With the rapid development of Internet technology,the importance of blockchain technology has become increasingly prominent.Faced with this situation,extensive research has been carried out at home and abroad.Through ...With the rapid development of Internet technology,the importance of blockchain technology has become increasingly prominent.Faced with this situation,extensive research has been carried out at home and abroad.Through the analysis of relevant literature on blockchain in recent years,it is found that there are many research results of blockchain technology in medical care,finance,education,etc.,but its application in the field of resource allocation efficiency is rare.From the existing studies on the influencing factors of resource allocation efficiency in China,it is found that there are significant differences in resource allocation efficiency between China and some developed countries or between various provinces and cities of China.展开更多
The real economy is the main body of high-quality development,and the efficiency of capital allocation is an important manifestation of the development of the real economy.Therefore,it is very important to study the e...The real economy is the main body of high-quality development,and the efficiency of capital allocation is an important manifestation of the development of the real economy.Therefore,it is very important to study the efficiency of capital allocation.As a representative of horizontal finance,commercial credit has a significant impact on the improvement of capital allocation efficiency.In view of this,this article combs the literature on commercial credit and capital allocation efficiency from the following aspects:firstly,by studying the literature,combing the literature on the macro-level,micro-level and economic effects of commercial credit;secondly,the measurement method of capital allocation efficiency And the influencing factors are systematically sorted out,and finally sorted out and evaluated the existing literature on the influence of commercial credit on the efficiency of capital allocation.展开更多
Agricultural mechanization and custom machine services have developed rapidly in China,which can influence rice production efficiency in the future.We calculate technical efficiency,allocative efficiency,and scale eff...Agricultural mechanization and custom machine services have developed rapidly in China,which can influence rice production efficiency in the future.We calculate technical efficiency,allocative efficiency,and scale efficiency using data collected in 2015 from a face-to-face interview survey of 450 households that cultivated 3096 plots located in the five major rice-producing provinces of China.We use a one-step stochastic frontier model to calculate technical efficiency and regress the efficiency scores on socio-demographic and physical land characteristics to find the influencing variables.Variables influencing technical efficiency are compared at three different phases of rice cultivation.We also calculate technical efficiency by using the Heckman Selection Model,which addresses technological heterogeneity and self-selection bias.Results indicate that:(1)the average value of technical efficiency using a one-step stochastic frontier model was found to be 0.74.When self-selection bias is accounted for using the Heckman Selection Model,the average value of the technical efficiency increases to 0.80;(2)mechanization at the chemical application phase has a positive effect on technical efficiency,but mechanization does not affect efficiency at the plowing and harvesting phases;(3)machines are overused relative to both land and labor,and high machine input use on the small size of landholding has resulted in allocative inefficiency;(4)rice farmers are overwhelmingly operating at a sub-optimal scale.Future policies should focus on encouraging farmland transfer in rural areas to achieve scale efficiency and allocative efficiency while promoting mechanization at the chemical application phase of rice cultivation to improve technical efficiency.展开更多
In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only conside...In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only.This paper presents a novel comprehensive utility function for resource allocation in MEC.The utility function considers the heterogeneous nature of applications that a UE offloads to MES.The proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for MESs.Moreover,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility function.We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes.The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources.The utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE requests.Choosing(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task.We show that MES resource allocation is sub-optimal if CPU is the only resource considered.By taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.展开更多
Discussing results in asset pricing and efficient portfolio allocation,we show that mixed success and errors in these results often follow from a lack of information about the asset return distribution and wrong assum...Discussing results in asset pricing and efficient portfolio allocation,we show that mixed success and errors in these results often follow from a lack of information about the asset return distribution and wrong assumptions about its properties.Some mistakes in asset pricing come from the assumption of symmetry in return distributions.Some errors in efficient portfolio allocation follow from Markowitz’s approach when applying it to portfolio optimization of skewed asset returns.The Extended Merton model(EMM),generating skewed return distributions,demonstrates that(i)in skewed asset returns,the variance is not an adequate measure of risks and(ii)positive skewness in the asset returns comes together with a high default probability.Thus,the maximization of the mean portfolio returns and skewness with controlled variance used in mainstream papers can critically increase portfolio risks.We present the new settings of the optimal portfolio allocation problem leading to less risky efficient portfolios than the solutions suggested in all previous papers.展开更多
Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize...Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the Internet.In a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency.It is also known as a server pool or server farm.When a single node is overwhelmed,balancing the workload is needed to manage unpredictable workflows.The load balancer sends the load to another free node in this case.We focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling process.One of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at hand.Depending on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.展开更多
As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while pro...As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while providing opportunities for these firms to take the path of independent transformation.Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences(DID)model,we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms.The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities.The conclusions remain stable after a series of robustness tests.The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency,R&D investment,and green technology innovation.The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement.This study also observes that the upgrading impact is more promi‐nent for state-owned enterprises,enterprises with higher bargaining power,and enterprises whose managers have a long-term vision.The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win-win target of environmental protection and economic transfor‐mation.展开更多
This paper improves the estimation of firm-level total factor productivity(TFP)by considering energy use and including small-and medium-sized enterprises using data from the Chinese National Tax Survey Database(2008-2...This paper improves the estimation of firm-level total factor productivity(TFP)by considering energy use and including small-and medium-sized enterprises using data from the Chinese National Tax Survey Database(2008-2011).It analyzes the production efficiency of Chinese manufacturing firms using the improved TFP data and finds that(i)the TFP data frequently used in previous studies overestimated firms'real production efficiency;(i)the TFP of manufacturing firms decreased from 2008 to 2011 due to declines in both technical efficiency and allocation efficiency,(ii)the lower capacity utilization of production factors led to lower technical efficiency;and(iv)allocation efficiency decreased more in provinces and industries with higher shares of state-owned enterprises.The findings have policy implications for enhancing growth potential in the long run.展开更多
Based on methods such as stochastic frontier production function,this paper analyses the changes of single factor productivity(SFP)and total factor productivity(TFP)of agriculture in the five Central Asian countries,d...Based on methods such as stochastic frontier production function,this paper analyses the changes of single factor productivity(SFP)and total factor productivity(TFP)of agriculture in the five Central Asian countries,during the period of 1992 to 2017.The research results show that the agricultural output in most of the five Central Asian countries has increased steadily,while agricultural labor productivity has shown a growth trend.With the exception of Kazakhstan,the land productivity of the other four countries shows a growth trend.In terms of factor input,the number of agricultural workers in the five Central Asian countries mainly shows a trend of decrease,with the input of chemical fertilizer increasing,and the amount of agricultural machinery increasing or decreasing within a small range.The total factor productivity in the five Central Asian countries has improved,but it is still at a low level.The policy suggestions contained in the research conclusions are as follows:(1)Promote the growth of agricultural TFP in the five Central Asian countries,and strengthen the emphasis on the input and allocation of agricultural factors;(2)be aware of the innovation of agricultural technology,as well as the promotion and diffusion of existing agricultural technologies,and improve the overall technical efficiency of agriculture;and(3)accelerate the effective flow of capital and other elements to the agricultural sector,improve infrastructure,better release the'dividend'of science and technology,and enhance the output efficiency.展开更多
基金Supported by National Innovation Planning Project for University Students in 2021 (202110414021)
文摘With the rapid development of Internet technology,the importance of blockchain technology has become increasingly prominent.Faced with this situation,extensive research has been carried out at home and abroad.Through the analysis of relevant literature on blockchain in recent years,it is found that there are many research results of blockchain technology in medical care,finance,education,etc.,but its application in the field of resource allocation efficiency is rare.From the existing studies on the influencing factors of resource allocation efficiency in China,it is found that there are significant differences in resource allocation efficiency between China and some developed countries or between various provinces and cities of China.
