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The business environment is a critical element in promoting high-quality urban economic development. To evaluate the quality of the business environment in Chinese cities, we first build a business environment evaluation system in this paper. Then using the entropy weight, Dagum Gini coefficient, β-convergence model, and Markov chain method, we evaluate and analyze the development level, spatial differences, and dynamic development patterns of the business environments in 278 cities in China from 2003 to 2019. Our findings reveal that the overall business environment level in Chinese cities is not high. There are significant differences in business environment levels between megacities, large cities, and small–medium-sized cities, and within-group differences and between-group differences are the main reasons for the differences in business environment levels. However, the contribution rate of within-group differences shows a downward trend, while the contribution rate of between-group differences shows an upward trend. In addition, the business environment levels in Chinese cities have spatial correlation, and the national and three major city groups' business environment levels exhibit strong absolute and conditional β-convergence processes. Finally, the business environment levels in Chinese cities demonstrate path dependence, which is typified by low mobility and makes achieving upward mobility challenging. [ABSTRACT FROM AUTHOR]

The development of green agriculture is conducive to accelerating the agricultural modernization process, making a significance for the sustainable development of agriculture and the environment in China. This paper constructs a comprehensive evaluation model by selecting 17 representative indicators from the perspective of sustainability. Then, this paper uses the entropy value method to measure the level of green agricultural production in 31 provinces from 2011 to 2021. We use Dagum's Gini coefficient and decomposition method, the kernel density estimation method and spatial Markov chain analysis method to explore the main sources of spatial differences and dynamic evolution trends. The results show that: (1) The overall level of green production in Chinese agriculture is low, and the level in the central region is higher than that in eastern and western regions; (2) There are significant regional differences in the level of green production in China's agriculture, and this is likely to widen. The interaction of inter- and intra-regional differences is the main reason for overall differences; (3) The trend of polarization in the level of green agricultural production is more obvious in the eastern part of China, while it is weaker in central and western regions; (4) There is a spatial spillover effect in the dynamic evolution of China's agricultural green production level. And the longer the overall time, the more obvious the spillover effect is. This paper analyzes the past development of green agriculture in China, makes predictions and provides constructive suggestions for the improvement and development of green agricultural production in China in the future. [ABSTRACT FROM AUTHOR]

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China's power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system. [ABSTRACT FROM AUTHOR]

Accelerating the building of a new development pattern with domestic market as the mainstay and domestic and international markets reinforcing each other is a major decision made by the CPC Central Committee with Comrade Xi Jinping at the core in light of changes in the development stage, environment and conditions in China. Trade gains are an important indicator of the development trend of domestic and international circulations for a country. With the quantitative model of trade gains containing the multi-sectoral input-output relations, this paper estimates the trade gains from the economic dual circulation in China between 1987 and 2017, based on the kernel density estimation and Markov chain, and the main findings are as follows. (1) The trade gains from the domestic circulation in China showed a downward trend, while the trade gains from the international circulation presented a trend of first rising and then declining. But the former still outweighed the latter. (2) Compared with the international circulation, the continuity, spatial correlation effect and rising potential of the trade gains from China's domestic circulation were greater. (3) There was no positive spatial correlation effect between the trade gains from the domestic and those from the international circulation. China should make overall plans for opening up both externally and internally, and unblock sticking points between the two circulations, so as to realize the positive interaction of the trade gains from the dual circulation, and accelerate the formation of a new development pattern with domestic market as the mainstay and domestic and international markets reinforcing each other. [ABSTRACT FROM AUTHOR]

To cope with climate change, China commits that it will strive to achieve carbon peaking by 2030. Using the Cointegration model and the Markov Chain model, this paper forecasts China's carbon emissions during 2019–2030 in six scenarios, and assesses the contribution of optimizing the energy mix to China's carbon peaking. The research obtains three main conclusions. Firstly, optimizing the energy mix will contribute to achieving China's carbon peaking. In the economic slow-growth scenario, taking China's planned target of energy mix (PTEM) into account, the carbon peaking year will be brought forward from 2028 to 2023. In the economic medium-speed-growth scenario, optimizing the energy mix will make China achieve carbon peaking in 2028. Without considering the PTEM, however, the carbon emissions will not peak before 2030. In the economic fast-growth scenario, the peaking year will not occur whether considering the PTEM or not, but the growth rate of carbon emissions with the PTEM will be far lower than that without considering the PTEM. Secondly, in all three economic growth scenarios, optimizing the energy mix will largely reduce the growth rate of carbon emissions, and thus significantly reduce the peak value of carbon emissions. Thirdly, optimizing the energy mix has a negative adjusting effect on the impact of economic growth on the growth rate of carbon emissions, and the negative effect rise as the economic growth rate increases. [ABSTRACT FROM AUTHOR]

