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Guiding industries to produce in an effective and environmentally sustainable manner has become a key issue for governments around the world. Over the past 15 years, series of regional and industrial development policies have been introduced by the Chinese government. This has influenced the geographical distribution and energy efficiency performance of China’s industries. This paper quantifies the influence of enterprises geographical distribution on energy efficiency improvement in the paper industry and aims at providing some helpful suggestions on industrial development to policy makers. The main results show that, firstly, the paper industry in eastern China shows obvious characteristic of agglomeration. The average location quotient is 1.2278. However, following the industrial and regional development policies, the characteristic of agglomeration is weakening. Secondly, only when agglomeration reaches a certain level (location quotient is above 0.5447) will industrial agglomeration positively impact on industrial energy efficiency improvement (a 1% increase in agglomeration will increase dynamic energy efficiency by at least 0.23%). Thirdly, the regional development policies carried out by the government slow the pace of energy efficiency improvement in China’s paper industry. The empirical results indicate that the government needs to consider regional characteristics and consciously guide industrial enterprises to concentrate in the dominant area. [ABSTRACT FROM AUTHOR]

China’s paper industry consumes a lot of energy and emits huge amounts of pollutions. The Chinese government over the past decades has done a lot to promote energy efficiency of the industry. This paper studies the impact of industrial polices to improve energy efficiency. The energy efficiency change is firstly studied under a framework of total factor efficiency. On this basis, different econometric models are built to discuss the detailed energy efficiency characteristics of the paper industry. The main results are as follow: (i) ownership structure did influence the energy efficiency, but the influence is different in different conditions; (ii) energy efficiency of the paper industry can be improved by economies of scale; (iii) market competition can to some extent overcome the motivation and supervision problem that often occur in state-owned companies; (iv) energy price cannot play the role of resource allocation well in China. Finally, based on our findings, some suggestions are provided. [ABSTRACT FROM AUTHOR]

China's paper industry accounts for a larger proportion of the global paper industry and consumes large amounts of energy. Energy conservation and pollution reduction of the industry have become the focus of China's national development strategy. Energy efficiency is an important index which directly determines the amounts of energy consumption and pollution emissions. To understand the energy efficiency evolution of China's paper industry, this paper establishes an input-output data of the paper industry for 29 provinces in China and applies DEA method and econometric models to estimate and comprehensively discuss the energy efficiency issues based on the framework of total factor efficiency. Our results indicate that China's paper industry has a large energy saving potential even though its energy efficiency has improved during the period 1990–2013. The saving potential is about 65% under the meta-frontier and 44% under the group-frontier. Moreover, there are significant differences between different regions in production technology and energy efficiency. The performance of eastern China is better than other regions. But the technology gaps between the different regions are shrinking and converging over time. Finally, some policy recommendations are proposed to improve the energy efficiency of the industry. [ABSTRACT FROM AUTHOR]

China has become the world largest paper and paperboard producer. However, pulp and paper industry has involved high resource consumption and severe pollution especially for water. This research has assessed the eco-efficiency of China's pulp and paper industry at the national level and provincial level. An overview regarding the cleaner production of China's pulp and paper industry was presented to reveal the measures for reducing environmental impact in the last two decades. Slacks-based measure was used to analyze the efficiency levels of 16 provinces' pulp and paper industries. To uncover the underlying causes of eco-efficiency performance, Malmquist–Luenberger index was calculated to discover the drivers of productivity growth of pulp and paper industries. Our results showed that the pollution treatment of China's pulp and paper industry has made progress in terms of water consumption and water pollution, although the absolute amount of pollution discharge is still large. Chemical oxygen demand emissions are still the first critical influencing factor of pulp and paper industry's inefficiency. Furthermore, efficiency progress was the dominating contribution of the industry's productivity growth between 2010 and 2013. The policies for adjusting the industrial structure of pulp and paper industry have resulted in the scale effects through eliminating backward production capacity and accelerating merger and acquisitions. Moreover, the productivity of pulp and paper industry was underestimated when the undesirable outputs were ignored. It indicates that the stricter environmental regulations have positive effects on paper companies to internalize environmental pressures in the production activities through environmental management. In the future, pulp and paper companies should further internalize the cost of pollution treatment through scale effects and technology improvement. [ABSTRACT FROM AUTHOR]

