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In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling. [ABSTRACT FROM AUTHOR]

The mining industry plays an extremely important strategic role in China's economic and social development. In the new era of pursuing circular/green/efficient development, the evaluation of the total factor environmental efficiency (TFEE) of China's mining industry is essential for alleviating resource waste and environmental pollution. The Epsilon-Based Measure (EBM) model effectively solves the shortcomings of radial and non-radial DEA models. In addition, the Malmquist–Luenberger (ML) index can measure the dynamic change of efficiency value. Combining the EBM model and the ML productivity index, this paper evaluates the TFEE from the static and dynamic perspective in China's 31 provincial mining industries over the period 2007–2016. The Theil index is employed to reveal the root of the overall provincial TFEE gap (OGTFEE) in China's mining industry. The results show that the average total factor static environmental efficiency (TFSEE) of China's provincial mining industry exhibits a low score of 0.6589 and with significant spatio-temporal differences. The provincial TFEE gap within four major areas (WGTFEE), especially that in east and west areas, is the main cause of the OGTFEE in China's mining industry. Technical change contributes more to the TFEE decline in China's mining industry. There are differences in improving the TFEE among China's 31 provincial mining industries, and corresponding countermeasures can be formulated accordingly. This study provides theoretical and practical basis for the clean and green development of China's mining industry. [ABSTRACT FROM AUTHOR]

In response to extreme climate change, China has set a goal of reaching emission peak by 2030 and achieving carbon neutrality by 2050. Energy conservation and emission reduction of building heating in northern China are key to achieving this goal. Based on building area prediction and energy consumption intensity, this paper establishes a model for calculating heating energy consumption and carbon emissions in northern residential buildings at the macro level, which provides a basis for formulating policies related to heating and emission reduction in northern buildings. Based on the research method of scenario projection, combining the carbon emission subsets and future heating energy mix projections in northern China, the heating energy consumption in northern China decreases to 175, 149 and 135 Mtce in 2050 under the baseline, medium control and strict control scenarios, respectively. The heating energy consumption in the northern region should be controlled at least under the medium control scheme. Under this scenario, building heating carbon emissions in the north could be reduced to 450 MtCO2 by 2050, or 280 MtCO2 if more stringent abatement technologies are adopted. In order to achieve this goal, a combination of energy-saving technologies must be used. The use of biomass and solar technologies should be emphasized in rural heating, while envelope renovation makes the greatest contribution to heating energy savings, with envelope renovation contributing up to 92 Mtce to urban heating. [ABSTRACT FROM AUTHOR]

With the reform of the power system in China, investments in power grid projects across the whole power system are increasing. However, there are various objectives to achieve in the investment decision processes of power grid projects, so the rational investments of a grid project can be seen as a multi-objective optimization problem. Meanwhile, these issues have rarely been studied at home and abroad, and this paper will fill this gap. As a result, this study critically analyzed the application of a multi-objective optimization model to power grid investment. Firstly, the objective factors of grid investments were explored, which were quantified through quantitative methods. Secondly, based on the characteristics of power grid investment, a multi-objective optimization model was established, and the assumptions and constraints of the model were presented. Finally, NSGA-II was used for solving the multi-objective optimization model. The results show that: (1) Multi-objective optimization models are suitable for the study of and deriving solutions for power grid investment by establishing suitable objective functions, assumptions and constraints, (2) According to the conventional steps of NSGA-II, suitable steps can be established to search for an optimal solution to the objective set of a power grid investment and (3) Due to the different concerns of different project scenarios, Pareto frontier solutions can be selected as the practical references of power grid projects. Therefore, the solution set makes the implementation scheme more flexible. [ABSTRACT FROM AUTHOR]

This paper analyzes the macro-economic impact of controlling fossil energy consumption in China by using a social accounting matrix framework. The empirical results show that, first, restricting the exploitation of fossil energy resources will lead to a decline in the output of other production activities, thereby reducing the factor income and the living standards of residents. Second, improving energy efficiency can promote increased output in other production sectors, and raise factor returns and residents' living standards. Third, raising the price of energy products will result in higher costs of the entire society. The stronger the dependence on the energy sector, the greater the increase in the industrial cost. Therefore, the government should increase investment in energy efficiency, keep to energy intensity constraints, restrict the extraction amount of energy resources, and reasonably control energy prices based on market environmental factors. [ABSTRACT FROM AUTHOR]

In this paper, the panel data of 30 provinces in China from 2011 to 2019 are analyzed by the spatial measure model and the threshold regression model. The results show that the air quality level is positively correlated with green finance, but there is no spatial effect. The spatial effect of the three influencing factors, including the degree of openness, the level of infrastructure, and the level of education, is the crowding-out effect. At the same time, variables such as human resource level, air quality, and infrastructure construction level all have threshold effects in the relationship between green finance and economic development. The research conclusions suggested that local governments at all levels should formulate policies according to the actual situation to promote the development of provinces' intensive, intelligent, and green development, and build a regionally-linked green finance development model, thereby promoting the improvement of green finance. [ABSTRACT FROM AUTHOR]

