Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&...Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&D)investments that have resulted in increasing size,the sector seems to be falling behind in technological areas.To guide future policies and understand proper ways of promoting R&D efficiency,we looked into the lithium battery industry of China.Specifically,data envelopment analysis(DEA)was used as the primary approach based on evidence from 22 listed lithium battery enterprises.The performance of the five leading players was compared with that of the industry as a whole.Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019.However,during this period,a significant increase in R&D expenditure was witnessed.This finding was supported,as the results showed that the average technical efficiency of the 22 enterprises was 0.442,whereas the average pure technical efficiency was at 0.503,thus suggesting that they were suffering from decreasing returns to scale(DRS).In contrast,the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry's average.Moreover,they were experiencing increasing scale efficiency(IRS).We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale;rather,it should also encompass assisting businesses in developing efficient managerial processes for R&D.展开更多
Data envelopment analysis has been successfully used in resource allocation problems.However,to the best of our knowledge,there are no allocation models proposed in the literature that simultaneously take both the glo...Data envelopment analysis has been successfully used in resource allocation problems.However,to the best of our knowledge,there are no allocation models proposed in the literature that simultaneously take both the global efficiency and growing potential into account.Hence,this research aims at developing an allocation model for extra input resources,which maximizes the global technical efficiency and scale efficiency of a decision-making unit(DMU)set while maintaining the pure technical efficiency(i.e.,growing potential)of each DMU.To this purpose,we first discuss the optimal resources required by each DMU.We prove that the optimal inputs for the DMU are actually the inputs of some most productive scale size(MPSS).We then propose the allocation model based on the discussion on the case of one DMU.The allocation model is illustrated using two numerical examples.展开更多
基金This workwas supported by R&D and Application Demonstration of Common Key Technologies in Modern Service Industry,Key Special Sub Topics of National Key R&D Plan(Grant No.2018YFB1402500).
文摘Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&D)investments that have resulted in increasing size,the sector seems to be falling behind in technological areas.To guide future policies and understand proper ways of promoting R&D efficiency,we looked into the lithium battery industry of China.Specifically,data envelopment analysis(DEA)was used as the primary approach based on evidence from 22 listed lithium battery enterprises.The performance of the five leading players was compared with that of the industry as a whole.Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019.However,during this period,a significant increase in R&D expenditure was witnessed.This finding was supported,as the results showed that the average technical efficiency of the 22 enterprises was 0.442,whereas the average pure technical efficiency was at 0.503,thus suggesting that they were suffering from decreasing returns to scale(DRS).In contrast,the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry's average.Moreover,they were experiencing increasing scale efficiency(IRS).We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale;rather,it should also encompass assisting businesses in developing efficient managerial processes for R&D.
文摘Data envelopment analysis has been successfully used in resource allocation problems.However,to the best of our knowledge,there are no allocation models proposed in the literature that simultaneously take both the global efficiency and growing potential into account.Hence,this research aims at developing an allocation model for extra input resources,which maximizes the global technical efficiency and scale efficiency of a decision-making unit(DMU)set while maintaining the pure technical efficiency(i.e.,growing potential)of each DMU.To this purpose,we first discuss the optimal resources required by each DMU.We prove that the optimal inputs for the DMU are actually the inputs of some most productive scale size(MPSS).We then propose the allocation model based on the discussion on the case of one DMU.The allocation model is illustrated using two numerical examples.
