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This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption. [ABSTRACT FROM AUTHOR]

Existing literature ignores to consider multiple types of pollutants when analyzing energy efficiency and its convergence. Under this background, using SuperSBM-GML model, the improved entropy method, and convergence model, this paper calculates the green total-factor energy efficiency and its evolution trend of 35 China's industrial sub-sectors considering multiple pollutants, and analyzes its convergence. The results indicate that the average score of industrial green total-factor energy efficiency is low, and there are significant differences among sub-sectors: "Utilization of waste resources" is the highest, while "Manufacture of paper and paper products" is the lowest. The green total-factor energy efficiency shows an overall upward trend from 2006 to 2021, the main driving force comes from technological progress, but numerous sub-sectors have not sufficiently caught up with existing cutting-edge technologies. Specifically, the growth rate of green total-factor energy efficiency in high-energy-consumption sub-sectors is higher than low-to-medium-energy-consumption sub-sectors. There are both σ-convergence and β-convergence in low-to-medium-energy-consumption group, indicating that development of sub-sectors is stable and outstanding. Nevertheless, the high-energy-consumption group only exhibits conditional β-convergence, revealing an imbalance in energy efficiency development. Consequently, formulating the benchmark level of energy efficiency and developing energy efficiency "leader" system are suggested for low-to-medium and high-energy-consumption sub-sectors, respectively. [ABSTRACT FROM AUTHOR]

Based on panel data for 30 provinces in China from 2000 to 2020, this paper uses entropy method to construct the integrated development indicators of energy Internet, and explores the impact of the development of the energy Internet on reducing carbon emissions in China and its internal transmission mechanism. The research results show that the development of energy Internet can significantly reduce regional carbon dioxide emissions, and increasing the use of renewable energy is an important mechanism for the comprehensive development of energy Internet to curb carbon emissions. Heterogeneity analysis shows that the emission reduction effect brought by the development of energy Internet is more remarkable in the regions with lower carbon emissions and western China. Furthermore, this paper finds that in the setting of the development of energy Internet, the development of renewable energy can promote natural gas consumption and advance energy conversion efficiency. The government's emphasis on regional technology development has an external strengthening effect on carbon emission reduction. The research in this paper expands and diversifies carbon reduction pathways and provides reference for more developing countries to utilize the development of energy internet to achieve carbon reduction. [ABSTRACT FROM AUTHOR]

An in-depth investigation into the effect of embedment in global value chain (GVC) on energy conservation and emissions reduction is of great significance for scientifically assessing the environmental impact of GVC participation, and promoting high-quality development in China. This paper incorporates GVC embedment, energy consumption and carbon emissions into the same analysis framework for the first time. Based on the WIOD database, this paper theoretically and empirically examines the impact and mechanism of global value chain embeddedness on carbon emission reduction from two dimensions: energy consumption intensity and energy consumption structure. The study found that GVC embedment significantly reduced the industry's carbon emission intensity; developing economies' embedment in GVC helped reduce their carbon emission intensity, while the effect was not obvious in developed economies. GVC embedment had a significant inhibitory effect on the carbon emissions in both upstream and downstream industries, but not conducive to carbon reduction of low-tech manufacturing. The mechanism test shows that the GVC embedment not only exhibits the dual effects of energy conservation and emissions reduction, but also has a significant impact on carbon emissions by reducing the energy consumption intensity and improving the energy consumption structure. [ABSTRACT FROM AUTHOR]

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

Given the increasing severity of energy shortages, the exploration of effective strategies to optimize energy structures has become imperative. This requires careful consideration of energy consumption patterns, especially since these data are fundamental inputs for policy formulation. Given the uncertainty in the rate of change in energy consumption, this paper proposes an optimized multivariable grey Bernoulli model that is rooted in the grey Bernoulli model and incorporates background values and genetic algorithms. The grey Bernoulli model effectively linearizes nonlinear problems, thus simplifying computational procedures. In addition, to account for random fluctuations of relevant factors that may affect the model's predictions, this model introduces nonlinear correction terms that allow simulation and prediction values to adhere to the grey index law. The incorporation of background values enhances the model's ability to process information, providing it with a superior grasp of real data. Genetic algorithms can be used to refine the model's parameters, increasing its adaptability and precision. Finally, this paper applies the refined model to examine China's energy consumption patterns, validating its efficacy and versatility. Furthermore, energy consumption patterns for the next four years are forecast, with the analysis revealing that the growth rate of energy consumption from 2021 to 2024 shows a downward trend, particularly notable in 2024, where the growth rate is 1.64%. [ABSTRACT FROM AUTHOR]

