Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data.

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers' overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.

Non-farm income and environmental efficiency of the farmers: Evidence from India.

In the face of various agro-climatic shocks when agricultural income becomes highly volatile, farmers often undertake multiple jobholding and engage in non-farm activities for income smoothing. The earnings from these activities are often used to purchase productivity enhancing agricultural inputs. In this context, the impact of non-farm income on intensification of agricultural inputs and the consequent impact on over-all farm efficiency is well documented in the literature. However, with a rapid rise in usage of agricultural inputs with environmentally detrimental impact, very little is known about whether non-farm income has any impact on farmers' environmental efficiency-ability to reduce the amount of polluting inputs to the largest extent possible without reducing the amount of agricultural production. Our study fills the gap in the literature by analysing the impact of non-farm income on environmental efficiency of the farmers. We first estimate the environmental efficiency scores adopting the non-parametric data envelopment analysis (DEA) method and using a household level panel data from Village Dynamics of South Asia project on Indian states for a span of five years (2010-2014). We then estimate the impact of non-farm income on environmental efficiency using Instrumental Variable Tobit Model. Our results show that average environmental efficiency of the Indian farmers was 46 % during the study period indicating the fact that a reduction in polluting agricultural inputs by 54 % was possible without compromising the level of farm production. We also find that for 1 % increase in non-farm income, environmental efficiency of farmers rises by around 4 %. This reflects the environment friendly behaviour of farmers as a channel through which non-farm activities affect usage of environmentally linked inputs. These results provide vital policy insights in terms of how non-farm activities could be integrated with policies related to farming, in order to ensure sustainable agricultural practices.

A breakthrough in macro-scale circularity and eco-efficiency assessment: A case study of OECD countries.

Countries' circularity performance and CO2 emissions should be addressed as a part of the UN net-zero Sustainable Development Goals (SDGs) 2030. Macro-scale circularity assessment is regarded as a helpful tool for tracking and adjusting nations' progress toward the sustainable Circular Economy (CE) and SDGs. However, practical frameworks are required to address the shortage of real-world circularity assessments at the macro level. The establishment of CE benchmarks is also essential to enhance circularity in less sustainable nations. Further, monitoring the extent to which nations' circularity activities are sustainable and in line with the SDGs is an area that lacks sufficient practical research. The current research aims to develop a macro-level framework and benchmarks for national sustainable circularity assessments. Methodologically, we develop a dynamic network data envelopment analysis (DN-DEA) framework for multi-period circularity and eco-efficiency assessment of OECD countries. To do so, we incorporate dual-role and bidirectional carryovers in our macro-scale framework. From a managerial perspective, we conduct a novel comparative analysis of the circularity and eco-efficiency of the nations to monitor macro-scale sustainable CE trends. Research results reveal a significant performance disparity in circularity, eco-efficiency, and benchmarking patterns. Accordingly, circularly efficient nations cannot necessarily be considered eco-friendly and sustainable. Although Germany (as a superior circular nation) can be regarded as a circularity benchmark, it cannot serve as an eco-efficiency benchmark for less eco-efficient nations. Hence, the new method allows decision-makers not only to identify the nations' circularity outcome but also to distinguish sustainable nations from less sustainable ones. This, on the one hand, provides policymakers with a multi-faceted sustainability analysis, beyond the previous unidimensional analysis. On the other, it proposes improvement benchmarks for planning and regulating nations' future circularity in line with real sustainability goals. The capabilities of our innovative approach are demonstrated in the case study.

Measuring value in health care: lessons from accountable care organizations.

Accountable care organizations (ACOs) were created to promote health care value by improving health outcomes while curbing health care expenditures. Although a decade has passed, the value of care delivered by ACOs is yet to be fully understood. We proposed a novel measure of health care value using data envelopment analysis and examined its association with ACO organizational characteristics and social determinants of health (SDOH). We observed that the value of care delivered by ACOs stagnated in recent years, which may be partially attributed to challenges in care continuity and coordination across providers. ACOs that were solely led by physicians and included more participating entities exhibited lower value, highlighting the role of coordination across ACO networks. Furthermore, SDOH factors, such as economic well-being, healthy food consumption, and access to health resources, were significant predictors of ACO value. Our findings suggest a "skinny in scale, broad in scope" approach for ACOs to improve the value of care. Health care policy should also incentivize ACOs to work with local communities and enhance care coordination of vulnerable patient populations across siloed and disparate care delivery systems.

Investigating potential supply of ecosystem services in cultural landscapes through efficiency analysis.

