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As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants' thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi'an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants' subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants' thermal comfort responses. [ABSTRACT FROM AUTHOR]

Climate change is directly linked to a broad array of changes because of disorganized activities within the economic system and human intervention. Climate change affects the well-being status of both non-living and living things. Relevant policies to mitigate and adapt to climate change conditions concentrate on solutions that intend to use renewable energy instead of fossil fuels or other conventional energy sources. This study aims to process a review focused on how renewables advance environmental quality and create relevant benefits within severe economic activities. This study elaborates on the case of China for two main reasons. First, China has a pivotal role in the economic system worldwide. Second, China is making serious attempts to transition into a low-carbon economy. An integrative review was processed to receive selected publications. The advantage of this process is that it considers empirical and non-empirical studies, policy papers, and conceptual frameworks. Inclusion and exclusion criteria were applied to retrieve the final number of publications. Review results from 39 well-acknowledged Journals provided 180 selected publications categorized into seven study groups: data analyses, model optimization studies, market issues, renewable energy technology publications, nuclear energy publications, ocean energy publications, and policy-related studies. Policy implications concern China's efforts to accelerate the integration of renewables in the energy mix. Hence, the country should increase energy efficiency in consumption and process investment plans based on robust research and development efforts. [ABSTRACT FROM AUTHOR]

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy. [ABSTRACT FROM AUTHOR]

How to ensure the "carbon peaking and carbon neutrality" goal is a crucial problem for China with good performance, low cost and limited time. As a pillar industry, the manufacturing industry need offer more great potential helps. In this paper, for the intelligent workshop, firstly, an energy consumption architecture, based on mutual inductance sensing technology & a multi-granularity production line energy consumption modeling, is put forward. Secondly, the device-level sensing technology demonstrates the detailed implementation for the multi working condition machines whose power is unknown. And then, the research devoted the basic aspects of the proposed multi-granularity production line energy consumption modeling and summarized the implementation elements of 3-matrix (the attribute attributes, power attributes and cumulative timing attributes). Above the 2 sections, the robot and SMT production lines case studies are presented separately. Results show that the architecture can effectively sense and model the energy consumption of the devices and the workshop, which provides an available method for the fine management and control of energy consumption and energy efficiency. Through this study, we hope to provide some reference ideas for future researchers. [ABSTRACT FROM AUTHOR]

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context. [ABSTRACT FROM AUTHOR]

Renewable energy (RE), the key to realizing the low-carbon transformation of energy, is significant in reducing social carbon emissions (CEs). However, the regional differences in the impact of RE on CEs and the impact mechanism are still unclear. Clarifying the regional differences and impact mechanisms will encourage the government to formulate a development plan for RE in line with the actual situation. To explore the heterogeneous impacts and conduction paths of RE on CEs, this paper uses province-level data from 2000 to 2019 in China. Based on clarifying regional CE efficiency, panel quantile regression models are introduced to assess the differential impacts of RE at different CE efficiencies. And the conduction mechanism of the effect of RE on CE efficiency is explored through the parallel multiple mediation effect model. The results show that RE development, the accumulation of social affluence, and technological innovation can improve carbon efficiency while increasing population size and energy consumption inhibit carbon efficiency. Urbanization rate, industrial structure, and opening to the outside world affect travel alienation in regions with different levels of CE efficiency. RE development improves regional CE efficiency, which is more evident in regions with low and high efficiency. In addition, RE can increase CE efficiency indirectly through effects on population size, urbanization rate, scientific and technological innovation, and intensity of fossil energy consumption. However, it decreases CE efficiency indirectly through social enrichment. These findings will provide broader insights for improving the development of regional RE and formulating differentiated policies. [ABSTRACT FROM AUTHOR]

Ports in China are facing significant pressure to reduce carbon emissions in alignment with carbon peak and carbon neutrality goals. Onshore power supply (OPS) is regarded as a promising approach to accomplish these targets, necessitating a thorough evaluation of its impact for port authorities to make informed decisions regarding its adoption. This research focuses on Ningbo Zhoushan Port, the largest port globally, as a case study. Two metrics are proposed to quantify the energy efficiency of ships powered by onshore energy while berthed. The installation and connection status of OPS in the port area are analyzed. Subsequently, the energy demand of berthed ships is assessed, and the potential for energy efficiency improvement resulting from OPS implementation is evaluated using Monte Carlo methods. The findings reveal untapped potential in the studied port area, with OPS demonstrating the ability to improve energy efficiency of berthed ships at a rate parallel to the connection rate, excluding indirect emissions. However, considering indirect emissions and energy loss diminishes the effectiveness of OPS. The paper discusses practical implications for enhancing the energy efficiency of OPS, enabling port authorities to make well-informed decisions. These findings are invaluable for Chinese port authorities striving to achieve carbon reduction goals and enhance sustainability in the maritime industry. [ABSTRACT FROM AUTHOR]

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc. [ABSTRACT FROM AUTHOR]