CO2 capture from coal power plants is an important and necessary solution to realizing carbon neutrality in China, but CCS demonstration deployment in power sector is far behind expectations. Hence, the reduction potential of energy consumption and cost for CCS and its competitiveness to renewable powers are very important to make roadmaps and policies toward carbon neutrality. Unlike the popular recognition that capturing CO2 from flue gases is technically and commercially mature, this paper notes that it has been proved to be technically feasible but far beyond technology maturity and high energy penalty leads to its immaturity and therefore causes high cost. Additionally, the potential energy penalty reduction of capture is investigated thermodynamically, and future CO2 avoidance cost is predicted and compared to renewable power (solar PV and onshore wind power). Results show that energy penalty for CO2 capture can be reduced by 48%-57%. When installation capacity reaches a similar scale to that of solar PV in China (250 GW), CO2 capture cost in coal power plants can be reduced from the current 28-40 US$/ton to 10-20 US$/ton, and efficiency upgrade contributes to 67%-75% in cost reduction for high coal price conditions. In China, CO2 capture in coal power plants can be cost competitive with solar PV and onshore wind power. But it is worth noting that the importance and share of CCS role in CO2 emission reduction is decreasing since renewable power is already well deployed and there is still a lack of large-scale CO2 capture demonstrations in China. Innovative capture technologies with low energy penalties need to be developed to promote CCS. Results in this work can provide informative references for making roadmaps and policies regarding CO2 emission reductions that contribute towards carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition. [ABSTRACT FROM AUTHOR]

The issues of scaling, pipeline corrosion, and the growth of sludge, bacteria, and algae within circulating cooling water systems have been consistently limiting the enhancement of production efficiency in enterprises. The trending new electrochemical water softening technology, with its environmentally friendly, high efficiency, and low energy consumption characteristics, offers a fresh approach to addressing the stable operation of industrial circulating water systems. This paper begins by detailing the mechanisms of active scale removal, sterilization, and algae control facilitated by electrochemical water softening technology. Subsequently, it compares and analyzes the crucial core components of electrochemical devices (including the selection and preparation of anode and cathode materials, as well as the power system). Furthermore, it introduces the main types and installation modes of electrochemical equipment available in the market. Additionally, it uncovers the key factors influencing the efficiency of electrochemical descaling and validates the impact of electrochemical water softening technology on industrial circulating water systems through illustrative examples. Lastly, the paper concludes by summarizing and forecasting the future development direction of this technology. [ABSTRACT FROM AUTHOR]

China's total energy consumption and production rank first in the world. However, China's energy structure is not reasonable. Therefore, accurate prediction of future energy trends is of great significance for the Chinese government to adjust the energy structure. In this paper, we propose an optimized Grey Euler model with polynomial term, which is abbreviated as OSGEM(1,1,N), to forecast the total energy consumption and production of China in comparison with the commonly used prediction models. The data from 2002 to 2018 are used to simulate the parameters in the proposed model, and the data from 2019 to 2021 are used to test the improved approach. The results show that the OSGEM(1,1,N) model outperforms the other models. Finally, the OSGEM(1,1,N) model is used to forecast the total energy consumption of China from 2022 to 2025 and different results from the previous research results have been obtained. [ABSTRACT FROM AUTHOR]

This paper uses the exogenous shock of China's formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development. [ABSTRACT FROM AUTHOR]

Tourism has become a major driver of China's economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consumption in tourism. Specifically, firstly, this paper decomposes the tourism energy consumption factors in each province into six factors and identifies the driving forces of different factors on energy consumption. Secondly, K-means clustering method is used to classify different provinces into three categories using the latest dynamic influencing factors as clustering factors and provincial targeted suggestions are made according to the characteristics of different categories. This paper combines the LMDI model with cluster analysis to find targeted energy optimization strategies for the energy consumption of the Chinese tourism industry. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to address environmental constraints and achieve high-quality economic development. The existing literature suggests that technological progress has an important impact on energy efficiency, but ignores the role of different paths of technological progress. Herein, the impact of different technological progress paths on energy efficiency was analyzed using the dynamic panel data model and the threshold model based on the energy efficiency measurement using the Meta-frontier and Nonradial Directional Distance Function model. The findings of this study were as follows: (1) During the study sample period, a fluctuating upward trend was observed in China's energy efficiency, and there were significant differences in energy efficiency in different regions of China. (2) The energy efficiency in China could be significantly improved by domestic innovation and regional technology diffusion. Even though the imported technologies do not play a significant role in promoting energy efficiency and have no positive effect on the improvement of energy efficiency from the perspective of domestic technology absorption or foreign technology absorption. (3) The eastern and central regions of China improve their energy efficiency through domestic innovation. However, none of the technological progress paths in the western and low-energy-efficiency regions have played a positive role in promoting energy efficiency. (4) The effect of promoting domestic innovation and regional technology diffusion on energy efficiency is gradually increasing with human capital improvement, and the restriction of foreign technology import on energy efficiency has obviously decreased. In a nutshell, different paths of technological progress have differential effects on energy efficiency improvements. Based on the above results, this paper makes some targeted policy recommendations on the choice of technological progress paths to improve energy efficiency in different regions of China. [ABSTRACT FROM AUTHOR]

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

Typical public institutions such as government offices, hospitals, and schools play important leading and exemplary roles in the green and low-carbon development of the whole society. This paper analyzes the business and energy consumption characteristics of public institutions, as well as the characteristics of personnel travel, and constructs a carbon accounting model for public institutions. It innovatively proposes a methodology for carbon accounting of personnel travel related to public institutions and validates and analyzes it using a comprehensive hospital as an example. The paper analyzes the carbon emission characteristics of various energy types, systems, and types of travelers in hospitals, provides schemes for the transformation of main energy systems, and corresponding emission reduction effects, thereby providing technical support for the full-chain carbon accounting of public institutions. Additionally, this paper explores the carbon reduction pathways for hospitals to support the peak carbon and carbon neutrality goals of public institutions, and promote the high-quality development of public institutions in China. [ABSTRACT FROM AUTHOR]

Under the new development model, the digital economy has become a new engine to promote the green development of the economy and realize the goal of "double carbon". Based on the panel data of 30 provinces in China from 2010 to 2020, this paper empirically investigates the impact of the development of the digital economy on energy and carbon emissions using a series of econometric models such as baseline regression, a mechanism test, and the spatial Durbin model, etc. Common prosperity plays an intermediary role between digital economy development and carbon emissions; digital economic development optimizes resource allocation, effectively solves the problem of uneven resource distribution, and reduces energy and carbon emissions while achieving common prosperity. In addition, green innovation, industrial structure, urbanization level, R&D intensity, and the degree of marketization also have different degrees of influence on energy and carbon emissions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the digital economy development strategy according to local conditions, so as to promote the digital economy to produce a more significant carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

This research investigates how supply chain finance (SCF) affects the investment efficiency of new energy firms by adopting a sample of Chinese listed new energy enterprises from 2011 to 2019. With the help of data envelopment analysis (DEA) combined with the Banker-Charnes-Cooper (BCC) model, we calculate efficiency values and analyze the regression results of the Tobit model, and find that SCF significantly boosts the investment efficiency of new energy corporations by improving their cash-holding position. The results of mechanism analysis also suggest that both supply chain concentration and digital finance positively magnify the relation between SCF and corporate efficiency. After comparing the impact in the new energy firms with different characteristics, we conclude that the SCF effect in promoting corporate investment efficiency is more prominent for non-stateowned new energy enterprises located in the western region that have low equity concentration. The conclusions of this paper shed new light on the fusion of the SCF business into building a new energy industry cluster. [ABSTRACT FROM AUTHOR]

Energy is known as the lifeline of national development, and from the last decade or so, the security of human energy supply has been frequently disturbed by natural disasters due to global warming and frequent geological activities. In this paper, by combing the literature, we found that the past literature on the relationship between energy and natural disasters mainly focused on measuring energy consumption under natural disasters, but less on the integrated evaluation of human economic activities, energy security and natural disasters, and less on the evaluation of the energy governance efficiency of each provincial, municipal, and autonomous region government in preventing and managing exposure to natural disasters from the perspective of Chinese provinces. Therefore, in order to fill the gap in the literature in this field, this paper collects panel data of energy consumption efficiency stages and natural disaster treatment stages from 2013 to 2017 for 30 provinces in China (excluding Hong Kong, Macao and Taiwan as well as Tibet Autonomous Region) and uses the two-stage undesirable dynamic DDF model as a framework to study the relationship between economic, environmental pollution and natural disasters for the sample data and to analyze the phase-by-phase evaluation of energy and natural disaster efficiency and make corresponding policy recommendations. The following conclusions are drawn: (1) The overall efficiency of China's eastern coastal provinces is higher than that of the central and western provinces. (2) The first-stage efficiency of Chinese provinces is better than the second stage, and the difference in efficiency of the first stage of each province is smaller than that of the second stage. (3) In terms of the efficiency of disaster prevention and relief inputs, the efficiency values are generally lower in most regions of China. (4) Finally, the annual efficiency of natural disaster losses is not high in all regions, and the efficiency values are higher in the eastern coastal regions than in the central and western regions. Accordingly, this paper proposes that each province should formulate relevant disaster prevention and economic development strategies according to regional characteristics, while the central government should also propose locally appropriate coordinated governance policies to effectively control carbon dioxide emissions and air pollution, as well as increase disaster prevention publicity and incorporate disaster prevention education-related work into the performance assessment mechanism of local governments to promote the interactive development of the two. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is crucial for China's power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China's novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated. [ABSTRACT FROM AUTHOR]

In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China's provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China's digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China's energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure. [ABSTRACT FROM AUTHOR]

China's green financial pilot policies (GFPPs) are an important start to build a green financial system in China. It is also an important part of achieving the "dual-carbon" goal. This paper calculates China's total factor energy efficiency (TFEE), analyzes the impact of GFPPs on TFEE, and explores the conduction mechanism. The results show that the relationship between GFPPs and TFEE in the pilot areas is statistically significant. The GFPP mainly improves the TFEE through the green innovation effect and the optimization of the energy structure. The GFPP has a significant effect on TFEE enhancement in eastern coastal cities, non-old traditional industrial cities, and non-resource-based cities with high initial energy. China should continue to deepen the pilot work of green finance, formulate differentiated green finance policies, and ensure green technology innovation. This study provides empirical evidence for further optimization of green finance and energy efficiency. [ABSTRACT FROM AUTHOR]

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality. [ABSTRACT FROM AUTHOR]

In the context of increasing global resource and environmental problems, it is of great practical significance to accurately test the impact of various factors on energy use efficiency for maintaining national energy security and formulating relevant policies. This paper measures firms' total factor energy efficiency (TFEE) using the two-stage stochastic frontier method within the data envelopment analysis (DEA) framework, leveraging data from listed firms in China spanning 2010 to 2022. Employing the establishment of free trade zones (FTZs) as a quasi-natural experiment, we apply the staggered differences-in-differences (DID) and stacked DID methods and analyze the impact of FTZs on firms' TFEE. The results show that the establishment of FTZs significantly promotes the improvement of firms' TFEE, and it has a greater promotion effect on heavily polluting, non-manufacturing, state-owned, private, and small-scale firms. The results of the mechanistic analysis showed that the promotion effect of FTZs on firms' TFEE is mainly realized through three channels: increasing government subsidies, reducing the financing constraint effect, and encouraging the technology innovation effect. Furthermore, industry-level decomposition results indicate that the surge in industry energy efficiency primarily results from improvements within firms rather than inter-industry variations. This paper's results propose that countries can enhance energy efficiency by progressively endorsing the implementation of FTZs. [ABSTRACT FROM AUTHOR]

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime. [ABSTRACT FROM AUTHOR]

This paper proposes a temperature-adaptive radiative cooling (TARC) coating with simple preparation, cost effectiveness, and large-scale application based on a thermochromic powder. To determine the energy efficiency of the proposed TARC coating, the heat transfer on the surface of the TARC coating was analyzed. Then, a typical two-story residential building with a roof area of 258.43 m2 was modeled using EnergyPlus. Finally, the energy-saving potential and carbon emission reduction resulting from the application of the proposed TARC roof in buildings under different climates in China were discussed. The results showed that the average solar reflectivity under visible light wavelengths (0.38–0.78 μm) decreases from 0.71 to 0.37 when the TARC coating changes from cooling mode to heating mode. Furthermore, energy consumption can be reduced by approximately 17.8–43.0 MJ/m2 and 2.0–32.6 MJ/m2 for buildings with TARC roofs compared to those with asphalt shingle roofs and passive daytime radiative cooling (PDRC) roofs, respectively. This also leads to reductions in carbon emissions of 9.4–38.0 kgCO2/m2 and 1.0–28.9 kgCO2/m2 for the buildings located in the selected cities. To enhance building energy efficiency, TARC roofs and PDRC roofs are more suitable for use on buildings located in zones with high heating demands and high cooling demands, respectively. [ABSTRACT FROM AUTHOR]

In recent years, companies have employed numerous methods to lower expenses and enhance system efficiency in the oilfield. Energy consumption has constituted a significant portion of these expenses. This paper introduces a normalized consumption factor to effectively evaluate energy consumption in the oilfield. Statistical analysis has been conducted on nearly 45,000 wells from six fields in China. Critical factors such as lifting method, daily production, pump depth, gas–oil ratio (GOR), and well deviation angle were evaluated individually. Results revealed that higher production could lead to lower normalized consumption for beam pumps, progressive cavity pumps, and electric submersible pump systems, thus enhancing system efficiency. Additionally, a higher GOR might result in lower normalized consumption for the beam pump system, while the deviation angle of the well showed negligible impact on the normalized consumption factor. This manuscript offers a method to assess the impacts of artificial lift methods on production and discusses suggestions for reducing consumption associated with each lifting method in the oilfield. [ABSTRACT FROM AUTHOR]

Promoting high-quality economic development, boosting high-level ecological environment protection, and integrating deeply digital economy and energy efficiency has become a general trend. This paper, based on 284 prefecture-level Chinese cities from 2008 to 2018, uses the entropy weight method and super-efficient SBM-DEA model to measure the digital economy and energy efficiency (decompose into energy pure technical efficiency and energy scale efficiency), respectively. After the spatial and temporal evolution characteristics, a fixed-effect model is constructed to study the impact of the digital economy on energy efficiency. The research results show that: (1) the development of the digital economy effectively promotes energy efficiency, mainly reflected in the improvement of pure technical efficiency of energy; (2) the development of both the digital economy and the energy efficiency in China has complex spatial and temporal evolutionary characteristics, and there is heterogeneity in the impact of the digital economy on energy efficiency at the temporal and spatial levels; and (3) digital economy effectively improves energy efficiency by reducing unnecessary energy consumption through the R&D innovation effect. The development of the digital economy has far-reaching significance on the optimization and upgrading of the economic system, accelerating the improvement of energy efficiency and achieving green transformation. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]

Public institutions, emblematic of public infrastructure, exhibit extensive reach and operational scope, positioning them as vanguards in China's dual carbon initiatives and serving as exemplars. Electricity and natural gas predominantly fuel the operations of public institutions. Notably, the fixed commute routes and consistent procurement patterns of office personnel yield a standardized energy consumption profile within these entities. Researching carbon emissions related to commuting and evaluating procurement strategies for reducing carbon footprints in public institutions demonstrate a precision-tailored approach. This paper, through an analysis of the energy consumption characteristics, utilization structure of public institutions, and the commuting behaviors and procurement practices of office personnel, establishes a bespoke carbon accounting model specifically designed for public institutions, seamlessly embedded within a comprehensive platform. By providing fundamental methodological frameworks and advanced technological foundations for carbon accounting in public institutions across China, this work propels the nation's efforts towards carbon peak and ultimately carbon neutrality. [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]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building's exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs. [ABSTRACT FROM AUTHOR]

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China's inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships. [ABSTRACT FROM AUTHOR]

As an essential material basis and power source for economic and social development, Western China's low energy use efficiency has hindered its sustainable economic development. This study aims to evaluate the total factor energy efficiency of the region and identify its influencing factors. A three-stage DEA model was used to measure the efficiency of 11 provinces from 2006 to 2021, and the Tobit model was employed to investigate internal factors. The findings show that (i) external environmental factors and stochastic perturbations have a significant impact on TFEE in the western region, overestimating integrated efficiency and scale efficiency and underestimating pure technical efficiency. (ii) the study of external influencing factors finds that the level of economic development increases input redundancy; the industrial structure increases capital input and labor input redundancy while decreasing energy input redundancy; and the energy consumption structure increases capital input and energy input redundancy while decreasing labor input redundancy. (iii) the study of internal influencing factors finds that the level of scientific and technological innovation, the level of openness to the outside world, and the TFEE have a positive correlation. In contrast, the intensity of environmental regulation has a negative correlation. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

China's rapid economic development has consumed a large amount of energy, causing serious environmental pollution problems and contributing to global warming. This paper calculates the carbon emissions of the fishery sector and uses Random Forest (RF) for the first time to analyze the influencing factors of future carbon emissions. The results of the study show that increasing carbon sinks dominate the reduction of carbon emissions in the fisheries sector. Carbon sinks will continue to dominate emission reductions in the fisheries sector if positive mitigation measures are taken. Continuing the current pattern of fisheries development, the fishery population has a significant impact on future carbon emissions. Per capita incomes under a crude economic model will inhibit carbon emission reductions. The research results can provide guidance for the development of fishery low carbon economy and the formulation of emission reduction policies. [ABSTRACT FROM AUTHOR]

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [ABSTRACT FROM AUTHOR]

Recycling carbon tax revenue is a significant part of the design of a nation's carbon tax system. The impact of different recycling principles on the socio-economy and carbon emission reduction varies greatly. Under the background of China's goal of reaching peak carbon emissions, this paper designed three carbon tax recycling principles based on the goal of Nationally Determined Contributions (NDCs), including recycling principles based on output ratio, export ratio, and carbon intensity. A recursively dynamic CGE model was developed in this study to evaluate the economic effects of these three carbon tax recycling principles based on tax neutrality under China's goal of reaching peak carbon emissions. Carbon tax recycling measures could mitigate the economic losses caused by carbon tax, and the results could be greatly affected according to different recycling principles. The results showed that: 1 The recycling principle based on output ratio could reduce economic losses and improve households' social welfare. 2 China's carbon intensity in 2030 could be reduced by more than 65% compared with that in 2005, and the proportion of non-fossil energy in primary energy consumption could reach 25% using the recycling principle based on carbon intensity, but the economic loss mitigation effect of the recycling measures based on this principle was small. 3 Adopting the recycling principle based on export ratio would narrow the competitive gap between China and foreign countries, and the exports of high-carbon-intensity goods would not increase under this principle. 4 According to principles based on output ratio and export ratio, the NDC goal of non-fossil energy accounting for about 25% of primary energy consumption by 2030 could not be achieved, and only the NDC goal of reducing carbon emissions per unit of GDP by more than 65% compared with that of 2005 could be achieved. In brief, as an essential policy tool to achieve China's goal of reaching peak carbon emissions, the carbon tax should be implemented as soon as possible. At the same time, according to China's national conditions, a targeted choice of carbon tax recycling principle is needed. The role of the carbon tax in the process of achieving the goal of Nationally Determined Contributions should be fully utilized. [ABSTRACT FROM AUTHOR]

With the implementation of China's carbon-peaking and carbon-neutrality strategy, new energy will achieve leapfrog growth. Due to the good economics of distributed new-energy generation, it can not only save users' own investment, but also help to achieve local consumption of new energy. However, it will also bring about a series of incremental costs to the power grid. This paper first enumerates the concept, development status and scheduling mode of a distributed new-energy storage system. Based on the above, it establishes a new-energy power generation model and an energy storage system charging and discharging model, and proposes a global optimization scheduling model for a distributed new-energy storage system, considering the time-of-use electricity price and taking the lowest total operating cost of the distributed new-energy power generation system as the objective function. Finally, it proposes a distribution network incremental cost analysis model based on the penetration of distributed new energy. The calculation results show that the incremental cost of grid-connected distributed new energy is 1.0849, 1.2585 and 1.3473 yuan/kWh, respectively, which indicates that the global dispatching model can optimize the power consumption structure of a distributed power generation system, and has the function of peak shaving and valley filling, but the incremental cost of the distribution network will also increase. [ABSTRACT FROM AUTHOR]

Rice production is the agricultural activity with the highest energy consumption and carbon emission intensity. Water and fertilizer management constitutes an important part of energy input for rice production and a key factor affecting greenhouse gas emissions from paddy fields. Water–fertilizer integration management (AIM) is an automated water and fertilizer management system for large-scale rice production, which can effectively save water and fertilizer resources. At present, the energy utilization and environmental impact of AIM in rice production are not clear. To clarify whether AIM is a water and fertilizer management measure that combines energy conservation and carbon emission reduction, a comparative study between the widely used farmers' enhanced water and fertilizer management (FEM) in China and AIM was conducted in this paper. Field experiments were conducted to evaluate the rice yield, carbon emission, energy utilization, and economic benefits of the two management methods. The results showed that AIM reduced water and fertilizer inputs, energy inputs, and economic costs by 12.18–28.57%, compared to FEM. The energy utilization efficiency, energy profitability, and energy productivity under AIM were improved by 11.30–12.61%. CH4 and N2O emissions and carbon footprint were reduced by 20.79%, 6.51%, and 16.39%, respectively. Compared with FEM, AIM can effectively improve the utilization efficiency of water and fertilizer resources and reduce carbon emissions. This study presents a mechanized water and fertilizer management approach suitable for large-scale rice production systems in China. By analyzing rice yield, resource utilization efficiency, and environmental benefits, AIM can serve as a crucial management strategy for enhancing productivity, economic returns, and environmental conservation within profitable rice production systems. In the future, further investigation into the impact of AIM on the microbial mechanisms underlying rice yield formation and greenhouse gas emissions is warranted. [ABSTRACT FROM AUTHOR]

In the context of carbon peaking and neutrality goals, this paper examines the impact of industrial clusters on firms' carbon emissions based on listed manufacturing firms in China from 2008 to 2020. To avoid the endogeneity issue, we constructed an industrial cluster database ten years earlier than the sample period. The results of the empirical analysis suggest that industrial clusters are conducive to decreasing carbon emissions. The stronger the industrial cluster, the lower the carbon intensity. Examining the channel reveals that industrial clusters have an emission reduction effect through digital transformation. The heterogeneity analysis demonstrates that the variations in industrial clusters, including pollutant emissions, energy intensity, and energy consumption structure, significantly affect a firm's carbon emission reduction performance. In addition, environmental policies and the internal and external green development impetus of firms also affect emission reduction behavior. Our findings provide the rationale for governments to promote green transformation and enhance digital competitiveness through industrial cluster development. [ABSTRACT FROM AUTHOR]

The energy consumption of office buildings has increased sharply since the 21st century with the increasing urbanization coverage. At this stage, the energy consumption of office buildings in China mainly comes from air conditioning and lighting. This paper obtains the regression equation of natural lighting illumination and radiant heat gain under different curtain conditions through experiments and simulations, and gives the theoretical calculation process of curtain regulation of office thermal and light environment and energy consumption. DeST and Ecotect software were used to analyze the thermal and light environment and energy consumption of the office. The results show that setting curtains in the room can effectively improve the indoor thermal light environment, the higher the transmittance of curtains the lower the lighting energy consumption, the higher the number of high temperature hours of natural room temperature and the greater the radiant heat gain. The room with thick curtains has the lowest energy consumption for air conditioning in summer, followed by the room with thin curtains, and the room without curtains has the highest energy consumption for air conditioning. The room without curtains has the lowest energy consumption for artificial lighting, the room with thin curtains has the second lowest, and the room with thick curtains has the highest electricity consumption for artificial lighting. The air conditioning energy consumption accounts for the main building energy consumption, followed by the lighting energy consumption. After setting curtains in the office, the air conditioning energy consumption caused by the solar radiation heat entering the room through the exterior windows is smaller than the lighting energy consumption caused by artificial lighting. Therefore, the installation of internal shading with moderate transmittance and good thermal insulation performance can make the energy saving of lighting and air conditioning energy consumption reach the optimal value. [ABSTRACT FROM AUTHOR]

Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the "energy performance difference between housing units" (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units. [ABSTRACT FROM AUTHOR]

Green consumption has become an important part of an environmentally friendly society. Policy guidance can effectively promote green consumption. Green consumption policies are extensive because of the diversity of green consumption objects. However, the various objects are not completely isolated, and policy must have a certain level of consistency. Therefore, from the perspective of green consumption objects, this paper first uses the text mining method to mine 308 green consumption policies in China, analyzing the objects covered in China's green consumption policies. Then, the evaluation system of green consumption policy consistency was built, seven key policies were selected, and the PMC-index method was used to measure policy consistency. The results show that (i) the main objectives of China's green consumption policy are the promotion of green products and the efficient use of energy. Green services and green recycling have been subject to fewer policy efforts. (ii) In terms of the links involved in green consumption, China pays more attention to the supply and the consumption chain and less attention to the recycling chain. (iii) The quantitative evaluation results of the seven typical policies show that the policy consistency is good but not perfect. The policy shortcomings are mainly reflected in the incentive methods. Therefore, it is recommended to further improve the green consumption objectives and enrich the means and methods of green consumption guidance. [ABSTRACT FROM AUTHOR]

Since the power generation structures and load characteristics in each province in China are quite different, the distribution of peak regulating resources and demands are extremely imbalance. Restricted by a low power marketization degree, peak regulating resource shortages, and transmission channel blocks, the efficient utilization of new energy is facing greater pressures. In order to improve the mutual aid in regional power grids and to obtain more precise simulation results, this paper proposes a two-layer optimization dispatching model, considering the mutual aid of peak regulation resources between each province. It determines the optimal startup mode and the units' power output in each province and obtains the power output arrangements for all the units and the technical and economic indicators. The model and the solution method are original and innovative. And it effectively solved the unequal distribution problem between the peak regulating demands and resources of each provincial power grid. Finally, taking an actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the utilization rate of new energy, which verifies the effectiveness and feasibility of the proposed model and methods presented in this paper. [ABSTRACT FROM AUTHOR]

Encouraging farmers to adopt greener and cleaner energy is crucial for reducing energy pollution and achieving carbon neutrality goals. In rural China, the decision making of farmers is often closely related to the whole family. At different stages of the family life cycle, the family has different characteristics, which leads to heterogeneity in the focus and final decision of farmers in adopting living clean energy. Therefore, this paper studies the farmers' living clean energy adoption behavior from the perspective of the family life cycle. It is helpful to identify the different policy needs and the evolution of farmers in different stages in order to provide a reference and inspiration for encouraging the adoption of living clean energy by farmers and for promoting the development of clean energy in rural areas. Based on the survey data of 1382 farmers in Jiangxi Province, this paper uses a multiple linear regression model to explore the impact of the family life cycle on farmers' clean energy adoption behavior. The results show the following: (1) The family life cycle has a significant impact on farmers' living clean energy adoption behavior, which is reflected in four aspects: energy demand, livelihood strategy, health demand and support burden; (2) Awareness of environmental ecology and frequency of government promotion have significant positive effects on farmers' living clean energy adoption behavior, while gender has significant negative effects on farmers' clean energy adoption behavior; (3) There are also differences in the influencing factors of farmers' living clean energy adoption behavior at different stages of the family life cycle. Therefore, when promoting clean energy in rural areas, a precise clean energy incentive mechanism should be adopted to treat families in different family life cycle stages differently. [ABSTRACT FROM AUTHOR]

The significant acceleration of China's urbanization process has greatly promoted economic development. At the same time, the massive construction of urban buildings has also caused many problems such as environmental pollution and increased energy consumption. Therefore, in architectural design, it is particularly important to pay attention to the sustainable development of the environment, handle the relationship between good people and nature under the guidance of the concept of green development, and focus on the recycling of resources. Focusing on resource utilization in architectural design and based on certain planning can better control the construction process of urban buildings, save energy consumption, reduce construction costs, and contribute to the green development of cities. The comprehensive evaluation of energy conservation and emission reduction of construction projects is a classical MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) method has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are used as a tool for characterizing uncertain information during the comprehensive evaluation of energy conservation and emission reduction of construction projects. In this paper, the single-valued neutrosophic number Logarithmic TODIM (SVNN-LogTODIM) method is built to solve the MAGDM under SVNSs. In the end, a numerical case study for comprehensive evaluation of energy conservation and emission reduction of construction projects is given to validate the proposed method. [ABSTRACT FROM AUTHOR]