The coal power policy has been influencing the development of China's coal power industry from the aspects of quantity control, technological progress, and market-oriented reform. Meanwhile, the strategic coal power policy of the mid- and long-term future is yet to be well-designed under the carbon peak and neutrality targets. Therefore, it is necessary to have a comprehensive overview of the coal power policy during the past and give targeted prospects for policy-makers. This paper discusses the policy effect on the development and technological progress of the coal power industry by giving an overview of the policy and reform processes of the coal power industry in China in the past 20 years. The results indicate that China's coal power policy has promoted the progress of coal power generation technology, optimized the coal power structure, accelerated the diversified investment in the power industry, and alleviated the overcapacity problem of coal power generation. After discussion, we conclude that: (1) It is necessary to gradually reduce the average generation hours of coal-fired power units while retaining high-quality coal-fired power units. (2) The renewable energy enterprises that adopt the support services of coal power enterprises should provide reasonable capacity compensation costs to create favorable conditions and achieve sustainable development for China's coal power industry. (3) To deploy coal power units combined with large-scale renewable energy in the western and northern regions will achieve efficient energy utilization. (4) Different strategies should be adopted for different coal power units, and the peak regulation potential of coal power units with low-capacity parameters should be explored. [ABSTRACT FROM AUTHOR]

Reasonable regional integrated energy planning is an important prerequisite for the construction of a Near-Zero Carbon Emission Demonstration District (NCEDD). An integrated energy planning scheme that is based on a three-step planning method with the objective of achieving an NCEDD is proposed in this paper. First, the planning objectives should be determined. After that, the planning strategies should be established. Finally, the planning approaches should be proposed according to the previously determined objectives and strategies. A case study considering the integrated energy planning of the Meishan International Near-Zero Carbon Emission Demonstration District (MINCEDD) is investigated to explain the planning method. In addition, the planning results, which are indicated as indexes, are explained, analyzed, and compared to the ones of other districts. The indexes include a proportion of renewable energy to primary energy (73% by 2030 and 108% by 2050), a proportion of renewable power to total power consumption (98% by 2030 and 111% by 2050), and CO2 emission reduction rates (70% by 2030 and 100% by 2050) and are more advanced than other districts in China. This planning scheme and method can provide a reference for the integrated energy planning of NCEDDs in developed urban areas. [ABSTRACT FROM AUTHOR]

Green finance integrates the concept of environmental governance into the financial industry, which is conducive to sustainable development. Applying the mediating effect model, this paper investigates the effect of green finance on urban haze pollution and explores the mediating role of technological innovation of enterprises between them. Based on a sample of 639 enterprises in China over 2016–2019, a significantly negative effect of green finance on urban haze pollution is found. An increase of one standard deviation in green finance decreases PM2.5 concentration by 8.8 μg/m3, ceteris paribus. Further, green finance may improve environmental quality by promoting technological innovation. Considering the heterogeneity of enterprise ownership, this mediating effect exists in non-state-owned enterprises, while it cannot be observed in state-owned enterprises. This study proposes a new solution for pollution: using green financial tools to promote environmentally friendly technological progress. [ABSTRACT FROM AUTHOR]

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid. [ABSTRACT FROM AUTHOR]

With the improvement of China's economic strength, the energy consumption of public buildings is continually increasing, notably for hotels. The energy consumptin of a hotel accounts for more than 15% of its revenue, and the average energy consumption per floor area is more than 10 times that of urban residents. Therefore, the energy-saving retrofit of existing hotels is imperative. This study investigates the economic evaluation methods for existing hotel retrofit projects, and constructs an economic evaluation model using economic evaluation and cost-effectiveness ratio as the base. The energy-saving retrofit measures of 15 existing hotels in Jiangsu Province, China, are used as a case study to demonstrate the research framework. The results indicate that the heating, ventilation and air conditioning (HVAC) system, monitoring system, lighting system, domestic hot water system, and building envelope system are the five energy-saving retrofit technical measures with the highest application ratio. The average dynamic investment payback period of hotels in Jiangsu Province is 2.96 years, which meets the requirement of no more than 10 years specified in the energy-saving building standard. The cost-effectiveness ratio of different technologies differs significantly. Lighting and kitchen systems have the highest energy-saving efficiency, followed by monitoring systems, while HVAC and domestic hot water systems have the lowest. The research presented in this paper contributes to the economic evaluation of the energy-saving retrofit of hotels. [ABSTRACT FROM AUTHOR]

There is a global move toward being "carbon neutral". Reducing the use of coal to generate power has become an inevitable choice for many countries when transforming their energy structures. Many countries have proposed phasing out coal. China is a major energy producing and consuming country and intends to reach a carbon peak by 2030 and become carbon neutral by 2060. China has repeatedly emphasized coal reduction, but has not explicitly proposed phasing out coal, due to the influence of local governments, coal-related enterprises, and the public. This paper explores whether China could declare a "coal phase-out", and the possible reasons for doing so, by constructing an evolutionary game model with two correlations. MATLAB was used to simulate the model results to determine the effectiveness of the fractal results of the model, and the entropy method was used to calculate the development level of "coal phase-out" related indicators in China and Germany. The results show that: (1) The government can phase out coal only when coal-related enterprises and the public can benefit from reducing coal production and consumption. In addition, these benefits are needed to ensure stable economic and social development without affecting people's daily lives; (2) The development level of relevant indicators of "coal retreat" in China is lower than that in Germany. Based on these results, it is concluded that it is difficult for China to announce a "coal phase-out" at present. Faced with this reality, China should improve the efficiency of coal use, install carbon capture and storage facilities, vigorously develop renewable energy and reduce the share of coal in the energy system. [ABSTRACT FROM AUTHOR]