In-depth research on the spatiotemporal patterns and evolution trend of urban land use carbon emission intensity (ULUCEI) can reveal the internal relationship between urban land use and carbon emissions, which is crucial for achieving carbon emission reduction and "double carbon" targets. This paper proposed a conceptual framework of ULUCEI; the methods of kernel density estimation (KDE), exploratory spatial data analysis (ESDA), and spatial Markov chains were adopted for exploring the spatiotemporal patterns and evolution trend of China's ULUCEI from 2000 to 2017. The following conclusions are drawn through research. (1) There was an increasing trend in ULUCEI in China from 0.102 in 2000 to 0.283 in 2017. From the regional perspective, the ULUCEI in the eastern region is markedly higher than that in the central and western regions. Moreover, the results of nuclear density estimation indicate that China's ULUCEI shows an obvious upward and polarized trend. (2) China's ULUCEI shows a positive spatial autocorrelation. The types of spatial agglomeration include "high-high" agglomeration, "high-low" polarization, "low-high" collapse, and "low-low" homogeneity, and there are obvious disparities in the distribution rules of cities with different spatial agglomeration forms. (3) China's ULUCEI presents strong stability and "club convergence" trend. Moreover, spatial factors significantly affect the dynamic transition of China's ULUCEI, and its effect on the shifting upwards gradually enhances with increasing lag type. This paper therefore suggests that policymakers should formulate differentiated urban land low-carbon use models and carbon emission reduction policies to reduce ULUCEI. [ABSTRACT FROM AUTHOR]

Background: The RESCUE BT2 trial recently showcased the efficacy of tirofiban in treating acute ischemic stroke (AIS) without large or medium-sized vessel occlusion. To further assess the value of tirofiban from the perspectives of Chinese and US healthcare system, a study was conducted to evaluate its cost-effectiveness. Methods: A hybrid model, integrating a short-term decision tree with a long-term Markov model, was developed to assess cost-effectiveness between tirofiban and aspirin for stroke patients without large or medium-sized vessel occlusion. Efficacy data for tirofiban was sourced from the RESCUE BT2 trial, while cost information was derived from published papers. Outcomes measured included respective cost, effectiveness, and incremental cost-effectiveness ratio (ICER). We conducted a one-way sensitivity analysis to assess the robustness of the results. Additionally, we performed probabilistic sensitivity analysis (PSA) through 10,000 Monte Carlo simulations to evaluate the uncertainties associated with the results. Results: The study revealed that tirofiban treatment in AIS patients without large or medium-sized vessel occlusion led to a considerable reduction of 2141 Chinese Yuan (CNY) in total cost, along with a lifetime gain of 0.14 quality-adjusted life years (QALYs). In the US settings, tirofiban also exhibited a lower cost ($197,055 versus $201,984) and higher effectiveness (4.15 QALYs versus 4.06 QALYs) compared to aspirin. One-way sensitivity analysis revealed that post-stroke care costs and stroke utility had the greatest impact on ICER fluctuation in both Chinese and US settings. However, these variations did not exceed the willingness-to-pay threshold. PSA demonstrated tirofiban's superior acceptability over aspirin in over 95% of potential scenarios. Conclusion: Tirofiban treatment for AIS without large or medium-sized vessel occlusion appeared dominant compared to aspirin in both China and the US. [ABSTRACT FROM AUTHOR]

Land contracting is an important system in China. As we know, farmers and agricultural organizations acquire land management rights from collective economic organizations to carry out agricultural production. Over the past few decades, it has proved to make a huge contribution to food security and agricultural development in China. However, as land values increased, landowners, contractors, and operators were increasingly in competition over land interests and, as a result, the number of land contract disputes has rapidly increased. Land contract disputes are not only involved in social and economic issues but also related to government management and grassroots governance. Studying the temporal and spatial changes of disputes is the premise to deal with this subject. Based on the data of China Judgment Online from 2016 to 2021, this paper used descriptive statistical methods, spatial analysis tools, and Markov Chains to reveal the temporal evolution characteristics, spatial distribution trends, and grade transfer tendency of land contract disputes in the Yangtze River Economic Belt (YEB). The results showed the following: (1) From 2016 to 2021, the number of land contract disputes in the YEB increased sharply and then decreased gradually; (2) In terms of spatial distribution, land contract disputes were significantly clustered, and the level of clustering has increased in volatility. Meanwhile, the agglomeration area has continuously transferred; (3) There existed the "club convergence effect" and "spatial spillover effect" in the process of dispute grade transfer, but the overall trend was to change for the better. This study attempted to comprehensively describe the changes in land contract disputes in the YEB, and the results would serve as a useful reference for relevant regions to explore the differentiated paths to deal with land contract disputes. [ABSTRACT FROM AUTHOR]

Carbon emissions from animal agriculture are a major source of global greenhouse gases. This paper measures the spatial and temporal characteristics and evolution patterns of carbon emissions from livestock farming in China and 31 provinces from 2001 to 2020 based on IPCC coefficients. The paper also uses Moran's I index, kernel density estimation, and spatial Markov chains for the analysis. The results show that the total carbon emissions from China's livestock sector show a fluctuating downward trend. And livestock carbon emissions are concentrated in areas with better resource endowments, with grassland and grain-producing areas dominating China's livestock carbon emissions. The spatial analysis shows that the spatial correlation of the national livestock carbon emissions is increasing, showing prominent local aggregation characteristics, mainly in the form of high-high and low-low aggregation. The transfer of carbon emissions from China's livestock industry shows strong spatial and temporal dependence, and the transfer of regional carbon emissions is limited by the original type and stock of carbon emissions, showing growth inertia and path dependence. The findings of this paper can provide suggestions for planning and modifying policies to reduce carbon emissions in China's livestock industry. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal fractional-order accumulative Grey Markov model with variable parameters (FOGMKM (1,1)) to predict the annual total energy consumption in China and improve the accuracy of energy consumption forecasting. The new model is built upon the traditional Grey model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The particle swarm optimization algorithm (PSO) is used to determine the number of optimal fractional order and nonlinear parameters. An experiment is conducted to validate the high prediction accuracy of the FOGMKM (1,1) model, with mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.51% and 1886.6, respectively, and corresponding fitting values of 0.92% and 6108.8. These results demonstrate the superior fitting performance of the FOGMKM (1,1) model when compared to other six competitive models, including GM (1,1), ARIMA, Linear, FAONGBM (1,1), FGM (1,1) and FOGM (1,1). Our study provides a scientific basis and technical references for further research in the finance as well as energy fields and can serve well for energy market benchmark research. [ABSTRACT FROM AUTHOR]

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model. [ABSTRACT FROM AUTHOR]

In the coronavirus epidemic, many Chinese hospitals have established buffer zones to prevent the spread and transmission of the virus. The buffer zone is a monitored and separate area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the beds in the buffer zones are limited, the managers face the patient admission control problem for the buffer zone. This management and control problem is challenging since the patient arrivals are uncertain, and the patients’ conditions are different. In this paper, we build the infinite- and finite-horizon Markov decision process (MDP) models for this problem. We use the uniformization method to discretize the patient flow. We propose various iteration algorithms to solve the MDP models and obtain the optimal and threshold policies. Numerical experiments validate the advantages of the policies obtained by the algorithms in this paper over the current policies of hospitals. Note to Practitioners—The ongoing COVID-19 pandemic has been causing enormous damage to people’s health, jobs, and well-being. COVID-19 has affected almost all countries globally and has changed the operation mode of the healthcare system, especially the hospitals. The hospitals are the frontlines of healthcare service and the battle with the COVID-19 pandemic. This article is motivated by our collaborations with hospitals in Shanghai, China. In China, many hospitals establish buffer zones: a monitored area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the zone’s capacity is limited, the managers must make dynamic patient admission control decisions according to multiple factors, such as patients’ health conditions and the usage of beds in the zone. We propose two MDP models to solve this complex problem. Several iteration algorithms are designed to solve the MDP models and obtain the optimal and threshold policies. Based on hospitals’ real-life data, we show the methods presented in this paper can help hospital managers make more reasonable decisions. Although we focus on the hospital’s buffer zone in China, the methodology and approach for this problem can be extended to other practical hospital management scenarios in the coronavirus pandemic. For example, For example, some hospitals have admission control problems for coronavirus patients due to hospital capacity limitations. The hospital has to decide if a patient is accepted as an inpatient or suggested to home quarantine. In such a case, the admission control problem can also be solved by the methodologies in the paper. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1) model. This combined model takes the advantage of high predictable power of GM(1,1) model and at the same time takes the advantage of the prediction power of ARIMA model. For prediction accuracy improvements, three-points average model is further applied to ARGM(1,1) model. We call the improved version as 3P-ARGM(1,1) model. In addition, Markov chain model from stochastic process theory to enhance the prediction power of 3P-ARGM(1,1) model. The generated model is called as M3P-ARGM(1,1) model. As an illustrative example, we use the statistical data recording the number of China's fixed telephone subscribers from 1989 to 2004 for a validation of the effectiveness of the M3P-ARGM(1,1) model. (Contains 4 tables and 3 figures.)

In response to the 14th National Five-year Plan of China and to better explore new strategies for promoting regional coordinated green development, the eco-efficiency values of Chengdu-Chongqing Economic Circle and the corresponding temporal analysis from 2004 to 2018 were assessed in this paper using the super-SBM model and Markov chain. Meanwhile, the spatial analysis of eco-efficiency was conducted by a geographically weighted regression model. Although eco-efficiency has risen at an increasing rate, the economic development of Chengdu-Chongqing Economic Circle was still ecologically ineffective. This means there is an urgent need to improve the efficiency of resource utilization and promote technological innovation. During the study period, the evolution of the eco-efficiency presented as a "π" shape, and was accompanied by the phenomenon of "club convergence". There was also a strong tendency for eco-efficiency to maintain the original status quo, which indicates that it lacked sufficient momentum for improvement, so it was difficult to achieve a leapfrog transfer. Spatially, the eco-efficiency was distributed from northwest to southeast in a high-low-high manner. The spatial-temporal differences of eco-efficiency narrowed but the effect of agglomeration was relatively weak and there was a polarization trend. Further investigation suggests that the differences in the development level of urbanization, opening, technology, environmental regulation and advancement of industrial structure led to the spatial differences of eco-efficiency. Each city in the Economic Circle should make every effort to improve eco-efficiency accordingly, and thus to promote the green development of the whole region, so as to lay a foundation for driving the green and coordinated development of the central and western regions. [ABSTRACT FROM AUTHOR]

