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A stable and accurate finite-difference discretization of first-order elastic wave equations is derived in this work. To simplify the origin and proof of the formulas, a symmetric matrix form (SMF) for elastic wave equations is presented. The curve domain is discretized using summation-by-parts (SBP) operators, and the boundary conditions are weakly enforced using the simultaneous-approximation-term (SAT) technique, which gave rise to a provably stable high-order SBP-SAT method via the energy method. In addition, SMF can be extended to wave equations of different types (SH wave and P-SV wave) and dimensions, which can simplify the boundary derivation process and improve its applicability. Application of this approximation can divide the domain into a multiblock context for calculation, and the interface boundary conditions of blocks can also be used to simulate cracks and other structures. Several numerical simulation examples, including actual elevation within the area of Lushan, China, are presented, which verifies the viability of the framework present in this paper. The applicability of simulating elastic wave propagation and the application potential in the seismic numerical simulation of this method are also revealed. [ABSTRACT FROM AUTHOR]

In the current study four grey models in matrix form for forecasting binaty and ternaiy interval number time series are given. First, the development coefficient of GM (1, 1) is set as a second-order square matrix, and the grey input is set as a two-dimensional column vector. Then the matrix form of GM (1, 1) model applicable to interval number sequence is proposed. Furthermore, the background value of the model is further improved and transformed into matrix form. The matrix forms of the original difference GM (1, 1) and the discrete GM (1, 1) are also given. By Cramer's law, it is proved that the above models with matrix form are all in the same unified form. Further, the matrix form of GM (1, 1) model applicable to ternaiy interval number sequence is also proposed. Using the new models, the interval values of China's total electricity consumption and retail consumer goods are predicted and the accuracy is the best compared with the model based on the overall development coefficient and the method of converting interval sequence to real number sequence. [ABSTRACT FROM AUTHOR]

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The study examines the digital finance (DF) and regional sustainable development (RSD) across 90 cities within six major city clusters in China over the period from 2011 to 2020. By constructing an evaluation index system for DF and RSD, we employed the entropy value method to assess their levels, and the coupling coordination degree (CCD) model to evaluate their interplay. Our analysis extended to temporal and spatial disparities, distribution dynamics, and the convergence of CCD through kernel density estimation, Markov chain analysis, σ -convergence, and β -convergence techniques. The results indicate a consistent upward trend in CCD, yet it remains at a low level with pronounced regional disparities and temporal characteristics. The kernel density distribution's central tendency has shifted rightward progressively, albeit with a decelerating pace annually. The Markov transition probability matrix suggests a stable CCD across various levels, hinting at "club convergence". Furthermore, both σ -convergence and β -convergence analyses reveal significant convergence trends in CCD, enhanced by economic growth factors. Using the Quadratic Assignment Procedure (QAP) method, we found that regional economic growth disparities significantly influence the CCD's regional variances. [ABSTRACT FROM AUTHOR]

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China's city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China's city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes. [ABSTRACT FROM AUTHOR]

The deep sea has abundant resources and is regarded as the "sixth continent" that humans can use. Although research on the deep sea has made some progress, its exploration is not deep enough. The acquisition and analysis of deep-sea images can provide important technical means for deep-sea exploration and the acquisition of information. However, there is currently no complete underwater image dataset. For this reason, this paper is the first deep-sea visual dataset of the South China Sea, contains over 100 TB videos and 1 million high quality images. This paper also applies the dataset to underwater image enhancement and image superresolution, and the experiment proves the effectiveness of the dataset. [ABSTRACT FROM AUTHOR]

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

China has been facing a contradiction between its water supply and demand for a long time, and it is urgent that it builds a "water-saving society". Previous research on water pricing mainly focused on the policy level, only considering the supply and demand sides and ignoring the participation of local governments. Due to the inconsistent approval mechanisms, different times of cost formation, and lack of a scientific pricing relationship between multiple water sources and multiple users, with the current water pricing in China, it is difficult to solve the complex problem of water resource allocation. This study aimed to promote the qualitative supply of water and the reuse of reclaimed water, achieve a high-quality and low-cost water supply, and explore the role of water-pricing mechanisms in the regulation of multi-source water supply configurations. The mechanism of regulation is constructed for the complex, multi-source, multi-user water resource system, and a three-party game model is established among local governments, water supply enterprises, and water users. A reasonable pricing system with a rational price relationship is obtained, and Pinghu City in Zhejiang Province is taken as the research area. The prices of the external water supply to residential users, non-resident users, and special users are 4.48 CNY/m3, 6.28 CNY/m3, and 7.12 CNY/m3. The external supply prices for resident users, non-resident users, and special users are 3.81 CNY/m3, 5.25 CNY/m3, and 6.05 CNY/m3. The external supply price for non-resident users is 2.62 CNY/m3 for reclaimed water. The results of the study showed that when the government's contribution did not exceed the amount that it would have been without the inclusion of recycled water and the annual payment of water users did not exceed their ability to pay, the income of water suppliers increased by 69%. The three balanced objectives of rational water resource configuration by the local government, reasonable profit for the water supply enterprise, and reasonable payment by water users have been achieved. This study provides theoretical and methodological support for the implementation of water pricing for multi-source and multi-user water supplies in water-scarce areas. [ABSTRACT FROM AUTHOR]

