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Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

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]

To explore the impact of promotion of electric vehicles on carbon emissions in China, this paper used the principal component analysis (PCA)-logistic regression model to predict the demand for traditional vehicles, and used the scenario analysis method to analyze the proportion of electric vehicles in traditional vehicles qualitatively. Then this paper calculated the carbon emissions during the power generation process based on the existing power structure. In addition, the IPCC carbon emission calculation method is used to compare the CO2 emissions produced by electric vehicles and fuel vehicles with similar vehicle quality while driving the same distance and consuming different energy sources. The results showed that the CO2 emissions of electric vehicles accounted for only 37.05% of fuel vehicles. By 2050, the annual electricity demand of electric vehicles will be 828.7, 776.9, and 752.1 billion kWh under the radical scenario, the reference scenario, and the negative scenario, respectively. According to the current power structure, the carbon emissions will be 1.2, 1.1, and 1 billion tons, respectively. The rapid growth of electric vehicles has a substantial impact on the grid load. Studying the changes in CO2 emissions from energy substitution is significant to formulate the development strategy of the automobile industry and adjust energy structure policies. [ABSTRACT FROM AUTHOR]

As the world's largest carbon dioxide emitter, China is curbing coal consumption and increasing natural gas and renewable energy consumption to curb the increase in carbon emissions. At the same time, China is still the largest developing country in the world. Therefore, it is very important to investigate the links between different types of energy consumption, economic growth, and carbon emissions. In this paper, impulse response functions and Granger causality test based on time-varying parameters vector autoregressive model are used to explore the time-varying relationship among CO2 emissions, economic growth, and heterogeneous energy consumption (coal, gas, oil, and renewable energy) in China over the period 1980–2019. The results show that reducing coal and oil consumption and increasing natural gas and renewable energy consumption will not significantly hinder economic growth. In addition, in the long run, reducing coal and oil consumption and increasing renewable energy consumption can significantly reduce carbon emissions. However, after 1998, increasing natural gas consumption will significantly increase carbon emissions. This study reveals the time-varying relationship between heterogeneous energy consumption, economic growth, and carbon emissions, and provides suggestions for China to achieve low-carbon economic growth. [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]

The petrochemical industry is traditionally a high energy consumption industry, thus energy saving in this industry is of great significance in terms of trying to achieve the national goal of "carbon-peak and carbon neutrality" in China. Diesel is one of the main products of petrochemical enterprises. The energy consumption of a diesel hydrotreating (HDT) unit is relatively high due to the high reaction temperature and pressure of HDT reactors. In order to further explore the energy consumption of diesel HDT units under different operating conditions, this paper establishes a full-process simulation based on the kinetics of hydrodesulfurization, hydrodenitrogenation, and aromatics saturation, in Aspen Plus to discuss the effect of different operating conditions on the electricity, steam, and fuel gas consumptions. The results show that with the increase of reaction temperature or reaction pressure, the sulfur content in diesel oil decreases, but the consumption of related utilities and comprehensive energy consumption increases. In addition, this paper also analyzes the energy consumption changes under the operating condition boundaries when the diesel quality is unchanged. When the reaction pressure is 8 MPa and the reaction temperature is 317.5 °C, the comprehensive energy consumption is 5% lower than the energy consumption with the reaction pressure of 6 MPa and the temperature of 320 °C. Therefore, on the premise of satisfying the diesel product quality, the comprehensive energy consumption of the diesel HDT unit can be reduced by increasing the reaction pressure and decreasing the reaction temperature. [ABSTRACT FROM AUTHOR]

In the 14th Five-Year Plan, China clearly proposed to achieve carbon peak by 2030 and carbon neutralization by 2060, which will be incorporated into the Ecological Civilization Construction. Therefore, it is particularly important to control the consumption of fossil energy in rural areas. Under this background, the paper is based on the filed survey data of rural households and uses the method of CLAD (the censored least absolute deviations) estimation of Tobit model to study the influencing factors and structural characteristics of rural households' energy consumption. The results show that the consumption of traditional energy with low-quality takes a main proportion of total energy consumption in rural households, which is unreasonable and needs transforming urgently. Also, there is heterogeneity among regions. Family characteristics, family wealth, and energy conservation initiative have an impact on total energy consumption and different types of energy. Especially, households with high frequency of energy conservation behavior and strong policy perception will reduce the consumption of high-polluting firewood and increase the use of coal and electricity. [ABSTRACT FROM AUTHOR]

