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In recent years, there has been a growing interest in assessing the level of performance and efficiency of the foodservice industry. As a result, an increasing number of studies applied efficiency frontiers methods to quantify firm (in)efficiency. Starting from the benchmarking studies on restaurant efficiency, this paper aims to develop a meta-analysis based on 77 observations from 25 studies published in scientific journals from 1998 to 2020. The estimated effect size in our meta-analysis is equal to 0.842 and is statistically different from zero, while the Cochran's Q test for the heterogeneity in our sample hints at the absence of heterogeneity in the previous studies on restaurant efficiency. A meta-regression analysis partially supports this result but also highlights the importance of assuming an appropriate return to scale, given the peculiarity of the sector. [ABSTRACT FROM AUTHOR]

The study analysed the efficiency of Watermelon (Citrullus lenatus) Production in Nigeria. A multi-stage sampling technique was used in selecting three hundred and sixty (360) respondents. Selection was done with purposive and simple random sampling, and data collected with a structured questionnaire. The objectives of the study were to identify the socio-economic characteristics of the respondents, determine the technical efficiency and measure the total resource productivity of watermelon production in the study area. The data were analyzed using descriptive statistics and quantitative analytical tool of stochastic frontier model (Cobb Douglas production function). Socio-economic attributes like age, farm size, educational status and farm experience were described to show their relationship with watermelon production in the study area. Results of the stochastic frontier model showed that all the estimated coefficients of the variables of the production function were positive except fungicide. They included: farm size (0.0795), labour (0.0201), number of seed grown (0.926) and fertilizer (0.0207). This implied that watermelon output increases with increase in these variables. It was also shown that labour (0.441), fertilizer (0.475) and fungicide (-1.662) did not exert any significant effect on watermelon output as shown by their t-ratio values. For the factors affecting technical inefficiency of watermelon farmers, age of farmers and farm size were negative and significant at 0.05 levels of probability, while household size, educational qualification and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Non-farm income was positive and significant at 5% level of probability. This means that one unit increase in these variables would increase technical inefficiency of the farmers and hence decreasing their technical efficiency. Finally, the return to scale parameter returned the value 0.967 which indicated that watermelon production in the study area was in the Stage II of the production surface. Based on the results of the analysis the following were recommended. Watermelon farmers should be provided and encouraged to take loans, be assisted with extension services and become members of farmer associations, in order to boost their production. Also inputs such as farm size, labour, seeds, fertilizer and fungicide should be increased for optimum production. [ABSTRACT FROM AUTHOR]

In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to measure the technical efficiency of 20 Algerian insurance companies from 2016 to 2020, by using slacks-based measure (slacks-based measure [SBM] data envelopment analysis [DEA]) model. This research aims to provide a comprehensive assessment of this companies' efficiency, taking into account both the desirable and undesirable outputs. Design/methodology/approach: The study uses a nonoriented, SBM model with the assumption of constant returns to scale to estimate the technical efficiency of commercial insurance companies over a five-year period. The inputs used are labor expense, agent expense and investments, while the outputs included are gross premiums and investment income as desirable outputs and gross claims as undesirable output. Findings: Among 20 insurance companies evaluated, only 5 companies consistently achieved technical efficiency during the study period (Caisse Nationale de Mutualité Agricole [CNMA], MACIR, CARDIF, MUTUALISTE and AGLIC); so they represent the best practices in the Algerian insurance sector, with overall average of the technical efficiency is 81%. However, the reference sets analysis showed that CNMA and AGLIC had high robustness. Also, the results demonstrate the impact of ignoring the undesirable outputs on the accuracy of the assessment. Research limitations/implications: The sample of the study consists of the active insurance companies in Algeria, based on the Annual Insurance Reports of Algeria; there are 20 companies as shown in the table. The data are taken from the annual reports of insurance companies during the 2020 period, issued by the Algerian Ministry of Finance. Practical implications: The challenge for insurance company is how to find a balance between reducing claims paid and simultaneously improving the quality of insurance services. In fact, it is observed that studies evaluating their efficiency ignore claims in the analysis process. Therefore, the study highlights the importance of considering undesirable outputs within the DEA framework; this allows for a more accurate assessment of the company's performance and helps in improvement. Furthermore, although the insurance sector plays a crucial role, it has not received enough research attention compared to other financial sectors, especially in Arab and developing countries. Originality/value: The literature on efficiency assessment in the insurance companies shows a lack of addressing undesirable outputs (such as claims) within the DEA framework; so this study aims at bridging this research gap. Also, the study provides an overview of the efficiency of Algerian insurance companies. [ABSTRACT FROM AUTHOR]

This paper aims to propose a method to measure the operational efficiency of the movie theater businesses. The method is based on a bootstrap data envelopment approach to test the statistical significance of the performance difference between companies in the movie theater industry. The proposed method is utilized to investigate the operational efficiency of the three major U.S. movie theater chains. This article utilizes data from each company's individual financial filings to estimate their bias-corrected efficiency scores. An aggregate report of the efficiency scores shows that the movie theater industry is experiencing a significant decline in both scale and technical efficiencies. The efficiency assessment of the three movie theater chains reports statistically significant differences. Based on the constant returns to scale assumption, the efficiency scores are different among the three movie theater chains. Closely, two significant differences are identified when considering the variable returns to scale assumption. The kernel density of the efficiency scores is estimated as well. Results suggest that the movie theater chains are in imperative need of developing an innovative strategic plan to tackle poor attendance and weak managerial practices. Other contributions and managerial issues are also discussed. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to provide a comparative analysis of several possible approaches to applying data envelopment analysis (DEA) in the case where some decision making units (DMUs) in the original sample have negative system outputs. In comparison to the traditional model of Charnes, Cooper, and Rhodes (CCR) and the CCR model with a scale shift to measure second-stage outputs, the range directional measure (RDM) model produces the most appropriate results. In this paper, an approach is proposed for estimating returns to scale. The study applies a two-stage DEA model with negative second-stage outputs to assess the public support for research, development, and demonstration projects in the energy sector in 23 countries over the period from 2010 to 2018. The assessment of government performance depends on its contribution to the growth of energy efficiency in the national economy and the reduction of its carbon intensity. Intermediate outputs (patents in the energy sector) are included in the analysis as both outputs of the first stage and inputs of the second stage. Taking the similarity between the calculations obtained without stage separation and the system efficiency calculations from the two-stage model as a measure of model adequacy, the RDM model shows the highest similarity scores. [ABSTRACT FROM AUTHOR]