In 2017, a carbon emissions trading market will be launched nationwide by the China government. Calculating the enterprise carbon emissions is an important prerequisite and basis for trading. This paper discussed types of greenhouse gases, calculation boundaries and methods, energy consumption, carbon emissions, and intensity of a representative integrated pulp and paper mill in China based on China Guidance and Greenhouse Gas Protocol Tools. The results showed that there were 435,000 tonnes (t) of CO2 emissions from that mill in 2014 that did not contain emissions of biomass energy, which was 8 times higher than that of fossil energy. The pulp carbon intensity based on the mill was 0.230 t CO2/Adt, which accounted for 7.50% to 57.4% in other pulps' based on the product. Intensity based on Gross Domestic Product (GDP) was 1.090 t CO2e/1000 USD and accounted for 56.8% intensity in the China paper industry. The intensity based on sales accounted for 52.6% in the firstclass enterprise in the developed country. It also showed that intensity was influenced by the species of raw material, energy, and products, which provided the mill with measures for energy saving and emissions reduction to obtain the redundant carbon emissions in the trading. [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]

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]

Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]

Papermaking is a water intensive industry. The growth of paper demand will further intensify the need of fresh water resources. Papermaking involves complex processing routes. Previous investigations of water consumption in papermaking industry have focused primarily on key processes and pathways, ignoring the impacts of many intermediate and inter-related processes in paper production cycles and there underestimating the sustainability impacts. Herein, the results of a life cycle analysis of water consumption for papermaking industry in China using an extensive system boundary that includes the water embedded in intermediate processing are presented. The results show the life cycle water consumption and water saving potentials for different papermaking pathways. The advocacy of "forest pulp and paper integration" may have few practical contribution to reducing water consumption due to the low indirect water consumption of raw materials and energy for transportation. [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]

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]

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]

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]

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]

This paper investigates whether and how tax and fee support policies at the firm level in China influence the total factor energy productivity of enterprises. Using panel data from Chinese public trading companies for the period 2004–2020, this study employs a panel model for estimation. The findings suggest that tax rebates contribute to the improvement of the total factor energy productivity of enterprises. Specifically, a 1% increase in tax refunds leads to a growth of approximately 0.008% in total factor energy productivity. Robustness tests and endogeneity checks confirm the validity of the results. Heterogeneity analysis reveals that tax rebates have a significant impact on state-owned enterprises, small- and medium-sized enterprises, and non-technology firms in terms of enhancing their total factor energy productivity. Mechanism analysis indicates that tax rebates facilitate firms in alleviating financing constraints and enhancing their innovation capabilities, thereby improving energy efficiency. The research findings of this paper provide empirical support for optimizing policy supply, improving energy usage efficiency, and promoting the development of a globally sustainable economy. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

To reduce energy use and achieve a low carbon production in pulp and paper industry (PPI), it is essential to improve energy efficiency in pulp and paper production. In this study, the energy savings from the PPI were estimated based on a techno-economic analysis by EAGER tool for PPI at the factory level. The structure and implement procedure were introduced initially. Two typical pulp and paper mills from China were determined to estimate the impact of selected energy efficiency technologies (EETs) on energy savings as the cases in the base year. One Factory (A) only produced paper products (i.e. corrugated paper and Kraft linerboard) while the other Factory (B) also produced market pulp besides sack paper. In totally 22 selected EETs were applied to assess their saving potential in Factory A, and 29 measures were applied in Factory B. The results show that 2290 TJ and 1179 TJ of final energy could be saved annually in Factory A and B, corresponding to reduction of 29.4% and 13.6% total energy consumption respectively for the case factories. The CO 2 reduction potential was 32.3% for Factory A compared with that of 16.6% in Factory B. It suggests that the pulp and paper mills should encouraged to use this method estimating energy saving potential, and improve their energy efficiency with suggested measures in order to reduce energy consumption and related carbon emissions further in the coming years. • Energy savings of pulp and paper production were estimated at the factory level. • A techno-economic evaluation tool was applied to access the energy savings and carbon emission reductions. • Two typical pulp and paper factories from China were investigated to estimate their energy savings in the base year. • It is useful to assess energy savings and select feasible EETs in achieving energy saving and emission reduction targets. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this paper broadens the perspective by evaluating energy consumption across various cryptocurrencies. We analyze nine cryptocurrency projects, chosen for their market value, technology, and data availability. These span a spectrum from pioneering to emerging digital coins, offering a holistic view of the crypto realm. To contextualize, we juxtapose the energy usage of these digital currencies with traditional payment means like Visa and Mastercard. Our analysis shows vast differences in energy use among cryptocurrencies, largely tied to their consensus algorithms. Notably, while Bitcoin stands out as highly energy-intensive, several newer digital currencies have energy footprints mirroring those of conventional payment methods. Additionally, CO2 emissions estimation presents challenges due to variances in miner locations and regional energy sources, with potential higher emissions if concentrated in carbon-intensive regions like China. Nonetheless, the silver lining emerges as many cryptocurrencies, especially those beyond Bitcoin, register considerably lower CO2 emissions. By moving the lens beyond Bitcoin, this paper paints a more nuanced picture of the environmental ramifications of the crypto world. [ABSTRACT FROM AUTHOR]

After the carbon peaking and carbon neutrality goals were proposed, different institutions and scholars carried out research on China's medium- and long-term mitigation pathways. Through the literature review of China's research on carbon neutrality, this paper finds that the zero-carbon energy transition is the key to achieving carbon neutrality. The main driving factors of reducing energy-related carbon emissions include the cleanliness of primary and secondary energy supply systems, the electrification of energy consumption and the development of hydrogen energy, energy efficiency improvement, carbon capture and storage (CCS), and negative emissions. Furthermore, based on the literature, this paper conducts a quantitative comparative analysis, and selects key indicators for comparative analysis and summary from seven dimensions, including economic and social development, carbon emission pathways, primary energy consumption, final energy consumption, final hydrogen energy consumption, electricity demand and supply, and installed capacity and structure of power generation. Main conclusions are as follows: (i) In terms of carbon emission pathways, the institutions generally believed that China will peak carbon emissions around 2028 and achieve carbon neutrality between 2050 and 2060. Achieving net-zero or near-zero emissions first in the power sector is the key to carbon neutrality across the society; (ii) In terms of energy supply, it is a consensus to increase the proportion of clean energy and reduce carbon emissions from the source. The proportion of clean energy in primary energy will increase to more than 85%, and the proportion of clean energy power generation and installed capacity will reach more than 90%; (iii) In terms of energy use, electricity will become the core of final energy consumption in the future. The predicted electricity consumption across the society will range 14.3–18.4 PWh, and the predicted electrification rate will exceed 65%. [ABSTRACT FROM AUTHOR]

Currently, coal is still the main energy source for China's economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China's coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China's coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China's coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China's coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1,055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential, using present-day discharge from the CaMa-Flood model (2011-2015, 0.05 degree), and future discharge from the WaterGAP2 model used for ISIMIP2b (2021-2030, 0.5 degree). The dam count of 1,055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1,242 MW in the 1980s to 69,199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+16,854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+18,223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1,334,683 MW, where Laos is the highest at 514,887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2,000,000 MW. Laos and China are the highest at around 900,000 MW each, together forming over 80% of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better planning and management in the future hydropower hotspot Laos. The Mekong dam database is publicly available at https://doi.org/10.21979/N9/ACZIJN (Ang et al., 2023). [ABSTRACT FROM AUTHOR]

The escalating issue of global climate change necessitates urgent measures to reduce carbon emissions globally. Within this context, the construction industry emerges as a critical sector to address given its high energy consumption, substantial CO2 emissions, and low utilization rate. Therefore, it is pivotal to foster energy conservation and reduce emissions in this sector. To this end, this paper delineates two primary objectives: (1) identifying optimal research methodologies and index parameters for evaluating carbon emission efficiency in the construction industry, and (2) assessing the variance in carbon emission efficiency at disparate stages and regions. Leveraging the Malmquist index, we scrutinize the carbon emission data from 30 Chinese provinces spanning from 2010 to 2019. Our findings indicate a geographical dichotomy in China's construction industry's carbon emission efficiency—lower in the west and higher in the east. Additionally, this study delves into the distinguishing features of emission efficiency alterations across regions, the main influencing factors, and avenues for enhancement. Subsequently, it proposes policy recommendations tailored to the unique attributes of various regions and the overarching framework. [ABSTRACT FROM AUTHOR]