The fan-delta reservoir play has become an important exploration target within the Junggar Basin, especially in the Mabei area within the Mahu Sag, where a fan-delta oil and gas pool has been recently discovered. The sedimentary characteristics, distribution patterns and formation mechanisms of the fan-delta clastic bodies in Lower Triassic Baikouquan Formation (T1b) were studied using seismic, well log and core data accompanied by a flume tank experiment and a modern analogue depositional study. The T1b in the Mabei area is composed of a fan delta consisting of fan-delta plain (including debris flow deposits, sheetflood deposits, braided channel deposits, and floodplain deposits), fan-delta front (including subaqueous reworked sheetflood deposits and distal sheetflood deposits) and muddy lake deposits. The sheetflood deposits, characterised by moderately sorted conglomerates with brown matrix, form during the peak flooding. They are widespread and sheet-like, occupying the major portion of the fan-delta plain. The braided channel deposits are characterised by well-sorted and clast-supported conglomerates and pebbly sandstones, formed later during the falling flow stage. Owing to the decline in volume and velocity, and the formation of continuously braided fluid flow, the sediments of the flood period are reworked, generating the braided channels. Laterally, the braided channels occur as belts of clastic bodies surrounded by continuous sheetflood deposits. Vertically, the braided channels are interbedded between the sheetflood deposits. The subaqueous reworked sheetflood deposits are characterised by greyish-green, well-sorted and clast-supported conglomerates, whereas the distal sheetflood deposits are characterised by well-sorted sandstones, intercalated with mudstone. The subaqueous reworked sheetflood and distal sheetflood deposits are a distal partly subaqueous extension of the main sheetflood deposits, albeit reworked by basinal currents and waves. The distal sheetflood deposits form on distal fringes of the sheetfloods and are more thoroughly reworked by longshore and wave currents. The braided channel, subaqueous reworked sheetflood and distal sheetflood deposits can form high-porosity reservoirs. These findings challenge the common view and suggest that the channelised facies on the fans are not the main flood events; rather, the more extensive sheetfloods are the major flood events. [ABSTRACT FROM AUTHOR]

The probability integral method is one of the most widely used methods for predicting surface subsidence induced by underground mining in China. In its parameter calculation, the least square algorithm is commonly employed for fitting parameters. However, in the process of fitting parameters, the results are easily affected by ill-conditioned normal matrices and the interference of outliers, resulting in divergent problems. To solve these problems, the principle of robust ridge estimation was introduced in this paper, and a parameter calculation model for the probability integral method based on this principle (hereafter referred to as the established model) was established. Besides, a parameter calculation experiment with manual intervention was conducted in combination with engineering examples. The results demonstrate that the parameter calculation method based on robust ridge estimation can suppress the interference of outliers, overcome the problem of ill-conditioned matrix, and ensure the effectiveness and reliability of parameter estimation results. Compared with the conventional least squares method, the robust ridge estimation method demonstrates greater accuracy in predicting surface subsidence parameters, which validates its rationality and accuracy in underground mining engineering. The research findings provide technical support for obtaining similar parameters for surface subsidence in mining areas and hold significant engineering application value. [ABSTRACT FROM AUTHOR]

Urban agglomerations (UAs), which serve as pivotal hubs for economic and innovative convergence, play a crucial role in enhancing internal circulation and strengthening external linkages. This study utilizes the China city-level multi-regional input-output tables, incorporating the Dagum Gini coefficient and kernel density estimation methods, to perform a thorough quantitative analysis. Disparities within the national and global value chains ("dual value chains") of Chinese UAs from 2012 to 2017 were assessed. Additionally, the logarithmic mean Divisia index (LMDI) method was applied to disaggregate the drivers of both national and global intermediate inputs (NII and GII). The study's key findings include the following: (1) The national value chain (NVC) within UAs exhibits robust growth, contrasting with the decline in the global value chain (GVC). (2) The inter-UA disparity contribution rate significantly surpasses the combined rates of intra-UA contribution and super-variation density. (3) Distinct evolutionary peak trends are discerned among various UAs within the "dual value chains", highlighting diverse spatial polarization characteristics and expansiveness. (4) The growth of the NVC has transitioned from a negative to a positive impact on NII, while the decline in GVC has substantially counteracted GII growth. Economic and demographic factors notably drive positive improvements in both NII and GII, whereas the efficiency of outflows presents a negative driving effect. Based on these findings, this study offers strategic recommendations to facilitate the effective integration of UAs into the new development paradigm, thereby providing a scientific basis for related decision-making processes. [ABSTRACT FROM AUTHOR]

Cooperative Technology Transfer (CTT) is a technology transfer model where universities and enterprises jointly participate throughout the entire process of technology transfer activities. Most discussions focus on its mechanisms and influencing factors, yet a framework to guide CTT projects in practice is still lacking. This study proposes an assessment model based on the life-cycle of CTT projects, covering the initial cooperation relationship, project management during the mid-term, and technological achievements at the end. The model was evaluated by 14 experts first and then validated through two CTT projects in China. Gray Relation Analysis was employed to calculate the weights of different factors based on their relative importance, while the Dempster–Shafer theory was utilized to combine evidence from various sources and address the uncertainty in the assessment. The results of the case analysis indicate that the attitudes of universities and enterprises are considered critical in influencing the success of CTT projects, while management issues that arise during the projects can pose potential risks. This research serves as an applied exploration and has three functions. Firstly, the model can be used as a feasibility study before the project commences. Secondly, it can be utilized to analyze and improve potential issues during the project. Finally, it can be used for a post-project experience summary. [ABSTRACT FROM AUTHOR]

Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows can more clearly show what causes the carbon peaking stress and which carbon flow paths are more critical. In this study, the decoupling index of carbon emissions and economy development was applied to characterize the carbon peaking stress in each region, and the environmental extended multi-regional input–output model was applied to re-evaluate the criticality of regional embodied carbon flows. The results showed that the carbon peaking stress in China improved from 2007 to 2012, but the rebound of carbon peaking stress in 2017 made most regions reverse the previous downward trend. The stress to reach carbon peaks varies considerably from region to region, and the stress in the northwest is much higher than that in developed eastern China. Considering the heterogeneity of carbon peaking stress, additional concerns should be given to the net embodied carbon output in the northwestern, northern, and central regions, which can help avoid the dilemma between outsourcing embodied carbon and reducing carbon emissions from production. The policy to reduce emissions should be implemented in all regions that benefit from the net embodied carbon output of the northern and northwestern regions, where the carbon peaking stress is higher. The focus should be on the actual improvement of the carbon peaking stress, not just on the transfer of stress. The increasing urgency of achieving carbon peaking targets and unequal stress for regional peaking emissions calls for differentiated regional mitigation measures to help the Chinese government scientifically and in an orderly manner promote the overall and local carbon peaking work. [ABSTRACT FROM AUTHOR]