This paper is the first to examine both technical efficiency and scale elasticity of the forest sector across different European (EU) regions in an uncertain environment. In doing so, a dataset from 29 EU forest sectors have been collected; where the input and output data have been assumed to be random while their prices (or importance weights) were known. A chance-constrained data envelopment analysis approach was derived from several directional value-based models aimed at evaluating technical efficiency in the first stage. The results indicated that the number of efficient EU forest sectors were reduced substantially with extremely weak efficiency dispersion (about 18%), and the total average technical efficiency scores were moderately reduced by 0.74 as well. However, in the second stage, the findings of the modified cost/revenue-based scale elasticity model interestingly revealed that almost 7% of the EU forest sectors are in increasing returns to scale regions, which means they are still attractive acquisition targets and have the opportunity to increase the level of outputs and shift to the fully efficient border and finally increase their overall efficiency. As the major conclusion, all of our applied stochastic data envelopment analysis models (specially the cost/revenue-based approach) have high discriminating power in order to clearly distinguish the efficiency of the EU forest sector. [ABSTRACT FROM AUTHOR]

Background: This research was focused on measuring the TFP bioeconomy post-Covid-19 in six regions of the world. Methods: The panel data was organized with FAO Statistics data. Linear programming with an enveloping data analysis (DEA) approach was used to measure the Malmquist TFP indices to determine the inter-annual productivity and technical efficiency changes by region. Results: The results show that the effect of Covid-19 on the bioeconomy productivity during the period 2012–2021 on average decreased by 11.6%. This effect was explained by the decomposition of the productivity change into the changes in technical efficiency. The workers decreased their efficiency by 11.7%. In the Northern American region, it decreased by 21.6%, in the Southern European region by 10.1, and in Western Europe by 11.7%. Conclusion: The results show a downward trend that was affected in the year 2019 by Covid-19, however, it was possible to recover in the following year. One of the conclusions of these results is the effect of the immediate strategies that the governments of the region implemented. This effect was a little slower in the North American, Southeastern, and Eastern European regions. Finally, it is concluded that the measures implemented by the governments in the studied regions had an increasing effect in conditions of variable scale returns. In other words, the companies that remained on a constant scale decreased. [ABSTRACT FROM AUTHOR]

Nowadays, it is seen that the COVID-19 virus has changed with mutation and the most effective method in combating this virus is vaccination. In addition to increasing the number of tests, the development and implementation of policies to increase vaccination rates also increase the efficiency of countries in the fight against the virus. This study aims to measure the efficiency levels of OECD countries in the fight against the COVID-19 pandemic in different periods with data envelopment analysis (DEA). To achieve this goal, efficiency analyses were conducted using the output-oriented DEA-constant return to scale (CRS) model with data for 2021. As a result, Colombia, Denmark, New Zealand, Slovakia, and the USA were efficient, while Slovenia, Hungary, and Chile were the countries with the lowest efficiency scores. For inefficient countries to be efficient in the future, it is suggested that policies should be developed and rapidly implemented to reduce the number of cases and deaths, as well as to increase the number of tests. With the study, it has been concluded that the use of the DEA method together with multi-criteria decision making (MCDM) methods such as TOPSIS, EDAS and CODAS for efficiency evaluation of OECD countries allows for a better ranking of efficient decision-making units. [ABSTRACT FROM AUTHOR]

The profitability, resource-use efficiency and technical efficiency of organic crops in the Chamba district of Himachal Pradesh is estimated from primary data collected from 60 organic farmers selected randomly using three-stage sampling. The cost return data showed that maize and paddy were profitable organic crops, and increasing return to scale was supported by the Cobb-Douglas production function. Mean technical efficiency was 75, 55, 47, and 31% in maize, followed by barley, wheat, and paddy, respectively. [ABSTRACT FROM AUTHOR]

Objective: To create, develop, and validate the Fear of Return to Sport Scale (FRESS) for injured professional or recreational athletes in rehabilitation. Methods: This is a questionnaire validation study. To determine the structural and construct validity, 192 injured professional or recreational athletes of different sports modalities were included. We used a subsample with 32 participants to analyze test–retest reliability and internal consistency. Main outcome measures were the FRESS, Numerical Pain Scale (NPS), Pain-Related Catastrophizing Thoughts Scale (PCTS), Self-Estimated Functional Inability because of Pain Questionnaire for athletes (SEFIP-sport), and Hospital Anxiety and Depression Scale (HADS). Results: Initially, 25 questions were proposed by the specialists. Of these, 4 questions were excluded due to similarity with others. After applying the content validity coefficient, 8 questions were excluded for presenting a value lower than 0.80, leaving 13 items. The exploratory factor analysis identified the one-dimensional structure of the FRESS with 13 items. However, five items were excluded for presenting high covariance with the error of several other FRESS items in the confirmatory factor analysis. Thus, the final version of the FRESS was defined with one domain and eight items. Regarding the construct validity, we observed a magnitude of correlation varying between 0.257 and 0.470 between the FRESS and the instruments used here. We observed adequate test–retest reliability (intraclass correlation coefficient = 0.896) and internal consistency (Cronbach's alpha = 0.868). Ceiling and floor effects were not observed. Conclusion: The FRESS with one domain and eight items has acceptable measurement properties and its use in clinical and sports environments to measure the fear of returning to sport in injured professional or recreational athletes is supported. [ABSTRACT FROM AUTHOR]

The standard theory of optimal jurisdictional size hinges on the existence of economies of scale in the provision of local public goods and services. However, despite its relevance for forced local amalgamation programs and related policies, the empirical evidence on the existence of such economies of scale remains elusive. The main goal of this paper is to produce an updated and comprehensive quantitative review of the existence of economies of scale in the provision of local public goods using a meta‐analysis approach to systematize the wide range of empirical approaches and modeling frameworks found in the previous literature. Our analysis confirms the presence of moderately increasing to constant returns to scale in the provision of local services with no reduction in the average costs of production in the delivery of most local public services beyond a certain, modest jurisdictional size, which many studies have estimated at 10,000 residents. Also, the potential for economies of scale differs at least across three traditional services: education, water and sanitation, and garbage collection, being highest for education and lowest for garbage collection. Our analysis also offers guidelines for future empirical research in this area. Physical output and production cost data should be used, together with translog specifications for the modeling of cost functions. Last, we find evidence that the determinants of output cost elasticity include bidirectional publication bias and population density but do not include the presence or absence of modern "lean" production technologies or the (perceived) capital intensity of the sector, contrary to conventional wisdom. These findings have significant policy implications for countries considering jurisdictional consolidation programs. [ABSTRACT FROM AUTHOR]