In order to make a complete ranking of intertemporal environmental efficiency in a dynamic manner, this paper combines the network-based dynamic data envelopment analysis (DEA), super-efficiency with the unified efficiency under natural and managerial disposability, and designs a dynamic DEA model and the corresponding dynamic super-efficiency DEA model. Compared with previous studies, the proposed measure can fully rank the overall environmental efficiency of all decision making units (DMUs) in a dynamic manner, and more importantly, it provides the information about when and what factors lead to inefficiency or efficiency of DMUs. The proposed models are applied to examine the environmental efficiency of 30 provinces in China from 2008 to 2017. The results show that there are significant regional differences of environmental efficiency in China. In addition, slack analysis shows that most eastern efficient provinces have no obvious advantages in energy consumption, labor and waste water emission; most central and western efficient provinces have no advantages in sulfur dioxide (SO2) emissions and GDP. To improve overall efficiency, eastern inefficient provinces should mainly focus on reducing energy consumption, SO2 emissions and labor, and increasing capital investment in right years, central and western inefficient provinces can focus on reducing SO2 emissions and labor in most years, most of provinces need to increase gross domestic capital formation. [ABSTRACT FROM AUTHOR]

To address the complexity of siting and sizing for the renewable energy and energy storage (ES) of offshore oil–gas platforms, as well as to enhance the utilization of renewable energy and to ensure the power-flow stability of offshore oil–gas platforms, this paper proposes a hierarchical clustering-and-planning method for wind turbine (WT)/photovoltaic (PV) ES. The proposed strategy consists of three stages. First, the WT/PV power generation is forecast by a LightGBM model. The WT/PV siting and sizing at each node of the distribution network is optimized with a particle swarm optimization (PSO) algorithm, with the objectives of economy and stability. In the second stage, the distribution network is partitioned into sub-clusters, based on a voltage and loss-sensitivity index. Finally, the ES siting and sizing is optimized with PSO to minimize the line loss and the voltage fluctuation for each sub-cluster. The relationship between the economic and stability indicators is conducted quantitatively in the joint-planning approach. Considering the 10 kV distribution network of an oil–gas platform in the Bohai Sea of China as an example, our experiments demonstrated that by adjusting the WT/PV ES capacity for different gas-turbine power outputs, line losses can be reduced by 55–66% and voltage fluctuations can be reduced by 30.4–47.5%. [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]

The development of foreign electricity markets has been relatively complete, while China's electricity market started late and the design of the market mechanism needs to be improved, so the selection of a suitable market model and the design of the market mechanism in hydropower‐rich regions is conducive to the healthy development of the electricity market. While most of the first eight pilot regions in China have entered the long‐period continuous operation stage, and all the second batch of six power spot pilots have started the simulation trial run. However, the construction of power markets in hydropower‐rich regions of China, Sichuan, and Yunnan, still faces multiple problems to be solved, such as ensuring the reliability of medium‐ and long‐term power supply, improving the efficiency of hydropower resources utilization and consumption scale, and maintaining the stability of market prices. In this paper, the authors establish a multi‐scale joint clearing model for different market models considering both long‐term and short‐term operation strategies, and conduct simulations of different market models at different time scales to compare and analyze the optimization results and electricity price fluctuations of different market models. It can stabilize the market price and reasonably reflect the true value of hydropower, and also promote the consumption of clean energy and guarantee the reliability of power supply in the medium and long terms. Finally, suggestions are made for the selection of electricity market models, the security of energy supply and the design of sustainable development mechanisms for hydropower in hydropower‐rich regions such as Yunnan and Sichuan in China, with a view to providing useful references for the construction of China's electricity market. [ABSTRACT FROM AUTHOR]

Hydrogen is receiving unprecedented momentum and is projected to meet a sizable share of the final energy demand in the future. The coupling between the hydrogen and power systems can help integrate volatile renewable energy, reduce curtailment, and realize long-term energy storage. Different coupling modes are being proposed, yet quantitative comparisons are absent. To fill this gap, this paper firstly summarizes the system composition of potential power–hydrogen coupling modes and analyzes their advantages and disadvantages. Then, a model for power–hydrogen coordinated planning optimization is proposed for quantitative analysis. Southern Xinjiang is chosen as a representative of the northwestern area in China, which has plentiful renewable resources and a relatively small local load at present, for a case study. Through result analysis, it is found that the mode of power–hydrogen coupling at the source side, either for in situ utilization or for long-distance transport via pipelines, shows better economic competitiveness. The discussion provides suggestions and a reference to policymakers for formulating infrastructure or industry plans in advance to better accommodate the rapidly developing renewable energy. [ABSTRACT FROM AUTHOR]

There are differences in economic level, population size, and technical level in different stages. At the same time, as a coal-dependent country, other regions have foreign dependence on coal. Then the primary factors and influence degrees of CO2 emissions in different stages and areas with different coal dependents will be various. This paper adds urbanization rate, industrial structure, and energy consumption structure to the traditional STIRPAT model. Based on the expanded STIRPAT model, the panel data of 30 regions in China from 2005 to 2019 are used to analyze the different stages and areas with foreign coal dependence. The main influencing factors and degree of influence of CO2 emissions. Regression finds that population size, energy consumption, and industrial structure will increase regional carbon dioxide emissions. There is an "inverted U-shaped" curve relationship between economic development, technological level, and carbon dioxide emissions. The improvement of the urbanization rate can reduce carbon dioxide emissions. Carbon dioxide emissions. An "inverted U-shaped" curve relationship between per capita GDP and CO2 emissions in three research periods from 2005 to 2010, 2011 to 2015, and 2016 to 2019. At the same time, the inhibition effect of the urbanization rate is constantly strengthened, and the promotion effect of the coal consumption ratio is decreasing year by year. The influence of population size, technical level, and industrial structure on carbon dioxide emissions in the whole country and areas with different coal dependents varies with the study period. To provide a theoretical basis for local conditions and regional management in the future direction of energy-saving and carbon reduction. [ABSTRACT FROM AUTHOR]

The reduction of carbon emissions in the power industry will play a vital role in global decarbonization. The power industry has three main strategies to achieve this reduction in emissions: to implement lean marketing strategies that effectively target users of power and encourage them to adopt decarbonizing technologies and services; to optimize the efficiency of these users of power; and to improve the efficiency of renewable energy sources. This paper establishes a comprehensive evaluation system of indexed data from power industry customers for the development of lean marketing strategies. This system evaluates indexes derived from customer data on renewable energy sources, carbon emissions, energy efficiency, and customer credit. It adopts the DEMATEL–CRITIC combination weight assignment and VIKOR method for system evaluation and conducts simulation experiments on customer data in a region of Northeastern China to give an example of how this method could be applied in practice to lean marketing. The results show that the evaluation system proposed in this paper can govern the lean marketing decision-making of power sales enterprises. [ABSTRACT FROM AUTHOR]

Heating systems are essential municipal infrastructure in winter, especially in severe cold cities of China. The safety and efficiency of heating systems greatly affect building energy efficiency and indoor thermal comfort. Heating networks (HNs), playing the role of transportation, are the key parts of heating systems. In HNs, hydraulic models could be affected by the accuracy of resistance characteristic parameters, which are expressed by pipe friction parameters (PFPs) in this paper. As the uniqueness of the estimation results of PFPs has not been discussed in previous studies, this paper builds an estimation method of PFPs by dividing two types of pipes, substituting variables and establishing a split-step linearization method. Combining with the theory of matrix equations, the decision conditions and solution methods for obtaining the unique estimation results of PFPs are determined. Theoretical analysis and case study results show that estimation values of PFPs can be obtained by utilizing measured data under multiple hydraulic conditions. In the example of DN and the simple actual HN, the average estimation deviation of PFPs is 1.42% and 1.86%, which are accurate enough for actual engineering. Estimation results of PFPs obtained by this method guarantee the accuracy of analysis and regulation in heating systems and improve social energy utilization efficiency. [ABSTRACT FROM AUTHOR]

China agricultural development has been facing the problem of resource constraints because its resources per capita such as land and energy are relatively lower than the global average. By applying the provincial agricultural panel data from 2000 to 2015 and fixed effect model based on the translog production function, this paper estimates both output elasticities and substitution elasticities of agricultural inputs, which may provide insights into sustainable agricultural development. The results show that, except for capital, the output elasticities of other production factors are all positive. Energy has always played an important role in agricultural production, whose elasticity in agriculture increased from 0.0203 in 2000 to 0.1694 in 2015. We also find a severe scarcity of land, and the high intensity of energy in the field of agriculture. Moreover, there exists a substitute relationship between all factors, which means that in the short term, one production factor can be employed to replace another to maintain agricultural development. From the empirical results of this paper, some policy suggestions are proposed as follows: it is crucial that more attention should be placed on land and to plan energy use wisely. In addition, on account of the current situation in China, the input of labor force should be stepped up and energy should be used more efficiently to make up for the shortage of land resources. The empirical results and policy suggestions in this paper may benefit the sustainable development of China's agricultural economy. [ABSTRACT FROM AUTHOR]

In the extant literature, there are numerous discussions on China's environmental sustainability. However, few scholars have considered renewable energy consumption and trade policy simultaneously to debate environmental sustainability. Therefore, this paper attempts to examine how renewable and non-renewable energy consumption, bio-capacity, economic growth, and trade policy dynamically affect the ecological footprint (a proxy for environmental sustainability). Using the data from 1971 to 2017 and employing the auto-regressive distributed lag model to perform an empirical analysis, the results demonstrate that renewable energy consumption and trade policy are conducive to environmental sustainability because of their negative impacts on the ecological footprint. However, the results also indicate that bio-capacity, non-renewable energy consumption, and economic growth are putting increasing pressure on environmental sustainability due to their positive impacts on the ecological footprint. Moreover, to determine the direction of causality between the highlighted variables, the Yoda-Yamamoto causality test was conducted. The results suggest a two-way causal relationship between renewable energy consumption and ecological footprint, non-renewable energy consumption and ecological footprint, and economic growth and ecological footprint. Conversely, the results also suggest a one-way causal relationship running from bio-capacity and trade policy to the ecological footprint. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such as particle swarm optimization (PSO), multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, a lack of comparison between these approaches poses a challenge to the selection of optimization approach for stormwater drainage pumping stations. In this paper, a new framework for comparing multi-objective approaches is proposed. Two kinds of evolutionary approaches, single-objective optimization and multi-objective optimization, are considered. Three approaches representing these two types are selected for comparison, including PSO with linear weighted sum method (PSO-LWSM), MOPSO with technique for order preference by similarity to an ideal solution (MOPSO-TOPSIS), and NSGA-II with TOPSIS (NSGA-II-TOPSIS). Four optimization objectives based on the number of pump startups/shutoffs, working hours, energy consumption, and drainage capacity are considered, of which the first two are new ones quantified in terms of operational economy in this paper. Two comparison methods—TOPSIS and operational economy and drainage capacity (E&C)—are used. The framework is demonstrated and tested by a case in China. The average values of the TOPSIS comprehensive evaluation index of the three approaches are 0.021, 0.154, and 0.375, respectively, and for E&C are 0.785, 0.813, and 0.839, respectively. The results show that the PSO-LWSM has better optimization results. The results validate the efficiency of the framework. The proposed framework will help to find a better optimization approach for pumping systems to reduce energy consumption and maintenance costs. [ABSTRACT FROM AUTHOR]

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side. [ABSTRACT FROM AUTHOR]

How to utilize human, financial, and material resources reasonably in the technological innovation of new energy vehicles to obtain the maximum benefit with the least investment is an important issue that needs to be solved urgently. This paper employs the stochastic frontier model to analyze the innovation efficiency and its influencing factors of new energy vehicles in China. The investment of innovation is divided into four dimensions: human resource input, R&D input, technology acquirement input and environment support input. The results demonstrate that the mean of innovation efficiency of the new energy vehicle industry is low in general. The Yangtze River Delta and Pearl River Delta regions have the highest innovation efficiency comparable to that of other regions. There is a significant positive impact of asset turnover ratio, per capita compensation of management, and education level on innovation efficiency. The effects of asset-liability ratio and government support on innovation efficiency are not significant. But the government support has a significant positive moderation effect on innovation efficiency through financial factors. Based on the conclusions, this paper proposes insights to promote the development of the new energy vehicle industry. [ABSTRACT FROM AUTHOR]

Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology. [ABSTRACT FROM AUTHOR]

Taking Hunnan District of Shenyang City as an example, this paper investigates 32 government agencies, 17 tertiary first-class hospitals, and 15 colleges and universities in Hunnan region and analyzes the energy consumption characteristics of three types of public institutions. Through the use of DeST energy consumption software to simulate the energy consumption of three types of public institutions, combined with the load density method to predict the total energy demand of hospitals, universities, and government agencies in Hunnan area and the MARKAL model coupling mechanism, the coupling model of active and passive energy suitable for public institutions is presented. According to the forecast results of energy demand, combined with the energy consumption structure and energy conversion efficiency, the energy supply of each subitem of the public institution was solved and the energy supply of the public institutions in Hunnan District was optimized in accordance with the relevant constraints. Through calculation, it is found that by 2020, the passive energy supply of public institutions in Hunnan District will account for 19.79% and active energy sources such as coal and petroleum will still be the main type of energy used by public institutions. Based on the research on the active and passive energy coupling supply of public institutions in Hunnan District, the active and passive energy coupling model and algorithm of public institutions constructed can provide a reference basis for the active and passive energy coupling utilization of public institutions in China. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the determinants of decline in energy intensity in four Chinese industries—pulp and paper, cement, iron and steel, and aluminum. This paper attempts to answer the following key question: For the purpose of promoting energy efficiency, do prices, technology, enterprise restructuring and other policy-related instruments affect various sectors uniformly so as to justify uniform industrial energy conservation policies, or do different industries respond significantly differently so as to require policies that are tailored to each sector separately? In this paper, we examine this question using data for China's most energy-intensive large and medium-size enterprises over the period 1999-2004. Our results suggest that in all four industries rising energy costs are a significant contributor to the decline in energy intensity over our period of study. China's industrial policies encouraging consolidations and scale economies also seem to have contributed to reductions in energy intensity in these four industries. [ABSTRACT FROM AUTHOR]

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure. [ABSTRACT FROM AUTHOR]

Prefabrication is undergoing significant growth in the building and construction sector in China. Meanwhile, the integrated measurement and reduction solution for energy consumption is quickly becoming an important target of scientific efforts and research, especially in prefabricated buildings. Embedded energy consumption requirements can potentially be reduced during the manufacturing process of prefabricated components: the opportunity for its reduction can be significant. This paper aims to propose a BIM and LCA integrated approach that enables the energy consumption of prefabricated components in the manufacturing stage to be extracted and calculated in order to obtain a clear, precise understanding of the energy consumption management in prefabrication. To enhance the energy consumption description of prefabricated components, this paper clarifies the contents of energy consumption inventory and related calculation formula in raw material extraction and production, material transportation, and factorized production of prefabricated components, respectively. Specifically, considering that current Industry Foundation Classes (IFC) standard does not accommodate entities with energy consumption information or relationships, it applies the IFC extension method to connect the energy consumption inventory with the BIM model. The proposed approach is applied and verified in a case study from China. This study reveals that energy consumption control should be investigated in the element level considering the differences of various prefabricated components' requirements in the manufacturing stage. [ABSTRACT FROM AUTHOR]

At the phase of 13-th five-year plan in China, natural gas will play an important role in energy revolution. With the growth of consumption, natural gas infrastructures will become hot spots of future investment and pipeline network construction will also usher in a period of rapid development. Therefore, it is of great theoretical and practical significance to study layout methods of transport pipeline network. This paper takes natural gas transport pipeline network as a research object, introduces dominance degree to analyse benefits of pipeline projects. Then, this paper proposes Dominance Degree Model (DDM) of transport pipeline projects based on Potential Model (PM) and Economic Potential Theory (EPT). According to DDM of gas transport pipeline projects, layout methods of pipeline network are put forward, which is simple and easy to obtain the overall optimal solution and ensure maximum comprehensive benefits. What's more, construction sequences of gas transport pipeline projects can be also determined. Finally, the model is applied to a real case of natural gas transport pipeline projects in Zhejiang Province, China. The calculation results suggest that the model should deal with the transport pipeline network layout problem well, which have important implications for other potential pipeline networks not only in the Zhejiang Province but also throughout China and beyond. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is China's substantial energy consumption base. The issue of carbon emission efficiency directly affects the ecological protection and high-quality development of the YRB. It is the key to achieving carbon peak in 2030 and carbon neutralization in 2060 ("30.60") double carbon emission reduction targets. Therefore, taking YRB as the research object, this paper first calculates the carbon emission and the decoupling state in the YRB. Secondly, the super-efficiency slacks-based measurement (SE-SBM) model is combined with the Malmquist index to analyze the temporal and spatial evolution characteristics of YRB's carbon emission efficiency from static and dynamic perspectives. Thirdly, the dynamic evolution characteristics of carbon emission efficiency are analyzed with the help of the Kernel density function. Finally, the Tobit model analyzes the influencing factors of YRB's and China's carbon emission efficiency. The results show that: (1) Among the nine provinces of YRB, the decoupling state between carbon emissions and economic growth in most provinces changes from weak decoupling to strong decoupling, and the decoupling elasticity index shows a fluctuating downward trend. (2) There are significant differences in carbon emission efficiency among provinces, but on the whole, it shows a stable growth trend. The high-value area of carbon emission efficiency is increasing, and the phenomenon of two-level differentiation is improving. The decline of the technological progress index causes the Malmquist index in Qinghai and Ningxia. On the contrary, the rise of the Malmquist index in the other seven provinces is caused by improving the technical efficiency index. (3) Industrial structure, economic development, and industrialization are the main positive factors affecting YRB's carbon emission efficiency. Urbanization level, green development level, and energy consumption level are the leading negative indicators hindering YRB's improvement of carbon emission efficiency. Therefore, targeted emission reduction suggestions should be formulated according to YRB's resource endowment and development stage characteristics. [ABSTRACT FROM AUTHOR]

The excessive carbon emissions not only intensify the global climate change, but also seriously restrict the sustainable development of social economy. However, improving industrial carbon emissions efficiency is the most directly effective way to reduce emissions. Therefore, accurate measurement and analysis of carbon emissions efficiency and evolution characteristics of China's industrial system is the basis for China to improve carbon emissions efficiency. Based on this, we adopted energy consumption method and input-output method to calculate and analyze the industrial carbon emissions efficiency and evolution characteristics of China from 2002 to 2015. The results show that (1) If carbon emissions from cement production are ignored and only energy-related carbon emissions are considered, the calculation results of carbon emissions efficiency of heavy industry will be overestimated about 30%. (2) Compared with 2002, China's industrial carbon emissions efficiency increased by about twice in 2015. Specifically, the optimization of carbon emissions efficiency in agriculture, construction, light industry and service industry is very obvious, which has increased by 5.65, 5.00, 4.69 and 4.68 times respectively; The optimization of carbon emissions efficiency in power sector, heavy industry, transportation and chemical industry was obvious, which increased by 2.55, 2.01, 1.86 and 1.47 times respectively; The carbon emissions efficiency of the fossil energy sector has decreased by 0.36 times. (3) The carbon emissions efficiency of various industries has significant differences. Among them, agriculture, service industry, light industry and construction industry are generally industries with higher carbon emissions efficiency, while power sector, fossil energy sector, transportation industry, chemical industry and heavy industry are generally industries with lower carbon emissions efficiency, especially power and fossil energy sectors. Based on these, this paper provides policy implications and scientific evidence for accurately improving carbon emissions efficiency from the perspective of carbon emissions efficiency. [ABSTRACT FROM AUTHOR]

As new energy vehicle (NEV) is the future of automobile development, it is of great significance to dig deeper into the technical topics and development trends of new energy vehicles for accurately understanding the technical trends of the new energy vehicle industry, grasping development opportunities, and scientifically formulating strategic plans. This paper takes the patent texts in the field of new energy vehicles from 2000 to 2020 in the patent database of CNKI as the data source, identifies 25 technical topics implied in the patent texts by using the LDA (Latent Dirichlet Allocation) topic model, analyzes the evolution trend of the 25 technical topics in terms of importance and popularity, and predicts the popularity and development trend of each technical topic in new energy vehicles from 2021 to 2025 by constructing the ARIMA model. The popularity and development trend of each technology topic of new energy vehicles in China from 2021 to 2025 are predicted by constructing ARIMA model. Drawing on quantitative evidence, the study found that there are top five technical topics in terms of importance in this field, namely, Topic 8 (Installation and Fixation), Topic 5 (Heat Dissipation), Topic 14 (Vehicle Data Monitoring), Topic 9 (Charging Pile), and Topic 15 (Damping). From 2014 to 2020, the importances of Topic 5 (Heat Dissipation), Topic 8 (Installation and Fixation), Topic 6 (Electric Drive System), Topic 9 (Charging Pile), and Topic 15 (Damping) are gradually rising. In terms of popularity of technical topics, from 2014 to 2020, the first to fifth topics are Topic 20 (Safety), Topic 8 (Installation and Fixation), Topic 3 (Cable Insulation Materials), Topic 15 (Damping), and Topic 10 (Pump Cooling). Based on the prediction of ARIMA model, it is found that the popularity of these five technical topics is steadily increasing from 2021 to 2025, among which the popularity of Topic 20 (Safety) will increase from 63.58 to 113.07, the largest increase in popularity among all technical topics. The paper provides implications for countries dedicated to developing the new energy vehicle industry. [ABSTRACT FROM AUTHOR]

The intensity in energy consumption due to food production systems represents a major issue in a context of natural resources depletion and an increasing worldwide population. In this framework, at least a third of global food production is being lost or wasted. Moreover, about 38% of the energy embedded in total food production is being lost. Consequently, the assessment of energy consumption in food systems, and in food loss and waste valorization systems, is an increasing trend in recent years. In this line, this work presents a systematic review, selecting 74 articles from a search of 16,930 papers regarding the key words "energy assessment food". The aim was to determine the current and historical trends in this field of research. Results pointed to a worldwide acceleration in trends since 2014, standing out in China and other Asian countries. Concerning the topics of the publications, energy consumption in the food sector is a research field which has existed since 1979. Moreover, the study of energy valorization systems using food loss and waste is an increasing trend since 2010. Additionally, publications focused on the water–energy–food nexus appeared firstly in 2014 and have grown exponentially. Moreover, life cycle assessment highlights as the most widespread methodology used. [ABSTRACT FROM AUTHOR]

Forecasting energy demand accurately is the basis for the formulation and implementation of energy planning. In this paper, energy demand influencing factors are mainly decomposed into scale economy effect, population size effect, energy structure effect, and residential consumption effect based on the Logarithmic Mean Divisia Index (LMDI). Then, the Cointegration and Granger Causality tests are used to discover the influencing factors of energy demand in China. On this basis, a hybrid optimization algorithm, the least-squares support-vector regression optimized by particle swarm optimization (PSO-LSSVR), is proposed to forecast the energy demand of China. Then, three scenarios are set up to analyze the further development of drive factors of energy demand. Finally, in accordance with the forecasting results, some suggestions related to China's energy development policy are given. The main results are as follows. First, gross domestic product (GDP), the total population at the end of the year (POP), the coal consumption ratio in energy (CCR), and residential consumption levels (RCLs) are dominant indicators of energy demand in China. Second, the improved PSO-LSSVR model has significant superiority than other models in energy demand forecasting, a complex and nonlinear system with small samples. Third, China's energy demand will peak in 2022, which is 4.9 million tce in all scenarios. [ABSTRACT FROM AUTHOR]

The unavoidable option for socially sustainable development is a low-carbon economy. One of the essential steps for China to attain high-quality development is reducing carbon emissions. It is necessary to realize low-carbon development in Sichuan, as it is not only an important economic zone but also an ecological protected area. The concurrent relationship among energy consumption, carbon emissions, and economic growth was examined in this study using the Tapio decoupling indicator, and the factors affecting energy consumption and carbon emissions in Sichuan were broken down using the logarithmic mean Divisia indicator (LMDI). The findings demonstrate a fundamental relative decoupling relationship between Sichuan's energy use and carbon emissions. Analysis of energy consumption and carbon emissions in Sichuan Province from 2005 to 2020 shows distinct patterns. From 2005 to 2012, in 2014, and from 2016 to 2020, the relationship between energy use and carbon emissions was relatively decoupled, with decoupling values ranging between 0 and 1. Absolute decoupling occurred in specific years: 2010, from 2013 to 2018, and in 2020. These periods are characterized by economic growth alongside reductions in carbon emissions. Factors affecting energy consumption and carbon emissions were consistently analyzed, showing similar impacts throughout the study periods. We find that population and economic growth are the main driving forces of these effects. The effects of energy intensity and industrial structure mainly play restraining roles, and the latter has a slightly weaker effect than the former. [ABSTRACT FROM AUTHOR]

Green innovation possesses dual externalities of "innovation" and "environmental protection", and enhancing energy efficiency serves as a crucial means to promote high-quality economic development. Building upon the energy rebound effect, we use the balanced panel data of cities at prefecture level and above in China from 2008 to 2018 to explore the impact of urban green innovation on total factor energy efficiency (TFEE). The findings of this study indicate that, firstly, the impact of green innovation on TFEE exhibits a positive U-shaped pattern, characterized by initial suppression followed by subsequent promotion. This conclusion remains robust after undergoing a series of rigorous robustness tests. Second, high-quality green innovation is found to reach the turning point more quickly, implying that substantial green innovation can cross the turning point in smaller quantities. Thirdly, on the whole, in comparison to non-resource-based cities, resource-based cities are capable of reaching the turning point at an earlier stage. Finally, the new energy demonstration cities have not yet reached the turning point, while the non-new energy demonstration cities have crossed the turning point. This study contributes novel insights into the relationship between green innovation and TFEE, which holds significant implications for the formulation and implementation of sustainable development policies. [ABSTRACT FROM AUTHOR]

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China's impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path "innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy" has significantly positive direct and indirect effects in both the treatment group and the control group. However, "innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector's impossible triangle coupling coordination degree" and "innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector's impossible triangle coupling coordination degree" have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group. [ABSTRACT FROM AUTHOR]

Environmental protection tax is of great significance to the realization of an environment-friendly society. Using data from 280 Chinese cities from 2010 to 2020, this paper measures total factor energy efficiency in China by the super slack-based measure model. The impact of China's environmental protection tax reform on total factor energy efficiency is tested through differences-in-differences method, and its transmission mechanism is explored. Environmental protection tax reform policies have a significant positive impact on total factor energy efficiency. Environmental protection tax reform policies have greater impact on resource-intensive cities in central region and cities with lower initial energy efficiency. Cities with increased tax have positive spillover effects on the neighboring cities. This study provides empirical evidence for further optimization of environmental protection taxes and energy efficiency. The structure of the environmental tax system should be optimized, and the environmental tax policy should be implemented according to local conditions. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China. [ABSTRACT FROM AUTHOR]

The green fiscal policy stands as a potent catalyst for fostering the green and low-carbon transition, with aims to garner multifaceted social, economic, and ecological gains. However, empirical evidence elucidating the impacts of green fiscal policies on firm energy performance remains scarce. Based on a unique firm-level dataset from 2008 to 2016, this paper utilizes the staggered difference-in-differences strategy to examine the energy-saving benefits of China's energy saving and emission reduction (ESER) fiscal policy. The results indicate that the ESER fiscal policy effectively enhances firm energy performance, supported by various robustness checks. Mechanism analysis reveals that technology innovation and energy consumption restructuring are significant channels through which the ESER fiscal policy improves firm energy performance. Moreover, the ESER fiscal policy contributes more remarkably to the energy performance of large-scale firms and non-resource-based urban firms. This study highlights the essential policy insights for further optimizing green fiscal policies to facilitate the upgrading of firm energy consumption. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces (cities) in China, this paper first measures the total factor energy efficiency, technical efficiency index and technological progress index from 2003 to 2018 by using the DEA-GML index method. Then, a panel Tobit model is constructed to analyze the impact of different industrial agglomerations on total factor energy efficiency and its components. It can be found from the empirical results that the agglomeration of manufacturing industry has no significant impact on the performance of total factor energy efficiency, and the agglomeration of producer services and the collaborative agglomeration of manufacturing and producer services promote the improvement of energy efficiency. Among them, the main way for industrial agglomeration to improve total factor energy efficiency is technological progress, and the promoting effect of technological progress is enough to make up for the inhibiting effect of industrial agglomeration on technical efficiency. Further research finds that the impacts of manufacturing agglomeration, producer services agglomeration and collaborative agglomeration in different regions on total factor energy efficiency are different. Specific suggestions to improve total factor energy efficiency are put forward, such as accelerating the deep integration between industries, improving technological innovation ability, implementing differentiated development strategy, and improving the market mechanism about energy conservation and emission reduction strategies. [ABSTRACT FROM AUTHOR]

With the continuous development of intelligent transportation technologies, new ways of energy usage in transportation continue to emerge, which puts forward new requirements for the planning and design of energy systems. However, comprehensive analyses on the characteristics of transportation energy systems and the development trend of energy usage patterns brought by intelligent technologies have rarely been carried out so far. This paper explores this subject by reviewing the recent development and utilization of intelligent technologies in the transportation system and its impacts on energy usage. This review is carried out from three aspects, covering the representative intelligent transportation and energy technologies on vehicles, infrastructures and systems. The scope is limited within road, railway and water transport domains, with a focus on the recent developments in China as a representative. In terms of vehicles, the development trend of the power systems for new energy vehicles, the characteristics of energy usages in electric vehicles and the effects on energy saving and emission reduction are summarized. In terms of infrastructures, new technologies on smart road, smart port, intelligent railway energy system and the usage of clear energy on electric grid for transportation are reviewed, with the consideration of their potential influences on energy usages and the energy consumption characteristics of typical facilities also being analyzed. As for the transportation system, this review has focused on intelligent and connected transportation systems, train control and autonomous systems, and intelligent shipping system, with the emphasis on the energy saving and emission reduction effects of applying these intelligent technologies. The overall development trend of the transportation energy system is then analyzed based on the above materials, in particular, the future energy usage patterns in transportation system are given and the major challenges and obstacles approaching the future scenarios are also identified. [ABSTRACT FROM AUTHOR]

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric–thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3 contains energy storage and a flexible load. Compared with scenario 1, the total cost of scenario 3 was reduced by 51.5%, and the abandonment cost of wind energy was reduced by 43.3%. The use of a flexible load and energy storage can effectively reduce the cost and improve new energy consumption. By increasing the capacity of the energy-storage device, the wind power is completely absorbed, but the operation and maintenance cost is increased, so the capacity of energy storage equipment is allocated reasonably according to the actual situation. [ABSTRACT FROM AUTHOR]

Occupant behavior has an important impact on building energy consumption, and the accuracy of an occupant behavior model directly affects the reliability of energy consumption simulation results. Ultra-low energy buildings are crucial to achieving building energy conservation and carbon dioxide reduction in China. In order to effectively promote the development of ultra-low energy buildings in Hot Summer and Cold Winter Climate Zones. where most residents adopt a "part-time, part-space" pattern of intermittent energy use behavior, and to solve the problem of poor indoor thermal environments and the high incremental cost of ultra-low energy, the study described in this paper takes Changsha as an example to carry out a multi-objective optimization study on ultra-low energy housing using a probabilistic behavioral model. On the basis of a probability model representing the residents' actual behavior in Changsha, the optimization objective indicators, key variables and the technology benchmarks for ultra-low energy building were determined, then multi-objective optimization was carried out for a range of energy efficient technologies to obtain the Pareto optimal solutions. The results showed that the set of optimal solutions could reduce energy demand by 50.2 to 60.2% and reduce indoor thermal discomfort time by 3.52–11.09% compared with those of a reference base case, which just meets the requirements of the current design standard for energy efficient domestic buildings. An optimum solution for energy savings and indoor thermal comfort, along with economic costs, was identified, which can assist in decision-making by providing different preferences and provide useful reference for the design of ultra-low energy buildings in Hot Summer and Cold Winter Climate regions. [ABSTRACT FROM AUTHOR]

This paper explores the effect of carbon trading on low-carbon transformation of high energy consumption enterprises in China. Based on the mechanism of interaction and restriction among high energy consumption enterprises, carbon verification agencies and the government, a tripartite evolutionary game model is constructed. The three-dimensional dynamic system is built to analyze the behavior patterns of the three parties. The evolution path of the tripartite game is visualized, and the low-carbon transformation states of high energy consumption enterprises in different situations are described. The results show that the high energy consumption enterprises, verification organization and the government cannot reach the optimal game equilibrium (low-carbon transformation, verification and supervision) temporarily when seeking their own interests. The corresponding measures should be taken with different situations of the tripartite game. No matter what strategy the government chooses, the low-carbon transformation could be promoted by carbon trading through carbon verification mechanism. [ABSTRACT FROM AUTHOR]

Energy consumption in transportation industry is increasing. Transportation has become one of the fastest energy consumption industries. Transportation energy consumption variation and the main influencing factors of decomposition contribute to reduce transportation energy consumption and realize the sustainable development of transportation industry. This paper puts forwards an improved decomposition model according to the factors of change direction on the basis of the existing index decomposition methods. Transportation energy consumption influencing factors are quantitatively decomposed according to the transportation energy consumption decomposition model. The contribution of transportation turnover, transportation structure and transportation energy consumption intensity changes to transportation energy consumption variation is quantitatively calculated. Results show that there exists great energy-conservation potential about transportation structure adjustment, and transportation energy intensity is the main factor of energy conservation. The research achievements enrich the relevant theory of transportation energy consumption, and help to make the transportation energy development planning and carry out related policies. [ABSTRACT FROM AUTHOR]

The presence of mycotoxins in cereals can pose a significant health risk to animals and humans. China is one of the countries that is facing cereal contamination by mycotoxins. Treating mycotoxin-contaminated cereals with established physical and chemical methods can lead to negative effects, such as the loss of nutrients, chemical residues, and high energy consumption. Therefore, microbial detoxification techniques are being considered for reducing and treating mycotoxins in cereals. This paper reviews the contamination of aflatoxins, zearalenone, deoxynivalenol, fumonisins, and ochratoxin A in major cereals (rice, wheat, and maize). Our discussion is based on 8700 samples from 30 provincial areas in China between 2005 and 2021. Previous research suggests that the temperature and humidity in the highly contaminated Chinese cereal-growing regions match the growth conditions of potential antagonists. Therefore, this review takes biological detoxification as the starting point and summarizes the methods of microbial detoxification, microbial active substance detoxification, and other microbial inhibition methods for treating contaminated cereals. Furthermore, their respective mechanisms are systematically analyzed, and a series of strategies for combining the above methods with the treatment of contaminated cereals in China are proposed. It is hoped that this review will provide a reference for subsequent solutions to cereal contamination problems and for the development of safer and more efficient methods of biological detoxification. [ABSTRACT FROM AUTHOR]

This paper studies the impact of the development of green finance on China's energy consumption structure. 17 basic indexes and the improved entropy weight method are used to construct the green finance index (GFI). Multiple regression, panel regression, and spatial regression are used to study the impact of green finance on China's traditional energy and renewable energy consumption. The results show that there is a positive spatial spillover effect in the development of green finance among provinces in China. The development of green finance contributes to the conversion of traditional to renewable energy consumption. The effect of green finance on the transformation of energy consumption structure is mainly reflected in the direct effect. The green finance in each province not only helps the local development of green energy but also plays a good role in the production and utilization of clean energy consumption in surrounding provinces. Therefore, the government should support the green finance, reduce traditional energy consumption, and increase renewable energy consumption. [ABSTRACT FROM AUTHOR]

This paper analyzed the current situation and development trends of energy consumption and carbon emissions, and the current situation and development trend of coal consumption in China. In the context of recently established carbon peak and carbon neutralization targets, this paper put forward the main problems associated with the green and low-carbon development and utilization of coal. Five key technological innovation directions in mining were proposed, including green coal development, intelligent and efficient mining, low-carbon utilization and conversion of coal, energy conservation and emission reduction, carbon capture, utilization and storage (CCUS). Focusing on the above technological innovation directions, it is suggested to carry out three basic theories, including the theory of green efficient intelligent mining, clean and low-carbon utilization and transformation of coal, and CCUS. Meanwhile, it is proposed to develop 12 key technologies, including green coal mining and ecological environment protection, efficient coal mining and intelligent mine construction, key technologies and equipment for efficient coal processing, underground coal gasification and mining, ultra-high parameter and ultra-supercritical power generation technology, intelligent and flexible coal-fired power generation technology, new power cycle coal-fired power generation technology, the development of coal-based special fuels, coal-based bulk and specialty chemicals, energy conservation and consumption reduction, large-scale and low-cost carbon capture, CO2 utilization and storage. Finally, necessary measures from the governmental perspective were also proposed. [ABSTRACT FROM AUTHOR]

Mandatory energy efficiency label is an effective way to change consumers' consumption habits and guide them to buy energy-saving appliances. However, few studies concerned about the impact of energy efficiency label on consumers' purchasing behavior. Based on the theory of planned behavior (TPB), social cognitive theory and signaling theory, this paper constructs a theoretical model of the effect of the energy label on consumers' purchasing behavior of energy-saving household appliances. The survey data of 396 household appliance consumers in Mianyang City, China, are collected by the interception method, and the theoretical model is tested by structural equation modeling (SEM). Empirical results of this study indicate that consumers' cognition and perceived value of energy efficiency label significantly affect label trust. Perceived value has a significant impact on consumers' purchasing behavior of energy-saving appliances, while label cognition and label trust indirectly influence consumers' purchasing behavior through the intermediary variable of purchase intention. External environmental factors such as publicity and education as well as subjective norms affect consumers' actual purchasing behavior through the intermediary effect of purchase intention. This study provides important insights into the policy intervention measures to promote consumers' purchasing behavior of energy-saving appliances. [ABSTRACT FROM AUTHOR]