基金We are grateful for the financial support from Surface Project of“Nature Science Found of Shandong Province”(Project Title:Trade credit and TFP of Shandong Manufacturing Enterprises:a Study from the Perspective of Enterprises and Clusters,No.ZR2020MG037)Key Project of“Shandong University Humanities and Social Sciences”(Project Title:the Mechanism of Trade Credit Influencing Technological Innovation:an Empirical Study of Shandong Enterprises,No.J17RZ005)Surface Project of“Social Science Found of Shandong Province”(Project Title:Study on the Mechanism of Informal Finance Promoting Innovation in Shandong Province,No.19CJJJ23).
文摘The real economy is the main body of high-quality development,and the efficiency of capital allocation is an important manifestation of the development of the real economy.Therefore,it is very important to study the efficiency of capital allocation.As a representative of horizontal finance,commercial credit has a significant impact on the improvement of capital allocation efficiency.In view of this,this article combs the literature on commercial credit and capital allocation efficiency from the following aspects:firstly,by studying the literature,combing the literature on the macro-level,micro-level and economic effects of commercial credit;secondly,the measurement method of capital allocation efficiency And the influencing factors are systematically sorted out,and finally sorted out and evaluated the existing literature on the influence of commercial credit on the efficiency of capital allocation.
基金financial support from the National Social Science Foundation of China(14BGL094)the Rice Research System in Guangdong Province,China(2019KJ105)+2 种基金the EU Project H2020 Program(822730)supported by the United States Department of Agriculture(USDA)National Institute of Food and Agriculture(NIFA)funded Hatch projects(#94382 and#94483)。
文摘Agricultural mechanization and custom machine services have developed rapidly in China,which can influence rice production efficiency in the future.We calculate technical efficiency,allocative efficiency,and scale efficiency using data collected in 2015 from a face-to-face interview survey of 450 households that cultivated 3096 plots located in the five major rice-producing provinces of China.We use a one-step stochastic frontier model to calculate technical efficiency and regress the efficiency scores on socio-demographic and physical land characteristics to find the influencing variables.Variables influencing technical efficiency are compared at three different phases of rice cultivation.We also calculate technical efficiency by using the Heckman Selection Model,which addresses technological heterogeneity and self-selection bias.Results indicate that:(1)the average value of technical efficiency using a one-step stochastic frontier model was found to be 0.74.When self-selection bias is accounted for using the Heckman Selection Model,the average value of the technical efficiency increases to 0.80;(2)mechanization at the chemical application phase has a positive effect on technical efficiency,but mechanization does not affect efficiency at the plowing and harvesting phases;(3)machines are overused relative to both land and labor,and high machine input use on the small size of landholding has resulted in allocative inefficiency;(4)rice farmers are overwhelmingly operating at a sub-optimal scale.Future policies should focus on encouraging farmland transfer in rural areas to achieve scale efficiency and allocative efficiency while promoting mechanization at the chemical application phase of rice cultivation to improve technical efficiency.
基金National Research Foundation of Korea-Grant funded by the Korean Government(Ministry of Science and ICT)-NRF-2020R1AB5B02002478.
文摘In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only.This paper presents a novel comprehensive utility function for resource allocation in MEC.The utility function considers the heterogeneous nature of applications that a UE offloads to MES.The proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for MESs.Moreover,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility function.We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes.The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources.The utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE requests.Choosing(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task.We show that MES resource allocation is sub-optimal if CPU is the only resource considered.By taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.
文摘Discussing results in asset pricing and efficient portfolio allocation,we show that mixed success and errors in these results often follow from a lack of information about the asset return distribution and wrong assumptions about its properties.Some mistakes in asset pricing come from the assumption of symmetry in return distributions.Some errors in efficient portfolio allocation follow from Markowitz’s approach when applying it to portfolio optimization of skewed asset returns.The Extended Merton model(EMM),generating skewed return distributions,demonstrates that(i)in skewed asset returns,the variance is not an adequate measure of risks and(ii)positive skewness in the asset returns comes together with a high default probability.Thus,the maximization of the mean portfolio returns and skewness with controlled variance used in mainstream papers can critically increase portfolio risks.We present the new settings of the optimal portfolio allocation problem leading to less risky efficient portfolios than the solutions suggested in all previous papers.