One of the paramount challenges in natural resource management revolves around the delicate equilibrium between the demand for and the supply of diverse Ecosystem Services (ESs) within a cultural landscape. Recognizing the centrality of cultural landscapes to human well-being, the sustainability of these landscapes hinges upon the health and stability of ecosystems that can effectively provide the required ESs. Over the long term, the sustainable supply of ESs is constrained by the potential supply of ESs. Understanding the potential supply of ESs is crucial for averting compromises to the ecosystems within a landscape. This article introduces a novel perspective on evaluating the ESs of a landscape by means of efficiency analysis. Instead of presenting the potential supply of ESs in absolute terms, we offer a comparative analysis of ESs' relative supply to associated management costs. In principle, the efficiency of Landscape Units (LUs) is defined as the ratio of the potential supply of multiple ESs to the costs associated with land use and land cover management. The resultant efficiency maps serve as hot and cold spot maps, revealing efficient ecosystem compositions that yield multiple ESs. This composition reflects management efforts, incorporating various management costs. Forests emerge as pivotal ecosystems in landscapes, delivering the most ESs at the lowest costs. These efficiency maps offer valuable insights for regional planners, enabling them to enhance the supply of ES in inefficient LUs by studying the ecosystem structure and associated costs of the most efficient LUs.

Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM.

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources.

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

Evaluating bank technical efficiency in SADC region.

Efficiency is generally defined as the capacity to deliver desirable results with little effort or input. A bank cannot afford to allocate limited resources at random in a competitive market. Only once the efficiency factors have been identified can resources be allocated in a conscious and effective manner. The study investigates the determinants of technical efficiency of banks in the SADC region. The study is significant in the SADC region as the block is trying to create a robust and stable banking system. This is driven by the desire to stay away from the current global financial system volatility and the region is working to develop an integrated banking system. The results show that the banks are relatively inefficient with the level of inefficiency around 40 percent. The efficiency of the banks is determined by the level of capitalisation, size of the bank, research costs and automation of the banks. The results of the study imply that that there is great scope for the banks in the SADC region to increase their efficiency. Improved efficiency will ensure banks provide services at a lower cost to clients. The study recommends adequately capitalizing banks, increasing the asset base of the banks, investing in research and the automation of the banking systems.

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

Evaluating the decentralisation of the Spanish healthcare system: a data envelopment analysis approach.

OBJECTIVES: The aim of the study was to answer whether the central government has been more efficient than the regional governments or vice versa. Likewise, through the analysis of the data, the aim was to shed light on whether decentralisation has had a positive impact on the efficiency of the hospital sector or not. DESIGN: In this paper, we have used data envelopment analysis to analyse the evolution of efficiency in the last 10 Autonomous Regions to receive healthcare competences at the end of 2001. PARTICIPANTS: For this study, we have taken into account the number of beds and full-time workers as inputs and the calculation of basic care units as outputs to measure the efficiency of the Spanish public sector, private sector and jointly in the years 2002, 2007, 2012 and 2017 for the last Autonomous Regions receiving healthcare competences. RESULTS: Of the Autonomous Regions that received the transfers at the end of 2001, the following stand out for their higher efficiency growth: the Balearic Islands (81.44% improvement), the Madrid Autonomous Region, which practically reached absolute efficiency levels (having increased by 63.77%), and La Rioja which, together with the Balearic Islands which started from very low values, improved notably (46.13%). CONCLUSION: In general, it can be observed that the transfer of responsibilities in the health sector has improved efficiency in the National Health Service. JEL CLASSIFICATION: C14; I18; H21.

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System.

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

Measuring safety performance of construction employees using data envelopment analysis: A case in Australia.

INTRODUCTION: Safety is one of the critical and persistent challenges in the construction industry. Measuring safety performance could allow decision-makers to check safety production processes and enhance the health and safety environment. METHOD: This study developed a total-factor framework based on the global Data Envelopment Analysis (DEA) method to measure safety performance. The performance trend and influencing factors of pure technical efficiency and scale efficiency were separately investigated. The safety performance of construction employees in Australia was measured as a case study. RESULTS: The results demonstrate that the safety performance in the Australian construction industry has been evidently enhanced, which is mainly promoted by the progress of pure technical elements. The scale factors did not play a positive and important role in driving the performance. CONCLUSIONS: The increasing regional differences in safety performance could be reduced by learning the practices from the benchmark construction industry, such as a young worker program, small business safety program, workplace mental health program, and advanced construction technologies. PRACTICAL APPLICATIONS: The method can be utilized to measure safety performance and investigate the pathways to enhance performance without influencing production inputs and outcomes. The model and experiences of how to promote safety performance on the policymakers and employers were recognized.

Level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia: an application of the data envelopment analysis.

BACKGROUND: Besides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia. METHODS: A facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers' data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance. RESULTS: In this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers. CONCLUSIONS: The mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers.

Quantifying greenhouse gas emissions mitigation potential in China's agricultural plastic greenhouses.

To promote sustainable intensification and provide guidance for decisionmakers, we must understand the potential for mitigating greenhouse gas emissions in greenhouse cultivation. Therefore, this study aimed to evaluate the greenhouse gas emission mitigation potential of agricultural plastic greenhouses in China using a combination of life cycle assessment, data envelopment analysis, and scenario analysis. We found that: (1) In China, the mean mitigation potential for greenhouse gas emission intensity related to the crop production inputs of agricultural plastic greenhouses is 1.67 kt CO2-eq/km2, demonstrating a remarkable mitigation rate of 47 %. This mitigation potential, driven mostly by fertilizer use, exhibited a spatial pattern characterized by high mitigation rates in the southern regions compared to those in the northern regions; (2) in scenarios S1 and S2, the greenhouse gas emission intensity mitigation potential of greenhouse construction materials was 0.645 kt CO2-eq/km2 and 0.968 kt CO2-eq/km2, respectively, with steel being the primary contributor to mitigation; (3) under scenarios S1 and S2, the greenhouse gas emission mitigation rates for China's agricultural plastic greenhouses were 42 % and 48 %, respectively. This study highlights the greenhouse gas emission mitigation potential of China's agricultural plastic greenhouses. To ensure sustainability in plastic greenhouses, emphasis should be placed on optimizing fertilizers and extending the lifespan of construction materials.

Evaluating health systems' efficiency towards universal health coverage: A data envelopment analysis.

To estimate the technical efficiency of health systems toward achieving universal health coverage (UHC) in 191 countries. We applied an output-oriented data envelopment analysis approach to estimate the technical efficiency of the health systems, including the UHC index (a summary measure that captures both service coverage and financial protection) as the output variable and per capita health expenditure, doctors, nurses, and hospital bed density as input variables. We used a Tobit simple-censored regression with bootstrap analysis to observe the socioeconomic and environmental factors associated with efficiency estimates. The global UHC index improved from the 2019 estimates, ranged from 48.4 (Somalia) to 94.8 (Canada), with a mean of 76.9 (std. dev.: ±12.0). Approximately 78.5% (150 of 191) of the studied countries were inefficient (ϕ < 1.0) with respect to using health system resources toward achieving UHC. By improving health system efficiency, low-income, lower-middle-income, upper-middle-income, and high-income countries can improve their UHC indices by 4.6%, 5.5%, 6.8%, and 4.1%, respectively, by using their current resource levels. The percentage of health expenditure spent on primary health care (PHC), governance quality, and the passage of UHC legislation significantly influenced efficiency estimates. Our findings suggests health systems inefficiency toward achieving UHC persists across countries, regardless of their income classifications and WHO regions, as well as indicating that using current level of resources, most countries could boost their progress toward UHC by improving their health system efficiency by increasing investments in PHC, improving health system governance, and where applicable, enacting/implementing UHC legislation.

Impact assessment of construction waste policy intensity on environmental efficiency based on system generalized method of moments.

With the acceleration of urbanization in recent years, China has witnessed large-scale construction across its provinces, generating massive amounts of construction waste that pose challenges to environmental protection and sustainable development. This study evaluated the impact of construction waste policy intensity on its environmental efficiency. Firstly, the content analysis method was used to analyze the construction waste policy text quantitatively. Second, this study constructed a slack-based measure (SBM) model based on data envelopment analysis (DEA), considering resource input and construction waste output to measure environmental efficiency. Finally, we built and tested an econometric model of how policies affect environmental efficiency using the system generalized method of moments (SYS-GMM). The findings indicate a non-linear U-shaped link between policy intensity and environmental efficiency. Among all five control variables, population density, urbanization level, and technological innovation enhance environmental efficiency, while economic development and highway density will lower it. This study advances the research on construction waste policies and offers some insights for the construction industry to pursue sustainable development.

A multidimensional analysis of corporate governance mechanisms and their impact on sustainable economic development: A case study of Ghana's financial sector.

Efficiency remains pivotal to the banking sector, serving as a linchpin for resource allocation and competitive prowess. This study delves into the intricate dynamics between corporate governance and banking efficiency in Ghana, with an analytical lens on cost efficiency (CE) and total efficiency (TE). Utilizing Data Envelopment Analysis (DEA), our investigation spans over a decade (2008-2019) and encompasses a data set of 23 Ghanaian banks. The study findings unveils that rigorous corporate governance mechanisms, as quantified by the Corporate Governance Index (CGI), exert a salutary influence on both cost and total efficiencies. Moreover, a well-defined Risk Management Index (RMI) positively correlates with cost efficiency, albeit without a substantial impact on total efficiency. Conversely, the study identifies a counterintuitive effect: the current make-up of supervisory boards, as gauged by the Supervisory Board Index (SBI), inversely impacts both efficiency metrics, signaling sub-optimal governance structures. Significantly, the research also highlights a pressing concern: the average total efficiency of Ghanaian banks lags behind the global benchmarks prescribed by the World Bank. This discrepancy underscores an exigency for efficiency optimization within the sector. The study thereby offers invaluable insights for multiple stakeholders-including regulatory bodies, investment communities, and policymakers-by delineating the governance variables that can enhance or impede banking efficiency. It also identifies actionable avenues for improvement, specifically in the realms of risk management and board composition, with the potential to catalyze a transformation in Ghana's banking landscape.

Sustainable approach towards alternatives for the use of iron ore tailings in the construction sector using Data Envelopment Analysis methodology.

Iron ore tailings (IOTs) need to be properly managed to mitigate the environmental, social, and economic impacts of mining activities. To cope with this issue, we use data envelopment analysis (DEA) to evaluate alternatives for using IOT in the construction sector. The classical and weight restriction output-oriented DEA models were used in this analysis. The results show that the ranking of alternatives depends on the aspect being evaluated. Concrete block is the most environmentally friendly alternative when analysing both models. For both social and economic aspects, ceramics produced better results in the classical model, whereas Portland cement showed better outcomes in the weight restriction model. In this sense, the results suggest great potential for the use of IOT in the construction sector, enabling the reduction of risks and social and environmental impacts of tailings dams.

Measuring performance of supply chains based on data envelopment analysis and multi-regional input-output analysis: An application to 18 manufacturing sectors in 43 countries.

This study developed a novel framework that combined data envelopment analysis and multi-regional input-output database to investigate the economic and environmental productivity change in the global supply chains associated with 18 manufacturing sectors in 43 countries from 2000 to 2014. Two models are developed; manufacturer model is used to evaluate performance of direct production activity of a sector in countries and supplier model is used to evaluate performance of indirect production activity of upstream suppliers of the sector. The proposed framework enables us to separately analyze the performance of supply chains into direct production activity and indirect production activity of suppliers. The empirical results show that the environmental productivity of direct production activity of 18 manufacturing sectors was improved by 12.9 percent, while the environmental productivity of the upstream suppliers was improved by only 4.7 percent during 2000-2014 on average. Different patterns of economic and environmental productivity growth were observed between the direct production activity and upstream suppliers in all sectors. The finding suggests that the performance of an entire supply chain should be separately analyzed to consider industry-specific policies. The proposed framework is used to identify countries that succeed/fail to improve economic and environmental performance. Based on the results, this study discusses policies regarding production and supply chain management toward CO2 mitigation.

Testing data envelopment analysis models on the performance of European Union regions in sustainable waste management.

Achieving a Circular Economy requires effective waste management. Monitoring the transformation path taken by the European Union (EU) and its Member States is crucial for assessing the impact and effectiveness of current European waste legislation. Even though numerous studies have shown positive performance and convergence in waste treatment, regional efficiency evaluation studies are still lacking. This research aims to benchmark the performance of municipal solid waste management in 167 regions - at level 2 of the Nomenclature of Territorial Units for Statistics - across European Member States during the period from 2008 to 2013. It looks to assess whether there is any positive convergence among these regions. The efficiency assessment employs four different Data Envelopment Analysis models. One of these models, the Benefit-of-Doubt, is a non-parametric approach and represents a special case within the Data Envelopment Analysis framework. Our findings indicate a yearly reduction in the coefficient of variation of 3.6%. Despite this progressive convergence, substantial differences in municipal solid waste management performance exist between EU-20 regions, even within the same country. Particularly, the analysis demonstrates that the best performers are registered in the Central and Northern EU regions, while the Eastern and Southern EU regions performed worse.