A high-speed urban expansion in China over the past two decades has been accompanied by a great leap forward for energy consumption. However, such a significant socio-economic transition may increase the potential risk of energy inequality, which deserves special attention. Using China's provincial panel data covering the periods of 1997–2020, this paper mainly studies the impact of urbanization on urban-rural electricity consumption inequality with a modified STRIPAT model. The results of the Generalized Method of Moments (GMM) estimation show that there is a significant U-shaped relationship between urbanization and urban-rural electricity consumption inequality. The estimated short-run turning point arrives at the urbanization level of around 63.54% and 61.18% for the long-run estimates. We further carry out a regional heterogeneity analysis and then have two interesting findings: firstly, the colder northern region's turning point (70.95%) arrives later than the south (57.69%). Secondly, the baseline U-shaped relationship remains for developed eastern regions and the estimated turning point is 57.91%, while for the undeveloped midwestern regions, the relationship is not nonlinear but linearly negative. As an extension, we lastly explore the mechanism underlying the U-shaped relationship, and find that the interaction of urbanization's scale and efficiency effect determines the U-shaped relationship. Our findings remind policymakers that, to narrow the urban-rural development gap, the future preference of energy policy should be dynamically adaptive to varied regions and development stages. [ABSTRACT FROM AUTHOR]

Household energy consumption accounts for almost one third of global primary energy demand and significantly affects the environment. As such, it has served as a classic and compelling theme in the literature, with a range of studies having analyzed various aspects of household consumption, including energy conservation, energy poverty, and energy efficiency. Nonetheless, overall trends and frontiers in this research area have not been characterized and are poorly understood. This study aims to assess the current status, evolution, and emerging topics in this area through a bibliometric and network analysis of 1134 extracted publications from 1983 to 2018. This systematic review shows that nearly half the studies on household energy consumption were published in just three journals (namely, Energy Policy; Energy; and Energy Economics) with a focus on three areas (Environmental Sciences and Ecology; Energy and Fuels; Business and Economics). The findings also show that among contributing countries, the USA and China have the closest ties and wield the most academic influence. Furthermore, emerging and pioneering studies on behavioral interventions, energy conservation, and energy poverty and those pertaining to climate and electricity consumption may constitute the research frontier. [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]

Aluminum production is a major energy consumer and important source of greenhouse gas (GHG) emissions globally. Estimation of the energy consumption and GHG emissions caused by aluminum production in China has attracted widespread attention because China produces more than half of the global aluminum. This paper conducted life cycle (LC) energy consumption and GHG emissions analysis of primary and recycled aluminum in China for the year 2020, considering the provincial differences on both the scale of self-generated electricity consumed in primary aluminum production and the generation source of grid electricity. Potentials for energy saving and GHG emissions reductions were also investigated. The results indicate that there are 157,207 MJ of primary fossil energy (PE) consumption and 15,947 kg CO2-eq of GHG emissions per ton of primary aluminum ingot production in China, with the LC GHG emissions as high as 1.5–3.5 times that of developed economies. The LC PE consumption and GHG emissions of recycled aluminum are very low, only 7.5% and 5.3% that of primary aluminum, respectively. Provincial-level results indicate that the LC PE and GHG emissions intensities of primary aluminum in the main production areas are generally higher while those of recycled aluminum are lower in the main production areas. LC PE consumption and GHG emissions can be significantly reduced by decreasing electricity consumption, self-generated electricity management, low-carbon grid electricity development, and industrial relocation. Based on this study, policy suggestions for China's aluminum industry are proposed. Recycled aluminum industry development, restriction of self-generated electricity, low-carbon electricity utilization, and industrial relocation should be promoted as they are highly helpful for reducing the LC PE consumption and GHG emissions of the aluminum industry. In addition, it is recommended that the central government considers the differences among provinces when designing and implementing policies. [ABSTRACT FROM AUTHOR]

This paper develops a dual sectors dynamic equilibrium model and introduces agricultural energy consumption and agricultural water consumption in a growth model that tested by using a panel data set from 2004 to 2016 in China. It presents a theoretical prediction on the interactions between agricultural energy consumption, agricultural water consumption, and food yield. Consistent with this prediction, agricultural electricity consumption and agricultural water consumption by themselves appear to have significant effects on food yield. The fixed-effects models verify that agricultural electricity consumption, agricultural water consumption, and food yield have long-run equilibrium relationships. The results of weather-based robustness checks reveal that food yield is positively correlated with agricultural electricity consumption. Also consistent with theory, agricultural water consumption is positively associated with food yield. These results are generally stable and hold with tests of panel data unit roots, with alternative estimation strategies, and with or without controlling for covariates. [ABSTRACT FROM AUTHOR]

To achieve China's carbon peak and carbon neutrality goals, conducting systematic research on the energy, economy, and emission factors that affect the sustainable development of society is of great significance. This paper first uses the vector error correction model (VECM)-based Granger causality test to analyze the joint causal relations and feedback correction mechanisms among energy consumption, economic growth, and CO2 emissions in China from 1980 to 2019 at the energy heterogeneity level; then, analyzes the decoupling effect of China's four major energy sources (coal, oil, natural gas, and electricity) and economic growth from the perspective of energy heterogeneity; finally, the Tapio decoupling elastic model is decomposed into the emission reduction elasticity and energy saving elasticity to analyze the decoupling causality chain of the economy, energy, and carbon emissions. The research results show that there is a long-term, two-way causal relation between coal consumption and CO2; coal consumption has a one-way causal relation with economic growth; and long-term, two-way causal relations exist between oil and CO2, natural gas and CO2, electricity and CO2, electricity, and economic growth. In addition, when energy consumption, economic growth, and CO2 emissions deviate from their equilibrium states in the short term, various energy consumption, economic growth, and CO2 emissions in the previous year will be adjusted by 19.5%, 0.6%, ..., 7.7%, and 3.4% to bring the nonequilibrium states back to the long-term equilibrium states. Furthermore, the energy-saving elasticity of China's total energy, coal, oil, and natural gas is the main factor affecting the corresponding decoupling elasticity, but the emission reduction elasticity of electricity has a stronger impact on the decoupling elasticity than the emission reduction elasticity does. [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]

To investigate how to obtain the optimal balance between energy consumption and economic development, this paper proposes a multifactor optimization model (MFOM). Different from previous input–output optimization models, our proposed MFOM comprehensively considers the direct and indirect impacts of sectoral consumption activities on total energy consumption. The results show that MFOM achieves higher energy-saving goals by smaller adjustment to consumption activities than the previous input–output optimization models do. The results also show that the consumption of some products should be limited to achieve the energy-saving and economic growth goals, such as Non-ferrous Metal Ore Mining, Chemical Products and Ferrous Metal Ore Processing. Furthermore, the results indicate that the share of some sectors, especially high-level manufacturing sectors, significantly decreases in the consumption structure after optimization. To maintain the sustainable development of these sectors, the dependence of their production activities on energy-intensive products should be reduced. [ABSTRACT FROM AUTHOR]