Actively exploring a reduction in carbon emissions from rural residents' living consumption (RRLC) is necessary to address climate change and achieve high-quality development of the rural economy. Based on the measurement of the carbon emissions from RRLC in China between the years 2000 and 2021, and it uncovers regional differences, dynamic evolution and convergence. The main findings are as follows: (1) Using the Dagum Gini coefficient, it was found that the differences in carbon emissions from RRLC in the nationwide and low-income level group (LLLG), low-middle-income level group (LMLG), upper-middle-income level group (UMLG), and high-income level group (HHLG) are all significantly decreasing, and the intensity of transvariation is the primary source of the overall difference. (2) Using the kernel density estimation, it was found that the level of carbon emissions from RRLC in the nationwide and the four major regions have generally gone upward, as well as a polarisation phenomenon. (3) Using the Markov chain, it was shown that there is an instability in the carbon emissions from RRLC, which can be transferred downward to the ideal state, but there is also a risk of increasing the upward shift of carbon emissions. (4) The nationwide level and the four regions showed typical σ convergence characteristics and absolute β convergence. After considering the influence of socio-economic and natural climatic factors, conditions β convergence trend is shown. And there are significant regional differences in spatial β convergence. The limitation of this study is that the data on carbon emissions from RRLC are only obtained at the macro level, which cannot accurately reflect the micro and individual impact on RRLC. On this basis, the paper puts forward policy recommendations to reduce the spatial imbalance of carbon emissions from RRLC. [ABSTRACT FROM AUTHOR]

The environment for tourist development will be increased further if the people-centered development concept is fully implemented, and people's desire for a colorful tourism life will be boosted. This includes the new kinetic energy of digitalization and networking, which has contributed more and more to economic development. The people-centered development concept also includes the sudden new crown pneumonia outbreak that has had a major influence on the tourism sector; the tourism industry's robust expansion will not change as a result. There is a reason to assume that tourism will become a key business that contributes to the long-term economic and social growth of China. With the Markov chain to forecast the development tendency of the tourism economy, this paper constructs characteristic indicators such as the number of duty-free shopping on outlying islands, the per capita spending of outlying island rabbit tax shopping, and the number of ecological civilization villages, using the era of free trade ports as a backdrop. The experimental results shows that the proposed idea of this paper is realistic and can have a significant impact towards designing a national ecological civilization pilot zone, international tourism consumption center, and the creation of tropical rainforest national parks. [ABSTRACT FROM AUTHOR]

The coordination of urbanization with the ecological environment has become a significant sustainable urban development problem given the current acceleration in China's urbanization. The existing research lacks in-depth discussion on the coordination relationship between urbanization and energy eco-environment from the perspective of urban agglomeration. This paper takes the Yangtze River Delta Urban Agglomeration as its research area. The combination of CRITIC and entropy weight methods is used to measure the comprehensive urbanization level (CUL) from the four dimensions of population, economy, land, and society, and the SBM-Undesirable model is used to measure the energy eco-efficiency (EE). The spatiotemporal pattern and dynamic evolution law of the coupling coordination degree (CCD) between CUL and EE are analyzed using the coupling coordination model and the spatial Markov chain. The research results reveal that (1) from the comprehensive development level standpoint, both CUL and EE are on the rise from 2010 to 2021; however, a problem of unbalanced development is observed. (2) The overall CCD displays an upward trend and evinces significant spatial differentiation characteristics from the coordinated development level viewpoint. (3) The CCD transition state is not isolated to certain geographic spaces: adjacent cities will impact changes in the coupling coordination types of an urban center. Therefore, we propose strategies to improve the CCD between CUL and EE to offer theoretical and policy inferences for the formulation of sustainable urban development strategies. [ABSTRACT FROM AUTHOR]

