Carbon emission efficiency of forest processing industries in China

This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute β convergence and a conditional β convergence model,to conduct an in-depth analysis of dynamic changes and spatial convergence.Carbon emission efficiency of forest processing industries in 25 regions,including Shanghai,Chongqing,Zhejiang,and Jiangsu are increasing,whereas those of Tianjin,Liaoning,Heilongjiang,and Tibet are decreasing.The main contributing factors of carbon emission efficiency in three major regions vary over time.Further,carbon emission efficiency in the eastern,central,and western regions all have absolute β convergence and conditional β convergence,indicating that different regions are developing toward their own goals and industry,yet regions with lower efficiency are catching up with those where with more efficient strategies in place.Finally,this paper proposes according recommendations.

A Study on Input-Output Efficiency of Citrus in Jiangxi Province

This paper analyzes the citrus input-output efficiency by using the DEA-Malmquist productivity index methods based on the data of six counties in Jiangxi Province from 2010 to 2013.The result shows that TFP of Jiangxi citrus is at low level overall,but the development is rapid.Technological progress is the driving force to promote productivity growth.Based on the static analysis study of six counties in 2013,it is found that the available input factors on the whole are in an ideal condition,and the citrus input factors in a few counties are redundant.Therefore,according to the results,the following policy recommendations are proposed:speeding up the intensive production of citrus in Jiangxi Province,to maximize the scale effect;adjusting the input ratio of various factors of production,so as to improve the citrus input-output efficiency.

Near is more:learning efficiency in research and development innovation among interlocking firms

Research question/issue:This study examines whether geographic proximity produces a proximity preference as interlocking firms observe each other and learn innovative behaviors through information transmission among interlocking directors.Research findings/insights:We study the performance of A-share-listed companies in China from 2007 to 2017 on the basis of resource dependence theory,agglomeration effect theory,and Porter’s competitive theory.When target firms learn about research and development–related innovation behaviors from interlocking firms closer to them,they experience more efficient learning effects and have improved convergent traits.Moreover,this proximity advantage increases the willingness of the target firm to communicate with and learn from interlocking firms closer to them.Highly developed areas and research and development–intensive industries positively affect the learning efficiency of interlocking firms.Theoretical/academic implications:Our conclusion is consistent with resource dependence theory;target firms in highly developed areas are more willing to imitate and study nearby interlocking firms to maintain their peer relations,innovation potential,and competitiveness.Our conclusion is also consistent with competition theory,which states that the exchange of information between target firms in highly research and development–intensive industries and distant interlocking firms increases innovation differentiation,innovation potential,and competitiveness,even when such exchange has a high cost.Practitioner/policy implications:The results support resource dependence theory and peers’effects.The information obtained by interlocking directorates through external social relations guides firm decision-making,and closer distances reveal more obvious effects.

Evaluating R&D efficiency of China's listed lithium battery enterprises

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.

A DEA MODEL FOR IDENTIFYING CRITICAL INPUT-OUTPUT PERFORMANCE MEASURES

In this paper,the authors propose a new model for identifying the critical input-output combinations for each DMU.A real-world application case of 21 international tourist hotels(ITHs) is used to illustrate the proposed approach.

Material flow analysis(MFA)of an eco-economic system:a case study of Wujin District,Changzhou,China

The economic and ecological aspects of a social system are coherently linked and can be examined by its material and energy flows.In this study,we used the material flow analysis(MFA)to model the material input and output of the Wujin District of Changzhou City,Jiangsu Province in China.It was revealed that:(1)total material input and material input per capita increased with economic development,while the total material out-put and material output per capita decreased consistently;(2)except for water,the total material input continued to increase.Input of solid materials grew faster than that of gaseous materials,while the total material output declined.The gas output accounted for the largest pro-portion of the output resulting in primary environmental pollution as burned fossil fuel;(3)water use in agriculture continued with an increasing trend while that in industrial and residential sectors decreased per capita.The total wastewater discharge and wastewater discharge per capita decreased with a faster decreasing rate of residential was-tewater discharge followed by industrial wastewater discharge;(4)material input per unit GDP fluctuated and material output per unit GDP decreased.A decreasing trend in both water use and wastewater discharge per unit GDP was disclosed.These results suggest that the efficiency of resource use in the Wujin District has improved.This weakened the direct link between economic development and environmental deterioration.Additionally,we discussed the harmonic development between environment and economy.Potential limitations of MFA’s application were also discussed.It is suggested that effective measures should be taken for the enforcement of cir-cular economic strategies and the construction of a resource-saving economy.

Toward cost-effective residential energy reduction and community impacts: A data-based machine learning approach

Many U.S.utilities incentivize residential energy reduction through rebates,often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating assets.The assumption built into incentive programs is that the least efficient residences will be more likely take advantage of the rebates.This,however,is not always the case.The main goal of this study was to determine the potential for prioritized incentivization,i.e.,prioritizing incentives that deliver the greatest energy savings per invest-ment through an entire community.It uses a data mining approach that leverages known building and energy characteristics for predicting energy consumption of houses that collectively can be considered representative of all residences within an entire community.From this model,it estimates natural gas consumption and savings,and corresponding implementation costs associated with the adoption of the most impactful energy reduction measures.The resulting savings and cost estimates allow us to develop a sequential energy reduction strategy whereby the most economic measures within the whole utility district are addressed.The results show that an energy reduction of 36%can be achieved at a levelized cost of less than$14 per mmBTU($14,780 per MJ),demonstrating the strong potential of this approach.A corresponding Economic Input–Output Analysis captures the cascading community economic impacts of this strategy.The results show that for the roughly 45,000 single-family residences in the studied region,an initial energy efficiency investment of$26M could result in a total cascading multiplier economic impact of$41M and additional economic impacts of$2.2M for the lifetime of the considered energy efficiency measures.