to promote renewable energy sources and other alternatives can positively affect natural gas resource rents. China needs to develop an energy policy considering the policy implications of energy imports and natural gas resource rents. Such a policy should focus on increasing domestic production, reducing energy imports, and ensuring adequate revenue from natural gas resource rents. Additionally, regulations could be implemented that support the development of alternative energy sources, such as requiring utilities to purchase a certain percentage of their power from renewable sources., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Electricity load forecasting is one of the important tasks of the power marketing department, and accurate load forecasting is extremely important to ensure real-time dispatch and security of the power system. In order to obtain accurate and reliable load forecasting results, an ultra-short-term power load forecasting model based on an improved random forest regression algorithm is proposed in this paper. First, data pre-processing is performed on the original dataset. Then the pre-processed time data and historical load data are used as inputs to the model, and optimization of the model using the Gaussian mixture-based tree-structured Parzen estimator algorithm is carried out. Finally, the final prediction results were derived. Experimental analysis was conducted with real load data from a region of China, and the experimental results show that the method has better prediction accuracy than the original random forest algorithm and other traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper. [ABSTRACT FROM AUTHOR]

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) is used to estimate the technical efficiency (TE) score of manufacturing companies. Subsequently, the TE score is used to evaluate the electricity shortage index and other factors that are postulated to affect enterprise productivity. Two estimation methods have been adopted including ordinary least squares (OLS), which is less robust to endogeneity and instrumental variable (IV) estimation, which turns out to be more robust to endogeneity in the data. The empirical results show that, under OLS estimation, electricity shortage has a significantly negative impact on the technical efficiency of the listed manufacturing companies. However, when IV regression is implemented to address endogeneity issues in the data, electricity shortages tend to have a significantly positive impact on the technical efficiency, underscoring the importance of capturing endogeneity in the data. Extending the baseline results, this study also finds that, while the size of an enterprise may have no bearing, state-owned companies are more likely to be negatively affected by electricity shortages compared to privately owned companies. These results have significant implications for industrial policy design in China in particular, and developing countries in general. Most importantly, the results of this study underscore the importance of policies and measures to promote a shift in the ownership structure towards the private sector. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately predicting electricity consumption is crucial for reducing power waste and maintaining power system stability. To address the non-linear and seasonal fluctuations of electricity consumption, this paper proposes a seasonal prediction method based on Seasonal and Trend decomposition using Loess (STL) algorithm and gray model by introducing time series decomposition method. The STL decomposition algorithm decomposes fluctuating electricity data into three components: trend, seasonal, and remainder. Then reasonable methods are used to predict components with different data characteristics. The novel model is employed to analyze the quarterly electricity consumption in Zhejiang province of China from 2014Q4 to 2022Q3. The experimental results show that the prediction accuracy of this model is superior to the state-of-the art models; the MAPE and RMSPE values are 1.77% and 2.37%, respectively. Our model that can effectively identify seasonal fluctuations in data sequences provides a new method for predicting seasonal fluctuation data and optimizing seasonal electricity supply schemes., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The worldwide transformation of electricity production goes hand in hand with increasing use of wind energy. The German 'Energiewende' project is no exception and relies heavily on the construction and use of an ever-increasing number of wind turbines. While the operation of wind turbines does not lead to the emission of pollutants (in contrast to, e.g. coal, oil or gas), the production processes of the construction materials do. Since the raw materials' production primarily takes place outside Germany, radioactivity and doses related to these processes occur at remote places in the world. This effect might be called an 'export of doses'. In the present paper, we perform a life cycle analysis of wind turbines, investigating the mining and production of the construction materials. We focus on rare-earth elements needed for the generator magnets and assess the associated releases of radioactive materials during mining and processing, primarily in China. Estimates of dose to the public in selected Chinese cities are calculated. Different electricity generation techniques are compared by the use of the quantity (collective) dose per GW per year., (© The Author(s) 2023. Published by Oxford University Press.)