The prediction of disaster risk paths can foresee the spread of disaster risk and lay a foundation for reducing or avoiding the harm of disaster risk. In this paper, we have improved the embedding layer of convolution neural network (CNN), and we literally refer to the specific CNN framework as IECNN. The IECNN model is proposed to predict the disaster risk path. Here, we first establish a disaster risk network in which each node represents two attributes: place and disaster type. The random walk paths are generated from the disaster risk network, and the structural characteristics of nodes in the disaster risk paths are considered in the improved embedding layer of IECNN. This study also applies Markov chain algorithm to calculate the probability of each disaster risk path and trains the IECNN model with the determined disaster risk paths. ROC curve, AUC, Accuracy, F1-measure, Precision, and Recall are used to evaluate the prediction model. In order to verify the feasibility and advantage of the proposed model, we use a dataset consisting of natural disasters in southwest China in the experimental section. Results of the comparative analysis display that the proposed model cannot only effectively predict the disaster risk path, but also provide the high performance in terms of every evaluation index. Moreover, some characters of disaster risk path are found and discussed. Resultantly, our results can provide an efficient way to prevent and control risk spread in disaster events. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth study of the assembly production variance problem through the largest supplier of General Motors China. We focus on the production variance problem in an unreliable assembly (MOBA) system with a finite inter-station buffer, focusing on two of the central issues, namely the output variance as well as the delivery schedule variance. We model every subsystem's departure procedure in the MOBA system using the Markov Arrival Process (MAP) approach. Through the approximate use of MAP, we successfully shorten the time needed to calculate the output variance as well as delivery schedule variance of a large-scale MOBA system, which improves the efficiency of the system while ensuring that it meets the customer's needs. The relationship between production variance and system parameters is also studied, which is of substantial significance for optimizing the productivity of MOBA systems and improving customer satisfaction. [ABSTRACT FROM AUTHOR]

In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs' acceptance behaviour and endogenous prices for WICs. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme [Grant 71661167009, N_PolyU531/16], National Natural Science Foundation of China [Grants 72171016, 71831001], and British Academy\Leverhulme Small Research [Grant SRG19\190059]. This work was also supported by Zhejiang Shuren University Research [Grant KXJ0121605] and Beijing Logistics Informatics Research Base. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4425. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the relationship between FDI and CO2 emissions in a nonlinear framework using data from countries along the "One Belt, One Road" from 1979 to 2017 as the sample. First, the linear analysis method was used to examine the stability of FDI and CO2 emissions in countries along the "One Belt, One Road." In addition, the BDS (Brock-Dechert-Scheinkman test) method and nonlinear Granger causality test are used to investigate the nonlinear relationship between variables. Finally, a threshold vector autoregressive model (TVAR) is used to analyze the dynamic change mechanism between China's FDI and CO2 emissions, and a threshold vector error correction model (TVECM) is used to test how the variables respond to deviations from equilibrium. Then, the Markov switching model is used to test the robustness of the results. The research results show that China, India, South Africa, and other countries all have a nonlinear causal relationship between FDI and CO2 emissions. At the same time, the comovement of FDI and CO2 emissions in China has obvious structural break features, which are relevant for the underlying regime. Furthermore, the results also show that the adjustment process of the FDI toward equilibrium is highly persistent in the first regime, and CO2 emissions will adjust to an equilibrium state at a faster speed in the second regime. Therefore, this paper puts forward different policy suggestions for different countries. For China, we should pay attention to the long-term benefits of FDI and introduce high-tech green FDI. [ABSTRACT FROM AUTHOR]

It has been widely certified that hyperspectral images can be effectively used to monitor soil organic matter (SOM). Though numerous bands reveal more details in spectral features, information redundancy and noise interference also come accordingly. Due to the fact that, nowadays, prevailing dimensionality reduction methods targeted to hyperspectral images fail to make effective band selections, it is hard to capture the spectral features of ground objects quickly and accurately. In this paper, to solve the inefficiency and instability of hyperspectral feature selection, we proposed a feature selection framework named reinforcement learning for feature selection in hyperspectral regression (RLFSR). Specifically, the Markov Decision Process (MDP) was used to simulate the hyperspectral band selection process, and reinforcement learning agents were introduced to improve model performance. Then two spectral feature evaluation methods were introduced to find internal relationships between the hyperspectral features and thus comprehensively evaluate all hyperspectral bands aimed at the soil. The feature selection methods—RLFSR-Net and RLFSR-Cv—were based on pre-trained deep networks and cross-validation, respectively, and achieved excellent results on airborne hyperspectral images from Yitong Manchu Autonomous County in China. The feature subsets achieved the highest accuracy for most inversion models, with inversion R2 values of 0.7506 and 0.7518, respectively. The two proposed methods showed slight differences in spectral feature extraction preferences and hyperspectral feature selection flexibilities in deep reinforcement learning. The experiments showed that the proposed RLFSR framework could better capture the spectral characteristics of SOM than the existing methods. [ABSTRACT FROM AUTHOR]

This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE) of conditional variance calculated by semiparametric model are significantly smaller than that of the parametric model. The results of Kupiec backtesting on VaR obtained by the two models show that the failure days of the former are less than or equal to that of the latter, so it can be inferred that the semiparametric RS-GARCH model constructed in this paper is more effective in estimating the Value at Risk of the basis for Chinese stock index futures. In addition, the mean value and standard deviation of VaR obtained by the semiparametric RS-GARCH model are smaller than that of the parametric method, which can prove that the former model is more conservative in risk estimation. [ABSTRACT FROM AUTHOR]