Intelligent unmanned mining is a key process in coal mine production, which has direct impact on the production safety, coal output, economic benefits and social benefits of coal mine enterprises. With the rapid development and popularization of 5G+ intelligent mines and coal mine intelligent equipment in China, the intelligentization of intelligent unmanned mining has become an important research topic. Especially with the promulgation of some Chinese policies and regulations, intelligent unmanned mining technology has become one of the key technologies of coal mine production. To understand the connotation, status quo and development trends of intelligent unmanned mining, this paper takes the relationship between key technologies and engineering application of intelligent unmanned mining in China as the perspective. It is proposed that the intelligent unmanned mining technology is in the whole process of working face mining. A research structure of unmanned follow-up operation and safe patrol is changing to the mode of intelligent adaptive mining, followed by the basic concepts and characteristics of intelligent unmanned mining. Relevant researches that maybe beneficial to the proposed research content are reviewed in four layers, which include basic theory, key technology, mining mode, and overall design system theory and technology. Finally, the current intelligent unmanned mining mode and future trends in this field in China are summarized. [ABSTRACT FROM AUTHOR]

Inter-provincial power transmission in China solved the problem of electricity production and consumption spatial mismatch, which also facilitated Chinese green energy transition and sustainable development. Aiming to understand the spatial patterns and main driving factors, this paper accounted the aggregate carbon intensity per electricity produced by each province, built the inter-provincial Out–Destination matrixes of carbon emission embodied in power transmission among 27 provincial areas of 2006 and 2019, and quantitatively analyzed the change in spatial patterns of carbon flow and carbon intensity difference between power inward and outward provinces. We found that the inter-provincial power transmission has led to the reduction in the national average carbon intensity per electricity produced from 0.855 kg/kWh in 2006 to 0.628 kg/kWh in 2019 and the green energy transition of all provinces. The spatial morphology characteristics of carbon flow show the 'three corridors of West-East Power Transmission (WEPT)' pattern, in which power is transmitted from some main thermal power-dominated provinces in central and north-west China to the eastern coastal provinces. Further, it resulted in the carbon leakage from electricity consumption of the coastal provinces and carbon overload of the hinterland provinces. Despite natural resource endowment, the location of the provinces in the national power grid, the national low-carbon energy transition strategy, as well as advances in long-distance ultra-high-voltage power transmission technologies were the main factors to the formation and evolution of Chinese electricity green transition. [ABSTRACT FROM AUTHOR]

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%. [ABSTRACT FROM AUTHOR]

Building energy waste has become one of the major challenges confronting the world today, so specifications and targets for building energy efficiency have been put forward in countries around the world in recent years. The schematic design stage matters a lot for building energy efficiency, while most architects nowadays are less likely to make energy efficiency design decisions in this stage due to the lack of necessary means and methods for analysis. An integrated multi-objective multivariate framework for optimization analysis is proposed for the schematic design stage in the paper. Here, the design parameters of the building morphology and the design parameters of the building envelope are integrated for analysis, and an integrated performance prediction model is established for low-rise and medium-rise residential buildings. Then, a comparison of the performance indicators of low-rise and medium-rise residential buildings under five typical urban climatic conditions is carried out, and the change patterns of the lighting environment, thermal environment, building energy demand, and life cycle cost of residential buildings in each city under different morphological parameters and design parameters of the building envelope are summarized. Specific analysis methods and practical tools are provided in the study for architectural design to ensure thermal comfort, lighting comfort, low energy consumption, and low life-cycle cost requirement, and this design method can inspire and guide the climate adaptation analysis and design process of low-rise and medium-rise residential buildings in China, improve architects' perception of energy-saving design principles of low-rise and medium-rise residential buildings on the ontological level, as well as provide them with a method to follow and a case to follow in the actual design process. [ABSTRACT FROM AUTHOR]

Developing energy from renewable sources and modernizing the energy system are critical components of China's efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China's energy revolution is financial limitations, which are inextricably linked to the country's economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources. [ABSTRACT FROM AUTHOR]

Smog pollution is a severe social and environmental concern for the space-heating regions in China due to fossil-intensive space heating. To reduce polluting emissions and improve social and environmental performance, local government agencies should choose adequate cleaner space-heating technologies based on diverse local conditions. This implies that all cleaner heating solutions should be considered, including low-emissions fossil fuel district heating and low-emissions fossil fuel decentralized heating as transitional technologies, as well as biomass and electricity-driven heat pumps as long-term solutions. However, stakeholders such as policy makers, equipment manufacturers, and house owners, often lack necessary information to assess the feasibility for installing adequate heating solutions at the local level. It is therefore necessary to establish a systematic method to evaluate each heating solution in various geolocations of China. This paper reviews the current heating situation in China and proposes a spatial system analysis method as a tool for heating-solution feasibility evaluation. By applying the spatial system analysis method, a qualitative investigation on the choice of heating solution in different regions of China is provided. [ABSTRACT FROM AUTHOR]

This study examines the relationship between urbanization, economic growth, industrial transformation, technological change, public services, demographical change, urban and natural environmental changes, and carbon emissions using a dataset of 182 prefecture-level cities in China between 2001 and 2010. Specifically, this paper differs from previous studies in two aspects. First, the extant literature has focused on how economic processes accompanying rapid urbanization affect carbon emissions in urban areas but gives little attention to the other dimensions of urbanization, including social and environmental changes, which may have important effects on carbon emissions. We assessed the effects of 17 key processes accompanying urbanization in a full range of economic, social, and environmental dimensions on carbon dioxide emissions in urban areas. The results showed that social processes accompanied with rapid urbanization had distinct effects on carbon emissions, compared to economic and environmental processes. Specifically, improvement in public services, indicated by education and cultural developments, reduces the increase in carbon emissions during urbanization, while economic growth and urban construction reinforces the growth in carbon emissions. Second, we examined the impact of various urbanization processes on carbon dioxide emissions using a unique dataset of 182 prefecture-level cities that covers a wide span of regions in China. The results of our analyses on the city level have important implications for the formulation of comprehensive policies aimed at reducing carbon dioxide emission in urban areas, focusing on different urbanization processes in economic, social, and environmental phases. [ABSTRACT FROM AUTHOR]