This research evaluates the efficiency of two packaging lines: PK1 and PK2, over the year 2020 using window analysis and data envelopment analysis (DEA) techniques, including CCR, BCC, and slack-based model (SBM). Seven-decision making units (DMUs) of the window of six months were considered. For each DMU, the planned production quantity (PPQ), defect quantity (DQ), and idle time (IT) were set as the inputs, whereas the produced quantity was the output. Then, the technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) scores were calculated for all DMUs. Based on the PT and PTE scores, it is found that the efficiency scores of the PK2 line showed more stability properties during the year 2020 and were larger than their corresponding scores of PK1. Moreover, the SE scores indicated that pure technical inefficiency (PTIE) was the main contributor to technical inefficiency (TIE) for PK1 in all the seven DMUs, while the PTIE and SE were the contributors to TIE of PK2. The majority percentages of 61.90% and 52.38 % efficient lines for PK1 and PK2 were operating under constant returns to scale, respectively. Finally, the results of SBM revealed that the averages of input excess slacks of PK1 (PK2) in PPQ, DQ, and IT were 59.5793 (14.9193), 1.3275 (2.0703), and 58.2518 (12.8491), respectively. Moreover, the inefficiency scores by SBM were larger than their corresponding TIE values. In conclusion, the results of this research can guide decision-makers to the appropriate actions that reduce inefficiency scores and enhance lines' performance. [ABSTRACT FROM AUTHOR]

Background: Cambodia is undergoing a series of reforms with the objective of reaching universal health coverage. Information on the causes of inefficiencies in health facilities could pave the way for a better utilization of limited resources available to ensure the best possible health care for the population. Objectives: The purpose of this study is to evaluate the technical efficiency of health centers and the determinants for inefficiencies. Methods: This cross-sectional study used secondary data from a costing study on 43 health centers in six Cambodian provinces (2016–2017). Firstly, the Data Envelopment Analysis method with output-orientation was applied to calculate efficiency scores by selecting multiple input and output variables. Secondly, a tobit regression was performed to analyze potential explanatory variables that could influence the inefficiency of health centers. Results: Study findings showed that 18 (43%) health centers were operating inefficiently with reference to the variable returns to scale efficiency frontier and had a mean pure technical efficiency score of 0.87. Overall, 22 (51%) revealed deficits in producing outputs at an optimal scale size. Distance to the next referral hospital, size and quality performance of the health centers were significantly correlated with health center inefficiencies. Conclusion: Differences in efficiency exist among health centers in Cambodia. Inefficient health centers can improve their technical efficiency by increasing the utilization and quality of health services, even if it involves higher costs. Technical efficiency should be continuously monitored to observe changes in health center performance over time. Highlights: • A total of 41% of the sample health centers were identified as technically inefficient with the potential to increase their output capacity. • With the given financial resources of health centers, a 13%-increase in service delivery would be possible. • Substantial efficiency gains could be generated by improved utilization and increased quality of health services. • Technical efficiency of health centers in Cambodia will improve if quality of services, organizational and health center management are strengthened. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) obtains the relative efficiency of decision-making units (DMUs) based on their inputs and outputs. In many situations, some variables can play the role of input or output for a DMU. It is important to provide a suitable model that can maximize the efficiency of the unit under evaluation by correctly choosing the role of these variables. One of the production technologies in DEA is semi-additive production technology. In addition to the observed DMUs, this technology also considers the set of all aggregations corresponding to these DMUs in the evaluation of efficiency. In this paper, we presented the semi-additive production technology in DEA in the presence of flexible measures. This technology is created based on the observed DMUs and their corresponding aggregations DMUs. We have shown that we can provide semi-additive production technology in the presence of flexible measures based only on the observed DMUs by removing the region with decreasing returns to scale from the production possibility set (PPS). In the following, we present two different approaches for measuring the efficiency of DMUs in the presence of flexible measures in semi-additive production technology. These two approaches allocate flexible measure as input or output in such a way that the efficiency of the unit under evaluation is maximized. Also, we use the proposed approach to evaluate the efficiency of data sets related to academic units. [ABSTRACT FROM AUTHOR]

[Display omitted] • Evolutions of carbon emissions from MSW treatment in 2011–2020 was estimated. • A joint framework of LMDI-PDA based on variable return to scale was proposed. • The proposed approach can quantify both socioeconomic and policy factors. • The annual average growth rate of carbon emissions in most provinces slowed down. • Technology progress was the dominant factor for carbon emissions mitigation. Reducing carbon emissions from municipal solid waste (MSW) treatment is non-negligible for China to meet its "carbon peaking and carbon neutrality" targets. It is critical to objectively evaluate the spatiotemporal patterns and drivers of carbon emissions from MSW treatment. This study estimates the carbon emissions from MSW treatment across 30 Chinese provinces from 2011 to 2020. The joint approach LMDI-PDA model is further used to refine the impact of policy on carbon emission changes from technical and efficiency perspectives, while considering the socio-economic factors. The results showed that carbon emissions from MSW treatment grew significantly until peaking at 202.05Mt CO 2 e in 2017 and then stabilized, finally dropping to 165.10 Mt CO 2 e in 2020 due to the impact of COVID-19. Compared with the "12th Five-Year Plan" period, the MSW emissions intensity declined significantly during the "13th Five-Year Plan" period, indicating the effective implementation of waste emission control measures. Furthermore, the slowdown in the growth of national emissions was primarily driven by technological advances in waste treatment. Technical efficiency change effect, MSW generation intensity effect, economic scale effect, and population scale effect impeded national emissions decline. Since the performance of various drivers varied greatly in different provinces, a cluster analysis was conducted to provide policy recommendations in provinces with similar characteristics. Both the methods and results of this study can provide better decision-making support for national and provincial carbon emissions control policies targeting MSW treatment. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare the efficiencies of 36 OECD countries in the fight against the COVID 19 pandemic. A three-stage Data Envelopment Analysis (DEA) is proposed. While the first stage analyzes the pre-covid situation, the contagion control and medical treatment stages evaluate the current COVID status of the countries. In the three-stage model, 8 input, 3 intermediate and 3 output variables are used. Efficiency analysis was carried out using output-oriented and constant return to scale CCR model and variable return to scale BCC models. First of all, efficiency analysis was performed for all countries at each stage. Target values have been calculated for countries that are not on the efficient frontier. Considering the target values and percentage changes, suggestions were made for countries to reach the efficient frontier. It has been observed that Turkey is efficient in the pre-Covid and medical treatment stages, but inefficient in the contagion control. Suggestions have been made by comparing the countries that are in the reference position for Turkey in the contagion control stage. Afterwards, with a study that can be described as a sensitivity analysis, overall efficiency scores were calculated for each country by giving different weights to the stages. No multi-stage DEA study has been found that calculates overall efficiency scores and deals with the pre-Covid and Covid pandemic period together. [ABSTRACT FROM AUTHOR]

This article investigated the effects of production parameters on thecultivation of rice in the Ketu North District of Ghana. Out of 1,024 farmers, 290 rice farmers were chosen to take part in the study using a two-stage sampling procedure. A structured interview schedule was used to collect primary data from 285 respondents resulting in a response rate of 93%. A translog stochastic frontier production function with a model for inefficiency effects was employed in data analysis, using the Maximum Likelihood Method. Land area under cultivation, fertilizer input, irrigation cost, and equipment were identified as the major input factors that significantly influenced yield of rice in the Ketu North District. Also, results indicated 0.642 returns to scale; which implies that an aggregate increase in inputs results in a less than proportionate increase in the yield of rice in the study area. This indicates a decreasing returns to scale. The findings of this study would guide governments and civil society organisations to understand where public investments can best be directed to boost rice production in Ghana. Increased rice output would raise farmers' income and improve their livelihood security. Also, increased output of rice will help reduce rice imports to save foreign exchange and strengthen the local currency. Finally, the findings of this study would fill the gaps in literature and contribute to knowledge. [ABSTRACT FROM AUTHOR]

Consider a quasiconcave, upper semicontinuous and homogeneous of degree γ function f. This paper shows that the reciprocal of the degree of homogeneity, 1 / γ , can be interpreted as a measure of the degree of concavity of f. As a direct implication of this result, it is also shown that f is harmonically concave if γ ≤ - 1 or γ ≥ 0 , concave if 0 ≤ γ ≤ 1 and logconcave if γ ≥ 0 . Some relevant applications to economic theory are given. For example, it is shown that a quasiconcave and homogeneous production function is concave if it displays nonincreasing returns to scale and logconcave if it displays increasing returns to scale. [ABSTRACT FROM AUTHOR]

With growing environmental legislation and growing public awareness of the importance of pursuing sustainable growth, developed nations are increasingly recognizng the value of waste reduction, recycling, and reuse maximisation. MSWM efficiency is the focus of this study, which also attempts to identify the most effective ways to put MSWM policies into practice and make significant strides in increasing MSWM efficiency. This empirical study examines the ecological and economic performance of MSWM for 26 major cities of Madhya Pradesh state, India in 2016 to 2020. A non-parametric approach, Data Envelopment Analysis (DEA) method has been employed. More precisely, output-oriented DEA models have been used to analyse both constant and variable returns to scale in the context of environmental performance. The results from the DEA CRS model indicate that 12 units were efficient in 2016-2017, 10 units in 2017-2018, 13 units in 2018-2019, and 16 units in 2019-2020. In contrast, the DEA VRS model predicts that the number of efficient units were from 16 in 2016-17 to 14 in 2017-18 to 17 in 2018-19 and 19 in 2019-2020. Additionally, the Shapiro-Wilk normality test shows that the distribution of efficiency scores is not normal. Out of the 26 units analysed, 15 have an efficiency score of 1, indicating they are technically efficient. The mean efficiency score for all units is 0.959, with 57.7% of units having an efficiency score of 1. Only 3.8% of units have an efficiency score between 0.7 and 0.8. The study finds that the Northern and Central large cities have higher efficiencies compared to the Eastern and Southern regions. Cities in the north, centre, and south had Potentially Toxic Elements (PTEs) that were comparatively less. The PTE of western cities was comparably more, while the eastern region's efficiency distribution was dispersed. [ABSTRACT FROM AUTHOR]

In this paper, we formulate an inverse data envelopment analysis (DEA) model for a serially linked two-stage production process operating under constant returns to scale technology. The inverse DEA model determines a set of intermediate and output targets for an input augmented decision-making unit (DMU) to maintain its relative efficiency at a pre-specified level. We solve the inverse DEA model using the constraint method used in multi-objective optimization. The input augmented DMU with intermediate and output targets obtained in the inverse DEA model is a hypothetical DMU. Under our modelling framework, when such a hypothetical DMU established on an inefficient DMU is included in the observed DMU set, the frontier established with observed DMU set remains intact. This is important in practice as the intermediate and output targets of the hypothetical DMU would be feasible. When overall efficiency of the hypothetical DMU is decomposed, individual stages have the same efficiency level as that of the hypothetical DMU. This is important to DMU managers as sub-processes also maintain the desired overall efficiency level. We apply our inverse DEA model to a sample of Australian superannuation funds. We demonstrate that each unique Pareto optimal solution of the inverse DEA model obtained through the constraint method provides a specific set of intermediate and output targets and they may offer trade-off between intermediates and outputs. When fund managers anticipate expansion or growth in their funds, choice of targets allows comparison of different trade-off scenarios and makes informed decisions. [ABSTRACT FROM AUTHOR]

Education, innovation, the labour force, and new businesses are considered the key important sectors (factors) for developed and emerging economies. The paper analyses are performed by using the Cobb Douglas production function for analysing the impact and correlation of these factors (variables) on the economic growth of Kosova during the period 2013 - 2021. The variables of public expenditures on education as a percentage of GDP and the labour force have shown a negative impact on growth and they have operated under decreasing returns to scale (based on the Cobb-Douglas function), while the variables of information, computer and telecommunication services (which refer to innovation technology) and new business creation have indicated a positive impact on growth and they have operated under increasing returns to scale (based on the Cobb-Douglas function). In addition, this paper summarized a few useful conclusions: first, public expenditure on education should provide an important contribution on growth; second, enhancing the role of education to improve and develop new knowledge for the labour force in Kosova, and at the same time, to adapt with new labour market demand; third, information, computer and telecommunication is one of the leading export sectors and this sector should provide higher potential and contributions on the economic growth of Kosova; fourth, new businesses creation should provide a significant contribution to increase economic growth. These variables in many countries have made a key value contribution to the economy by driving it toward sustainability, development, and growth. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance. Design/methodology/approach: The authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador. Findings: Most uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant. Social implications: The results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador. Originality/value: This paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers. [ABSTRACT FROM AUTHOR]

