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In recent years, China has proposed the goal of peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060, which will significantly alter its existing coal-based energy mix. Since coal is China's primary source of energy and the largest contributor to carbon emissions, coal reduction is an important measure toward carbon neutrality. In order to guarantee the stable development of economy and society in the process of coal reduction, the path and cost of coal reduction need to be studied in depth. Based on coal use in China, this paper examines and measures the stages and costs of coal reduction. It also gives a definition for coal reduction costs for the first time, including economic cost, environment and ecology cost, and health cost, as well as proposes a framework for analyzing the "full cost, full process, and full scenario". We measure the cost in combination with the KAYA formula, and take into account the time value of the cost. Based on the above measurement framework, we calculate the unit coal reduction cost and estimate the coal reduction cost between CNY 454.38 and 827.1 billion. [ABSTRACT FROM AUTHOR]

The Luzhou area in the southern Sichuan Basin has experienced multiple tectonic movements, forming a complex fault system; the activity has an important impact on the enrichment of shale gas in this area. In order to reveal the influence of the fracture system on the differential enrichment of shale gas, this paper takes the southern Sichuan Basin as the research object. The structural evolution process and fracture development characteristics of the different tectonic units in Luzhou area of southern Sichuan were characterized by conducting a seismic profile analysis, structural recovery using a back-stripping method, and core hand specimen description. We clarified the control effect of the structural deformation and fracture on the differential enrichment of shale gas, and we established a differential enrichment model of shale gas in the Luzhou area. The results show that: (1) The Luzhou area has undergone the transformation of a multi-stage tectonic movement. There are many sets of detachment structures in the longitudinal direction, and the plane structural form is a thin-skin fold-thrust belt composed of wide and narrow anticlines in the north–south direction. (2) The faults in the study area are affected by the Himalayan tectonic movement. The high-angle reverse faults are developed, and the number of large faults is small. The second and third faults are mainly developed. The second faults are only developed at the high position of the structure, which has a significantly destructive effect on shale gas reservoirs, while the third and fourth faults have no significant destructive effect on shale gas reservoirs. (3) In the study area, the types of cracks are categorized into transformational shear cracks, bed-parallel shear cracks, intraformational open cracks, lamellation cracks, shrinkage cracks, and abnormal high-pressure cracks. The thickness of the shale rock mechanical layer, brittle mineral content, and organic matter content jointly control the crack development degree in the shale of the Wufeng–Longmaxi Formation. (4) The uplift erosion, structural deformation, and fracture development caused by the structural evolution have affected the preservation of shale gas, resulting in the differential enrichment of shale gas reservoirs in the region. Based on the enrichment factors of shale gas, we established a differential enrichment model of shale gas in typical structural units and optimized the favorable enrichment areas, which are important contributions for guiding shale gas exploration and development in the Sichuan Basin. [ABSTRACT FROM AUTHOR]

The current legal and policy system of renewable energy in China has set up a framework of policy instruments, which provides an important foundation for dealing with climate change and promoting the healthy development of the renewable energy industry with the goal of "carbon neutrality" and "emission peak". However, the current policy instruments cannot meet the needs for rapid development of the renewable energy industry. This paper investigates, analyzes, and concludes the status of renewable energy development along with related policy instruments in different renewable energy sources by literature and points out the problems of current policy instruments, and puts forward corresponding countermeasures. Moreover, from the perspective of energy consumption market transformation, the authors carried out an analysis of China's challenges of policy instruments, pointing out that China's renewable energy policy instruments have the potential to take one step further in the areas of the regulatory system, legal system and legal security system. [ABSTRACT FROM AUTHOR]

From a global perspective, carbon emissions are a global problem that needs to be solved urgently. At present, 61% of countries have committed to achieving net zero emissions. Compared with industry and construction, the transportation sector has become the focus and challenge for countries to achieve carbon neutrality due to the characteristics of strong mobility, scattered emission sources, and complex social behaviors. Therefore, the issue of carbon emissions in the transportation industry has become the focus of academic attention. This paper first calculates the carbon emission efficiency (CEE) of the regional transportation industry through the super-efficiency SBM model and then evaluates its regional differentiation characteristics through the Theil index, which has important practical significance for reducing regional carbon emissions. The results show that the national transportation CEE average value is 0.612, a relatively low level. The spatial distribution of China's transportation CEE shows an obvious characteristic of "east highest and west lowest". The regional differences in the transportation industry CEE are larger than those between regions. The differences in the transportation industry CEE among the eastern, central, and western regions are on the downward trend as a whole, and intra-regional differences are greater than inter-regional. The intra-regional differences cause the overall differences in transportation industry CEE; the eastern region contributed the most to the Theil index, while the central contributed the least. The biggest factor affecting the transportation industry CEE is the regional energy structure, and the smallest factor is the per capita GDP. This research has important reference significance on the target of carbon neutrality. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability. [ABSTRACT FROM AUTHOR]

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China's national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles. [ABSTRACT FROM AUTHOR]

This paper constructs an open economy dynamic stochastic general equilibrium (DSGE) model with oil to investigate the transmission mechanism and impact effects of oil price fluctuations driven by different factors on China's macroeconomy using quarterly data from 1996 to 2019. The results show that the international crude oil supply-driven oil price decline promotes positive output growth in the short run through the positive cost effect of the supply channel, and the production regulation cost will dampen the incentive to invest in the new energy sector in the long run. Domestic economic development demand-driven oil price increases act on the demand channel, driving output and oil prices to fluctuate in the same direction, generating a negative real balance effect on the economy through the interest rate channel. The oil-specific demand driven by foreign nominal interest rate shocks is transmitted through the exchange rate channel, triggering imported inflation, lower aggregate demand, and lower output. Different sources of oil price fluctuations have different transmission mechanisms and thus differential effects. For this reason, based on the root causes of oil price fluctuations, policy recommendations to deal with international oil price fluctuations in the new situation are proposed at the supply level, demand level, and international level. [ABSTRACT FROM AUTHOR]