Countries worldwide consider solid waste collection and recycling necessary due to the recent emphasis on conservation of resources and environmental protection. Due to the constraints from resource depletion and the need for sustainable economic growth, solid waste recycling has become a critical issue in China. Several indigenous researchers in China have studied the potential benefits of solid waste recycling. However, most studies limited the environmental assessment of solid waste recycling to greenhouse gas (GHG) emissions and considered only one type of solid waste (paper or plastic). Therefore, the present study analyzed the energy (electricity) and environmental (GHG and air pollutant emissions) benefits of recycling steel, nonferrous metal, plastic, and paper wastes from 2005 to 2017 in China. The study used the formulation of model equations method to estimate the electrical energy and environmental benefits. Prominent findings show that the total amount of electricity saved by recycling solid waste from 2005 to 2017 was 3743.3 Mtce. On average, solid waste recycling during the period led to a 43.2% saving on electricity. Solid waste recycling avoided 4765.9 billion kg of carbon dioxide emission and 22.502 billion kg of methane emission. It was also found that the recycling of solid waste saved a total amount of 10,669.8 M kg of NO X emission but had a burden of −6263.2 M kg of VOCs emission on the environment. Solid waste recycling avoided the emission of CO 2 , CH 4 , NO X , and SO X, but the recycling of steel, plastics, and paper waste had negative impacts on the environment in terms of VOCs and PM emissions. Proper measures such as installing air pollution control devices should be put in place to minimize the emission of pollutants during the recycling of these solid wastes. • The energy and environmental benefits of solid waste recycling is assessed. • The formulation of model equations method was used for the assessment. • On average, the recycling of solid waste saved 43.2% of electricity. • Recycling of waste avoided a significant amount of GHG and air pollutant emissions. • Solid waste recycling had no essential benefit on the emission of VOCs and PM. [ABSTRACT FROM AUTHOR]

Summary: With the continuous opening of China's electricity market, a large number of third‐party electricity retailers have poured into the electricity market. Electricity retailers are the bridge between the main player of the electricity market. In view of the large number and variety of electricity retailers in the current electricity market, this paper divides the electricity retailers that have been publicized in China into five categories. In view of the uncertainty of the credit evaluation standard of electricity retailers, this paper determines the cloud model scale of the risk level of the electricity retailers; selects three primary indicators, eight secondary indicators, and 20 tertiary indicators to construct the credit risk evaluation indicator system of the electricity retailer. This paper also builds a computing model combining intuitionistic fuzzy analytic hierarchy process (IFAHP) with cloud model; obtains the final evaluation score by combining the certainty of cloud model with intuitionistic fuzzy weight, the qualitative to quantitative change of the indicator is realized, and makes the fuzzy concept of credit rating of electricity retailers get a quantitative change. From the model results, it can be seen that the risk score of the electricity grid asset retailers is 14.8666, which is the lowest risk; the independent electricity retailers has a risk score of 26.9706, which is the highest risk. Compared with the traditional AHP‐cloud model, this method is not only more objective, but also can obtain more information such as the distribution of index values from entropy and hyper entropy. [ABSTRACT FROM AUTHOR]

Over the past decade, the photovoltaic (PV) industry in China has made great progress. However, this progress benefited from a series of subsidy policies, and with the continuous enlargement of the scale of centralized PV (CPV), the large subsidies have created great pressure on government finance, so achieving grid parity in CPV electricity is an urgent matter. This paper studies the grid parity of CPV electricity. First, this paper calculates the levelized cost of energy (LCOE) of CPV electricity. Second, using historical data and a dual learning curve, the paper predicts the LCOE of CPV electricity under scenarios with different resource zones and utilization times. Third, the paper predicts the LCOE of coal-fired electricity and draws conclusions based on changes in the price of coal and charcoal. Finally, the paper predicts when grid parity in CPV electricity in different resources areas will be achieved. [ABSTRACT FROM AUTHOR]