Significant negative impact caused by climate changes, such as economy and life losses, has been experienced globally in recent years, which has called for imminent development and adoption of low carbon technologies in order to mitigate the impact. In 2020, the Chinese government outlined the 'Dual Carbon' Goal where its carbon emission will peak before 2030 whilst China will become 'Carbon Neutral' before 2060. In China, the amount of carbon emissions from the transportation industry stands in second place and it is predicted that the carbon emission of China's automobile industry will reach between 21.5 and 30 billion tons in 2030. Actions should be taken as quickly as possible to facilitate the transition from traditional fossil fuel vehicles to low carbon vehicles such as electric vehicles in order to reduce carbon emissions effectively. Based on the questionnaire that is designed to survey the electrification of online ride-hailing vehicles, this paper first establishes a consumer purchase intention model according to the perceived value theory. By evaluating six aspects including functional value, emotional value, social value, functional risk, financial risk and physical and mental risk, the regression model of the consumer purchase intention for electric vehicles is built. Subsequently, the average operating models for petroleum fuel vehicles, hybrid vehicles and electric vehicles are established, and on top of this, a fossil fuel price model can be derived. This price model can identify from which price it will be advantageous to use electric vehicles to run an online ride-hailing service. Moreover, a multi-agent model is established to illustrate the spread of electric vehicles in the online ride-hailing sector and the private car sector, which is used to predict the trend of the EV market development in China from 2020 to 2040. [ABSTRACT FROM AUTHOR]

The synchronous variation and association of organic matter (OM) and minerals in the hydrocarbon-generated process of oil shales are poorly understood. The goal of the paper is to investigate OM occurrence and thermal variation so as to reveal the hydrocarbon generation potential of oil shales. Based on detailed analyses of particle, organic, mineral, and thermal data from lacustrine oil shales in the Songliao Basin, we observed three layers of shale particles after settling in the water column characterized by a distinct color, degree of consolidation, and particle size. The particle sizes are divided into three ranges of fine grain (<1 μm), medium grain (1–20 μm), and coarse grain (>20 μm) via laser particle analysis. The particle-size distribution indicates the presence of OM polymerization and dominant contribution of the associated mineral surface and bioclastic OMs to the OM abundance of oil shale. Various OM occurrences are influenced by OM sources and redox conditions, whereas the degree of biodecomposition and particle sizes affect the placement of OM occurrences. Based on multiple thermal analyses, a synchronous response of OM and minerals to thermal variation dominates at 300–550 °C. The I/S and chlorite minerals are characterized by an entire illitization, while solid/absorbed OMs and hydrocarbon-generated water were expelled in large quantities. This contributes to major loss weights of oil shales during heating. The peak hydrocarbon-generated rate occurred at 457 °C for oil shales, corresponding to around 1.3% vitrinite reflectance value. These results are suggested to improve the understanding of OM occurrences and the thermal degradation constraint on the hydrocarbon-generated process, and contribute to the interpretation of the hydrocarbon generation potential and in-situ exploitation of oil shales. [ABSTRACT FROM AUTHOR]

With the rapid progress of socio-economic development and urbanization in China, a wide variety of literature has focused on the phenomenon of energy-consumption disparity, which in turn could be related to numerous fundamental energy dilemmas that China must deal with now or soon, including energy use inefficiency, regional energy shortage, insufficient use of green energy, etc. However, in most cases, only a tendency scenario is discussed, while identifying which factors are more likely to affect the improvement of energy supply/use has not yet been fully explored. Therefore, this paper attempts to explore differences in energy consumption in specific, household-level aspects. Based on the information provided by Chinese General Social Survey (CGSS2015), the Gini coefficient and the Lorentz asymmetry coefficient are used to measure the difference among various categories of energy type and end use within and between different geographical sub-groups. The findings show that: (1) household energy-consumption behaviors exhibit a complicated effect on the overall level of energy-consumption difference; (2) from the supply side, energy users show the most significant difference in biomass energy consumption, while from the demand side, the contribution of space heating to the difference in total energy consumption is the highest; (3) a great urban–rural difference in energy consumption is generally observed with its difference within rural areas being much greater than in urban areas; (4) the eastern, middle and western regions weight differently in explaining the overall difference of energy consumption. These findings provide meaningful materials and references for policymakers in China to understand the overall situation of individual energy consumption to a great extent, and to locate key points to reform the current energy policy framework. [ABSTRACT FROM AUTHOR]

Passivhaus EnerPHit is a rigorous retrofit energy standard for buildings, based on high thermal insulation and airtightness levels, which aims to significantly reduce building energy consumption during operation. However, extra retrofit materials are required to achieve this standard, which raises a contradiction between how to balance the environmental impacts of the retrofitting material inputs and extremely low energy consumption after retrofit. This motivated the analysis in this paper, which aimed to evaluate the possibilities of reducing the required retrofitting material inputs when trying to achieve the EnerPHit energy standard using a typical suburban dwelling in China's hot summer–cold winter climate region as a case study. Firstly, how the insulation performance of each envelope component affected the building's energy consumption was analysed. Based on this, sensitivity simulations of combinations of different insulation levels with different fabric components were investigated under four scenarios of insulation levels, airtightness and glazing choice. The final proposed retrofitting plans achieved the EnerPHit standard with insulation materials' savings between 18% to 58% compared to a baseline retrofit plan, and this led, in turn, to 3.9 to 12.6 tonnes of carbon reductions. Moreover, an energy-saving of 87% in heating and 70% in cooling was achieved compared with the pre-retrofit dwelling. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system. [ABSTRACT FROM AUTHOR]

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active. [ABSTRACT FROM AUTHOR]

The mismatching between the multi-scale feature of complex fracture networks (CFNs) in unconventional reservoirs and their current numerical approaches is a conspicuous problem to be solved. In this paper, the CFNs are divided into hydraulic macro-fractures, induced fractures, and natural micro-fractures according to their mode of origin. A hybrid model coupling various numerical approaches is proposed to match the three-dimensional multi-scale fracture networks. The macro-fractures with high-conductivity and wide-aperture are explicitly characterized by a mimetic Green element method-based hierarchical fracture model. The induced fractures and natural micro-fractures that have features of low-conductivity and small-openings are upscaled to the dual-medium grid and enhanced matrix grid through the equivalent continuum-medium method, respectively. Subsequently, some benchmark cases are implemented to confirm the high-precision and high-robustness of the proposed hybrid model that indeed accomplishes accurate modeling of fluid flow in multi-scale CFNs by comparing with commercial software tNavigator®. Furthermore, an integrated workflow of simulation modeling for multiscale CFNs combined with a field example in Sichuan from China is used to analyzing the production information of fractured horizontal wells in shale gas reservoirs. Compared with the field production data from this typical well, it can be proved that the hybrid model has strong reliability and practicability. [ABSTRACT FROM AUTHOR]

The physical environment of classrooms has a strong relationship with student learning performance and health. Since the outbreak of COVID-19 in 2019, almost all universities have begun implementing closed instructional management, which has forced students to spend a much longer amount of time inside the classroom. This has also led to an increasing problem of thermal comfort in classroom indoor environments. In this paper, classrooms evolved from three dominant teaching modes at Zhejiang Sci-Tech University (ZSTU), located in the Hot Summer and Cold Winter (HSCW) zone of China, were selected as experimental spaces. Meanwhile, 12 learning groups with 60 students (30 of each sex) were selected as the tested samples. The relationship between thermal comfort and learning efficiency of the tested students was established through thermal comfort questionnaires and learning efficiency tests under the typical natural conditions in transition seasons. Based on this, improvement strategies were proposed for the current state of the classroom environment, providing a database for optimizing the environmental conditions of university classrooms in HSCW zone on the basis of improving students' learning efficiency. [ABSTRACT FROM AUTHOR]

Increasing economic and population growth has put immense pressure on energy, water and land resources to satisfy national and supra-national demand. Through trade, a large proportion of such a demand is fulfilled. With trade as one of its key priorities, the China Belt and Road Initiative is a long-term transcontinental investment program. The initiative gained significant attention due to greater opportunities for economic development, large population and different levels of resource availability. The nexus approach has appeared as a new viewpoint in discussions on balancing the competing sectoral demands. However, following years of work, constraints exist in the scope and focus of studies. The newly developed multi-regional input–output (MRIO) models covering the world's economy and its use of resources permit a comprehensive analysis of resource usage by production and consumption at different levels, and bring more knowledge about resource nexus problems. Using the MRIO model, this work simultaneously tracks energy, water and land use flows and investigates the transnational resource nexus. A nexus strength indicator is proposed which depends on ternary diagrams to grade countries based on their combined resources' use and sectoral weighting. Equal sectoral weighting is assigned. The analysis presented a sectorally balanced nexus approach. Findings support existing work by recognizing energy, water and land as the robust transnational connections, from both production and consumption points of view. Resource nexus issues differ from country to country owing to inequalities in industrial set-up, preferences in economic policy and resource endowments. The paper outlines how key resource nexus problems can be identified and prioritized in view of alternative and often opposing interests. [ABSTRACT FROM AUTHOR]

An extraction method for deep-seated thick seam deposits by underground mining with high resource recovery has remained a great challenge for Indian mining engineers, whereas the longwall top coal caving (LTCC) method has evolved as an effective method for various geo mining conditions in China and other counties. The percentage of top coal recovery (TCR) plays a predominant role in determining the feasibility of LTCC, which relies on the First Top Coal Caving Distance (FTCD). In this paper, the critical geotechnical parameters are identified, numerically simulated, and statistically analyzed, and the FTCD for Indian geo-mining conditions is developed and validated. A financial assessment is conducted, considering 70% top coal recovery at 85% performance level, cost of production escalated by 20% and fall in coal grade by two grades. The internal rate of return (IRR) for LTCC is 30.24% as per the sensitivity analysis where it is only 18% in single pass longwall method. This study contributes to evaluating both the technical and economic feasibility of introducing LTCC in Indian geo-mining conditions. [ABSTRACT FROM AUTHOR]

A tritium breeding blanket (TBB) is an essential component in a fusion reactor, which has functions of tritium breeding, energy generation and neutron shielding. Tritium breeding ratio (TBR) is a key parameter to evaluate whether the TBB could produce enough tritium to achieve tritium self-sufficiency (TBR > 1) for a fusion reactor. Current codes or software struggle to meet the requirements of high efficiency and high automation for neutronic optimization of the TBB. In this paper, the multiphysics coupling and automatic neutronic optimization method study for a solid breeder TBB is performed, and a corresponding code is developed. A typical module of China fusion engineering test reactor (CFETR) helium cooled ceramic breeder (HCCB) TBB was selected, and a 3D neutronics model of an initial scheme is developed. The automatic neutronic optimization was performed by using the developed code for verification. Results indicate that the TBR could increase from 1.219 to 1.282 (~5.17% improvement), and that the maximum temperature of each type of material in the optimized scheme is below the allowable temperature. It is of great scientific significance and engineering value to explore and study the algorithm for automatic neutronic optimization and the code development of the TBB. [ABSTRACT FROM AUTHOR]