In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model to forecast China's future energy consumption in 2022–2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions. [ABSTRACT FROM AUTHOR]

The Cretaceous condensate gas reservoir in Yakela is in a fan delta system in which the river channel swings frequently and the contact relationships between sandbodies are complicated both vertically and horizontally. Therefore, making the sandbody architecture clear is becoming the most urgent demand in locating the remaining oil. However, conventional well correlations and fine interpretation do not apply in this area due to the large-spacing of wells and the lack of reliable seismic data. In this paper, we analyzed the vertical characteristics of sandbody architecture including the type and thickness of architectural elements and their contact relationships based on well data, then simulated the lateral and planar distribution probabilities via a database containing a large number of dimension parameters from relevant architectural elements using Monte Carlo simulation. This simulation provides reasonable and efficient estimation of inter-well sandbody distribution. The workflow and data we present can be applied to similar clastic reservoir modeling and simulations, especially for areas with insufficient well and seismic data. [ABSTRACT FROM AUTHOR]

Based on the panel data from 30 provinces in China from 2010 to 2020, this paper employs the panel vector autoregression (PVAR) model to investigate the dynamic relationship between a green economy and energy utilization level. The results show that: (1) the green economic development level and energy utilization level of 30 provinces in China have been continuously enhancing in recent years, the increase in green economic development level is higher than energy utilization level, and the geographical characteristics of both levels are significant, decreasing from East to West; (2) the causal relationship between the green economy and the energy utilization level has passed the Granger test; the two have formed a relatively high-level balanced relationship over a long period, showing a benign and orderly development trend; (3) the green economy generates a positive pulse response on energy utilization level in the initial period, but the impulse response weakens in the period of lag 1, entailing the relationship needs to be further strengthened in the long term; (4) in the process of continuous enhancement of energy utilization level, it is difficult to drive economic benefits due to high input costs in the initial stage, but it can form a two-way interaction relationship with a green economy as time goes by. [ABSTRACT FROM AUTHOR]

China's green credit has mostly been invested in new energy areas with positive environmental externalities in recent years, while coal power enterprises have been neglected. This paper constructs a tripartite evolutionary game model among government, coal power enterprises, and banks to clarify the key factors and mechanisms for coal power enterprises undergoing green transformation. The research results show that: Firstly, to realize the spontaneous green transformation of coal power enterprises, spontaneous profitability must be achieved before the removal of policy incentives, which is reflected in the continuous increase in electricity price, carbon emission trading price, and decrease in green transformation cost. Secondly, the green credit adjustment factor cannot determine whether a company chooses to undertake green transition, but it provides a valuable window of green transition for companies. When the relative benefits of green transformation projects are greater than the relative costs, coal power enterprises will undergo green transformation spontaneously. Thirdly, lower green credit interest rates are not better. An optimal green credit interest rate exists, that allows coal power enterprises to obtain the longest transition window in which to achieve spontaneous profitability for green transition projects. [ABSTRACT FROM AUTHOR]

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propagation neural network (BP) pressure drop calculation model and particle swarm optimization for back propagation neuron network (PSO-BP) pressure drop calculation model are established. Two models were used to calculate and compare the measured data, and the average absolute error of the PSO-BP model was the smallest, which was 0.015%. Compared with the BP model, the average relative error is reduced by 13.16%. Therefore, The PSO-BP pressure drop calculation model has high accuracy and is of practical significance for predicting pipeline pressure drop. [ABSTRACT FROM AUTHOR]

Borehole cataloguing is an important task in geological drilling. Traditional manual cataloguing provides the stratification of underground boreholes based on changes in core lithology. This paper proposes a novel borehole cataloguing method using a drilling process monitoring (DPM) system. This DPM cataloguing method stratifies a borehole according to the drilling speed through the rock. A 102 m borehole was drilled and cored in Baota district, Yan'an city, Shaanxi Province, China. The rock-breaking response parameters of the drill bit displacement, drill rod rotation speed and inlet pipe and outlet pipe oil pressures were monitored throughout the drilling process, and the drilling depth-penetration rate curve during the net drilling process was obtained. The changes in drilling speed show that the DPM cataloguing can identify the depths of the layer interfaces of the borehole and describe the stratification. The interface depth values obtained by DPM have little difference from the interface depth values obtained by manual cataloguing, and the errors are between −0.04% and 4.29%. From the DPM stratification results, the engineering quality evaluation of the rock mass can be realized without coring. DPM is fast, convenient, accurate, can greatly improve the efficiency of existing catalogues, and can be applied to scientific research in any underground space. DPM is a measurement-while-drilling technology. According to DPM data, the operating state of a drilling rig and the parameter changes while drilling can be obtained in situ and in real time throughout the drilling process. [ABSTRACT FROM AUTHOR]