In this paper, a Markov transfer matrix is used to characterize exogenous sudden shocks, and a closed economic DSGE model including a financial accelerator is constructed to simulate the impact of sudden shocks on China's macroeconomic performance. The study found that: (1) Sudden impacts reduce output, investment, consumption, capital, technology, and enterprise value, but improve labor, inflation, and risk premium, thus weakeningmacroeconomic risk resilience. (2) The impact of sudden shocks on macroeconomic variables, from large to small, is net worth, technical level, labor, inflation, investment, capital, output, and external premium. (3) It is appropriate for the government to adopt the principle of combining broad finance measures and tight currency controls in order to improve the risk resistance ability of macroeconomic operations. [ABSTRACT FROM AUTHOR]

Background: Healthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country's coordinated economic and social development. Methods: In this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010–2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE. Results: The study found that China's HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an "East > Central > West" distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the "club convergence" phenomenon of "high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression" was formed in the spatial distribution. Conclusions: The results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper. [ABSTRACT FROM AUTHOR]

Improving China's agricultural greenhouse gases (GHG) emission efficiency has become an important way to cope with climate change in an ecologically—and ethically responsible manner. In this paper, we use a global slacks-based inefficiency model to evaluate the agricultural greenhouse gases (GHG) emission efficiency levels in China during 2000–2015. The regional disparity of China's GHG emission efficiency is examined by using a Dagum Gini coefficient. A spatial Markov chain technique is also employed to investigate the spatial dynamic evolution of agricultural GHG emission efficiency. The results show that: (1) China's agricultural GHG emission efficiency increased steadily during the study period; a certain gap in efficiency among provinces and regions also exists. (2) Between-group disparity is the main source of the overall regional disparities in China's agricultural GHG emission efficiency. The disparities between regions are on the rise, while the disparities within regions are relatively stable. (3) China's agricultural GHG emission efficiency demonstrates significant spatial dependence. This study provides policy implications for the sustainable development of China's agricultural sector. [ABSTRACT FROM AUTHOR]

This study evaluates housing price control policies in China between 2006 and 2016. A panel regression is employed to diagnose the explicit effects of these policies on housing supply and demand, followed by a nonstationary Markov model and t-copula to assess the policy sensitivity of the real-estate sector and the banking sector. This paper presents two important discoveries. First, total social welfare, represented by the sector fundamentals, is extremely sensitive to even tiny shocks from policy adjustments. Second, the current policies effectively restrain the supply of housing but not housing demand. As a result, the current policies fail to achieve their intended outcome by, on the one hand, driving up housing prices with a declining supply of housing and stable demand and, on the other, harming total social welfare. The empirical economic conditions in China are consistent with our findings. [ABSTRACT FROM AUTHOR]

HVE (Higher Vocal Education) in China is developing rapidly. With the expansion of HVE enrollment, the scale has also expanded rapidly. However, with the great development of HVE, HVE also faces many problems. In this paper, the M_BPNN (Markov neural network-BP) model is constructed for the prediction of the development scale of vocational education. Using BPNN's powerful nonlinear mapping ability and error correction thought, the data information of the future development scale of vocational education is predicted. The results show that the prediction accuracy of the M_BPNN model is the best, and MSE (mean squared error) and MRE (mean relative error) are 10.184 and 5.017, respectively, which are lower than the other two prediction models. It shows that the prediction effect of the M_BPNN model is better than that of the pure Markov model. The forecast results show that the population of school age in H province will decrease from 3.36 million in 2020 at an average annual rate of nearly 700,000 to the lowest value of 2.06 million in 2022. After that, the population of school age will increase steadily. The result shows that there is a relative shortage of regional students, and the enrollment scale is developing well, but it is still not optimistic. It is necessary to coordinate the cross-regional development. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) treatment is one of the major contributors to carbon emissions. The improvement in MSW treatment carbon emission efficiency is crucial for China to achieve its CO2 emission targets. Firstly, this study used the super-efficiency SBM-DEA model to calculate the MSW treatment carbon emission efficiency in 31 provinces in China from 2010 to 2019. The results show that the MSW treatment carbon emission efficiency in all provinces except Shanghai and Jiangsu is less than 1, and the provinces with high efficiency are mainly located in eastern China. Secondly, the spatial auto correlation model and spatial Markov chain are used to test the regional differences and the spatial spillover effect of efficiency. The results show that the national average efficiency shows a fluctuating downward trend, and only the western region achieves a gradual increase. The regional differences in China's MSW processing efficiency of carbon emissions show a fluctuating upward trend, and the regional background affects the spatiotemporal evolution pattern of the efficiency. Finally, the special error model was used to analyze the factors and influence paths that affect the efficiency, and to find that the degree of government intervention as an influencing factor that restricts the improvement of efficiency. Based on the research results, we put forward countermeasures and suggestions to improve the MSW treatment carbon emission efficiency in each province and the country as a whole. [ABSTRACT FROM AUTHOR]

Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs. Prestack seismic inversion is an important method for the quantitative characterization of elasticity, physical properties, lithology and fluid properties of subsurface reservoirs. In this paper, a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation (Jacobian and Hessian matrix) of rock moduli is derived. Besides, the contribution of porosity, shale content and fluid saturation to AVO reflectivity is analyzed. The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties. On the basis of this, one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model. The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model. Through the cross parallel of multiple Markov chains, multiple stochastic solutions of the model parameters can be obtained simultaneously, and the posterior probability density distribution of the model parameters can be simulated effectively. The posterior mean is treated as the optimal solution of the model to be inverted. Besides, the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results, so as to realize the simultaneous estimation of reservoir elasticity, physical properties, discrete lithofacies and dry rock skeleton. The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the concept of strong sustainability, this paper builds a dynamic evaluation system on ecological environment quality from the concepts of ecological environment pollution and ecological environment management and puts forward the evaluation indexes including ecological environment pollution index (EPI), ecological environment management index (EMI), and ecological environment quality index (EQI). On this basis, kernel density estimation, Markov chain, Theil index, and dynamic SDM are used to reveal and explain the spatio-temporal evolution of ecological environment quality in China. The results were as follows. Overall, the EPI is decreasing while EMI and EQI are on the rise. In the long run, there is low possibility of upward and cross-level transition for EPI and EQI. The differences of EPI, EMI, and EQI are mainly caused by the regional differences. The regional differences have been reflected in the value of EPI, EMI, and EQI. It has been shown that the internal regional differences are continuously narrowing and the inter-regional differences are expanding. The convergence speeds of EPI, EMI, and EQI are 13.77%, 23.80%, and 9.69%, respectively. In the regional convergence test, different regions have shown different convergence speeds. [ABSTRACT FROM AUTHOR]

The fact that the relationships among the returns of financial assets tend to be nonlinear and time-varying has important implications for asset allocation. To describe these two features, this paper first combines a copula function with the Markov switching technique to model the dependence structure across assets and then builds on this Markov Switching Copula model to present a procedure for the timing of portfolio adjustments. Our empirical evidence confirms that the dependence structure between high-risk and low-risk stocks in the Shanghai and Shenzhen markets is not static but switches between regimes over the course of the sample horizon considered in this paper. More importantly, as a result of such regime-switching characteristics of their dependence structure, our analysis of the out-of-sample asset allocation performance indicates that employing the procedure proposed in this paper to identify regime changes and decide when to adjust portfolio weights allows investors with the Constant Relative Risk Aversion utility to achieve both higher realized returns and higher certainty equivalent rate of returns than does the use of strategies based on static models. [ABSTRACT FROM AUTHOR]

The article presents a research paper which investigates the risk appetite in Hong Kong, China real estate and property security markets during the 2008-2009 global financial crisis. It uses Markov Chain Monte Carlo simulation to evaluate and measure the risk appetite indicators from real estate and property security markets. Results demonstrate the risk appetite started to drop in the third quarter of 2008, while the property security index were stabilized. It suggests that the attitude of investors in the real estate market are risk neutral, while they are susceptible in the property security market.

This paper applies Markov switching techniques to examine the different conditions of Chinese Stock Market from 1995 to 2004. Three contrasting regimes are identified: a bear market, a bull market and a speculative market. This paper also surveys the time period and probability of each regime. The timing of regime switching reflects the strong policy market feature of Chinese stock market. [ABSTRACT FROM AUTHOR]

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of "Zhuhai No. 1" as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042. [ABSTRACT FROM AUTHOR]

A scientific and comprehensive measurement of the efficiency of scientific and technological innovation activities is the basis and prerequisite for evaluating the existing environment and conditions of universities and is conducive for improving the efficiency and promoting the development of provincial economy. The paper selects the DEA (Data Envelopment Analysis) method to measure the efficiency of scientific and technological innovation in universities of China from 2007 to 2019 and studies its spatial differences. Then, the paper uses Markov chain estimation to describe the dynamic evolution process of the efficiency, and finally uses system GMM (generalized method of moments) model to identify the key influencing factors of its efficiency. The conclusions obtained are as follows: (1) The scientific and technological innovation efficiency in universities presents the highest distribution characteristics in the eastern part, the western part of China has the lowest, and the central part is midway of the two regions. There are significant differences between regions. (2) During the entire observation period, the efficiency in each region showed a path-dependent characteristic. After 2016, the mobility of different levels of technological innovation efficiency of universities in eastern part of China has increased, while in the central and western parts of China it tends to be stable. (3) Economic advantages, location advantages, government support, research and development foundation, and the efficiency of scientific and technological innovation in universities have a significant relationship, and the results are different in different regions. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of the Eurozone debt crisis on China and India. Using Markov-switching analysis, we discern regimes in economic growth as well as financial markets and study the impact of the global financial crisis and Eurozone crisis on the same. We identify vulnerability and robustness factors governing the degree of exposure and resilience to the crisis for both these economies. In view of strong trade and financial linkages, the Eurozone crisis may have marred prospects of recovery in the aftermath of the recent Great Recession in both China and India. China, however, is found to be more resilient to the crisis possibly due to stronger macroeconomic fundamentals. [ABSTRACT FROM AUTHOR]