Optimal scheduling strategy of integrated energy systems (IES) with combined cooling, heating and power (CCHP) has become increasingly important. In order to make the scheduling strategy fit to the practical implementation, this paper proposes a variable performance parameters temperature–flowrate scheduling model for IES with CCHP. The novel scheduling model is established by taking flowrate and temperature as decision variables directly. In addition, performance parameters are treated as variables rather than constants in the proposed model. Specifically, the efficiencies of the gas turbine and the waste heating boiler are estimated with the partial load factor, and the coefficient of performance (COP) of the electrical chillers and heat pumps are estimated with the partial load factor and outlet water temperature. Then, to deal with the model nonlinearities caused by considering the variability of COPs, the COP-expansion method is developed by adopting a specific representation of the COP and the expansion of the outlet water temperature. Finally, case studies show that the variable performance parameters' temperature–flowrate scheduling model can account for the variation of performance parameters, especially the impacts of water temperature and the part load factor on the COP. Therefore, the proposed scheduling model can obtain more adequate and feasible operation strategy, thereby suggesting its applicability in engineering practice. [ABSTRACT FROM AUTHOR]

China has become the largest CO2 emission country since 2014. The industrial sector is the largest contributor to CO2 emissions in China. This paper uncovers the spatiotemporal characteristics of the decoupling status of industrial CO2 emissions from economic growth at the provincial level during 1995–2019 in China and analyzed the structural characteristics of the industrial CO2 emissions. The results suggested that 2010 is an important turning point. Since 2010, the decoupling status of industrial CO2 emissions from economic growth has kept a continuously improving trend. During 2016–2019, all provinces achieved decoupling of the industrial CO2 emissions from economic growth. More than 20% achieved absolute strong decoupling. Four subindustries, including raw chemical materials and chemical products, production and supply of electric power and heat power, petroleum processing and coking products, and smelting and pressing of non-ferrous metals, with large CO2 emissions' contribution and a continuously increasing trend, should be paid more attention in the future CO2 reduction policies formulation. [ABSTRACT FROM AUTHOR]

In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction. [ABSTRACT FROM AUTHOR]

The present research aims to conduct a systemic review on Renewable Energy, Economic Growth and Economic Development and look for links between the papers published between 2008 and May 2021. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, it was possible to reach a sample of 111 articles selected by Web of Science and a sample of 199 academic articles selected by Scopus in that specific period. The analysis of the group of Renewable and Non-renewable Energy Consumption, Economic Growth and Economic Development shows that most of the articles published in this subsample use the quantitative methodology in economic sciences. The results indicate that research on the subject has a growing trend and that most of the articles are post-2015 publications. In addition, China has been the leading nation in published works. The journal Renewable and Sustainable Energy Reviews is considered the most relevant in this category, and Sustainability has the most publications. Finally, a research gap was identified to be explored, lacking studies aimed at understanding the consumption of renewable energies and economic development and studies that focus on renewable energies and economic growth in less developed economies. [ABSTRACT FROM AUTHOR]

This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the "time difference" between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long–short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%. [ABSTRACT FROM AUTHOR]

The new energy industry (NEI) is key to achieving a clean and low-carbon economy. Improving its technical efficiency, a factor reflecting the ability of an enterprise or industry to produce maximum economic outputs from a given set of inputs and production technologies, is vital for the healthy development of the NEI. Nevertheless, due to the fragmentation of industry data, it is still difficult to accurately measure the technical efficiency of China's NEI and understand the driving factors behind it. Based on the panel data derived from 17,457 observations on new energy enterprises in 29 Chinese provinces during 1998 and 2013 (latest data available), this paper uses data envelopment analysis (DEA) and geographically and temporally weighted regression (GTWR) for the first time to investigate the spatiotemporal characteristics and driving factors of the technical efficiency of China's NEI. The results show that the technical efficiency of China's NEI was relatively low and increased modestly from 0.44 in 1998 to 0.52 in 2013. Exploring the reasons from the perspective of spatiotemporal heterogeneity, we find that enterprise scale and technological progress are the major driving factors for increasing NEI's technical efficiency. However, the role of economic development in improving efficiency has gradually disappeared. Moreover, the negative effect of state-owned enterprises on efficiency becomes increasingly obvious. The effect of new energy resources is negligible. Our main contribution is the technical efficiency of China's NEI which is measured at the provincial level and its main driving factors are explored by considering spatiotemporal heterogeneity. Accordingly, we put forward some specific recommendations to improve the technical efficiency of China's NEI. [ABSTRACT FROM AUTHOR]

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of "unconditional-conditional simulation cooperation" (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application. [ABSTRACT FROM AUTHOR]

China is a global leader in methanol production volume, while coal is a major feedstock. The country also has the world's largest commercial coal-to-methanol operations. Coal-based methanol is used widely within China and is a competitive substitute for gasoline. Owing to this, it is plausible that the price of coal may be linked to international crude oil prices, with methanol prices serving as the connecting channel. We add supporting evidence to a recently emerging area of literature and observe statistically significant relationships among the three prices, and, therefore, the influence from international crude oil and methanol prices on the coal price determination in China. This paper investigates the relationships among these prices for the period from January 2010 to December 2019 through spectral Granger causality analysis, alongside more traditional cointegration tests to develop a comprehensive picture of causal association between the price series in both the frequency and time domain. Cointegration is found in our tri-variate system while the frequency domain Granger causality tests reveal the long-run causality in all directions except from crude oil to methanol, thus, emphasizing the structure of coal price dependence. According to the generalized impulse response functions, the coal price reacts positively to shocks in crude oil prices. [ABSTRACT FROM AUTHOR]