As the world's largest coal consumer, China's coal consumption in 2021 was 2934.4 million tons of standard coal. Thermal coal occupies an important position in the coal market and industry system, as an important raw material in the power industry, steel industry and other industries. The price of thermal coal in 2021 was at its highest level in a decade, and reached a historical level of about 2587.5 yuan per ton in October 2021. In the same month, the government intervened in the thermal coal price, which fell 51.9% by the end of the year under the influence of the policy. In previous studies, there has been little research on thermal coal and the impact of the variable "policy" on the thermal coal price. Thus, this paper analyzed the factors that affect the price fluctuation of thermal coal, and the impact of economic policy uncertainty on the thermal coal price. The cointegration test and forecast-error variance decomposition (FEVD) are adopted in this study. Our results show that the impact of policy uncertainty on the thermal coal price gradually increases over time, but the impact of policy uncertainty on price is negative and not as strong as expected. On the contrary, inventory and other energy prices have a greater positive impact on the price of thermal coal. The results of this study are of significance for the prediction of thermal coal prices in the future. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance has delivered significant benefits, such as sustainable development and economic growth. We explore the relationship between digital finance and green total factor energy efficiency (GTFEE) for the first time, filling a gap in the existing literature. This paper uses dynamic panel models to explore digital finance's impact on GTFEE at the Chinese city-level panel data from 2011 to 2018. The results show that digital finance can significantly improve urban GTFEE, and the findings remain robust with various tests. Second, the mechanism analysis indicates that digital finance can improve GTFEE by promoting urban green technology innovation and industrial structure upgrading. Further study shows that digital finance has a better effect on the improvement of GTFEE in central and western cities, small cities and non-resource-based cities, but has no significant or small impact on GTFEE in eastern cities, large cities and resource-based cities, reflecting the inclusiveness of digital finance. [ABSTRACT FROM AUTHOR]

Studies related to oil and gas wells have attracted worldwide interest due to the increasing energy shortfall and the requirement of sustainable development and environmental protection. However, the state of oil and gas wells in terms of research characteristics, technological megatrends, article-produced patterns, leading study items, hot topics, and frontiers is unclear. This work is aimed at filling the research gaps by performing a comprehensive bibliometric analysis of 6197 articles related to oil and gas wells published between 1900 and 2021. VOSviewer and CiteSpace software were used as the main data analysis and visualization tools. The analysis shows that the annual variation of article numbers, interdisciplinary numbers, and cumulative citations followed exponential growth. Oil and gas well research has promoted the expansion of research fields such as engineering, energy and fuels, geology, environmental sciences and ecology, materials science, and chemistry. The top 10 influential studies mainly focused on shale gas extraction and its impact on the environment. More studies were produced by larger author teams and inter-institution collaborations. Elkatatny and Guo have greatly contributed to the application of artificial intelligence in oil and gas wells. The two most contributing institutions were the Southwest Petr Univ and China Univ Petr from China. The People's Republic of China, the US, and Canada were the countries with the most contributions to the development of oil and gas wells. The authoritative journal in engineering technology was J Petrol Sci Eng, in environment technology was Environ Sci Technol, in geology was Aapg Bull, and in materials was Cement Concrete Res. The keyword co-occurrence network cluster analysis indicated that oil well cement, new energy development, machine learning, hydraulic fracturing, and natural gas and oil wells are the predominant research topics. The research frontiers were oil extraction and its harmful components (1992–2016), oil and gas wells (1997–2016), porous media (2007–2016), and hydrogen and shale gas (2012–2021). This paper comprehensively and quantitatively analyzes all aspects of oil and gas well research for the first time and presents valuable information about active and authoritative research entities, cooperation patterns, technology trends, hotspots, and frontiers. Therefore, it can help governments, policymakers, related companies, and the scientific community understand the global progress in oil and gas well research and provide a reference for technology development and application. [ABSTRACT FROM AUTHOR]

Coal and organic-rich shale in the Yan'an Formation in the southwestern margin of the Ordos Basin are widely developed, which is an important fact for oil and gas exploration in China that has been widely explored for a long time. In this paper, detailed sequence division and sedimentary environment analyses of the Yan'an Formation in the Pengyang area on the southwestern margin of the Ordos Basin were conducted using field outcrops, drilling cores, logging, wavelet transform and organic geochemistry. The results showed that the succession consists of some units with distinctly different characteristics. Based on the petrographic assemblage and transform wavelet characteristics, the Yan'an Formation in this area can be divided into a long-term cycle, five medium-term cycles, and eleven short-term cycles, among which coal and carbonaceous shale were mainly developed in the short-term cycles I2, III1, III2, V1 and V2. Coal and organic-rich mud shale have been developed in the Yan'an Formation and plant debris in mudstone and coal is common, indicating the development of swamps and shallow water-covered depressions in this area. The sandstones showed parallel bedding, cross-bedding and scours, thus indicating fluvial deposits. The saturated hydrocarbon gas chromatographic parameters of mud shale showed that the pristane/phytane (Pr/Ph) ratio is 2.24–6.22, the Ph/nC18 ratio is 0.15–0.93, and the Pr/nC17 ratio is 0.97–2.78, supporting the finding that the organic matter has mainly originated from terrestrial sources. [ABSTRACT FROM AUTHOR]

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the "market electricity" and "planned electricity" double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of "market electricity" and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, "market electricity" capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China. [ABSTRACT FROM AUTHOR]

The performance of a fuel cell stack is affected by the operating temperature of the stack. The thermal management subsystem of a multi-stack fuel cell system (MFCS) is particularly significant for the operating temperature control of each stack in the MFCS. To study the influence of different topologies of a MFCS thermal management subsystem, this paper proposes and establishes two different topologies. Firstly, the integrated topology is proposed. Secondly, seven component models, namely the mixer, thermostat, radiator, tank, pump, bypass value, and proton exchange membrane fuel cell stack temperature models, are described in detail. Finally, the performance of the two topologies of the MFCS thermal management subsystem under two working conditions, steady (200 A) and variable (China heavy-duty commercial test cycle, C-WTVC), is compared. Furthermore, there are two evaluating indicators, including the stability duration and deviation of the operating temperatures of the single stack in the MFCS. Results show that when the MFCS operates under steady working conditions, the integrated topology is superior in operating temperature control accuracy ( Δ T < 0.5   K ), while the distributed topology is superior in the adjustment process ( t   ≤   100   s ). Moreover, when the MFCS operates under variable working conditions, the distributed topology is superior in operating temperature control accuracy. [ABSTRACT FROM AUTHOR]

Green development is a significant concept that cannot be ignored in contemporary society. However, in the context of China's transition from high-speed growth to high-quality development, the complex impact of urban development has brought great challenges to the urban green environment. In this paper, the impact of urban development on green development efficiency (GDE) was studied. First and foremost, a Super-SBM model was introduced to measure the GDE of 41 cities in the Yangtze River Delta during 2009–2018. Moreover, a Tobit model was used to analyze the correlation between four urban development factors, including economic development and GDE. According to the results, the mean GDE of the Yangtze River Delta is 0.824, and the GDE in most cities there has shown a fluctuating growth trend in this decade. By comparison, the GDEs of coastal cities and cities in the southeast of the Yangtze River Delta were generally higher than those of cities in the north, indicating that the green development is geographically unbalanced, and there is spatial heterogeneity in the area studied. The study's results also suggest that the urban economic development, innovation level, and government planning play a significant role in stimulating urban green development, and that the expansion of urban construction area hinders the improvement of GDE. [ABSTRACT FROM AUTHOR]

Biogas resources in the Sanhu area of the Qaidam Basin have great potential, but there are few studies on biogas from shale, especially on the accumulation conditions of shale biogas. The study of biogas accumulation conditions of quaternary shale in the Sanhu area is of great significance to the theory of biogas accumulation and the guidance of exploration and development. This paper takes Quaternary shale in the Sanhu area as the research object. It is analyzed from multiple perspectives of shale hydrocarbon generation conditions, reservoir conditions, as well as hydrodynamic and structural conditions. Through the experiments of soluble organic carbon analysis and porosity and permeability analysis, the accumulation conditions of shale biogas reservoirs are clarified. The results show that the quaternary shale has a high soluble organic carbon content and high salinity formation water, which is conducive to late methane biochemical generation. Quaternary shale has the characteristics of high porosity and low permeability, mainly developing intergranular pores and intragranular pores. The large pore volume and specific surface area provide a lot of storage space for free gas and adsorbed gas, and the reservoir conditions are good. Under the structural characteristics of high in the south and low in the north and the action of formation hydrodynamics, biogas migrated from the south and deep to the north of the basin. The north slope is the main biogas-rich zone. On the whole, the quaternary shale in the Sanhu area has the characteristics of continuous hydrocarbon generation and dynamic accumulation, which has huge resource potential and exploration and development value. [ABSTRACT FROM AUTHOR]

Natural gas generation is the result of organic matter degradation under the effects of biodegradation and thermal degradation. Early Cambrian black shales in the Upper Yangtze Region are rich in organic matter and have shown great shale gas potentiality in recent years. Nevertheless, the enrichment mechanism and distribution of organic matter in these black shales between different sedimentary settings, such as intra-platform basin, slope, and deep basin, are still poorly understood. In this paper, based mainly on elemental geochemistry, a comprehensive study of the marine redox conditions, primary productivity, sedimentation rate, terrigenous input, hydrothermal activity, and water mass restrictions was conducted on the Early Cambrian Niutitang black shale in the Upper Yangtze Region. Our data showed that an intra-platform basin received a higher terrigenous input and that it deposited under more restricted conditions than the slope and deep basin settings. The primary productivity in the slope and deep basin settings was higher than that in the intra-platform basin setting. In the intra-platform basin, the productivity increased from its inner part to its margin. For the slope and deep basin settings, the high paleoproductivity generated large amounts of organic matter and its preservation was synergistically affected by the redox conditions. In contrast to the slope and deep basin, the preservation of organic matter in the inner part of the intra-platform basin was mainly controlled by redox conditions because the paleoproductivity in it was much lower than in the slope and deep basin settings. The intra-platform basin margin was the most favorable area for accumulating organic matter. [ABSTRACT FROM AUTHOR]

With the rapid development of agriculture in China today, the demand for agricultural machinery is rapidly increasing. A large amount of exhaust gas emissions poses a severe threat to the environment. To better promote engine fuel and air mixing, enhance engine performance, and achieve low emissions, we conducted a numerical study of the pre-chamber and nozzle design in a gasoline engine for an agricultural tractor by using the G-equation method in Converge CFD software. The relevant optimization of the three model parameters in the G-equation was performed using the improved particle swarm algorithm (PSO). The model parameters after optimization by the PSO algorithm were: a 1 = 0.77 , b 1 = 2.0 , b 3 = 1.0 . It was confirmed that the predicted engine performance was enhanced greatly with the pre-chamber system. More importantly, the results reveal that the volume and area ratios of the pre-chamber played a crucial role in the performance of the pre-chamber. Through a series of parametric studies on the pre-chamber and main chamber characteristics, we can identify the best sets of volume and area ratios based on the combustion reaction progress, the turbulent mixing profiles, and the exhaust gas emission. The turbulent maximum strength and the exhaust gas concentration of nitrogen oxides can differ by 13 and 18 times, respectively. In practical design, we recommend the optimization of the concerned metrics with the findings in the paper. [ABSTRACT FROM AUTHOR]