Due to ongoing urbanization and industrialization, the energy and electricity demand and consumption are rapidly growing in China, resulting in large air pollutant and greenhouse gas emissions. The coal‐fired power industry is a key to achieving synergistic reduction of air pollutant and carbon emissions. This paper quantifies and compares synergistic effects of technology and policy reduction measures in the coal‐fired power industry. Using 2158 coal‐fired power generators of different types at 833 coal‐fired power plants in 24 Chinese provinces as research samples, this study quantifies and assesses the synergistic effects of structural and technical emission reductions at coal‐fired power plants through top‐down analysis of structural emission reduction and bottom‐up analysis of technical emission reduction. Comparing the synergy coefficients of both types of emission reduction measures, it is determined that structural emission reduction has a significant positive synergistic effect on energy consumption, pollutant and carbon emissions, with no extra emissions and energy consumption. On the other hand, technical emission reduction results in the negative synergistic effect of CO2 emissions due to electric power consumption and chemical reactions emissions in end‐of‐pipe measures of emission control, which demonstrated that prevalent desulfurization measure WFDG synergistically led to 0.688 tons of CO2 and 5.132 MWh electric power consumption for 1 ton of SO2 removed. Therefore, the structural emission reduction is an important means for multi‐target energy conservation and emission reduction. It is necessary for technical emission reduction to reduce related negative synergistic effects while enhancing target pollutant removal. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to analyze the environmental impact of the viscose fiber/lyocell fiber manufacturing industry and obtain improvement information from China's analysis results. Our main research question is: How will the production of viscose/lyocell fiber affect the environment? Which raw material or process has the most significant impact on the environment? Is it possible for lyocell fiber to replace viscose fiber that has a greater impact on the environment? Methods: For the life cycle assessment (LCA) method, we use the analysis from the raw materials (pulp) to the end of the production (fiber) process. The paper mainly introduces the water consumption, energy utilization, and the production of three industrial wastes in producing the two fibers. The life cycle inventory input and output data were collected from two fiber manufacturers in China (manufacturing viscose fiber and lyocell fiber, respectively). Six impact categories were calculated using eFootprint online platform, including primary energy consumption (PED), non-living resource consumption potential (ADP), Resource depletion–water (WU), global warming potential (GWP), acidification potential (AP), and ecotoxicity (ET). Results and discussion: There is no significant difference between the ADP and WU values of the two processes from a comprehensive comparison. In PED, the value of lyocell fiber is approximately three times that of viscose fiber. Because viscose fiber uses more sulfide; its AP value is about twice that of lyocell fiber. The GWP of lyocell fiber is about two of that of viscose fiber. The ET value of viscose fiber is an order of magnitude higher than that of lyocell fiber. In the viscose fiber process, steam, pulp, ZnSO4, and sulfuric acid are the main contributors to the process's environmental impact. The main contribution rates of steam to PED, WU, GWP, and AP are 23.6%, 16.76%, 43.33%, and 18.99%, respectively. The use of ZnSO4 in viscose fiber resulted in an ET value of more than 90%. Alternatives can be found to reduce the environmental impact further. It is of great significance to study materials that can replace ZnSO4 as additives. In the lyocell fiber process, electricity, pulp, and steam are the main contributors to the process. The main contribution rates of electricity to PED, ADP, GWP, AP, and ET are 57.5%, 62%, 51%, 59.9%, and 69.3%, respectively. At present, this process mainly uses thermal power generation, which makes the environmental impact caused by electricity consumption great. If more economical or cleaner power supply can be considered, the environmental impact will be further improved. [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]

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]

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]

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]

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]

• 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]

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]

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]

• 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]

The challenge of harmonizing the integration of renewable energy in market-driven transactions and assured accommodations presents a predicament in the development of China's electricity spot market. Moreover, as renewable energy penetration escalates, the issue of reserve undeliverability due to transmission congestion diminishes the power system's capacity to utilize renewable energy resources. To address this concern, this study introduces a secondary clearing mechanism for the electricity spot market, taking into account the proportion of renewable energy consumption quotas. Based on the first clearing, when renewable curtailment occurs, the bid pricing of abandoned power units undergoes flexible adjustment through the optimization of the price correction coefficient, followed by the execution of a secondary clearing utilizing the revised bidding information to fulfill the stipulations of the renewable energy consumption quota ratio. Drawing on the outcomes of the two-stage clearing, an incentive-compatible settlement compensation mechanism is proposed to preserve the impartiality of the market operator. The spot market clearing model accounts for the transmission safety margin, effectively mitigating the likelihood of transmission congestion, reserve inaccessibility, and renewable energy curtailment issues in real-time dispatching. Finally, a modified IEEE 30-bus system serves to substantiate the efficacy of the proposed market mechanism. [ABSTRACT FROM AUTHOR]