One of the attractive issues in Data Envelopment Analysis (DEA) literature is to find the anchor points of the production possibility set (PPS). Each extreme efficient unit which is located on the intersection of the strong and weak efficient frontiers of the PPS, is called an anchor point. In the other word, a decision making unit (DMU) is an anchor point, if there is at least one supporting hyperplane at the unit under consideration, in the situation that some components of its gradient vector are equal to zero, and so some input or output factors do not play any role in the performance of that unit. This study presents a new method to identify the anchor points of the PPS under the variable returns to scale (VRS) assumption and in the presence of the uncertain data. The proposed method is based on the robust optimization technique and finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The potentially of the proposed method is illustrated by a data set, includes 20 banks in Iran. [ABSTRACT FROM AUTHOR]

Solar photovoltaic self-consumption is an attractive approach to increase autarky and reduce emissions in the building sector. However, a successful deployment in urban rooftops requires both accurate and low-computational-cost methods to estimate the self-consumption potential and economic feasibility, which is especially scarce in the literature on net billing schemes. In the first part of this study, a bottom-up GIS-based techno-economic model has helped compare the self-consumption potential with net metering and net billing in a Mediterranean municipality of Spain, with 3734 buildings in total. The capacity was optimized according to load profiles obtained from aggregated real measurements. Multiple load profile scenarios were assessed, revealing that the potential self-sufficiency of the municipality ranges between 21.9% and 42.5%. In the second part of the study, simplified regression-based models were developed to estimate the self-sufficiency, self-consumption, economic payback and internal rate of return at a building scale, providing nRMSE values of 3.9%, 3.1%, 10.0% and 1.5%, respectively. One of the predictors with a high correlation in the regressions is a novel coefficient that measures the alignment between the load and the hours with higher irradiance. The developed correlations can be employed for any other economic or demand scenario. [ABSTRACT FROM AUTHOR]

Background: The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. Methods and materials: The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. Results: The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. Conclusions: The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation. [ABSTRACT FROM AUTHOR]

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency. [ABSTRACT FROM AUTHOR]

Objective: To estimate the effects of changes in Veterans Health Administration (VHA) mental health services staffing levels on suicide‐related events among a cohort of Veterans. Data Sources: Data were obtained from the VHA Corporate Data Warehouse, the Department of Defense and Veterans Administration Infrastructure for Clinical Intelligence, the VHA survey of enrollees, and customized VHA databases tracking suicide‐related events. Geographic variables were obtained from the Area Health Resources Files and the Centers for Medicare and Medicaid Services. Study Design: We used an instrumental variables (IV) design with a Heckman correction for non‐random partial observability of the use of mental health services. The principal predictor was a measure of provider staffing per 10,000 enrollees. The outcome was the probability of a suicide‐related event. Data Collection/Extraction Methods: Data were obtained for a cohort of Veterans who recently separated from active service. Principal Findings: From 2014 to 2018, the per‐pay period probability of a suicide‐related event among our cohort was 0.05%. We found that a 1% increase in mental health staffing led to a 1.6 percentage point reduction in suicide‐related events. This was driven by the first tertile of staffing, suggesting diminishing returns to scale for mental health staffing. Conclusions: VHA facilities appear to be staffing‐constrained when providing mental health care. Targeted increases in mental health staffing would be likely to reduce suicidality. [ABSTRACT FROM AUTHOR]

This paper examines the financial and social efficiency of the microcredit programs offered by the Pakistan Poverty Alleviation Fund partner organizations. Panel data concerning variables of interest are collected from Pakistan Microfinance Network, covering a minimum of 14 partner organizations (in 2005) to a maximum of 35 partner organizations (in 2014). The data is analyzed using the Data Envelopment Analysis, assuming both constant and variable returns to scale scenarios and the operational scale of the partner organizations. Trends in average efficiency scores have been analyzed to assess the mission drift of the partner organizations. Results reveal that managerial inefficiency is more pronounced than the sub-optimal production scale in all three scenarios under consideration. Moreover, trends in the efficiency scores indicated a slight mission drift of the microfinance providers. About 77.5% of the partner organizations were financially sustainable over the entire study period. The study recommends providing objective-oriented training, workshops, and seminars for managing microfinance providers. [ABSTRACT FROM AUTHOR]

Economic-environmental performance or eco-efficiency is a topic of great interest due to the "green movement." Data Envelopment Analysis (DEA) is a non-parametric method for measuring the eco-efficiencies in comparable Decision-Making Units (DMUs) under various technology assumptions, e.g., constant or variable returns to scale. In the case of variable returns to scale, the returns to scale (RTS) values show whether the DMUs under consideration have the correct scale size or can be improved by upsizing or downsizing. However, sometimes the RTS values for some DMUs are unusually high or low and hence useless in practice. The RTS-mavericks test is devised to propose RTS bounds to fix this flaw. However, these bounds can be ineffective in practice. Even if this flaw is rectified, it needs to be clarified how the concept of RTS-mavericks influences eco-efficiency analysis. For the case of a single technology and a combination of two technologies (a so-called pollution-generating technology), we derive RTS equations and develop new median-based optimization problems to correct this flaw and show that the new concept can lead to non-convex technologies. We also demonstrate the applicability and exhibit the efficacy of the proposed model in the context of eco-efficiency analysis in the European Union. [ABSTRACT FROM AUTHOR]

A possible accession in the European Union raises a significant opportunities and challenges for the beef-fattening sector in Kosovo. Toward EU accession process, the Kosovo beef sector will have to improve efficiency and competitiveness. The goal of this research study is motivated by the need to understand better the forces that drive competitiveness and the efficiency of the beef fattening sector in Kosovo. Data Envelopment Analyses was used to evaluate the production efficiency of Kosovo beef fattening farms. Research data were collected through surveys from 35 beef fattening farmers across two Kosovo regions. In total 710 fattened bulls were monitored for 180 days. Considering for suspected bias with the bootstrap input-oriented model, input efficiency ranged from 0.60-0.96 with an average of 0.88. Based on the farm, a naive model would induce a bias of 0.01-0.06 in input efficiency use. Research results indicate further input use decreases because the sampled farms are found to operate under decreasing returns to scale. [ABSTRACT FROM AUTHOR]