Carbon neutrality is one of the most important goals for the Chinese government to mitigate climate change. Coal has long been China's dominant energy source and accounts for more than 70–80% of its carbon emissions. Reducing the share of coal power supply and increasing carbon capture, utilization, and storage (CCUS) in coal power plants are the two primary efforts to reduce carbon emissions in China. However, even as energy and water consumed in CCUS are offset by reduced energy consumption from green energy transitions, there may be tradeoffs from the carbon–energy–water (CEW) nexus perspective. This paper developed a metric and tool known as the "Assessment Tool for Portfolios of Coal power production under Carbon neutral goals" (ATPCC) to evaluate the tradeoffs in China's coal power industry from both the CEW nexus and financial profits perspectives. While most CEW nexus frameworks and practical tools focus on the CEW nexus perturbation from either an external factor or one sector from CEW, ATPCC considers the coupling effect from C(Carbon) and E(Energy) in the CEW nexus when integrating two main carbon mitigation policies. ATPCC also provides an essential systematic life cycle CEW nexus assessment tool for China's coal power industry under carbon-neutral constraints. By applying ATPCC across different Chinese coal industry development portfolios, we illustrated potential strategies to reach a zero-emission electricity industry fueled by coal. When considering the sustainability of China's coal industry in the future, we further demonstrate that reduced water and energy consumption results from the energy transition are not enough to offset the extra water and energy consumption in the rapid adoption of CCUS efforts. However, we acknowledge that the increased energy and water consumption is not a direct correlation to CCUS application growth nor a direct negative correlation to carbon emissions. The dual effort to implement CCUS and reduce electricity generation from coal needs a thorough understanding and concise strategy. We found that economic loss resulting from coal reduction can be compensated by the carbon market. Carbon trading has the potential to be the dominant profit-making source for China's coal power industry. Additionally, the financial profits in China's coal power industry are not negatively correlated to carbon emissions. Balance between the carbon market and the coal industry would lead to more economic revenues. The scenario with the most rapid reduction in coal power production combined with CCUS would be more sustainable from the CEW nexus perspective. However, when economic revenues are considered, the scenario with a moderately paced energy transition and CCUS effort would be more sustainable. Nevertheless, the ATPCC allows one to customize coal production scenarios according to the desired electricity production and emission reduction, thus making it appropriate not only for use in China but also in other coal-powered regions that face high-energy demands and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

China has become the major stakeholder in global photovoltaic (PV) technology. However, the existing mechanistic interpretation of "what promotes the technological progress of the Chinese PV industry" is controversial. This paper takes China's A-share listed PV enterprises from 1999 to 2019 as the research sample and uses a panel fixed-effect regression model to empirically test the impact of research and development (R&D) investment and government subsidies on the technological progress of PV enterprises. The results show that there is an "N"-shaped nexus between R&D investment and technological progress, and most PV enterprises are in the climbing stage of the N-shaped curve. With the development of the PV industry, the nexus will undergo a transformation from inverted U-shaped to N-shaped, indicating that R&D investment is a key driver of PV technological progress. Yet, government subsidies are a "double-edged sword". They have a significant positive direct effect on PV technological progress but also a negative moderating effect. Tax returns play a positive incentivizing role, while financial subsidies play a negative moderating role. This study provides a policy basis for the timely reduction of financial subsidies and increased R&D investment to promote technological progress in China's PV industry. [ABSTRACT FROM AUTHOR]

In the north of China, during winter, a large number of activities (such as leisure, work, sightseeing and sports) are engaged in. This paper mainly focuses on human thermal perceptions of outdoor activities in a winter climate, especially the change in thermal perceptions when humans enter a temporary rest space with a shelter effect. A climate chamber was applied in our experiments, and temperature up-steps of 4, 8 and 12 °C were set, respectively. Twenty four college students were invited to engage in activities of different intensities, such as standing, walking (slowly) and biking. Through questionnaire survey and field measurement, the subjects' thermal sensation, thermal comfort and skin temperature were obtained. Hypothesis testing and non-linear regression methods were introduced to analyze experiment data. Major results were as follows. After temperature up-step changes, thermal sensation and skin temperature reach steady state within 30 min. However, the change in skin temperature caused by a short-term thermal experience does not disappear completely within 40 min. In addition to the influence of ambient temperature changes, activity intensity also influenced the variation in thermal sensation (subjective) and skin temperature (objective). These study results provide a scientific reference for future research and design of a temporary rest space in low ambient temperatures. Experimental studies including broader age groups and outdoor field tests are valuable for future research. [ABSTRACT FROM AUTHOR]

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of high penetration DG in the distribution network operations, it is necessary to evaluate the acceptance capacity of DG. The correct evaluation can realize the secure, economic and low-carbon configuration of DG. In this paper, an evaluation method of acceptance capacity of DG in the distribution network considering the carbon emission is proposed. Firstly, a multi-objective evaluation model of acceptance capacity of DG is constructed with the objectives of minimizing carbon emission in the full life cycle, minimizing node voltage deviation and maximizing line capacity margin. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the model to determine the Pareto optimal solutions of DG configuration. Then, the comprehensive index of acceptance capacity evaluation is obtained based on entropy weight method to decide the optimal compromise solution. Finally, an actual 55-bus distribution network in China is used to verify the effectiveness of the proposed method. The simulation results show that the proposed evaluation method can comprehensively obtain the optimal compromise solution considering the reliability, economy and carbon emission benefits of distribution network operation, which guides the DG configuration in the distribution network. [ABSTRACT FROM AUTHOR]

Global warming is a critical crisis threatening human survival and development. International organizations and countries worldwide are introducing policies and practices to achieve carbon neutrality. In China, numerous carbon neutrality policies have been established; however, a systematic understanding of the underlying policy logic is lacking. Using the institutional analysis and development (IAD) framework, this paper analyzes selected carbon neutrality policies in China. We conducted a bibliometric visualization analysis of the texts of 20 policies and matched their logic to the elements of the IAD framework. We established 90 keywords with occurrences of no less than 10 times in China's carbon neutrality policies. The network visualization analysis identified six clusters. We discuss implementation challenges of China's carbon neutrality policies, address the policy implementation, and finally outline impacts on China's carbon neutrality governance. This study responds to the global concern over China's carbon neutrality commitments by clarifying the institutional logic of China's policies and actions. This study could provide a reference for countries worldwide that are designing and introducing carbon neutrality policies. [ABSTRACT FROM AUTHOR]