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO2) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO2 emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO2 emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO2 emissions is modeled. On this basis, China's future CO2 emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO2 emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO2 emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals. [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]

This empirical study investigates the dynamic interconnection between fossil fuel consumption, alternative energy consumption, economic growth and carbon emissions in China over the 1981 to 2020 time period within a multivariate framework. The long-term relationships between the sequences are determined through the application of the Autoregressive Distributed Lag (ARDL) bounds test and augmented by the Johansen maximum likelihood procedure. The causal relationships between the variables are tested with the Granger causality technique based on the Vector Error Correction Model (VECM). Empirical results reveal the existence of a statistically significant negative relationship between alternative energy consumption and carbon emissions in the long-term equilibrium. Furthermore, the VECM results demonstrate that both carbon emissions and fossil fuel consumption have unidirectional effects on economic growth. Additionally, the study highlights a short-term unidirectional causal relationship from economic growth to alternative energy consumption. These findings suggest that a reduction in fossil fuel consumption in the short run may indirectly impede the development of alternative energy. The study proposes that China should expedite the development of alternative energy and control the expansion of fossil fuel consumption to attain its carbon reduction target without hindering economic growth. [ABSTRACT FROM AUTHOR]

Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively. [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]

As a representative of the process industry, the cement industry consumes a large amount of coal and electricity resources. This is mainly due to the irrational energy scheduling caused by the rough production and independent statistics of each energy consumption indicator within the cement industry. The synchronous accurate prediction of energy consumption can provide a more resultful scheme for the production control process and energy scheduling. However, due to the time delay, variables coupling, and uncertainty of production, it is difficult to synchronously forecast multiple indicators. In this paper, a data-driven prediction method combining Sliding Window and Dual-Channel Convolutional Neural Network (SWDC-CNN) is proposed to achieve synchronous prediction of coal and electricity consumption in the next 5 min. The sliding window method is designed to extract time-varying delay characteristics of time series data to overcome its influence on energy consumption prediction. The effect of redundant parameters between weakly correlated variables on energy prediction is reduced by designing a dual-channel structure. We experimented and compared with excellent models Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gate Recurrent Unit (GRU) on an actual cement production data in Shanxi Province, China. Experimental results show that the proposed SWDC-CNN model has good performance, the highest prediction accuracy, and can meet the expected requirements. • A synchronous energy consumption prediction model consisting of sliding window and dual-channel CNN is proposed. • Design a sliding window to construct a high-dimensional time series matrix containing time-varying delay features. • Dual-channel convolutional neural network is designed to solve the problem of parameter redundancy. • The model can accurately predict coal and electricity consumption of cement clinker calcination process simultaneously. [ABSTRACT FROM AUTHOR]

Since the debate concerning economic growth, energy consumption, and environmental degradation is a burning issue in the last few decades. Yet the outcomes are controversial and requires more empirical work. This study used novel research methods to investigate the said issue in case of China since is the leading energy importing and pollution emitting economy. The time series data covers the period from 1969 to 2018 is analyzed via employing the novel Quantile-on-Quantile regression methods. The estimated results unveil that fossil fuel consumption significantly enhances carbon emissions in the country in the lower and middle quantiles. Besides, the economic growth showed an inverted U-shaped impact on the carbon emissions, where the lower-medium quantiles showed positive and the upper quantiles exhibit negative impact on carbon emissions. Besides, the positive association between economic growth and fossil fuel emissions are also evident in the analysis. The Granger causality indicates that there is a bidirectional causality between the study variables – validating that economic growth and fossil fuel consumption are the primary sources of increased emissions level. On the basis of empirical results, this study suggests the adoption of energy efficient/saving products and services, diverting economic growth to renewable and environmentally friendly energy resources, technological innovation and increased investment in the research and development to reduce dependency on fossil fuel energy consumption and attain low carbon economy. [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]