China's Belt and Road Initiative (BRI) will inevitably affect global energy cooperation. Along the Belt and Road, there are many developing countries. To understand the energy cooperation and development of these countries comprehensively is of great significance to guide their development and evaluate the impact of the BRI on the world energy and economic pattern. However, there is insufficient attention on those countries. Based on embodied energy analysis, a method which can track direct and indirect energy consumption in the economic system, effectively linking energy with the economy and environment, this paper proposes an evolution model of the embodied energy flow of the countries. Then, it simulates the evolution of the embodied energy flow under different cooperation strategies. The results show that if cooperation between countries positively affects their cooperation with other countries, adopting a mixed strategy is an advisable choice. On the contrary, cooperation with "powerful" countries in the network will be more conducive to the embodied energy flow. This article provides a new perspective and foundation for further discussion on the economy, trade, and energy cooperation along the Belt and Road. [ABSTRACT FROM AUTHOR]

Even with modern smart metering systems, erroneous measurements of the real and reactive power in the power system are unavoidable. Multiple erroneous parameters and measurements may occur simultaneously in the state estimation of a bulk power system. This paper proposes a gross error reduction index (GERI)-based method as an additional module for existing state estimators in order to identify multiple erroneous parameters and measurements simultaneously. The measurements are acquired from a supervisory control and data acquisition system and mainly include voltage amplitudes, branch current amplitudes, active power flow, and reactive power flow. This method uses a structure consisting of nested two loops. First, gross errors and the GERI indexes are calculated in the inner loop. Second, the GERI indexes are compared and the maximum GERI in each inner loop is associated with the most suspicious parameter or measurement. Third, when the maximum GERI is less than a given threshold in the outer loop, its corresponding erroneous parameter or measurement is identified. Multiple measurement scans are also adopted in order to increase the redundancy of measurements and the observability of parameters. It should be noted that the proposed algorithm can be directly integrated into the Weighted Least Square estimator. Furthermore, using a faster simplified calculation technique with Givens rotations reduces the required computer memory and increases the computation speed. This method has been demonstrated in the IEEE 14-bus test system and several matpower cases. Due to its outstanding practical performance, it is now used at six provincial power control centers in the Eastern Grid of China. [ABSTRACT FROM AUTHOR]

China is a large agricultural country with a high level of agricultural carbon emissions. Whether market prices can be used in agricultural production as a means of agricultural carbon emissions reduction is of great significance to improve the allocation of agricultural production factors and expand large-scale production. This paper applies an autoregressive distributed lag–pooled mean group(ARDL–PMG) model to evaluate the relationship between agricultural production factor prices, food consumption prices, and agricultural carbon emissions, using Chinese provincial panel data from 1994 to 2018. The results show that agricultural carbon emissions and agricultural production factor prices show environmental Kuznets curve (EKC) characteristics; agricultural carbon emissions and food prices show a U-shaped curve; and agricultural production factors are positively correlated with food price in both directions in the long-term. The results of Granger causality tests show that price is the cause of agricultural carbon emissions; the price of agricultural production factors and the price of food consumption are mutually causal. Such results have implications for price, agriculture, and environmental policies. The analysis implies that the market price can be applied to agricultural carbon reduction, which will help policymakers to implement effective price policies in order to reduce agricultural carbon emissions. One implication is that promoting the marketization of agricultural production factors and reducing price distortions will be conducive to carbon emissions reduction in agriculture, which in turn will increase food consumption prices. Therefore, subsidies are needed at the consumption end, which will eventually achieve further carbon emissions reduction at the production and consumption ends. [ABSTRACT FROM AUTHOR]

China has experienced rapid social and economic development in the past 40 years. However, excessive consumption of fossil fuel energy has caused an energy shortage and led to severe environmental pollution. To achieve sustainable development, China is striving to transform its growth mode. Adopting renewable energy (RE) including solar photovoltaic (PV) power is an effective measure. How to promote the further development of solar PV power under the scenario of China's aspirational target of carbon peak by 2030 and 20% RE ratio in the energy mix remains a theme that need to be addressed. This paper analyzes the potential opportunities and challenges confronting solar PV power in China. The analysis covers the dimensions of political, economic, social, and technological (PEST). The results revealed a significant prospect for the further deployment of solar PV power in the coming decades. The aggressive estimated installed capacity of solar PV power is expected to reach 80+ GW annually. To successfully achieve the goal of 80+ GW, barriers that hinder the further development of solar PV power have to be eliminated. Suggestions for policymakers include maintaining enforceability and continuity of policies, favorable financial supports, mandatory RE quotas for all parties, and supporting fundamental R&D. Suggestions for the solar PV industry include full utilization of integrated applications, set up an after-sales service network, collaborative innovation among the industry chain, and engaging in storage and hydrogen technology. The findings are greatly beneficial for policymakers and the solar PV industry. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations. [ABSTRACT FROM AUTHOR]