An increase in a currency internationalization levels can positively impact its credibility in international economic activities, and expand the effective demand and optimize the supply structure for the country's financial service trade. In this way, a state can improve its financial service trade competitiveness in the international market. This study builds a vector autoregressive model based on time-series data of China-US financial services trade from 2010 to 2021, analyzes the impact of different quantitative indicators of RMB internationalization on this trade from the impulse response results, and validates the conclusions using various inspection methods. The results show that the increase in RMB internationalization helps to narrow the China-US financial services trade balance, but with a significant lag. And this effect is heterogeneous in different dimensions, demonstrated by the fact that the development of overseas RMB securities business is more important for the level of RMB internationalization to narrow the China-US financial services trade balance. Finally, among the specific measures to improve its financial services trade, China should focus on developing the international competitiveness of the traditional RMB deposit and loan financial sector, while the competition in the overseas market for high value-added financial businesses must also not be neglected. Furthermore, China needs to implement more targeted RMB internationalization development policies at different levels in the future to provide high-quality financial services to the rest of the world and aid in the economic recovery of the world in the "post-pandemic" era. [ABSTRACT FROM AUTHOR]

Composite post insulators are crucial facilities in substations and are prone to significant damage during seismic disasters. However, existing research lacks seismic motion models suitable for power facilities and rarely involves stochastic models. Furthermore, considering the non-stationary characteristics of seismic motion, predicting the response of nonlinear systems under non-stationary excitation becomes exceedingly challenging. In view of this, the stochastic response of composite post insulators under the non-stationary stochastic seismic excitation appropriate for power facilities has been studied. First, a stochastic ground motion model, conforming to the Code for Seismic Design of Electrical Insulators in China, is established, incorporating amplitude and frequency non-stationarity. Next, the nonlinear dynamic system, accounting for multi-section composite post insulators and the nonlinearity of flange connections, is established under stochastic ground motion conditions. Based on this stochastic nonlinear dynamic model, the dynamic behavior of the system was analyzed using the stochastic dynamics method (the wavelet-Galerkin method), and the influence of nonlinear stiffness on the system response was discussed. The stochastic seismic response analysis method proposed in this paper can serve as a valuable reference for the seismic design of pillar-type electrical equipment. [ABSTRACT FROM AUTHOR]

The low-frequency oscillation (LFO) has occurred in the train–network system due to the introduction of the power electronics of the trains. The modeling and analyzing method in current researches based on electrified railway unilateral power supply system are not suitable for the LFO analysis in a bilateral power supply system, where the trains are supplied by two traction substations. In this work, based on the single-input and single-output impedance model of China CRH5 trains, the node admittance matrices of the train–network system both in unilateral and bilateral power supply modes are established, including three-phase power grid, traction transformers and traction network. Then the modal analysis is used to study the oscillation modes and propagation characteristics of the unilateral and bilateral power supply systems. Moreover, the influence of the equivalent inductance of the power grid, the length of the transmission line, and the length of the traction network are analyzed on the critical oscillation mode of the bilateral power supply system. Finally, the theoretical analysis results are verified by the time-domain simulation model in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

There is a significant time lag in the process of sectoral interactions, thus, it is necessary to identify the sectoral carbon emissions in time series. This paper calculates the time lag of sectoral carbon emissions of China's industrial sectors and constructs the monthly consumption data of sectors of 24 months, then simulates the monthly carbon emissions in the year 2022, the results are as follows: The terminal manufacturing industries receive carbon emissions from many sectors, and the energy supply industry generates carbon inflows to all sectors. Due to the time lag effect, some of the carbon emissions generated in the current period come from carbon accumulation in the previous periods. There are great differences in the time lag of sectoral carbon emissions, the carbon emissions of the energy supply industry are concentrated in the early stage while the time lag of the communication equipment manufacturing industry is long. Sectors show differences of carbon emissions in time series due to their different final consumption. In the whole year, the degree of carbon emissions in January and February is low and that in December is high. [ABSTRACT FROM AUTHOR]

Water and energy are mutually reinforcing elements in production and consumption activities, forming an inseparable relationship. This study, conducted at the provincial level, investigates the intricate connections between energy water footprint flow, water shortage, and water pollution. It aims to elucidate the vulnerability of provincial energy sectors to water use, fostering regional coordinated management for sustainable energy and water development. Utilizing a multi-regional input–output model, the research analyzes both the quantity and quality of water resources. The water footprint of China's provincial energy sector is quantified and scrutinized. Furthermore, an evaluation index system is constructed based on the water footprint concept. The entropy weight TOPSIS method is then applied to assess the vulnerability of inter-provincial energy sector water footprints. The key findings are as follows: (1) The energy water footprint has shifted from water resource-deficient areas, such as the northwest, North China, and northeast, to economically developed regions with relatively abundant water resources, such as East China and South China. This reveals that the transfer of water footprints in economic activities does not entirely alleviate water shortages in China and may even exacerbate shortages in certain water-deficient areas. (2) The graywater footprint of the energy sector is significantly larger than the blue water footprint and water shortage footprint. (3) The production and supply departments of power and heat emerge as the largest contributors to the transfer of energy and water footprints from water shortage areas to water-rich areas, followed by coal mining and dressing departments. (4) Top 10 Vulnerable Provinces: Tibet, Zhejiang, Ningxia, Tianjin, Jiangsu, Henan, Hunan, Beijing, Chongqing, and Hebei are identified as the top 10 provinces vulnerable to water footprints in the energy sector. This vulnerability closely correlates with the energy sector's dependence on water footprints and production leverage. Recommendations include optimizing the energy mix and establishing stable external water footprint supply channels for provinces with high water footprint dependence. Provinces with high production leverage are advised to accelerate innovation and application of water-saving technologies in key energy sectors, increase the utilization of clean energy, and enhance resource recycling. National government departments should focus on controlling and reducing water pollutants in the energy sector across regions, addressing water shortages by purifying water bodies and improving water quality. [ABSTRACT FROM AUTHOR]