To achieve China's "double carbon" goal, it is necessary to make quantitative evaluation of the power grid enterprises' contribution to carbon emission reduction. This paper analyzes the contribution of power grid enterprises to carbon emission reduction from three points: power generation side, power grid side, and user side. Then, PLS-VIP method is used to screen the key influencing factors of carbon emission reduction contribution of power grid enterprises from three aspects: consumption of clean energy emission reduction, reduction of line loss emission reduction, and substitution of electric energy. Based on GA-ELM combined machine learning algorithm, we establish an intelligent evaluation model of power grid enterprises' carbon emission reduction contribution. Furthermore, according to the distribution law of key influencing factors, this paper uses Monte Carlo simulation method to calculate the contribution of power grid enterprises to carbon emission reduction by scenario, so as to evaluate the contribution of power grid enterprises to carbon emission reduction. Finally, combined with the relevant data of power grid enterprises from 2003 to 2019, this paper makes an empirical study on the completion of carbon emission reduction contribution and the promotion path., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of the new normal, the global economy is entering a deep adjustment period, and the driving forces of development are also constantly changing. As a result, China's economy has also entered a "new normal" phase in which it is growing in a manageable and relatively balanced manner. In addition, the new normal characteristics of the power industry's development in China are also very significant, and they affect the adaptability of traditional power forecasting methods. By analyzing the new characteristics of China's economic development and the changing electricity demand in recent years, this paper quantitatively studied the effect of the national economy on the consumption of electricity. Meanwhile, a modified logistic model based on the change of the gross domestic product (GDP) growth rate is constructed to make a reasonable prediction of the future power consumption in China. Subsequently, by calculating the elasticity of electricity consumption in the future, it is found that the coefficient decreases each year, which indicates that electricity consumption in China is following a new trend. Based on the research results, this paper proposes rational suggestions for China's power development, and they are expected to provide references for the power planning and power industry layout in China. [ABSTRACT FROM AUTHOR]

With the rapid development of interregional power transmission, the redistribution of fossil and renewable energy resources has changed sharply, and its complexity poses a challenge to the evaluation of power carbon emission responsibility. This study constructs an interprovincial power transmission framework to measure the seasonal carbon emissions embodied in regional electricity consumption over the period of 2008-2015 based on quarterly data. Then, a structural decomposition approach was developed to identify the influential factors of carbon emissions embodied in provincial electricity consumption from a seasonal perspective. The results show that the assessment for embodied emissions of power consumption based on different levels of data may vary by as much as 20%, and the carbon emissions and carbon intensity of power consumption exhibit significant seasonal characteristics. Furthermore, it is revealed that the economic scale in the fourth quarter makes the most significant contribution to the emissions increment, especially in underdeveloped provinces, while the change in energy efficiency of power generation reduces more carbon emissions in the first and second quarters. In addition, the impact of the power transmission scale is more significant in the third and fourth quarters, and it has been close to or even more than the impact of traditional factors in some quarters. Finally, the impact of economic scale, power generation energy intensity, power generation mix and electricity utilization efficiency on the emissions of regional power grids shows a relatively stable increasing trend, but this trend of directional stability is not reflected in the effect of the power transmission structure and transmission scale. This study contributes to the identification of the impact of the power transmission structure and transmission scale. Moreover, this study highlights the importance of considering seasonal characteristics when estimating the carbon emissions of power consumption and formulating specific emission reduction policies. Additionally, it provides a more accurate evaluation of carbon emissions and proposes several prominent recommendations for policy makers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load data from two regional electricity grids in Shaanxi Province, China. The results show that combining the idea of point interval, point forecasting, and interval forecasting for components with different fluctuation regularity can effectively reduce the forecasting interval width while having high accuracy. The proposed model has higher forecasting accuracy and smaller mean interval width at various confidence levels compared to the commonly used models. [ABSTRACT FROM AUTHOR]

As China's distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model's input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection. [ABSTRACT FROM AUTHOR]