基金supported by the project:“Research and Implementation of Innovative Solutions for Monitoring Consumption in Technical Installations Using Artificial Intelligence”,beneficiary S.C.REMONI TECHNOLOGIES RO S.R.L in partnership with“Gheorghe Asachi”Technical University of Iasi,Financing Contract No.400/390076/26.11.2021,SMIS Code 121866,financed by POC/163/1/3.
文摘Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the Internet.In a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency.It is also known as a server pool or server farm.When a single node is overwhelmed,balancing the workload is needed to manage unpredictable workflows.The load balancer sends the load to another free node in this case.We focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling process.One of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at hand.Depending on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.
基金This paper was supported by the Fundamental Research Funds for the Central Universities[Grant number:JBK2202018].
文摘As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while providing opportunities for these firms to take the path of independent transformation.Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences(DID)model,we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms.The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities.The conclusions remain stable after a series of robustness tests.The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency,R&D investment,and green technology innovation.The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement.This study also observes that the upgrading impact is more promi‐nent for state-owned enterprises,enterprises with higher bargaining power,and enterprises whose managers have a long-term vision.The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win-win target of environmental protection and economic transfor‐mation.
基金the financial support of the National Natural Science Foundation of China(No.72273157)Research Program of Humanities and Social Sciences of the Ministry of Education of China(No.22YJA790096)Special Program of Philosophy and Social Science Research of the Ministry of Education.
文摘This paper improves the estimation of firm-level total factor productivity(TFP)by considering energy use and including small-and medium-sized enterprises using data from the Chinese National Tax Survey Database(2008-2011).It analyzes the production efficiency of Chinese manufacturing firms using the improved TFP data and finds that(i)the TFP data frequently used in previous studies overestimated firms'real production efficiency;(i)the TFP of manufacturing firms decreased from 2008 to 2011 due to declines in both technical efficiency and allocation efficiency,(ii)the lower capacity utilization of production factors led to lower technical efficiency;and(iv)allocation efficiency decreased more in provinces and industries with higher shares of state-owned enterprises.The findings have policy implications for enhancing growth potential in the long run.
基金Strategic Priorily Research Program of the CAS,No.XDA20040400National Natural Science Foundation of China,No.41871184,No.41401203+1 种基金The Agricultural Science and Technology Innovation Program.No.ASTIP-IAHD-2020-01,No.CAAS-ZDRW202012Central Research Institutes of Basic Research and Public Service Special Operations.No.161005202001-2。
文摘Based on methods such as stochastic frontier production function,this paper analyses the changes of single factor productivity(SFP)and total factor productivity(TFP)of agriculture in the five Central Asian countries,during the period of 1992 to 2017.The research results show that the agricultural output in most of the five Central Asian countries has increased steadily,while agricultural labor productivity has shown a growth trend.With the exception of Kazakhstan,the land productivity of the other four countries shows a growth trend.In terms of factor input,the number of agricultural workers in the five Central Asian countries mainly shows a trend of decrease,with the input of chemical fertilizer increasing,and the amount of agricultural machinery increasing or decreasing within a small range.The total factor productivity in the five Central Asian countries has improved,but it is still at a low level.The policy suggestions contained in the research conclusions are as follows:(1)Promote the growth of agricultural TFP in the five Central Asian countries,and strengthen the emphasis on the input and allocation of agricultural factors;(2)be aware of the innovation of agricultural technology,as well as the promotion and diffusion of existing agricultural technologies,and improve the overall technical efficiency of agriculture;and(3)accelerate the effective flow of capital and other elements to the agricultural sector,improve infrastructure,better release the'dividend'of science and technology,and enhance the output efficiency.