Regional population mortality correlates with regional socioeconomic development. This study aimed to identify the key socioeconomic factors influencing mortality patterns in Chinese provinces. Using data from the Seventh Population Census, we analyzed mortality patterns by gender and urban‒rural division in 31 provinces. Using a functional regression model, we assessed the influence of fourteen indicators on mortality patterns. Main findings: (1) China shows notable gender and urban‒rural mortality variations across age groups. Males generally have higher mortality than females, and rural areas experience elevated mortality rates compared to urban areas. Mortality in individuals younger than 40 years is influenced mainly by urban‒rural factors, with gender becoming more noticeable in the 40–84 age group. (2) The substantial marginal impact of socioeconomic factors on mortality patterns generally becomes evident after the age of 45, with less pronounced differences in their impact on early-life mortality patterns. (3) Various factors have age-specific impacts on mortality. Education has a negative effect on mortality in individuals aged 0–29, extending to those aged 30–59 and diminishing in older age groups. Urbanization positively influences the probability of death in individuals aged 45–54 years, while the impact of traffic accidents increases with age. Among elderly people, the effect of socioeconomic variables is smaller, highlighting the intricate and heterogeneous nature of these influences and acknowledging certain limitations. [ABSTRACT FROM AUTHOR]

China's constantly rapid economic growth accompanying by a low overall inflation has long been mysterious in macroeconomics. The core purpose of this paper is to solve this puzzle. Therefore, we integrate overdetermined set of equations into a MUCSVO model to explore the volatility mechanism of the overall inflation from a sectoral perspective. Our key findings include: 1) the hedging effect of sectoral inflation fluctuations principally accounts for China's long-run stable overall inflation; 2) the main contradiction of China's inflation has been shifting from high price levels in the traditional food and residence categories to rising prices in the health care category; 3) as the proportions of inflation in the food and residence categories fall steadily, sectoral inflation weights become more evenly distributed. In conclusion, China's overall inflation and deflation will be much less likely to occur, while inflation is still of sectoral imbalance. Unusual price fluctuations in the food and health care categories, which are highly relevant to basic living standards of the low-income group, deserve close attention in particular. Overall, besides solving the puzzle of China's low inflation, our model is applicable to economies that do not publish inflation weights, which is a useful extension of core inflation measurement. [ABSTRACT FROM AUTHOR]

Rapeseed blanket seedling transplanters have developed rapidly due to their high efficiency and adaptability to the soil in many areas of China. However, during the transplanter's longitudinal seedling conveying process, seedling blanket compression leads to inaccurate conveying and thus declined seedling picking performance. In this paper, a mechanical compression test was carried out on rapeseed seedling blankets. The longitudinal compression force of the rapeseed seedling blanket on a transplanter was calculated through mechanical analysis. A compression model of the rapeseed seedling blanket was established to determine how the blanket's mechanical characteristics and the device's structural parameters affect blanket compression. In addition, with the index of longitudinal compression Y1, the coefficient of variation in the longitudinal seedling conveying distance Y2, and the qualified-block-cutting rate Y3, the interactive influence between the seedling tray tilt angle A, the seedling blanket moisture content B, and the seedling blanket thickness C were analyzed using response surface analysis. Aiming to reduce blanket compression and enhance the accuracy of longitudinal seedling conveying and block-cutting quality, the optimized results show that the predicted optimal parameters were a 50.14° seedling tray tilt angle, a 71.86% seedling blanket moisture content, and a 22.13 mm seedling blanket thickness. Using these optimized parameters, the transplanter achieved a blanket longitudinal compression of 18.17 mm, a coefficient of variation in the longitudinal seedling conveying distance of 1.142, and a qualified-block-cutting rate of 90%. Subsequently, a validation test was performed, revealing a high degree of conformity between the optimization model and the experimental data. Thus, the predicted optimal parameters can provide significantly reduced compression and a high seedling conveying performance. The results of this study provide theoretical and empirical support for the optimized design and operation of mechanized rapeseed blanket seedling transplanting. [ABSTRACT FROM AUTHOR]

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

In order to reveal the characteristics of the competition and cooperation of multi-modal public transportation in the cities of cold areas and to study the competitive relationship of multi-modal public transportation in cold areas, the urban public transportation system in cold areas is divided into three sub-systems, namely, conventional buses, urban mass transit, and cabs, to analyze the competitive relationship and evolution mechanism of multi-modal public transportation in cities of cold areas. We construct a multi-modal public transit Lotka–Volterra competitiveness model, considering that conventional buses and urban mass transit are mainly dominant in urban public transit systems in cold regions, and introduce the competitive influence effect coefficients and cooperative influence effect coefficients to analyze the mechanism of the evolution of the competitiveness between conventional buses and urban mass transit. The data on traffic supply and demand and travel of the Harbin bus system, a typical city in China, are collected, and the simulation analysis of multi-modal competing relationship and evolution mechanism of conventional bus and urban mass transit competition is carried out, respectively, with Harbin as the research object. The results show that the constructed model can better describe the multi-modal bus competition relationship and evolution mechanism. [ABSTRACT FROM AUTHOR]

This paper wants to increase our understanding and computational know-how for time-varying matrix problems and Zhang Neural Networks. These neural networks were invented for time or single parameter-varying matrix problems around 2001 in China and almost all of their advances have been made in and most still come from its birthplace. Zhang Neural Network methods have become a backbone for solving discretized sensor driven time-varying matrix problems in real-time, in theory and in on-chip applications for robots, in control theory and other engineering applications in China. They have become the method of choice for many time-varying matrix problems that benefit from or require efficient, accurate and predictive real-time computations. A typical discretized Zhang Neural Network algorithm needs seven distinct steps in its initial set-up. The construction of discretized Zhang Neural Network algorithms starts from a model equation with its associated error equation and the stipulation that the error function decrease exponentially fast. The error function differential equation is then mated with a convergent look-ahead finite difference formula to create a distinctly new multi-step style solver that predicts the future state of the system reliably from current and earlier state and solution data. Matlab codes of discretized Zhang Neural Network algorithms for time varying matrix problems typically consist of one linear equations solve and one recursion of already available data per time step. This makes discretized Zhang Neural network based algorithms highly competitive with ordinary differential equation initial value analytic continuation methods for function given data that are designed to work adaptively. Discretized Zhang Neural Network methods have different characteristics and applicabilities than multi-step ordinary differential equations (ODEs) initial value solvers. These new time-varying matrix methods can solve matrix-given problems from sensor data with constant sampling gaps or from functional equations. To illustrate the adaptability of discretized Zhang Neural Networks and further the understanding of this method, this paper details the seven step set-up process for Zhang Neural Networks and twelve separate time-varying matrix models. It supplies new codes for seven of these. Open problems are mentioned as well as detailed references to recent work on discretized Zhang Neural Networks and time-varying matrix computations. Comparisons are given to standard non-predictive multi-step methods that use initial value problems ODE solvers and analytic continuation methods. [ABSTRACT FROM AUTHOR]