A Chinese solar greenhouse (CSG) is an agricultural facility type with Chinese characteristics. It can effectively utilize solar energy during low-temperature seasons in alpine regions. The low construction and operation costs make it a main facility for agricultural production in the northern regions of China. It plays an extremely important role in "Chinese vegetable basket projects". Energy is one of the important issues faced by CSGs. The better climate resources in the northern regions of China make it possible to apply solar energy as a green and sustainable energy source in the production of CSGs. Faced with the increasingly serious environmental problems of the new century, the Chinese government has made a decision and put it into practice to improve the rate and efficiency of solar energy utilization in agricultural facilities. In this paper, we summarize the research on the application of photovoltaic power generation and solar thermal technology in CSGs. The application of these advanced solar technologies has made great progress. With the further improvement of economic benefits and the establishment of relevant support policies and incentive mechanisms, the combination of CSG and solar energy technology will have certain application prospects and satisfy China's requirements for long-term sustainable development. [ABSTRACT FROM AUTHOR]

The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered. [ABSTRACT FROM AUTHOR]

• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China's Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy. [ABSTRACT FROM AUTHOR]

The development and utilization of biomass energy based on the thermochemical conversion of crop biomass to produce hydrogen are of great significance for promoting China's ecological civilization construction, energy revolution, and low-carbon economic development. Henan province is one of the largest agricultural and pasturage provinces in China. Based on the analysis of the status and trends of Henan's biomass energy (BE) development, this paper summarizes the present status of the construction of ecological civilization (CEC) and the factors restricting its development. Challenges in developing biomass energy are analyzed systematically, and strategies and key technical directions for future biomass energy development are discussed. Finally, the paper presents countermeasures and suggestions for CEC based on the development of BE, which will vigorously promote the development and utilization of BE and the process of CEC. This research provides a reference for the further development of BE and CEC in the future. [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]

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]

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]

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]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [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]

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]

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

Traditional historical buildings carry the culture and spirit of China for thousands of years and have a unique charm that modern buildings do not have. However, traditional historical buildings have gradually declined over time, and their restoration not only takes time and materials but also causes certain harm to the environment. Therefore, this paper has aimed to explore how traditional historical buildings can develop in a sustainable and ecologically adaptive image in modern times. For traditional historical buildings, this paper has taken traditional cave dwellings in northern Shaanxi as an example to analyze their ecological adaptability, and based on the analysis results, traditional cave dwellings have been transformed. Specifically, this paper has taken the comfort of residential houses as the evaluation standard and has selected the thermal stability of the building and the energy consumption of heat supply as indicators to compare and analyze the traditional cave dwellings and the renovated cave dwellings. The experimental results of this paper have found that the traditional historical buildings are the crystallization of the wisdom of the ancients for thousands of years. However, due to the limitation of technology, the lighting and thermal stability of traditional cave dwellings are not high. After scientific design, the lighting and thermal stability of the transformed cave are 100% and 30% higher than those of the traditional cave, respectively, and the heating energy consumption is reduced by 50%. [ABSTRACT FROM AUTHOR]