With the deepening of electricity market reform in China, the competition in the electricity retail market becomes increasingly intense. Electricity retailers (ERs) need to explore new business models to enhance their competitiveness in the retail market. Meanwhile, with the improvement of industrial production and people's living standards, more and more nonlinear electrical equipment have been put into use, leading to severe harmonic pollution problems. Harmonic pollution causes loss of electricity, resulting in the economic loss of customers, especially for large industrial customers. In the above contexts, this paper proposes a novel business model that incorporates harmonic control as a value-added service into electricity retail contracts for utility-owned ERs. Both utility-owned ERs and customers can benefit from the designed business model. For customers, it helps them to improve the power quality while saving the electricity cost. For ERs, it helps them to cultivate the customer loyalty and improve the customer satisfaction. A case study is performed to demonstrate the effectiveness of the proposed business model. [ABSTRACT FROM AUTHOR]

The concept of time-of-use (TOU) electricity pricing is widely recognized as a key strategy to bridge the gap between electricity availability and consumption, enhance the efficiency of electricity, and refine the patterns of electricity usage. Nonetheless, the existing policy on pricing electricity based on TOU electricity pricing is missing a theoretical approach that evaluates the load properties and the advantages of investing in the power grid. Consequently, the article suggests a method for optimizing electricity prices based on TOU electricity pricing to reduce the costs associated with investing in power grids. Initially, a model for optimizing electricity prices based on TOU electricity pricing is developed, offering support for the pricing strategy of the power grid; Subsequently, a method for dividing TOU electricity pricing using the Gaussian Mixture Module (GMM) clustering algorithm is introduced, offering theoretical backing for the creation of such pricing strategies; Following this, a detailed optimization approach for electricity pricing of electricity TOU electricity pricing is suggested, along with taking into account the benefits of grid investments and the power grid's load properties, the formulation of the electricity pricing strategy for TOU electricity pricing; Ultimately, this approach is corroborated by the Chongqing power system in China, aiming to minimize disparities in peak load valleys and enhance the advantages of grid investments, thereby offering technical assistance for the scientific determination of TOU electricity pricing. [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]

Due to the insufficient consideration of medium and long-term wind power contract power in short-term dispatch, long-term planning and real-time consumption of wind power cannot be effectively undertaken, resulting in a large amount of abandoned wind power. A way to improve the wind power absorption capacity has become an urgent problem to be studied. According to the characteristics of the market and dispatching in the process of wind-fire integration construction, this paper constructs a wind power consumption model that connects the mid- and long-term transaction power decomposition and short-term dispatch. Considering the unit output characteristics and maintenance, the monthly contract electricity is decomposed into daily electricity, and the nesting of medium and long-term transactions and short-term scheduling is realized; the second stage is a short-term multi-objective optimal scheduling model considering the decomposition of contract electricity and the output of non-bidding units to improve the real-time consumption of wind power. Finally, a province in northwest China is taken as an example to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Forecasting the electricity consumption has always played an important role in the management of power system management, which requires higher forecasting technology. Therefore, based on the principle of "new information priority", combined with rolling mechanism and Markov theory, a novel grey power-Markov prediction model with time-varying parameters (RGPMM(λ,1,1)) is designed, which overcomes the inherent defects of fixed structure and poor adaptability to the changes of original data. In addition, in order to prove the validity and applicability of the prediction model, we have used the model to predict China's total electricity consumption, and have compared it with the prediction results by a series of benchmark models. The result shows that the can better adapt to the characteristics of electricity consumption data, and it also shows the advantages of the proposed forecasting model. In this paper, the proposed forecasting model is used to predict China's total electricity consumption in the next six years from 2018 to 2023, so as to provide certain reference value for power system management and distribution., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China has significantly reduced the energy consumption for per unit of GDP by using both command-and-controls or market-based strategies. This paper examines empirically the relative effectiveness and efficiency of command-and-control strategy (energy reduction target) vs. market-based strategy (electricity price). We find that (1) electricity price was similarly effective in reducing electricity intensity across firms, but government targets were more effective for firms that were more technologically outdated and energy intensive; and (2) government targets led to expenditures that were not useful in reducing energy intensity, suggesting inefficiency associated with targets. Despite the Chinese governments' capacities and resources in directing and influencing enterprises, market-based approaches might still be more effective and efficient than command-and-control ones to reduce energy intensity., Competing Interests: The authors have declared that no competing interests exist.