The evaluation of caprocks' sealing capacity is exceedingly important for depleted gas reservoirs to be reconstructed into gas storage. In this paper, based on the physical sealing mechanism of caprock, four aspects of ten indexes of caprock quality evaluation were firstly selected, and the related classification standards were established. Secondly, based on the rock mechanical sealing mechanism, elastic and plastic indexes were selected to characterize the mechanical brittleness of caprock, and a brittleness evaluation method of caprock based on complete stress-strain curves was established. Then, a systematic comprehensive evaluation model (including 5 aspects and 12 evaluation indexes) for the sealing capacity of gas storage caprock was proposed, and the analytic hierarchy process (AHP) was used to determine the weight of the 12 indexes in the evaluation model, and the formula for calculating the suitability of the caprock sealing capacity was established. Finally, the geological data, laboratory, and field test data, including X-ray diffraction, poro-permeability test, displacement pressure, and tri-axial compression test, were used for the caprock sealing capacity evaluation of the X9 depleted gas reservoir, and the result from this model showed that the caprock quality is suitable for underground gas storage. [ABSTRACT FROM AUTHOR]

As a widely-used method of digging roadways in China, gob-side entry driving features specific advantages, such as a high recovery rate and good isolation effects. However, under the condition of hard overburden, the excessive bearing pressure of small coal pillars will easily cause serious internal damage in the coal and the run-through of the plastic zone, leading to harmful gas leakage in the goaf. Therefore, based on the engineering background of small coal pillars in the 18506 working face of Xiqu Coal Mine, this paper comprehensively adopts theoretical analysis, numerical simulation, industrial tests, and other methods, analyzes the evolution mechanism of isolated bearing and plastic fracture areas of small coal pillar under hard overburden, studies the influence law of hard overburden cutting parameters on the isolation and stability of small coal pillars, and puts forward the technology of actively cutting the top to weaken the stress concentration of coal pillars under hard overburden. With the reasonable cutting parameters determined, the controllable mechanism of hydraulic fracturing cutting under hard overburden further revealed, and the hydraulic fracturing cutting technology with "controllable cutting orientation of hydraulic fracturing with local stress field intervention" formed as the basic core, the stress situation on the roof is improved, realizing the stability control of the coal pillars for the roadway protection, and avoiding gas leakage and other disasters caused by small coal pillar destruction. [ABSTRACT FROM AUTHOR]

The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations. [ABSTRACT FROM AUTHOR]

The largest carbonate condensate field has been found in the Tarim Basin, NW China. Different from sandstone condensate gas reservoirs, however, the conventional gas injection for pressure maintenance development is not favorable for Ordovician fracture-cave reservoirs. Based on this, in this paper, 21 sets of displacement experiments in full-diameter cores and a pilot test in 11 boreholes were carried out to study enhanced oil recovery (EOR) in complicated carbonate reservoirs. The experimental results show that the seepage channels of the gas condensate reservoirs are fractures, which are quite different from sandstone pore-throat structures. Condensate oil recovery using water injection was up to 57–88% in unfilled fractured caves and at ca. 52–80% in sand-filled fractured caves. These values are much higher than the 14–46% and 17–58% values obtained from the depletion and gas injection experiments, respectively. The water injection in 11 wells showed that the condensate oil recovery increased by 0–17.7% (avg. 3.1%). The effective EOR for residual oil replacement using water injection may be attributed to fractures, as the gas channel leads to an ineffective gas circulation and pipe flow in fracture-cave reservoirs, which is favorable for waterflood development. The complicated fracture network in the deep subsurface may be the key element in the varied and lower oil recovery rates obtained from the wells than from the experiments. This case study provides new insights for the exploitation of similar condensate gas reservoirs. [ABSTRACT FROM AUTHOR]

A high-quality methane emission estimation in China's oil and gas sector is the basis of an effective mitigation strategy. Currently, the published emission data and studies of China's oil and gas sector only provide estimations of total emissions, which is not enough for good analysis of the trend and impact factors for the instruction of emission mitigation activities. The main problem is that published data for oil and gas infrastructure in China is incomplete, which makes it difficult to apply the conventional greenhouse gas inventory compiling method and the uncertainty estimation strategy. Therefore, this paper aims to develop a method to estimate infrastructure data using all available data, including partial data for the infrastructure, national production and consumption of oil and gas, and production and production capacity data of oil and gas enterprises, and then uses a Monte Carlo-based method to generate a source-based inventory and uncertainty analysis of methane emission for China's oil and gas industry from 1995 to 2018. We found that methane emission increased from 208.3 kt in 1995 to 1428.8 kt in 2018. Methane emission in 2018 has an uncertainty of about ±3%. Compared to former studies, our research found that the production stage of natural gas is the main contributor, which is further driven by the growth of natural gas production. The mitigation potential introduced by technology development on methane emission remains large. [ABSTRACT FROM AUTHOR]