The Semi-variable Coefficient Spatial Lag Model (SVC-SLM) not only addresses the "dimension disaster" associated with the Varying Coefficient Spatial Lag Model(VC-SLM), but also overcomes the non-linear problem of the variable coefficient, and fully explores the hidden information of the model. In this paper, INLA is firstly used to estimate the parameters of (SVC-SLM) by using B-spline to deal with the non-parametric terms, and the comparative experimental results show that the INLA algorithm is much better than MCMCINLA in terms of both time efficiency and estimation accuracy. For the problem of identifying the constant coefficient terms in the SVC-SLM, the bootstrap test is given based on the residuals. Taking the PM2.5 data of 31 provinces in mainland China from 2015 to 2020 as an empirical example, parametric, non-parametric, and semi-parametric perspectives establish three models of Spatial Lag Model (SLM), VC-SLM, SVC-SLM, which explore the relationship between the covariate factors and the level of urbanization as well as their impacts on the concentration of PM2.5 in the context of increasing urbanization; among the three models, the SVC-SLM has the smallest values of DIC and WAIC, indicating that the SVC-SLM is optimal. [ABSTRACT FROM AUTHOR]

This study explores the factors driving agricultural wages growth in China from 1981 to 2020. We propose a comprehensive framework that combines input-output analysis and structural decomposition analysis to investigate the drivers of agricultural wages growth from four perspectives: supply, demand, industrial linkages, and agricultural support policies. The findings indicate that changes in consumer demand, investment demand, and labor mobility play significant roles in driving the growth of agricultural wages in China. Additionally, agricultural support policies have contributed to an increase in agricultural wages to some extent. However, changes in industrial linkages negatively affect agricultural wages growth. A notable strength of this study lies in the methodology employed, which ensures a comprehensive and systematic analysis encompassing diverse factors rather than a restricted perspective. [ABSTRACT FROM AUTHOR]

Based on a large national sample of the China Family Panel Studies (CFPS) database, the study will to provide some thoughts and suggestions for the transformation and upgrading of China's agriculture, the enhancement of total factor productivity by exploring the agricultural production efficiency from the micro-farmers' perspective. By constructing the models of Charnes, Cooper & Rhodes (CCR), Banker, Charnes & Cooper (BCC), Slacks-Based Model (SBM) and technology optimization Malmquist-Luenberger index, it finally obtained the comprehensive technical efficiency value, pure technical efficiency value, farm household efficiency value and green total factor productivity level value at the micro-farm household level based on the comparative analysis. It was found that: the comparison of the measures based on different models found that although there are differences in the calculated efficiency values, the pure technical efficiency values calculated by BCC are the main factors affecting the micro agricultural production efficiency values at the farmer level, the SBM model should optimize the CCR, BCC models, and more suitable for Chinese government policy formulation and optimization; the technology optimization Malmquist-Luenberger index method is the micro agricultural production efficiency measurement method of choice, with the characteristics of diverse model selection, rich application scenarios and convenient processing of negative outputs; environmental factors in the current evaluation of agricultural green total factor productivity, mainly play a negative inhibitory role, reducing the negative externalities of environmental variables output, become one of the key issues facing the current micro farm layer green total factor production enhancement; the combination of subjective and objective measures of environmental non-desired output is an important way to measure environmental factors of agricultural green total factor productivity, it can be used in practical applications based on a combination of research objectives and data availability. [ABSTRACT FROM AUTHOR]

Electronic commerce plays a vital role in the digital age, and the creation of a good electronic commerce ecosystem is crucial to maintaining economic growth. The electronic commerce service industry is a leading indicator of electronic commerce development, and its possible changes imply the future trends and innovation directions of the electronic commerce industry. An accurate grasp of the possible future revenue scale of the electronic commerce service industry can provide decision-making information for government policy formulation. Electronic commerce companies must formulate operational plans based on the latest information to determine strategic directions that are reasonable and consistent with the actual situation. Although there exist many prediction methods, they often fail to produce ideal results when the number of observations is insufficient. The fractional-order grey model is a common method used to deal with small data set prediction problems. This study therefore proposes a new modeling procedure for the fractional-order grey model to predict the revenue scale of China's electronic commerce service industry. The results of experiments demonstrate that the proposed procedure can yield robust outputs under the condition of small data sets to reduce decision-making risks. Therefore, it can be regarded as a practical small data set analysis tool for managers. [ABSTRACT FROM AUTHOR]

Under the influence of international trade, labor flow not only exists in the waves of international labor migration but is also embodied in international products and services. This paper focused on members of the China–Africa Cooperation Forum (FOCAC). We computed and analyzed the sectoral embodied labor transfer between China and Africa from 2000 to 2015 based on the Multiregional Input-Output Method. Our results are as follows: (1) Both China and Africa play roles as labor suppliers in the global supply chain. By ameliorating the trade structure, both China and Africa can better utilize their labor surplus. (2) China and Africa share complementarity in sectoral labor allocation. In short, the embodied labor transfer via international trade between China and Africa has, to some extent, relieved the labor shortage on both sides. (3) Africa has transformed into a net exporter of industrial labor since 2011. By analyzing the embodied labor flow from the global perspective, this paper beats a new path in depicting the effect of international trade on labor allocation, enriches the evaluation of embodied labor transfer between China and Africa, and also provides a beneficial supplement to Multiregional Input-Output analysis in the field of factor flows. [ABSTRACT FROM AUTHOR]