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The matching between random wind supply and electric vehicle (EV) charging demand can reduce the requirement of traditional power sources and the emission of CO2. This problem is of great practical interest but involves system dynamics in multiple timescales. We consider this an important problem in this paper. In order to capture the randomness in the wind supply and EV charging demand, we formulate the problem as a bilevel Markov decision process. At the upper level, the charging demand of EVs in different locations is aggregated into multiple aggregators. The system operator dispatches power among the aggregators in a coarse timescale to maximize the wind power utilization. At the lower level, the aggregator schedules the charging process of individual EVs at a finer timescale to minimize the charging cost. In order to solve this large-scale problem, a bilevel simulation-based policy improvement (SBPI) method is developed. It is mathematically proved that the SBPI can improve from base policies in both levels. The performance of this multi-timescale and bilevel coordination approach is demonstrated through case studies in the city of Beijing. [ABSTRACT FROM AUTHOR]

Colorectal Cancer (CRC) is becoming a major threat to people’s life in China. Screening methods adopted by many other countries as effective counter-cancer methods have not been explicitly explored for people there. Thus, we present a Markov model with detailed precancerous adenoma states and then evaluate various screening strategies in this paper. Different from current researches, our model considers the population’s heterogeneous risk of developing adenomas and observation-based screening strategies. Furthermore, we also give a new cost-effectiveness metric. After calibrating, the model is simulated using the Monte Carlo method. Numerical results show that there are threshold values of compliance rates below which strategy with every ten-year colonoscopy becomes the most cost-effective method; otherwise, an observation-based screening strategy is the most cost-effective. We also find that strategy with single colonoscopy for adenoma-free individuals and every three-year colonoscopy for those with adenoma is recommended when the observation-based strategy is not considered. Our findings give an explicit and complete instruction in CRC screening protocol in average-risk Chinese. [ABSTRACT FROM AUTHOR]

As a typical univariate grey predication model, the GM(!,I) model is widely used in predication research in many fields. Based on the GM(l, I) model, the paper introduces the idea of fractional order to construct the FGM model. rn the current research, the particle swarm algorithm is used to optimize the fractional order, and then the FGMM model is constructed by combining the FGM model and the Markov model. According to the calculation standard of civil aviation carbon emissions issued by IPCC, the current research collects data related to civil aviation carbon emissions and total turnover to verify the effectiveness of the model improvement. rn addition, the current research uses an optimization model to predict the future carbon emission intensity of civil aviation. The example shows that the prediction accuracy of the grey model after multiple optimization is significantly improved than the original model. The prediction accuracy of the FGMM model is as high as 97.28%, which is 0.98% higher than that of the FGM model. Compared with the GM model, the accuracy of the FGM model is improved by 2.2%. This proves the effectiveness of the model improvement. At the same time, it is predicted that the carbon emission intensity of China's civil aviation in 2030 will be 0.59 kg/ton-km, a decrease of 44.02% compared with 1.06 kg/ton-km in 2005. Compared with the "13th Five-Year Plan", the five-year average has dropped by 29.80%. Accordingly, the current research provides policy recommendations that are helpful for civil aviation to save energy and reduce emissions, which can be referenced by relevant departments for decision-making. [ABSTRACT FROM AUTHOR]

In the analysis of cascading outages and blackouts in power systems, risky cascading fault chains should be accurately identified in order to do further block or alleviate blackouts. However, the huge computational burden makes online analysis difficult. In this paper, an online search method for representative risky fault chains based on reinforcement learning and knowledge transfer is proposed. This method aims at promoting efficiency by exploiting similarities of adjacent power flow snapshots in operations. After the “representative risky fault chain” is defined, a framework of tree search based on Markov Decision Process and Q-learning is constructed. The knowledge in past runs is accumulated offline and then applied online, with a mechanism of knowledge transition and extension. The proposed learning based approach is verified on an illustrative 39-bus system with different loading levels, and simulations are carried out on a real-world 1000-bus power grid in China to show the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

During the operation of production workshop, the scheduling process of raw materials directly affects the economic benefits of the production workshop, and the high-order Markov decision model can well describe the time series characteristics. In the actual process, the optimal scheduling of raw materials in the production workshop is uncertain and affected by many factors. This paper introduces the high-order Markov decision model to explore its application in the optimal scheduling of raw materials in the production workshop. The research results show that: the cost of raw materials is greatly affected by the scheduling scheme, and the cost of raw materials must be taken into consideration in the process of optimal scheduling. Based on the production of workpieces in an actual production workshop in Zhengzhou city, China, this paper applies the high-order Markov decision model to the scheduling of raw materials in a multi-resource workshop, and finds out that there is a certain scheme that can make the coordination degree, the influence degree of comprehensive efficiency, and the influence degree of comprehensive qualified rate of the optimal scheduling of raw materials at a relatively high level. This research provides a theoretical basis for improving the core production competitiveness of manufacturing workshops. [ABSTRACT FROM AUTHOR]