Repairing materials are well-known to play an important role in rehabilitating and extending the service life for hydraulic concrete structures. However, current underwater repairing materials possess several problems, including insufficient bond tensile strength, inconsistency with the deformation of the old substrate, and insufficient underwater self-sealing ability. In the present paper, an experimental study was carried out to evaluate the influence of silica nanoparticles (SNs) on the properties of underwater composite-repairing materials. The underwater deformation, impermeability, bond tensile strength, and compressive strength of the SN-modified underwater composite-repairing materials were used as the properties' evaluation indices. The results show that, within a certain range, the performance of the repairing material increase with increased SN percent. The deformability, impermeability grade, underwater bond tensile strength, and compressive strength of the SN-modified composite underwater repairing materials are 2.2%, 8, 2.91 MPa, and 115.87 MPa, respectively, when the mass ratio of the mortar, the curing agent and the SNs is 8:1:0.002. The proposed material is employed to repair the dam for a hydropower station in Guizhou province, China. Results show the seepage discharge is reduced by 8.6% when the dam is repaired. The annual average generating capacity is increased by 1.104 × 105 kWh. Meanwhile, CO2 and NOx emissions are reduced by 1.049 × 105 and 220.8 kg annually, respectively. [ABSTRACT FROM AUTHOR]

Analysis of the change of overall energy efficiency performance of an energy system is a fundamental work for the energy-saving policymaking. However, previous studies seldom focus on energy stages from useful energy to final service, while most attention are paid on stages from energy source to useful energy. In this paper, we develop a high-resolution the Societal Exergy Analysis and Logarithmic Mean Divisia Index (SEA-LMDI) method to analysis changes and driving factors of the aggregate exergy efficiency, in which the boundary of the SEA is extended to passive systems and final services, and a LMDI decomposition method is developed to quantify contributions of efficiency factors and structure factors of all six stages on the aggregate exergy efficiency. A case study of China during 2005–2015 reveals that: (a) the aggregate exergy efficiency from energy source to final service is only from 3.7% to 4.8% during 2005–2015, showing a huge theoretical potential of efficiency improvement. (b) Large passive losses are identified in passive systems and nearly 2/3 of useful energy can be theoretically saved by improving passive systems. (c) Deep analysis of industrial coal-fired boilers indicates that the internal structural adjustments are also important for the aggregate improvement. [ABSTRACT FROM AUTHOR]

With the increasing depth of coal mining, the quantitative evaluation of the degree of geological structure development is becoming increasingly important for the control of mine water hazards in coal mining areas. Understanding the complexity of geological structure development can improve the safety and efficiency of coal production. At present, various evaluation indicators of the geological structure development cannot fully reflect the complexity of faults and folds, and the evaluation process is usually affected by subjective human factors. In this paper, the fractal dimension from fractal theory is used as the evaluation indicator to quantitatively analyze and evaluate the complexity of fault and fold structure in the mining area. To verify the evaluation results, the mathematical geology method is applied in an analysis of the trend surface of fault and fold networks. The results indicate that the fractal dimension can be applied for the quantitative analysis and evaluation of the complexity of fault and fold networks. In addition, the outcome of this work provides new insights into how to characterize the fault and fold structures of coal mining areas in northern China, and has some important implications to ensure the coal production safety. [ABSTRACT FROM AUTHOR]

Due to the inappropriate treatment of dairy wastewater, which can easily cause underground water pollution, there is an increasing need for a novel approach to reuse dairy wastewater. The technology of microbially induced calcium carbonate precipitation with environmentally friendly characteristics and high efficiency has been widely used for underground infrastructure remediation. However, there is a lack of in-depth research on the application of this technology under extreme underground environments, such as the borehole of oil wells with high temperature, high pressure, alkaline, and aerobic conditions. In addition, to reduce the cost of this technology when applied on a large scale, we adopted dairy wastewater to cultivate bacteria. Then, we put the bacterial solution into cement slurry in the borehole to improve the cementing quality. In this paper, the rheology properties, mechanical strength, permeability, porosity, and pore distribution of microbial cementing slurry were studied. Moreover, we applied this microbial cement slurry in the Chunguang 17-14 well of China, and the sealing channeling ability of cement sheath on site was evaluated. The results showed that dairy wastewater could serve as an alternative medium to provide nutrients and energy for the growth of bacteria with low cost. Additionally, the microbial cement slurry exhibited a good right-angle thickening performance and high mechanical strength. The field application displayed an anti-gas channeling ability after microbial remediation. The application of dairy wastewater incubated bacteria to cement slurry not only provides an alternative method for the reuse of dairy wastewater but is also conducive to prolonging the lifespan of oil wells. [ABSTRACT FROM AUTHOR]

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers' index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers. [ABSTRACT FROM AUTHOR]

Offshore wind is considered a crucial part in the future energy supply. However, influenced by weather conditions, the maintenance of offshore wind turbine system (OWTs) equipment is challenged by poor accessibility and serious failure consequences. It is necessary to study the optimized strategy of comprehensive maintenance for offshore wind farms, with consideration of the influences of incomplete equipment maintenance, weather accessibility and economic relevance. In this paper, a Monte Carlo algorithm-improved factor is presented to simulate the imperfect preventive maintenance activity, and waiting windows were created to study the accessibility of weather conditions. Based on a rolling horizon approach, an opportunity group maintenance model of an offshore wind farm was proposed. The maintenance correlations between systems and between equipment as well as breakdown losses, maintenance uncertainty, and weather conditions were taken into account in the model, thus realizing coordination of maintenance activities of different systems and different equipment. The proposed model was applied to calculate the maintenance cost of the Dafengtian Offshore Wind Farm in China. Results proved that the proposed model could realize long-term dynamic optimization of offshore wind farm maintenance activities, increase the total availability of the wind power system and reduce total maintenance costs. [ABSTRACT FROM AUTHOR]