With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The conventional technique for positioning seafloor geophones in ocean bottom seismic exploration encounters several challenges, including the significant impact of outliers on positioning results, underutilization of high-precision observations, and low efficiency in real-time data processing. These issues inevitably affect the quality of seismic exploration outcomes. To address these challenges and enhance the accuracy of geophone positioning, this paper proposes a rigorous real-time acoustic positioning method for geophones based on sequential adjustment and Baarda's outlier detection approach. The proposed method comprises three key steps: grouping the original acoustic observations, constructing the intra-group acoustic positioning model, and synthesizing the positioning results across the different groups. The validity and practicality of this approach are confirmed through a simulation experiment as well as the field experiment conducted in the Bohai Sea, China. The results demonstrate that the proposed method effectively eliminates outliers in the original observations and maximizes the utilization of high-quality observations. Compared to traditional acoustic positioning methods, it significantly reduces positioning errors from meters to decimeters, and in some cases can achieve centimeter-level precision. When the sound velocity profile in the operating sea area is measured, the method can attain the posterior standard deviation at the millimeter level and positioning errors within 10 cm. When the sound velocity profile is unknown, the method can achieve the posterior standard deviation at centimeter-level and positioning errors of approximately 20 cm. [ABSTRACT FROM AUTHOR]

The southeast Sichuan in China is a key area for deep shale gas exploration. However, shale gas exploration in deep heterogeneous strata is greatly affected by in situ stress. Faults caused by tectonic movement strongly change the distribution of in situ stress. Inversion and prediction of in situ stress have become an important research hotspot in the field of resource exploitation. Based on the geological structure evolution and experimental data, the boundary stress iterative inversion method is used to inverse the in situ stress field of deep heterogeneous strata containing shale gas reservoirs, and the fracture characteristics around local fault are analyzed by fracture mechanics theory. The research shows that the stress field has the characteristics of non-uniform distribution under the influence of fault, and there is obvious stress concentration and deflection of principal stress direction around the fault. And magnitude and direction of stress are affected by the internal friction angle of the fault zone and the formation depth in different tectonic periods. Through the analysis and calculation of the plastic strain and stress intensity factors, it can be inferred that the cracks will propagate at the terminal portion of the fault. The research is important to guide engineering for the adjustment of resource exploitation scheme. [ABSTRACT FROM AUTHOR]

Resource-based cities are important strategic bases for securing resources in China and have made great contributions to the country's economic development. Long-term extensive resource development has made resource-based cities an important region constraining China from achieving comprehensive low-carbon development. Therefore, it is of great significance to explore the low-carbon transition path of resource-based cities for their energy greening, industrial transformation, and high-quality economic development. This study compiled the CO2 emission inventory of resource-based cities in China from 2005 to 2017, explored the contribution to CO2 emissions from three perspectives (driver, industry, and city), and predicted the peak of CO2 emissions in resource-based cities. The results show that resource-based cities contribute 18.4% of the country's GDP and emit 44.4% of the country's CO2 and that economic growth and CO2 emissions have not yet been decoupled. The per capita CO2 emissions and emission intensity of resource-based cities are 1.8 times and 2.4 times higher than the national average, respectively. Economic growth and energy intensity are the biggest drivers and main inhibitors of CO2 emissions growth. Industrial restructuring has become the biggest inhibitor of CO2 emissions growth. Based on the different resource endowments, industrial structures, and socio-economic development levels of resource-based cities, we propose differentiated low-carbon transition pathways. This study can provide references for cities to develop differentiated low-carbon development paths under the "double carbon" target. [ABSTRACT FROM AUTHOR]

The utilization of the Monte Carlo method in conjunction with probabilistic seismic hazard analysis (PSHA) constitutes a compelling avenue for exploration. This approach presents itself as an efficient and adaptable alternative to conventional PSHA, particularly when confronted with intricate factors such as parameter uncertainties and diverse earthquake source models. Leveraging the Monte Carlo method and drawing from the widely adopted Cornell-type seismicity model in engineering seismology and disaster mitigation, as well as a seismicity model capturing temporal, spatial, and magnitude inhomogeneity, we have derived a formula for the probability of earthquake intensity occurrence and the mean rate of intensity occurrence over a specified time period. This effort has culminated in the development of a MATLAB-based program named MCPSHA. To assess the model's efficacy, we selected Baoji City, Shaanxi Province, China, as our research site. Our investigation delves into the disparity between occurrence probability and extreme probability (a surrogate commonly employed for occurrence probability) in the Baoji region over the next 50 years. The findings reveal that the Western region of Baoji exhibits a heightened hazard level, as depicted in the maps, which illustrate a 10% probability of exceedance within a 50-year timeframe. The probability of earthquake occurrence under various intensities (VI, VII, and VIII) over 50 years follows a declining trend from west to east. Furthermore, the likelihood of seismic intensity exceeding VI, VII, and VIII indicates the lowest exceeding probability in the northeast and the highest in the northwest. Notably, for intensities VI-VII, the difference between occurrence probability and extreme probability approaches twice, gradually diminishing with increasing intensity. This study underscores the MCPSHA model's efficacy in providing robust technical support for mitigating earthquake risk and enhancing the precision of earthquake insurance premium rate calculations. [ABSTRACT FROM AUTHOR]

A trend in the development of spaceborne Synthetic Aperture Radar (SAR) technology is the shift from a single-satellite repeated observation mode to a multi-satellite collaborative observation mode. However, current multi-satellite collaborative geolocation algorithms face challenges, such as geometric model mismatch and poor baseline estimation accuracy, arising from highly dynamic changes among multi-satellites. This paper introduces a high-precision and efficient geolocation algorithm for a spaceborne bistatic interferometric SAR (BiInSAR) system based on an improved range–Doppler (IRD) model. The proposed algorithm encompasses three key contributions. Firstly, a comprehensive description of the spatial baseline geometric model unique to the bistatic configuration is provided, with a specific focus on deriving the perpendicular baseline expression. Secondly, IRD geolocation functions are established to meet the specific requirements of the bistatic configuration. Then, a novel BiInSAR geolocation algorithm based on the IRD's functions is proposed, which can significantly improve the target geolocation accuracy by modifying the range–Doppler equation to suit the bistatic configuration. Meanwhile, a low-coupling parallel calculation method is proposed, which can improve the calculation speed by two to three times. Finally, the accuracy and efficiency of the algorithm are demonstrated using experimental data acquired by the TH-2 satellite, which is China's first spaceborne BiInSAR system. The experimental results prove that the IRD algorithm exhibits geolocation accuracy with an average error of less than 1 m and a standard deviation of less than 2.5 m while maintaining computational efficiency at a calculation speed of 1,429,678 pixels per second. [ABSTRACT FROM AUTHOR]