With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China's electricity sales companies to predict electricity prices. [ABSTRACT FROM AUTHOR]

With the gradual opening of China's electricity market, it is effective for cascade hydropower plants to simultaneously participate in both the monthly contract market and the day-ahead spot market to obtain higher power generation benefits. Hence, this paper studies the optimal decomposition model for the monthly contracted electricity of cascade hydropower plants considering the bidding space in the day-ahead spot market. The close hydraulic and electric connection between cascade hydropower plants, the implementation requirements of contracted electricity, and the uncertainty of the day-ahead market clearing price are all well considered. Several linearization techniques are proposed to address the nonlinear factors, including the objective function and the power generation function. A successive approximation (SA) approach, along with a mixed-integer linear programming (MILP) approach, is then developed to solve the proposed model. The presented model is verified by taking the decomposition of the monthly contracted electricity of cascade hydropower plants in China as an example. The results indicate that the developed model has high computational efficiency and can increase the power generation benefits compared with the conventional deterministic model. The effect of the penalty coefficient for imbalanced monthly contracted electricity is also evaluated, which provides a practical reference for market managers. [ABSTRACT FROM AUTHOR]

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this study., (Copyright © 2021 Jiuyun Sun et al.)

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Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of −797.74 Mt, −619.99 Mt, and −47.76 Mt, respectively. [ABSTRACT FROM AUTHOR]

This paper explains the energy mix of China's overseas electricity investments across Belt and Road Initiative (BRI) recipient countries. We focus on Indonesia and Pakistan. Our research is based on both newly gathered project-level data and in-depth interviews with stakeholders of Chinese-backed power plants in Indonesia and Pakistan. We examine (1) why Chinese actors are involved in renewable power generation in Pakistan and not in Indonesia, and (2) why Chinese-backed coal-fired projects in Pakistan are cleaner than in Indonesia. We argue that variations along the three dimensions – scope, governance regime, and issue linkage – lead to different energy mixes in Chinese-invested power plants across BRI countries. This framework specifies how supply and demand factors interact across multiple levels regarding the formulation and implementation of China's overseas electricity projects. Our findings shed new light on the environmental implications of BRI projects and the dynamics of renewable energy development in emerging markets. [ABSTRACT FROM AUTHOR]

China's first hydroelectric station began producing electricity in 1912, a year better known for marking the end of imperial rule and the advent of republican governance. Located a short distance outside of the southwestern city of Kunming, Shilongba (Stone Dragon Dam) was a cross-cultural endeavour that involved long-distance encounters of both materials and expertise that spanned not just vast expanses within China, but also a world divided by competing imperial interests. The technologies involved were at once both new and old. Turbines and dynamos represented the latest in German innovation, but the techniques used to carve the canal and lower the water table had been perfected over centuries. A history of Shilongba thus allows us to approach China's transition from Empire to Republic not merely as a political process but also as one of multiple makings – of state, technology, energy, society, and not least, history itself. This paper explores these multiple makings, focusing on the first phase of construction from 1908 to 1912, when a dam was constructed, a canal dug, and the first power station established. [ABSTRACT FROM AUTHOR]

With the accelerating pace of China's electricity market reform, the construction of the electricity spot market has been put on the agenda. However, as the number and scope of market participants gradually expand, the market-oriented transaction power continues to rise, and the intraprovincial and interprovincial transaction varieties are increasingly abundant. How to design an intelligent and powerful system that can meet the performance requirements of high concurrency and high-frequency transactions in the future market is a major problem in power reform. Based on the research of theoretical research results, this paper builds the front-end interaction platform of the southern spot electricity system based on the regional center to provide a data declaration interface for market users, including market management, market declaration, market release, market evaluation, intelligent analysis, front-end data interface, security protection, and other functional modules, provide declaration information and some market evaluation results to the southern regional spot power system platform, and obtain clearance results and published information from this platform. [ABSTRACT FROM AUTHOR]