In 2017, China made an ambitious statement of high-quality development (HQD) with which to realize the goals of sustainability proposed by the United Nations. Our paper sheds new light on how the corporate high-quality development is affected by the responsibility system for environmental protection targets using a sample of energy-intensive firms from 2003 to 2018. We calculate the indexes for corporate high-quality development using entropy weighting for the five dimensions: efficiency, innovation, openness, greenness, and social responsibility. Then, we develop a difference-in-differences model to reveal that the responsibility system for environmental protection targets significantly dampens high-quality development of corporations, as the estimated coefficient is −0.0420 with a t-statistic of −2.9384. In contrast with private firms, the high-quality development of state-owned firms shows no significant correlation with environmental policy constraints. The efficiency of capital allocation by corporations mediates the effects of responsibility for environmental protection targets on high-quality development. Our study suggests several policy implications: first, understand the connotation of a high-quality development system, and formulate diversified regulatory policies. Second, the responsibility system for environmental protection targets in China should be implemented steadily within the firm's abilities. Next, the high-quality development of private firms should generate great attention. Finally, corporate internal governance should be designed to improve high-quality development. [ABSTRACT FROM AUTHOR]

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs. [ABSTRACT FROM AUTHOR]

Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonlinearity, nonstationarity, and multiple frequencies of regional carbon prices in China. This study constructs a model, called the VMD-ICEEMDAN-RE-SSA-HKELM model, to forecast regional carbon prices in China based on the idea of 'decomposition–reconstruction–integration'. The VMD is first used to decompose carbon prices and the ICEEMDAN is then used to decompose the residual term that contains complex information. To reduce the systematic error caused by increases in the mode components of carbon price, range entropy (RE) is used to reconstruct the results of its secondary decomposition. Following this, HKELM is optimized by the sparrow search algorithm and used to forecast each subseries of carbon prices. Finally, predictions of the price of carbon are obtained by linearly superimposing the results of the forecasts of each of its subseries. The results of experiments show that the secondary decomposition strategy proposed in this paper is superior to the traditional decomposition strategy, and the proposed model for forecasting carbon prices has significant advantages over a considered reference group of models. [ABSTRACT FROM AUTHOR]

In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits. [ABSTRACT FROM AUTHOR]

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

The dynamic ice load of conical offshore wind turbines (OWTs) in cold regions is unclear. The ice force period is the key parameter used to establish an ice force model for conical structures. To obtain ice load data, a field monitoring system was installed on an OWT in a cold region in China. Based on the monitoring data, a new formula for calculating the ice force period of conical structures was established. By comparing the period calculated with this formula and the measured ice force period, it was found that the calculated data generally agreed with the measured data. Then, a random dynamic ice force model for conical OWTs can be established. Based on this ice force model, the ice-induced vibration of an OWT was analyzed with the ANSYS finite element software. The results are in good agreement with the measured data obtained from the OWT in the time and frequency domains. Therefore, the random dynamic ice force model established in this paper can be used to evaluate the ice resistance performance of conical OWTs in cold regions. [ABSTRACT FROM AUTHOR]

Effective governance of air pollution requires precise identification of its influencing factors. Most existing studies attempt to identify the socioeconomic factors but lack consideration of multidimensional heterogeneous characteristics. This paper fills this long-ignored research gap by differentiating governance regions with regard to multidimensional heterogeneity characteristics. Decision tree recursive analysis combined with a spatial autoregressive model is used to identify governance factors in China. Empirical results show several interesting findings. First, geographic location, administrative level, economic zones and regional planning are the main heterogeneous features of accurate air pollution governance in Chinese cities. Second, significant influencing factors of air pollution in different delineated regions are identified, especially significant differences between coastal and non-coastal cities. Third, the trends of heterogeneity in urban air governance in China are to some extent consistent with national policies. The approach identifies factors influencing air pollution, thus providing a basis for accurate air pollution governance that has wider applicability. [ABSTRACT FROM AUTHOR]

Currently, the world is moving rapidly toward carbon neutrality, and renewable energy technology is very important in carbon neutrality. Among various renewable energy technologies, wind power is attracting much attention due to its sustainability, resource abundance, and high applicability. This study analyzed wind power patents from 2010 to 2021 to derive current global originality and convergence information. For IP5 countries, the growth stages of technology and the patent convergence networks were inferred by time series analysis and an association rule mining algorithm, respectively. The results showed that, during the analysis period, about 5000 patents were applied on annual average, and it was analyzed that China held the most patents at 48%, followed by the US, Europe, Korea, and Japan. According to the technology convergence network of patent codes from all the IP5 countries, patents related to 'wind turbines with rotation axes in the wind direction' occupied a very central position, and 'power conversion electric or electronic aspects' and 'integration of patents related to renewable energy sources in buildings with wind power' were found to show high connection strength. By country, it was analyzed that the US and China showed high patent competitiveness onshore and possessed many 'independent power conversion cluster' technologies. The research hypothesis was that technology development trends can be analyzed and the characteristics of each country can be understood through patent analysis in the wind power field. This hypothesis was analyzed through various patent analysis techniques, and this paper has novelty in that it presents the global megatrend in the wind power field through patent analysis and quantitatively presents the current status of technology development in five major countries. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

Shale oil is an unconventional oil resource that needs to be developed and utilized urgently. However, the Chang 7 shale in the Ordos Basin, as the most typical continental source rock in China, is limited by the study of organic matter (OM) enrichment factors in continental lacustrine facies, and there are still controversies about the controlling factors, which limit the progress of oil and gas exploration. This paper aims to reconstruct the paleoenvironment of Chang 7 shale in the southern margin of Ordos Basin and reveal the controlling factors of organic rich shale by organic and elemental analysis, X-ray diffraction (XRD) analysis, thin section observation, and scanning electron microscopy-energy dispersive spectrometer (SEM-EDS) analysis. The results show that during the deposition period of Chang 7 shale, the climate was warm and humid, the lake water has strong reducing, low salinity and rapid depth changes. Total organic carbon (TOC) is positively correlated with salinity and hydrothermal action and inversely proportional to terrigenous input. The high productivity, low consumption and low dilution result in high enrichment of shale OM in the southern margin of Ordos Basin. [ABSTRACT FROM AUTHOR]