Prediction of renewable energy consumption structure (RECS) can provide important guidance for energy development planning and energy structure transformation. The RECS refer to the proportion of various renewable energy consumptions and belong to compositional data, which could reflect the structural shapes of a complete system better. The multivariate compositional data's vector autoregressive model (CDVAR) on the basis of the Simplex space and its algebraic system is proposed in this study aiming at the multi-dimensional small sample size. Firstly, the algebraic system of the Simplex space is introduced and the statistics of the compositional data are defined. Secondly, the novel model with the form of the compositional data is obtained and the least square parameter estimation of the model is derived according to Aitchison geometry. Third, the validation of the novel model is verified by the data on RECS in countries (China, USA, and Canada). The validation presents that the proposed model performs better in fitting, prediction, stability, and applicability compared with other five models under transformation. Last, the proposed model is applied to analyze and forecast the RECS of the above countries in 2021–2025 to provide an important basis for the optimization of the RECS. [ABSTRACT FROM AUTHOR]

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author summary: Estimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated. [ABSTRACT FROM AUTHOR]

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China's carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China's future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China's carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China's transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions. [ABSTRACT FROM AUTHOR]

Water resources are the source of life for people to survive, especially in agriculture. Water resources provide necessary support for agricultural economic development and irrigation management. Using the panel data from 2013 to 2019, this paper uses the coupled coordination model to explore the degree of synergy between agricultural water resources efficiency (AWRE) and agricultural economic development level (AEDL), and analyzes the spatial correlation of coupling coordination degree (CCD) between the AWRE and AEDL, and further predicts the relationship between the two. The results are as follows: (1) There are superb variations in the CCD among provinces, and the diploma of coordination is greater in the south than in the north; (2) In the spatial correlation pattern, high–high agglomeration areas exhibit an growing trend, and the variety of low–low agglomeration areas is small; and (3) Through the prediction, it is found that in the further, the CCD will show a trend of slow increase in future. For purpose of the coordinated perfection of the two systems, we need to completely discover the connotation of coordinated improvement and promote regional cooperation. [ABSTRACT FROM AUTHOR]

Probing the determinants of energy conservation and decoupling energy use from economic growth in the iron and steel (IS) industry are at the forefront of China's environmental concerns. This study first constructed an extended logarithmic mean divisia index model to estimate determinants of the IS industrial energy consumption. The Tapio approach and decomposed decoupling analysis is further utilized to analyze the decoupling relationship between the industry's total energy use and its value creation as well as the decoupling states of the determinants. Finally, grey Verhulst technique is employed to predict the future trend of decoupling state. Empirical results based on statistical data of the IS industry during 2006–2017 demonstrate that: (1) Regulatory effect and scale effect contribute to energy conservation. Growth effect significantly increases energy consumption while technological effect and structural effect exert unstable impacts on energy use. (2) Weak decoupling states between energy use and value creation are mainly observed during the research period. Structural, technological, regulatory, and scale effects are mainly weak decoupling, while the growth effect is mainly expansionary negative decoupling. (3) A weak decoupling state between the two variables is predicted up to 2030 with the help of grey theory. [ABSTRACT FROM AUTHOR]

Depending on the trading modes, the effect of Outward Foreign Direct Investment (OFDI) on the manufacturing industry's position within the global value chain (GVC) may differ considerably. This paper examines the GVC position of China's manufacturing industry from 2003 to 2018, specifically focusing on the general trade and processing trade. Drawing upon this premise, this paper analyzes the effect and mechanism by which OFDI influences the GVC position of China's manufacturing industry. The result demonstrates that: (1) China's processing trade manufacturing industry has a much lower GVC position than general trade manufacturing industry. The GVC position of China's general trade manufacturing industry rose from 2.76 to 2.90 from 2003 to 2018, while processing trade manufacturing industry remained around 1.93. (2) OFDI boosts the GVC position of general trade manufacturing industry through facilitating reverse technology spillover, inducing industry structure upgrading, and enabling export scale expansion. (3) OFDI hinders the GVC position growth of processing trade manufacturing industry. The research findings offer theoretical backing for China to develop OFDI strategies that are tailored to different trading modes within the new framework of dual circulation. These strategies aim to facilitate the transformation and advancement of the manufacturing industry, as well as the growth of the GVC position. [ABSTRACT FROM AUTHOR]

The advancement of Financial Technology (FinTech) is crucial for government entities, the National Grid, and various energy corporations to facilitate the transition towards sustainable and green production methods. This study investigates the relationship between FinTech and Total Factor Energy Efficiency (TFEE) using data from a selected sample of 254 city groups in China. We examine how the development of FinTech impacts TFEE from both non-spatial and spatial perspectives. The results from the non-spatial panel model indicate that FinTech development has a significant positive impact on TFEE. Comparative studies were conducted using fixed effects (FE), feasible generalized least squares (FGLS) models, and system generalized method of moments (GMM) models, and the main findings remained consistent, confirming the robustness of our conclusions. Spatial autocorrelation results reveal a significant positive spatial spillover effect on TFEE. Both the spatial Durbin model and the dynamic spatial Durbin model demonstrate that FinTech also has a significant positive impact on TFEE, and this effect increases over time. These conclusions remain robust even after considering various spatial weight matrices and alternative methods for calculating TFEE. Additionally, we discovered that the digital economy plays a vital role in strengthening the relationship between FinTech and TFEE. Heterogeneity analysis indicates that, compared to cities without resource-based economies, FinTech development in growing resource-based cities has a more substantial impact on TFEE. [ABSTRACT FROM AUTHOR]

In existing models with an unknown link function, the issue of predictors containing both multiple functional data and multiple scalar data has not been studied. To fill this gap, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and responses, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and establish the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study, functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020, and scalar predictors are GDP and the number of beds in hospitals. The experimental results indicate that the unknown link function model has a smaller prediction error and better performance than both the model with the known link function and the model without a link function. [ABSTRACT FROM AUTHOR]