Technological progress is considered an important means of decreasing energy consumption. However, rebound effects caused by energy efficiency improvements directly affect the realization of energy savings and emission reduction. This paper focuses on the main theory and methodology of direct and indirect rebound effects. Using 30 sets of provincial panel data and national input-output data for China from 2007, this paper builds a co-integrating equation, a panel error correction model, and an 8-sector energy-input-output model. We subsequently estimate the direct and indirect rebound effects of urban residential electricity use. The results indicate that in the long term the direct plus indirect partial rebound effect is 0.79; in the short term it is 0.78. Thus, the majority of the expected electricity reduction in Chinese urban residential energy consumption arising from efficiency improvement may be offset. These rebound effects impair the functioning of energy efficiency policies. Therefore, the Chinese government should not improve energy efficiency alone-they must also take into consideration the relevant energy-pricing reforms when formulating energy policies. [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., Competing Interests: We have no conflicts of interest to disclose.

In recent years, northern China has suffered from the severe haze in autumn and winter, a phenomenon to which coal-fired heating appliances contribute significantly. To reduce pollution and achieve sustainability in the electricity industry, government departments have proposed the "Electricity Substitution" strategy, and the State Grid Corporation has also proposed the "coal-to-electricity" transformation movement. Against this background, our study aims to identify the impact factors of electricity consumption behavior among customers who have completed this transformation. Based on customers' monthly electricity consumption data in Shaanxi from 2017 to 2019 and on-site survey results, this paper constructs a multiple regression model and utilizes SPSS25.0 to analyze the relationship between residents' electricity consumption and related factors. The research results revealed that after the implementation of the "coal-to-electricity" transformation, residential customers' electricity consumption tended to rise. Consequently, winter temperature, appliance power, and heating area negatively correlate with residents' electricity consumption. Simultaneously, customers' habits, subsidies, and electricity pricing positively correlate with residents' electricity consumption. Finally, this paper offers some practical management suggestions for power companies based on the analysis. [ABSTRACT FROM AUTHOR]

This paper evaluates infrastructure's impact on regional industrial productivity in China and distinguishes between within- and cross-region effects. Three infrastructure assets are examined: electricity, roads and railways. The analysis reveals positive short-run spatial interaction of electricity and road infrastructure, but not of railways. In the long term, the spatial spillovers of infrastructure stocks are positive among provinces not in competition for production, but turn negative among provinces similar in productivity in the case of electricity and among locations similar in exports in the case of roads and railways. [ABSTRACT FROM AUTHOR]

Studies have shown that industrial structure (IS) adjustment, climate factors, energy intensity, etc. factors are the reasons causing the deviation between the growth rate of industrial value added (IVA) and electricity consumption of industry (Ele) in China. However, no one researches whether line loss (LL), calculated in industrial electricity consumption in China, is one of the reasons contributing to the deviation. In order to thoroughly figure out what is the cause of the deviation, theoretical method, correlation analysis, autoregressive distributed lag (ARDL) model and an error correction model (ECM) have been undertaken in the paper. Experimental results are as follows: (1) LL fluctuates violently in accordance with temperature, while IVA is not; (2) the correlation between IVA and Ele becomes stronger, if LL is eliminated from Ele; (3) both in the long-run and short-run equation of ARDL model, IS is the first driver of Ele, followed by LL and IVA; (4) compared with International Energy Agency and based on the experimental results, the practice statistics of LL in Ele is not reasonable in China; (6) policies for promoting the upgrading of the internal industrial structure and for improving the statistical method of electricity consumption in China are proposed, finally. [ABSTRACT FROM AUTHOR]

China's rapid economic growth has tremendously accelerated its energy use, calling for a more sustainable supply of scarce and nonrenewable energy. Using a firm‐level dataset of 30 major Chinese electricity utilities from 2010 to 2014, this paper applies a stochastic frontier analysis to determine the utilities' technical efficiency, incorporating their operational environments related to a recent policy reform to encourage sustainable development. Our main findings are (a) state ownership, consumer density, and a chief executive officer with a science and engineering background are factors that can improve technical efficiency; (b) asset‐related subsidy increases efficiency whereas income‐related subsidy lowers efficiency; and (c) the five largest regional electricity generation firms exhibit above‐average efficiency levels. These results provide evidence that supports the recent Chinese policy reform. The findings also suggest that electricity generation efficiency, which is essential to sustainable economic development, can be improved through performance‐based regulation and incentives. [ABSTRACT FROM AUTHOR]

