Evaluation of Urban Health Efficiency in Japanese Cities Using Network Data Envelopment Analysis

Food security is a global challenge. With rising world population and demand for food being compounded by resource and arable land constraints, raising the efficiency of food production and use has become increasingly important. While much of the research on food security is focused on farm efficiency and productivity, most neglect post-harvest (PH) handling which is critical in determining the availability of food. In this study, we employ the network Data Envelopment Analysis (DEA) model to evaluate the PH efficiency of milling, using data from Kenya’s rice processing industry. The results show lower efficiency scores when using a network DEA model, which reflects its greater discriminatory power when compared to the standard DEA approach. The study also quantified sources of productive efficiency using a fractional regression model and identified storage space and distance to market as having an impact on drying efficiency; while experience, age of mill, servicing and energy type influenced milling efficiency. The results suggest that policy makers should focus on investing in drying technologies and storage facilities to improve drying efficiency. To improve milling efficiency, policy recommendations include enhancing millers’ access to better technologies, investing in reliable sources of energy and providing PH handling workshops to reduce PH losses.

When the number of Decision-Making Units (DMUs) is not large enough compared to the total number of input parameters and outputs, traditional Data Envelopment Analysis (DEA) and Network Data Envelopment Analysis (NDEA) models often produce solutions that identify many DMUs as efficient, in addition to obtaining unrealistic weight distributions. In fact, this poor discrimination power and unrealistic weight distribution presented by DEA and NDEA models remain a major challenge, leading to the development of models to improve this performance. Thus, this paper proposes a Multiple Criteria Network Data Envelopment Analysis (MCNDEA), based on relational NDEA models. The idea of this model is to be used in network structures. To test the MCNDEA model, one a real instance linked to an evaluation problem of academic departments of a public university was used. Other instances were also used to validate the proposed MCNDEA, and these tests are included in the supplementary files. Finally, it should be noted that, in summary, the model proposed in this article had greater discrimination power of the analyzed DMUs, being able to identify the most efficient departments in each of the considered stages, besides pointing out to the University Management points of improvement regarding the best use of its resources for each department.

The Workshop titled “Salutogenic Cities: European experiences in Urban Health” - proposed and developed by the three EUPHA Section: Urban Public Health (URB), Public Mental Health (MEN) and Environmental Health (ENV) - is aimed to foster the dialogue between designers (architects and urban planners), Public Health experts (operators, professionals and epidemiologists), policy/decision makers and city users, to establish a multidisciplinary approach for understanding together how to create and manage healthy living outdoor urban environments. The Workshops mainly address both the “Climate emergency / Environment and health / Urban health” and the “Global health and Europe's role in global health” EPH23 conference topics. The Workshops purpose is to explore the link between the morpho-typological features of the urban environment and Public Health outcomes, that's crucial in the scientific scenario, taking into consideration the urbanization phenomena that characterizes contemporary society. Health is no longer a specifically individual health issue, but a collective priority strongly influenced by the environmental context and by strategies implemented by local Governments. Urban planning, at large and small scales, can be considered a key tool to protect and promote Urban Public Health. In this scenario, the COVID-19 emergency, has accelerated and emphasized several social, environmental and digital challenges, underling the focus on several aspects such as Urban mobility and walkable environment; Urban Green Spaces and ecosystem services assessment; Urban Health policies and actions. The Workshops program include inputs which they argue current experiences, emerging practices and scientific outcomes related to the Urban Health discipline. From methods, in “Improving health and wellbeing through health-centered, evidence-based urban planning methodologies” to actions, in “The urban burden of disease estimation for policy-making in 1000 European cities”. From epidemiological outcomes, in “Association between area-level walkability and glycated haemoglobin: a Portuguese population-based study” to hospitalizations implications, in “Daily temperature variability in cities and mental health-related hospitalizations”. Key messages • Explore the link between the morpho-typological features of the urban environment and Public Health outcomes, taking into consideration the urbanization phenomena that affect the contemporary society. • Establish a multidisciplinary approach for understanding together how to create and manage healthy living outdoor urban environments.

Background: Gains in bank efficiency improvement have widely been regarded as one of the most effective and efficient means of ensuring sustainability of a financial system. Aim: This article proposes a relative dynamic two-stage network data envelopment analysis model for measurement of bank efficiency based on the slack-based measure. Setting: Twenty-seven banks in Ghana during the period of 2009–2014. Methods: By considering simultaneous processes within the framework of two-stage data envelopment analysis, the slack-based measure approach identifies the sources of inefficiency in the banks. Results: In the empirical analysis, non-performing loans are an undesirable output in one production process, which should also be treated as a carry-over factor; that is to say, some non-performing loans from the preceding year can be collected in the current year. The carry-over factors should be used to indicate the presence of performance gaps that exist in the banks. The proposed model was used to measure the efficiency of the 27 banks in Ghana during the period of 2009–2014. We also present useful suggestions for improvement in bank efficiency based on the empirical results. Conclusion: The 27 main commercial banks in Ghana are far from efficient. For all banks, the efficiency score in the second stage is much higher than that of the first stage. That means more attention should be paid to the first stage of production in order to increase the banks’ efficiency.

No abstract available.

Nowadays, the growth of industry can be seen as a nature of the world. Each company race again each other to increase productivity to produce new, high quality and product that fulfil customer demand. One can achieve the Key Performance Indicator (KPI) or targeted goal but without considering the cost, manpower, time or others elements is inefficient toward productivity. Upgrade production line in manufacturing industry needs huge investment to come out with good performance. The company can receive Return of Investment (ROI) and save more money from paying labor salary and increase productivity. However, the company also may have the risk of losing their money from the investment done. In this research, we studied the effectiveness of production line that equipped with automation usage to determine the productivity and quality of the product produced. We apply Data Envelopment Analysis (DEA) to measure efficiencies of the production line where DEA is one of an excellent tool that can evaluate efficiencies and have been using widely in many sectors. The model that will be used in this study is Two-Stage Network DEA. As a case study, this research focuses on the production line that producing a product with a high and continues demand to observe how the investment on automation can give good return or otherwise.