The unstructured construction site with a complex and changeable environment brings challenges to human–machine interaction. In this study, the earthwork digital twin (DT) is proposed to realize accurate and real‐time perception, which can support the teleoperation of an automated bulldozer. The overall framework includes anterior‐time DT, real‐time DT, and posterior‐time DT, which integrates three‐dimensional modeling, edge detection, and blade trajectory algorithms. Multidimensional heterogeneous data in bulldozer teleoperation can be displayed in real‐time to ensure construction safety. Big data during teleoperation of the construction process can be collected, stored, and analyzed. The DT proposed in this study was successfully applied in a major construction project in China, and testing results show its universality, robustness, and advanced performance. The key technologies proposed in this study can be applied to solve the common problems in the construction industry, which is promising for future intelligent construction. [ABSTRACT FROM AUTHOR]

This study investigated factors of the green innovation performance of industrial enterprises in 30 provinces and autonomous regions across China. This study used literature analysis and DEMATEL method to identify the factors of green innovation performance of industrial enterprises. A green innovation performance evaluation index system was then constructed, and a mathematical model was established using factor analysis method to empirically analyze the factors of green innovation performance of industrial enterprises across 30 provinces and autonomous regions in China from 2012 to 2016. The results show that green innovation strength, technological development level, energy consumption, and environmental protection are the three factors of green innovation performance. Firstly, the influence of innovation strength on comprehensive green innovation performance has the largest weight, meaning that it has the largest impact on comprehensive green innovation performance. Secondly, the influence of the level of scientific and technological development on the comprehensive green innovation performance is weaker. Thirdly, the main influencing factor is the level of energy consumption and environmental protection. Finally, based on the empirical analysis, it is suggested that industrial enterprises should increase investment in green innovation, developing science and technology, and improving ecological environment, so as to enhance the green innovation performance of industrial enterprises. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals, championed by the United Nations, have elevated the importance of renewable energy development (RED) within China's energy landscape. As China strives to align its energy goals with these global objectives, it becomes imperative to delve into the intricate spatial dynamics of renewable energy. This study employs a modified gravity model and harness the power of social network analysis to scrutinize the landscape of RED in 30 Chinese provinces from 2012 to 2021. The findings reveal a captivating narrative of transformation. Over the years, we have witnessed shifts in the general trajectory of RED across China and nuanced variations within each province. The spatial network underpins RED's growth as a multifaceted tapestry, showing stability and adaptability. It paints a vivid picture of interconnected provinces with telltale signs of spatial spillover effects. Amid this intricate web emerges a core–edge pattern, signifying the ascent of core regions while the peripheries undergo a contraction. Nevertheless, beneath the surface, the demographics of network components play a significant role, contributing to the rich tapestry of RED variations across provinces. This spatial network thrives on the disparities in industrial policy, economic development, system adjacency, transportation infrastructure, and the diverse political systems across the Chinese provinces. In light of these compelling findings, our call to action is clear. Policymakers would reinforce their commitment to RED, encouraging regional collaboration and harmonizing policies to accelerate China's journey towards sustainable energy goals. [ABSTRACT FROM AUTHOR]

Large-scale exports of "Made in China" products are accompanied by a substantial outflow of embodied oil, which is a major threat to China's energy security. Based on a multi-region input–output model and structural path analysis, we estimate the quantity of embodied oil in China's exports, quantify the contributions of different production layers and industrial paths to China's domestic embodied oil outflow, and further identify the roles of different sectors in this process. The results show that China's embodied oil outflow was dominated by domestic embodied oil; the contributions from higher production layers to domestic embodied oil outflow noticed a rise, and export production tended to drive more oil consumption in China's domestic production system through intermediate inputs; and sectors played different roles in China's domestic embodied oil outflow, with electrical, electronic, and optical equipment and textile and leather products being the "consumers," coke and refined petroleum products being the "supplier," and transport, warehousing, and postal service, chemical and chemical products, and basic metals and fabricated metal products being the "transmitters." These findings can provide important references for China to reduce domestic embodied oil outflow by adjusting the trade structure and improving oil efficiency in critical industrial paths. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread, which can help in maintaining urban health within a county and between counties within a country. This distribution can be computed using the Volunteered Geographic Information (VGI) of the citizens in conjunction with other variables, such as climatic conditions, and used to analyze how human's daily density distribution quantitatively affects COVID-19 transmission. Based on the estimated population density, when the population density increases daily by 1 person/km2 in a county or prefectural-level administrative unit with an average size of 26,000 km2, the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days, which is the illness onset time for a new COVID-19 case. After 14 days, which is the maximum incubation period of the COVID-19 virus, there would be 5 new confirmed cases and 0.092 death cases. However, in neighboring regions, there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities. The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic. Additionally, the direct and indirect effects of the impact are estimated using spatial panel models. The models that control the unobserved factors improve the reliability of the estimation, as validated by random experiments and the use of the Baidu migration dataset. [ABSTRACT FROM AUTHOR]

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds. [ABSTRACT FROM AUTHOR]

The Zhuang ethnic minority in China possesses its own ethnic language and no ethnic script. Cultural exchange and transmission encounter hurdles as the Zhuang rely exclusively on oral communication. An online cloud-based platform was required to enhance linguistic communication. First, a database of 200 h of annotated Zhuang speech was created by collecting standard Zhuang speeches and improving database quality by removing transcription inconsistencies and text normalization. Second, SAformerNet, a more efficient and accurate transformer-based automatic speech recognition (ASR) network, is achieved by inserting additional downsampling modules. Subsequently, a Neural Machine Translation (NMT) model for translating Zhuang into other languages is constructed by fine-tuning the BART model and corpus filtering strategy. Finally, for the network's responsiveness to real-world needs, edge-computing techniques are applied to relieve network bandwidth pressure. An edge-computing private cloud system based on FPGA acceleration is proposed to improve model operation efficiency. Experiments show that the most critical metric of the system, model accuracy, is above 93%, and inference time is reduced by 29%. The computational delay for multi-head self-attention (MHSA) and feed-forward network (FFN) modules has been reduced by 7.1 and 1.9 times, respectively, and terminal response time is accelerated by 20% on average. Generally, the scheme provides a prototype tool for small-scale Zhuang remote natural language tasks in mountainous areas. [ABSTRACT FROM AUTHOR]