Quantifying the shock to the economy from the COVID-19 pandemic is difficult. Although this shock is easily linked to financial markets as a sort of monetary shock, few studies have been done on its effect on the real economy. This study takes a particular economic perspective, focusing on electricity uses in various sectors of the economy. We propose a novel method for comparing electricity use in 2019Q1 and 2020Q1, based on which we derive the degree of the real shock to some important economic sectors from COVID-19. In our theoretical framework, demand for energy and its influencing factors are related to the total scale of the economy, i.e., the gross domestic product. Using suitable empirical methods, we obtain certain marginal effects and then calculate the corresponding ratio as the real shock from COVID-19. The ratio between these marginal effects reveals the need for a balance between stocks and the corresponding differences in the economy. In our cases, the electricity use in various economic sectors plays a role in both stocks and the differences. We find that, although manufacturing and consumption are affected, the services are more vulnerable to the shock from the COVID-19 pandemic. Our findings offer implications for policymakers. [ABSTRACT FROM AUTHOR]

The main objective of this study is to investigate the impact of research and development (R&D) intensity on renewable energy consumption in selected Asian economies. We have relied on the autoregressive distributive lag (ARDL) method to get empirical estimates. The short- and long-run results show that a rise in R&D intensity increases renewable energy consumption in China and Japan. In the long run, energy intensity and financial development increase renewable energy consumption in China and India only. Among other control variables, a rise in CO2 emissions causes renewable energy consumption to rise in all three economies in the long run. The trade increases renewable energy consumption in India and Japan in the long run. This study shows the important policy implications of promoting R&D and renewable energy consumption in the selected Asian economies. [ABSTRACT FROM AUTHOR]

We designed and carried out a field experiment in which we imposed social comparison incentives and technical recommendations on student dormitories through electricity consumption reports and energy‐saving suggestions materials, respectively. Our findings are as follows: (1) Regression results on all users show that the effect of social norms is not statistically significant. (2) A social comparison message has a heterogeneous effect on consumers' energy use. Low‐ and high‐energy users reduced their electricity consumption by 26% and 14%, respectively, in the first week after the treatment. (3) The effect of social norms is time sensitive. [ABSTRACT FROM AUTHOR]

As one of the countries with the most rapid development of new energy, China has been committed to exploring countermeasures to the challenges of new energy consumption. After more than ten years of consideration and consultation, the "renewable portfolio standard"(RPS) for "renewable energy power consumption responsibility weighting" has landed in China. However, in the official affirmation issued by the National Energy Administration, theoretical support for the basis of the initial quota allocation is still lacking. In this study, we examine the efficiency of the weight allocation scheme for renewable energy power consumption responsibilities, which was announced by the National Energy Administration in 2018 and which is based on the BCC-DEA efficiency model. The results indicate that most provinces have low allocation efficiency under this allocation scheme. Therefore, we propose an optimal allocation scheme for a renewable energy consumption quota, based on the ZSG-DEA model. With the achievement of its target, this study's allocation scheme would ensure 100% efficiency in all provinces, improve provincial economic efficiency, and simultaneously bring economic growth. After analyzing the fairness before and after adjustment of the RPS, our findings suggest that the adjusted RPS allocation scheme can promote equity in per capita renewable electricity consumption. [ABSTRACT FROM AUTHOR]

China's huge regional differences are taken into consideration to study the influencing factors and their differences in CO2 emissions of the power industry from different regions. This study aimed to improve the efficiency of CO2 emission reduction policies. From the production and consumption perspectives, this study analyzes the influencing factors of CO2 emissions and utilizes the Logarithmic Mean Divisia Index (LMDI) to decompose CO2 emissions with consideration of the cross-regional power dispatching in the power industry. The results indicate that the trend of CO2 emissions in the eastern, central, and western China seems similar during years 2005 to 2017 no matter from which perspective. From the production perspective, power consumption is the main factor in CO2 emission increase and its affect extent may vary from different regions over a period of time. Energy efficiency inhibits CO2 emission increase in all regions. The power structure and power distribution across regions affect CO2 emissions significantly different in amount and direction from region to region. From the consumption perspective, economic activity plays a major role in CO2 emission increase and plays a similar role in the trend of CO2 emissions in three regions, but its affect extent on CO2 emissions varies in different regions. Targeted policy recommendations are provided to reduce CO2 emissions more effectively from China's power industry. [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]