The growing involvement of China in the African region has continued to stimulate questions on the impact of her involvement in the region. This paper attempt to contribute to this debate and as well pursue a development goal by empirically answering the question that "can China's FDI in sub-Saharan Africa (SSA) stimulate the sub-region's industrialization?" Consequently, data used for this study were obtained from China Africa Research Initiative, the World Bank and the Energy Information Administrator (EIA) websites for a sample of 26 economies in the SSA over the period 2003–2016. Panel-Corrected Standard Error (PCSE) was used to achieve the objectives of the study. The PCSE estimate result indicates that China's FDI in SSA has an insignificant but positive effect on SSA industrialization. Suggesting that China's FDI is not enough to boost industrialization in SSA. Furthermore, the result shows that the electricity supply in SSA has a significant and positive impact on industrialization in the continent. For SSA to benefit from China FDI significantly, SSA government must prioritize and where necessary modify future agreement to promote or prioritize Chinese investment in sectors with positive linkages with the manufacturing sector and increasing local outsourcing of inputs and intermediate production activities. [ABSTRACT FROM AUTHOR]

China's thermal electricity generation industry, the world's largest emitter of carbon dioxide, is experiencing a severe over-capacity problem. An effective solution to this problem is urgently needed to achieve the energy conservation and emission reduction goals, as well as a sustainable growth of China's economy. However, under China's existing political promotion system, there exists a market segmentation that hinders cooperation between regions. In the short term, market segmentation is conducive to the utilization of installed capacity in each place; however, in the long run, can market segmentation alleviate the over-capacity problem in the thermal electricity generation industry across China? Using the China Industrial Enterprise Database (CIED) from 2002 to 2013, this paper estimates the capacity utilization rate, an indicator of over-capacity, of China's thermal electricity generation firms through stochastic frontier analysis. Then, by applying the fuel and electricity price index, this paper measures the degree of market segmentation at three levels, (i) among all provinces, (ii) between neighboring provinces and (iii) among provinces covered by the same grid company. Based on these, this paper empirically estimates the impacts of market segmentation on the utilization rate of China's thermal electricity generation firms. The results indicate that market segmentation has significantly negative impacts on the capacity utilization rates. Heterogeneity analysis suggests that private firms, firms in regions with abundant coal resources and those in the western regions are affected to a larger degree. The results remain robust in the robustness check that replaces the data with the China Stock Market & Accounting Research (CSMAR) Database, the counterfactual test and the test that concerns endogeneity. These findings provide important policy implications for the solution of the over-capacity problem in China's electricity generation industry., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

• The financial and ecological viability of power from pyrolysis oil is assessed. • The process has high oil yield and power generation potential. • The project is economically feasible and profitable in China. • Power generation from the project contributes to global warming. • Consumption of power from the project reduces coal usage and global warming. The increased plastic waste generation worldwide poses ponderous issues for public health and the environment. China is the highest generator of plastic waste around the world. The current treatment process (incineration) of the increased plastic waste causes dangerous environmental consequences. Pyrolysis has recently surfaced as an ecologically friendly technique for energy and material recovery from plastic waste. The present study assesses the financial and ecological viability of power production from oil derived from the pyrolysis of mixed plastic wastes in China from 2009 to 2028. The prominent findings show that the amount of plastic waste collected in 2020 (24.16 Mt) increased by 53.19% in 2028.The pyrolysis of mixed plastic wastes during the project period yielded 359.29 Mt oil, which has a power potential of 1,060.86 GWh. The economic analysis indicated the project is viable and profitable with a positive net present value (US$8.80 million) and profitability index (1.26) greater than 1. The project has 10.6 y payback period, US$0.0752/kWh levelized cost of energy, 22.5% return on investment, and 13.0% internal rate of return. The life cycle assessment results show that conversion of mixed plastic waste to pyrolysis oil for electricity generation during the project period has a total global warming potential (GWP) of 1,311.4 kt CO 2 eq. The GWP is mainly from conversion of pyrolysis oil to electricity (73.42%), pyrolysis oil production (15.01%) and upgrading of pyrolysis oil (11.38%). The consumption of power from the project could avoid the combustion of 2,659.0 t coal, minimizing global warming by 11,278.8 kt CO 2 eq. Sensitivity analysis, which examines the influence of variation in sensitive factors on the success of the project, is presented. This paper provides scientific strategies for optimal investment and decision-making on the environmental sustainability of plastic waste-to-energy pyrolysis projects. [ABSTRACT FROM AUTHOR]