The goal of this paper is to use the variable returns to scale (VRS)-slacks-based measure (SBM)-data envelopment analysis (DEA) method to compare the energy resilience of different economies and areas. This study looks at the energy resilience scores of 26 economies from Europe, the Americas, and the Asia-Pacific area. It does this by looking at twelve sub-indicators in three dimensions: society, the economy, and the environment. According to the computational results, seventeen of these economies' total energy resilience achieved top-tier performance. South Korea, ranked 18th, is only second to these seventeen economies and is followed by, among others, Turkey, Luxembourg, Poland, Italy, Belgium, the Slovak Republic, the Czech Republic, and Hungary. Twelve of the twenty European economies, all three American economies, and two Asia-Pacific economies are relatively energy-resilient. There are sixteen economies in society dimensions, seventeen economies in economy dimensions, and seventeen economies in environment dimensions that are relatively energy-resilient. Sub-dimensional improvement suggestions for relatively less energy-resilient economies are provided according to empirical results. The outcome of the research provides policymakers with a benchmark for future policy planning. Due to data limitations, this study cannot benchmark all OECD economies and does not account for sub-dimensional resource inputs. [ABSTRACT FROM AUTHOR]

One of the main important issues in Data Envelopment Analysis (DEA) is to recognize the set of anchor points which is the subset of the extreme efficient points of the production possibility set (PPS). An anchor point is an extreme efficient point which is located on the intersection of the strong efficient frontier and the weak efficient frontier. In the other word, each anchor point delineate the strong efficient frontier from the weak efficient frontier. So, if a decision making unit (DMU) is an anchor point, then there is at least one supporting hyperplane whose the gradient vector has some of zero components, and so some input\output factor does not play any role in the performance of the unit under evaluation. The concept of anchor point was used in DEA for the generation of unobserved DMUs in order to extend the DEA efficient frontier and so, this concept plays a critical role in the DEA theory and its applications. Given the importance of the anchor pints in the DEA literature, this study focuses on finding the anchor points and presents a new method to search the anchor points of the PPS under the variable returns to scale (VRS) assumption. For this purpose, we use the definition of the anchor points and present an approach to find the anchor points of the PPS. The proposed method is based on finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The main advantage of the proposed method is that it exactly uses the definition of the anchor points to provide the approach and it is very simple to use and the anchor points can be easily identified by solving two simple models. In addition, the proposed approach is such that in addition to determining the anchor points, it also finds two important defining supporting hyperplanes on the PPS, which can be used in many problems in DEA. The potentially of the proposed method is illustrated by some numerical examples, reported in the literature to compare the proposed method with the existing methods. [ABSTRACT FROM AUTHOR]

This study examines the energy intensity (EI), energy efficiency (EE), and economic growth, measured by the type of returns to scale (RTS), of 37 nations in the Organization for Economic Co-operation and Development (OECD) from 2000 to 2019. We apply a non-parametric approach to estimate the three measures from their consumption of four primary energy sources, such as coal, gas, oil, and zero emission (e.g., renewable and nuclear power) as inputs and gross domestic product (GDP) as an output. In this study, we have the two types of efficiency measures over time: window-based and cross-sectional-based measures. Three findings are identified from our empirical study. First, the operationally efficient group, including France, Iceland, Japan, Switzerland, UK, and USA, presented a stable status of full efficiency in the window-based efficiency measure. Iceland and Switzerland were also in the higher efficiency group based on the cross-sectional measure. Their efficiencies were high and stable over the observed periods. Second, zero-carbon-emission (e.g., renewable and nuclear) energies outperformed other energy sources (coal, gas, and oil) in terms of a potentiality of EI/EE improvement. In other words, OECD nations can improve on their EI/EE measures by reducing fuel consumption of coal, gas, and oil while maintaining their high GDP levels. Finally, four industrial nations (France, Japan, UK, and USA) had a status of unity in their EI/EE measures for zero-carbon-emission energies with decreasing RTS. These nations would increase zero-carbon emission for energy consumption to increase GDP while keeping optimal EI/EE because such changes in consumption would not largely affect EI/EE due to their constant RTS status. Iceland showed increasing RTS. The nation may improve the EI level by increasing zero-carbon-emission energy consumption and economic size. The four nations can increase zero-emission energy consumption to achieve further economic growth without observing a large deterioration of EI/EE because it is very close to constant RTS. The examination of RTS provides policy directions for the improvement of EI and EE. Switzerland showed decreasing RTS and may deteriorate the EI/EE by increasing energy consumption and the size of each economy. The remaining countries, whose degree of EI/EE measures was less than unity, showed increasing or decreasing RTS. The examination of RTS provides important implications for energy policy to enhance the degree of EI/ EE. [ABSTRACT FROM AUTHOR]

Technology standard is widely employed in many industries and affects competition intensively. With game theory approaches, this article analyses technology standards under Cournot competition. First, the equilibrium is obtained with matrix approaches. The horizontal differentiation yields the complicated model and this article proposes a matrix method. Second, uniform technology standard improves the regional advantages and hurts the regions with low technology. Third, firms with cost advantages benefit from minimum technology standard (MTS) because MTS draws firms with high costs away from the corresponding industries. In this way, MTS stimulates the innovation of low technology. Moreover, MTS decreases the total outputs. Finally, MTS increases average costs because of the assumption of decreasing return to scale, while linear cost implies the reducing average costs under MTS. [ABSTRACT FROM AUTHOR]

We observe spatial cost dependence among medium‐sized and large U.S. banks (1998Q1–2020Q4). We contribute to the literature by accounting for this using an accessible dynamic spatial econometric cost model. For a movement along a bank's output expansion path, we calculate the cost returns that spillover to/from the bank. The noticeable impacts of the 2020 COVID pandemic are on the spillover cost returns and not the own returns. These spillover returns suggest the pandemic led to the smallest (largest) banks becoming suboptimally smaller (bigger). A number of banks with high‐ranking spillover returns have geographically concentrated branches and/or specialize in particular activities. [ABSTRACT FROM AUTHOR]

This study discusses the assessment of OE (operational efficiency) and RTS (returns to scale) over a time horizon. Many previous DEA (Data Envelopment Analysis) studies have discussed how to measure OE/RTS. However, their works did not consider the measurement over time. The important feature of the proposed approach is that our models are different from standard ones in terms of factor (inputs and outputs) unification. A problem with standard models is that they produce different efficiency measures for input and output orientations. Consequently, they yield different OE and RTS estimates depending upon which production factor is used for measurement. To handle the difficulty, we develop a new DEA formulation whose efficiency measure is determined after combining inputs and outputs, and then we discuss how to measure the types of RTS. The other methodological feature is that the proposed model incorporates a time horizon. As an empirical application, this study considers electricity generation and transmission across Chinese provinces from 2006 to 2019. The first key outcome is that the performance of China's electricity generation and transmission system tends to improve with an annual growth rate of 0.45% across time. The second outcome is that, during the observed periods, China has more occurrences of decreasing rather than increasing RTS. As an implication, some provinces (e.g., Jiangxi and Hainan) need to increase their generation sizes to enhance their OE measures, while other provinces (e.g., Jiangsu and Zhejiang) should decrease their generation sizes. Finally, this study confirms significant technological heterogeneity across Chinese provinces and groups. [ABSTRACT FROM AUTHOR]

The financial systems of the West Balkan countries are mainly based on banks. For an efficient financial system, it is of immense importance that the banks operating in it do so with high levels of efficiency. Furthermore, efficiency is needed when it comes to involving banking institutions in the financial flows in order to maintain sustainability of the financial construction. The aim of this paper is to determine whether there is a difference in efficiency between the considered countries and thus to show which changes the decisionmakers have to make in order to improve the efficiency of their banking systems. We analyze data from the revised financial statements of all banks operating in Albania, Bosnia and Herzegovina, Montenegro, North Macedonia and Serbia for the period from 2015 to 2019, using loans and investment as input variables and interest income, non-interest income and net income as output variables. The results obtained from the output-oriented DEA model with a variable return to scale have shown higher efficiency levels in North Macedonia, Bosnia and Herzegovina and Montenegro, while Serbia and Albania show lower efficiency. The individual analyses have shown that in order to improve efficiency levels, improvement is needed more in the correction of the investment amounts then in loan placements. [ABSTRACT FROM AUTHOR]

Taiwan is poised to become a super-aged society. We found there exist long-run relationships involving key macroeconomic variables and population age shares. The findings indicate that Taiwan's aggregate production exhibits increasing returns to scale where the quality of labor input is the single most important source of growth. The out-of-sample forecast plots envisage that Taiwan's aging labor force does not necessarily decelerate real GDP growth for at least a decade or more. In particular, there are viable policy measures to curb labor shortages due to the shift in the age structure of the labor force caused by population aging. [ABSTRACT FROM AUTHOR]

As a major energy consumer and carbon emitter, the low-carbon development transformation of China' s construction industry has attracted increasing attention. Clarifying the change trends and driving factors of carbon emissions in China' s construction industry is helpful in understanding the application of relevant policies in the low-carbon development of this industry. In this context, by using provincial panel data of China' s construction industry from 2000 to 2020 and combining the meta-frontier-production theory with the index decomposition model, this paper incorporated spatial heterogeneity and returns to scale changes into the analysis framework to reveal the actual effects of multidimensional technology and efficiency factors on carbon emissions of China' s construction industry. The results showed that: (J) Carbon emissions from the construction industry showed a significant growth trend during the study period, and the progress of energy-saving technology and output technology were the most important factors in restraining the growth of carbon emissions. Inter-regional gaps in energy-saving and output technologies were narrowed, and the management efficiency of the construction industry improved slightly, which facilitated emission reduction. The growth of the total output value of the construction industry and the decline in scale efficiency were the main factors for the growth of carbon emissions. © The central and western regions had a catchup effect with the eastern regions in terms of technology, which became the main emission reduction factor in these two regions. The decline in scale efficiency in the construction industry in the eastern and central regions led to a rise in carbon emissions. Energy-oriented scale efficiency went up in the western region, while output-oriented scale efficiency went down, which had hedging effects on the carbon emissions of the construction industry in the region. On the whole, the changes and driving factors of carbon emissions in different regions' construction industries showed significant differences. In the future, targeted carbon emission reduction schemes for the construction industry should be formulated according to the specific situations of different regions. [ABSTRACT FROM AUTHOR]

In conventional DEA methods, all inputs and outputs are assumed to be continuous. However, the implied presumption of continuous data may not maintain an acceptable level of precision in practical data. Many practical situations such as number of teachers in a school, number of students in a college, or research papers only take integer values. Presented work presumes subsets of input and output variables in traditional five-pronged axioms in DEA model to be integer values for the first time in the field. An additional effort in this work expands axioms and a new model is presented. This paper presents a novel two phase model that in the first phase produces more precise efficiency values and the best benchmark in its second phase, i.e. the nearest integer point to the efficiency boundary is selected based on constant returns to scale. A case study is presented that demonstrates a comparison of efficiency values obtained with this model compared to prior models that is show in table 1. And in Table 2, the values of the slack are compared with each other. [ABSTRACT FROM AUTHOR]

The aim of the article is to critically analyze Environmentally-Adjusted Multifactor Productivity (EAMP), by considering the Classical-Post-keynesian environmental framework Ecological Macroeconomics, integrated with the Evolutionary Environmental Economics. The paper introduces EAMP as rooted in in neoclassical economics and derived from Cobb-Douglas function with natural resources built by Solow (1974). I present theoretical critiques of EAMP's neoclassical assumptions by developing perspectives from heterodox Ecological Macroconomics literature. Finally, I discuss the shortcomings of EAMP's conceptual framework, making specific reference to the policy debate on Porter Hypothesis. The article puts in evidence how the assumptions of constant returns to scale, perfect competition and perfect input substitutability seriously alter the meaning and promise of sustainability policy. The analysis indicates that EAMP is a poor instrument to study complex issues regarding the promotion and effectiveness of green innovations and should therefore be abandoned to face the great challenges regarding the process of ecological transformation. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this study is to sensitivity analysis analyze the returns to scale in two-stage network based on DEA models. The main focus of the firms has always been to obtain the maximum output with the least available resources, which points to the improvement of the firm's performance and the importance of returns to scale and technical improvement. Design/Methodology/Approach. This study examines the sensitivity of returns to scale classifications in a two-stage DEA network. A new input-oriented model was progressed to identify the efficient decision-making units in the two-stage network, after which a new method of determining the returns to scale classifications in the efficient DMUs in two-stage network (constant, increasing, or decreasing returns to scale) was established. Findings. The stability of the returns to scale classifications in the two-stage network was analyzed. A stability region for changes in primary inputs and final outputs is only determined especially for DMUs that are efficient so that it maintains the classification of the returns to scale units. The results are shown by numerical examples. Practical Implications. The sensitivity analysis of returns to scale classifications is one of the most significant issues in data envelopment analysis (DEA), which plays an essential role in management decisions. Originality/Value. Using this model can help improve the performance of companies by using new tools and also improve the quality of work and increase acceptance competition. [ABSTRACT FROM AUTHOR]

Despite relative richness of the existing literature, it remains a challenge to consistently estimate the impacts of transport connectivity on agricultural production. The paper constructs pseudo-panel data with transport infrastructure defined at high resolution in two periods of time and examines spatially heterogeneous impacts of improved transport connectivity. The paper takes advantage of the unique circumstances in Mozambique where the Government invested intensively in road infrastructure during a relatively short period of time in the 2010s. Combining the highly disaggregated location-specific fixed effects with the instrumental variable method, the paper controls for the endogeneity to show that the improved road connectivity increased agricultural production significantly. Rural connectivity and domestic market accessibility are found to be of particular importance, but substantial heterogeneity exists across regions. The northern provinces, where transport connectivity is limited, have the potential for agricultural growth, exhibiting increasing returns to scale. [ABSTRACT FROM AUTHOR]

In this paper, the panel data of 19 out of 21 APEC members from 1993 to 2018 is selected as a research sample. Firstly, based on the extended Cobb-Douglas production function, the relationship between CO2 emissions and economic growth is explored via the panel data methods, which fully consider the endogeneity between variables and the unobserved individual heterogeneity. Secondly, to examine the driving effect of CO2 emissions on economic growth, the Empirical Likelihood for Generalized method of moments with nonparametric estimation characteristics is utilized to calculate the returns on economic scale and the corresponding confidence interval. The results show that CO2 emissions have a significant positive impact on economic growth. Moreover, the confidence interval of returns to economic scale is very small, indicating current levels of CO2 emissions have reached the limit to drive economic growth when the technical level, labor employment, capital, urbanization, trade, and other factors tend to stabilize. Finally, some recommendations were presented to achieve sustainable development goals. In combination with the extended Cobb-Douglas production function theory, employs 2SLS and GMM method that takes unobserved the heterogeneity between regions and endogeneity between variables in consideration to explore the relationship between CO2 emissions for economic growth. The extended model of the GMM-Empirical Likelihood for Generalized Moment Method (GMEL) is used to investigate whether the Driving effect of CO2 emissions on Economic Growth reaching its limit or not? Selected the panel data of APEC countries or regions as the research samples, which has never been studied by scholars. The GMEL is a non-parametric statistical inference method that has higher estimation accuracy than the traditional parametric estimation method. The confidence interval constructed by the GMEL method has the advantages of domain retention, transformation invariance. [ABSTRACT FROM AUTHOR]

Efficiency measurement is a key and strategic factor in improving an organization's performance and increasing their competitive advantage. Nevertheless, measuring efficiency in settings with multicomponent production technologies is a major issue with the existing approaches in the literature. The main contribution of the current paper is to develop a novel nonparametric approach to evaluate efficiency and obviate some of the theoretical barriers in multi-output settings. To this end, for the first time, new technologies assuming multiple hybrid returns-to-scale (MHRTS) with output-specific inputs, joint inputs, and outputs are developed. The new technologies are based on some of the axiomatic principles in data envelopment analysis (DEA) for forming a new production possibility set (PPS) to measure the efficiency of decision-making units (DMUs). By implementing the MHRTS technologies with output-specific inputs, joint inputs, and outputs, the proposed models can deal with undesirable outputs. Compared with the existing technologies in the DEA literature, the new technologies not only can incorporate output-specific inputs, joint inputs, and outputs for the performance evaluation of DMUs but also obviate existing theoretical barriers in the MHRTS technology. The applicability and usefulness of the proposed method are validated using a case study in the energy sector. [ABSTRACT FROM AUTHOR]

Covid 19 has led to deep inquiries in urbanization management mechanisms. In the city management systematic, there has been a process that compels the establishment of a more accessible city instead of large projects, which are the products of the understanding that sees the city as growth and rent-oriented. This approach, which marks a return to the human scale, necessitated many spatial arrangements. The most important of these is the reunion of people who have been locked in their homes with their neighborhoods that are within "eye reach"; "Social transparency of the neighborhood" has been accepted for both fear and viewing purposes. In Turkey and in the world, municipalities have implemented many innovative and pioneering urban health and social support practices in this process. In this study, the historical context on the subject, today's environment and most importantly, local service, neighborhood unit, neighborhood status, Covid-19 period municipality application examples are discussed. The selection of sample municipalities was made by random method, but pioneering examples from metropolitan city municipalities to provincial and district municipalities, from municipalities at every level, were tried to be evaluated within the article page limit. [ABSTRACT FROM AUTHOR]

Urban scaling analysis has shown that various aggregated urban quantities obey power-law relationships with the population size. Despite the rapid progress, direct empirical evidence that shows how the power-law exponents β depend on the spatial organization of the GDP has been lacking. Moreover, urban scaling studies are hardly reproduced in developing countries due to inadequate official statistics. We tackle these issues by performing urban scaling analysis on Indonesian cities using globally harmonized functional cities delineations and global-scale gridded Gross Domestic Product (GDP) datasets. First, we observe that the GDP and area of Indonesian cities scale linearly with the population size. For GDP in particular, the deviations from the scaling law follow a geographical pattern. Second, we determine the economic hotspots in each city and observe that the area of the hotspots scales mildly sublinear with the population size. Surprisingly, the GDP of hotspots also scales sublinearly with the population size, indicating a lack of increasing returns due to scaling. Third, by classifying the cities based on the spatial organization of the GDP in two dimensions (heterogeneity and spatial dispersion) and examining the scaling exponents of each class, we discover a non-trivial relation between scaling behavior and the spatial organization of the GDP. Spatial dispersion strongly affects the scaling behavior in heterogeneous cities, while such effect is weakened for homogeneous cities. Finally, we find that the scaling effect in terms of economies of scale (sublinearity of area) and increasing returns (superlinearity of GDP) is stronger for Indonesian cities with spatially compact GDP distribution. [ABSTRACT FROM AUTHOR]