The utilization efficiency of China's rural collective resource assets has always been at a low level, and excessive energy consumption and input have become the main limiting factor, which seriously affects the overall improvement of agricultural modernization and rural people's livelihood. In this paper, time measure and space measurement are adopted alternatively, and spatiotemporal vertical-horizontal progressive deduction is built with the improved model, so as to set up a perfect spatiotemporal dynamic effect evaluation system: First of all, improve the U-HDM-GML index model, study on the improvement strategy for input redundancy and unexpected output redundancy in the utilization efficiency of rural collective resources assets in various provinces of China, as well as the equilibrium rupture relation between the two from the perspective of global space and time, and at the same time of fulfilment of time measurement and estimation, provide support for better expressing the spatial effect in terms of improving approach and strategy; secondly, measure the improvement strategy of the total factor productivity of research samples with the spatial expletory ESDA test method, and it is also applicable to global spatial effect evaluation system; finally, apply FE-SDM to further analyze the difference of spatial effect of total factor productivity in rural resource asset utilization. Through analysis of the significant difference characteristics of the research samples with different geographical and spatial characteristics, the strategy deficiency in terms of difference discrimination is made up. Conclusions. This paper carries out the time and spatial effect measurement of total factor productivity for the performance of pilot strategies in various provinces of China in the last ten years and puts forward a more beneficial improved strategy and mechanism, preferentially selected according to the direction indicated by good research indicators, gets the redevelopment strategy for improving the utilization efficiency of areas at a low level of resources utilization currently, and indicates the direction for tapping the full potential of existing rural resources according to the practical conditions at local places, in the hope of establishing the trans-provincial cooperation supply and demand system based on the new rural collective economic organizations (cooperatives) after the reform experimentation and optimizing the spatial spillovers of rural resources assets, so as to make a breakthrough in the barrier due to growth of technical efficiency. [ABSTRACT FROM AUTHOR]

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]

In the conditions of the spread of globalization processes, growing interdependence, and interconnectedness of countries, a significant intensification of the academic society efforts to ensure national energy security and independence is necessary. The purpose of the paper is to provide a systematic review of the scientific environment aimed at analysing the contents and features of scientific publications dealing with new trends and patterns in sustainable energy consumption. In order to create a semantic and definition basis and research the main trends of scientific publications on the subject of developing energy efficiency and sustainable consumption, the paper carried out a bibliometric analysis concerning the categories «energy efficient» and «energy consumption» using the Scopus and VOSviewer tools. The main question is, what are the main scientific and research trends and patterns in the field of energy efficiency and sustainable consumption? For this purpose, the quantity and quality tendencies of the scientific articles which are studying issues connected with energy efficiency and sustainable consumer behaviour were determined. A sample of more than 24000 scientific publications on this issue was generated from publications indexed by the Scopus database. The study period covers 2001-2021. On the global level, the analysed investigated area is represented by the countries: China, the USA, and India. VOS viewer tools were used to systemize 5 clusters of scientific articles that analysed sustainable energy consumption from different points of view. Identification of regularities in the developing of scientific approaches to energy-efficient consumption and developing sustainable energy sector was carried out using bibliometric analysis, which has made it possible to describe the dominant retrospective evolutionary and temporal tendencies in the development of this theory concerning the concepts of sustainable development, environmental management, and development of renewables. As a result, one of the most significant trends is a green brand and tendencies connected with developing green marketing and green consumer awareness. [ABSTRACT FROM AUTHOR]

This study provides a viable path to save energy by means of e-commerce development. Taking the national e-commerce demonstration cities (NEDC) pilots policy implemented in China as a quasi-natural experiment, based on the city panel data from 2006 to 2019, this study applies the multi-period difference-in-difference (DID) method to evaluate the effect of NEDC on energy saving in pilot cities. The empirical results suggest that the NEDC policy obviously contributes to energy conservation. The treated cities reduced energy consumption by 14.2% as a result of the implementation of NEDC, relative to the untreated cities. The conclusions remain valid after conducting robustness tests such as placebo test, instrumental variables regression, propensity score matching-difference-in-difference (PSM-DID), and synthetic difference-in-difference (SDID). The NEDC achieves energy-saving effects through technological innovation, industrial restructuring, and economic agglomeration. Furthermore, the heterogeneity analysis indicates that, in cities with high levels of human capital, well-developed information infrastructure, non-resource-based cities, and favorable business environments, the impact of NEDC on energy saving is more significant. Analysis of spatial effects shows that the implementation of NEDC has negative externalities, increasing energy consumption in the surrounding area. In the context of the digital economy, this paper presents new insights on the relationship between e-commerce and energy consumption and provides policy direction for countries looking for energy-saving solutions. [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]

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]

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]

High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It's a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body's route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes' physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper's theories and methods can effectively help design the marathon route and determine the race time. [ABSTRACT FROM AUTHOR]