With the improvement of living standards, growing consumer demand for high-quality and natural foods has led to the development of new mild processes to enhance or replace conventional thermal and chemical methods for food processing. Pulsed electric fields (PEF) is an emerging and promising non-thermal food processing technology, which is ongoing from laboratory and pilot plant level to the industrial level. Chinese researchers have made tremendous advances in the potential applications of PEF for processing a wide range of food commodities over the last few years, which contributes to the current understanding and development of PEF technology. The objective of this paper is to conduct a systematic review on the achievements of PEF technology used for food processing in China and the corresponding processing principles. Research on the applicability of PEF in food processing suggests that PEF can be used alone or in combination with other methods, not only to inactivate microorganisms and extract active constituents, but also to modify biomacromolecules, enhance chemical reactions and accelerate the aging of fermented foods, which are mainly related to permeabilization of biomembranes, occurrence of electrochemical and electrolytic reactions, polarization and realignment of molecules, and reduction of activation energy of chemical reactions induced by PEF treatments. In addition, some of the most important challenges for the successful implementation of large-scale industrial applications of PEF technology in the food industry are discussed. The results bring out the benefits of both researchers and the industry., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The carbon-price mechanism has been proved to be an effective measure for promoting energy revolution and mitigating climate change. It is of vital importance to develop optimal energy development strategy for electric power-dependent regions by considering the complex interaction among carbon price, carbon emission control, and carbon-responsibility transfer. In order to fill the research gap on the optimal choice of carbon-price mechanism at the urban level, this study is the first attempt to express uncertainties embodied in the carbon price mechanism as interval values, probability distribution and downside risks. The developed risk-aversion-based interval two-stage stochastic programming (RITSP) model is effective in analyzing the effect of internal and electric-transmission related carbon-tax on power system structure. It is discovered that carbon compensation policy for imported electricity is more suitable for Tianjin's power system development. Tianjin would primarily purchase electricity from Inner-Mongolia. With the increase of carbon emission tax, Tianjin would import increasing proportion of electricity from Gansu. Due to the limited endowment of renewable energy in Tianjin, the impact of carbon emission limitations on the renewable energy power generation structure of is trivial, and it has a greater impact on stimulating the development of CCS technology. What's more, Tianjin's future power system planning is more inclined to develop CCS rather than renewable energy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

China's electricity industry has gone through three decades of market-oriented reform, and this process is often interpreted as a tug of war between marketization and state control. However, this paper argues that a dichotomous conception of the state-market relationship cannot explain the evolution and complexity of China's electricity governance. By systematically analyzing the institutions of China's electricity reform, this paper identifies a sequence of innovations that align well with both the history of China's economic transition and the institutional underpinnings of China's economic governance. Also, by interpreting the respective roles of state and market in China's reformed electricity industry, this paper shows that state control and marketization are complementary rather than contradictory, and that their complementarity is best exercised in addressing issues like market fragmentation and renewable energy curtailment. Moreover, this paper presents two case studies - both on China's most recent electricity market experimentation - to elaborate how state and market jointly serve sectoral functions as well as broader economic and political goals., Competing Interests: Declaration of competing interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (Copyright © 2020 Elsevier Ltd. All rights reserved.)