With the rapid growth of the oil and gas storage, transportation, and pipeline industries, it is necessary to improve the construction process of oil and gas pipelines. By combining the technical advantages of horizontal directional drilling and pipe jacking construction, the direct pipe laying method is suitable for pipeline crossing in different strata in the oil and gas, water conservancy and hydropower, and municipal industries due to its advantages of less construction land, high speed, and reversibility. Using the rapid jacking and laying of pipelines crossing Nanjuma River in the 'Jingshihan' gas pipeline double line project as a case study, this paper investigates the application status of the direct pipe laying method, summarizes the project, and introduces the construction of the working well, equipment selection, guiding control technology, supporting equipment installation, and drag reduction measures, as well as analyzes the influencing factors of thrust force and trajectory deviation combined with formation information. [ABSTRACT FROM AUTHOR]

The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China's CO2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO2 emissions in China's power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO2 emissions in China's power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO2 emissions. The energy demand structure and CO2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO2 emissions than that of construction and transportation. For achieving the CO2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors. [ABSTRACT FROM AUTHOR]

The shale gas exploration of the Longmaxi Formation in the Yangtze Region of China has made a significant breakthrough. As an important hydrocarbon generation organism, graptolite is widely distributed in the Longmaxi Formation shales, but its hydrocarbon potential is still unclear. Taking the Longmaxi Formation shales in the Middle Yangtze Region as an example, this paper discusses the organic petrological characteristics of graptolite and its contribution to buried organic carbon. The Longmaxi shales in the study area can be divided into organic-rich shales (TOC > 2.0%) and organic-bearing shales (TOC < 2.0%). The organic-rich shales have high quartz content and low clay mineral content, which is opposite in the organic-bearing shales. Organic maceral results show that graptolite is widely distributed in nearly all the samples, while solid bitumen is relatively developed in organic-rich shale. The equivalent vitrinite reflectance obtained from the conversion of graptolite reflectance ranges from 2.46% to 2.76%, indicating that the organic matter maturity of the Longmaxi Formation shale is overmature. Combining an optical microscope and a field emission scanning electron microscope, the proportion of graptolite area to organic matter area can be obtained, the average of which is 32.71%. Solid bitumen mainly contributes to buried organic carbon of the organic-rich shales in the Longmaxi Formation, while graptolites contribute little to organic carbon burial. However, solid bitumen in the organic-bearing shales is relatively undeveloped, and graptolite is an important hydrocarbon generation organism, which is the main contributor to buried organic carbon. [ABSTRACT FROM AUTHOR]

Prices of oil and other commodities have fluctuated wildly since the outbreak of the COVID-19 pandemic. It is crucial to explore the causes of price fluctuations and understand the source and path of risk contagion to better mitigate systemic risk and maintain economic stability. The paper adopts the method of network topology to examine the path of risk contagion between China's and foreign commodities, focusing on the dynamic evolution and transmission mechanism of risk contagion during the pandemic. This research found that among China's commodities, energy, grain, and textiles are net recipients of risk contagion, while chemical products and metals are net risk exporters. Among international commodities, industries have positive risk spillover effects on metals and textiles. During the first phase of the pandemic, China's commodities were the main exporters of risk contagion. However, international industries and metals became the main risk exporters and exerted risk spillover on China's commodities in the second phase of the pandemic. Moreover, based on total volatility spillover index of commodities, the risk contagion among the commodities follows three paths: "interest rate → commodities → money supply", "China's economic expectation → commodities → foreign economic expectation", and "commodities → consumer confidence". [ABSTRACT FROM AUTHOR]

How to consider both the influence of weather and wind power in the modeling process of probability distribution of wind power forecast error (WPFE), and to emphasize the application value of conditional modeling, is rarely studied at present. This paper proposes a novel method of conditional probability distribution modeling for WPFE. This method uses a proposed MNSGA-II-Kmeans algorithm to perform multi-objective clustering of multi-dimensional influencing factors (MDIF), including weather and wind power. It can maximize the difference between the probability distributions of each MDIF mode's WPFE while clustering, thus ensuring the application value of the conditional modeling way. Based on the clustering results, by using the versatile distribution to simulate the probability distribution of WPFE and the support vector machine to realize the recognition of MDIF modes, the specific conditional probability distribution function of WPFE can be provided to stochastic economic dispatch by identifying the forecast MDIF data. A wind plant of north China with historical data is selected for calculation. The results verify the effectiveness of the proposed method, and by comparison with the non-conditional probability distribution of WPFE that does not consider MDIF, it can effectively increase the wind power consumption of the power system. [ABSTRACT FROM AUTHOR]

Gas flooding has proven to be a promising method of enhanced oil recovery (EOR) for mature water-flooding reservoirs. The determination of optimal well control parameters is an essential step for proper and economic development of underground hydrocarbon resources using gas injection. Generally, the optimization of well control parameters in gas flooding requires the use of compositional numerical simulation for forecasting the production dynamics, which is computationally expensive and time-consuming. This paper proposes the use of a deep long-short-term memory neural network (Deep-LSTM) as a proxy model for a compositional numerical simulator in order to accelerate the optimization speed. The Deep-LSTM model was integrated with the classical covariance matrix adaptive evolutionary (CMA-ES) algorithm to conduct well injection and production optimization in gas flooding. The proposed method was applied in the Baoshaceng reservoir of the Tarim oilfield, and shows comparable accuracy (with an error of less than 3%) but significantly improved efficiency (reduced computational duration of ~90%) against the conventional numerical simulation method. [ABSTRACT FROM AUTHOR]