Accurate measurement of regional efficiency is a prerequisite for effective management. Prior studies have expanded on the overall "black box" evaluation with two stages of research and development (R&D) and commercialization, opening up the internal structure of the regional innovation process, but ignoring the independent innovation activities of universities, research institutes, and firms in the R&D stage. We construct a mixed structure with two stages, three actors, and four subsystems, and conduct an empirical analysis of China's provincial samples from 2017 to 2019 by using the network data envelopment analysis (DEA) model. Results show that the efficiency of the R&D stage at the provincial level is generally higher than that of the commercialization stage. However, the three subsystems of the R&D stage perform poorly. Spearman’s rank correlation coefficient suggests that there is a significantly positive correlation between total regional efficiency and commercialization. In addition, we use the k-means method to divide 27 provinces into three clusters, setting a more appropriate improvement benchmark for inefficient provinces. Based on enlightenment of regional tobacco industry, we put forward some proposals for specific stage and specific subsystem.

Unreasonable allocation of shared resources reduces the system efficiency and is a considerable operational risk. Sub-processes with insufficient portion of shared resources could not help accomplish complicated tasks, and overstaffing and idle resources will occur in the sub-processes assigned with redundant shared resources. This unfair portion distribution may cause internal contradictions among sub-processes and even lead to the collapsing of the entire system. This study proposes a data-driven, mixed two-stage network data envelopment analysis model. This method aims to reasonably define the allocation portion of shared extra intermediate resources among several nonhomogeneous subsystems and measure the overall system performance. A data set of 58 international hotels is used to test the features of the proposed model.

In this paper, we present for the first time the on-field production process of soccer teams as a mixed (serial and parallel) structure two-stage network system. According to this, the first stage consists of two distinct sub-processes (offense and defense) that operate in parallel. These respectively use players’ offensive and defensive actions as inputs to produce two different intermediate measures, namely, goals scored and prevention of goals conceded. These, in turn, are the inputs of the second stage (points’ accumulation sub-process) that produces accumulated points. Furthermore, based on a two-stage network Data Envelopment Analysis (DEA) model, we estimate the offensive, defensive, and athletic efficiency of soccer teams during a league season. According to our proposed framework, these three different efficiency scores are provided (for each soccer team under evaluation) by a single linear programming problem. For this purpose, aggregate-over-games statistics from the 2013–14 Greek premier soccer league are used.

This paper deals with urban sustainable development in China. We propose a network data envelopment analysis (DEA) model with a slack-based measure (SBM) to analyze the eco-efficiency of 284 Chinese cities, enabling us to find a way to open the “black box” in conventional DEA models and introduce social well-being factors into the model, and depict the role of local government in providing public service and improving social well-beings. We set up a framework of urban development by dividing the process of into two steps. The first stage is a production system translating inputs and natural resources into GDP and waste production, which will be inputs to the second stage for distribution and consumption to realize social welfare and environmental protection. The results show eco-efficiency of Chinese cities experienced a significant decrease from 2005 to 2016, which should be mainly attributed to the distribution and consumption processes. Structural differences are described by regions, administrative level and clusters. These results are compared with an existing urban sustainability index system developed by McKinsey and an ANOVA approach is conducted to reveal differences between cities across regions and clusters. This article sheds new light on the understanding of urban sustainable construction and development in China regarding the service performance of local government.

Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model

An efficiency measurement model of a university faculty is proposed with additional new sub-functions that produce new output variables, based on the network Data Envelopment Analysis (DEA) model for systems with a hierarchical structure.For production systems composed of hierarchical processes, the system efficiency is well represented as the aggregated performance of the components involved in the system. It is identified that the conventional DEA model ignores internal process activities in a university. Therefore, an improved DEA model based on a network structure that accounts for more activities in a university is proposed to measure its overall efficiency. The impact of major functions of a university are taken into account to represent the output variables in assessing the efficiency. Currently, collaboration activities have been given more attention, so, this variable is suggested as a new output for the hierarchical production system. In order to show the practicality of the model, a hypothetical set of data of 14 faculties has been used as a numerical example. The results show that none of the faculties is relatively efficient since its functions were found to be inefficient. The proposed model enables to help the management of university faculties to identify weaknesses of each function and thus to plan for suitable actions on improving the overall performance of the university.

The overarching goal of this article is to make explicit the multiple pathways through which the built environment may potentially affect health and well-being. The loss of close collaboration between urban planning and public health professionals that characterized the post-World War II era has limited the design and implementation of effective interventions and policies that might translate into improved health for urban populations. First, we present a conceptual model that developed out of previous research called Social Determinants of Health and Environmental Health Promotion. Second, we review empirical research from both the urban planning and public health literature regarding the health effects of housing and housing interventions. And third, we wrestle with key challenges in conducting sound scientific research on connections between the built environment and health, namely: (1) the necessity of dealing with the possible health consequences of myriad public and private sector activities; (2) the lack of valid and reliable indicators of the built environment to monitor the health effects of urban planning and policy decisions, especially with regard to land use mix; and (3) the growth of the "megalopolis" or "super urban region" that requires analysis of health effects across state lines and in circumscribed areas within multiple states. We contend that to plan for healthy cities, we need to reinvigorate the historic link between urban planning and public health, and thereby conduct informed science to better guide effective public policy.

Allocation of SO2 emission rights in city agglomerations considering cross-border transmission of pollutants: A new network DEA model

Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis