Sustainability efficiency assessment of wastewater treatment plants in China: A data envelopment analysis based on cluster benchmarking

Quantitative evaluation on the efficiency of wastewater treatment plants (WWTPs) is a key issue that needs to be solved. For this purpose, data envelopment analysis (DEA) was employed to establish a comprehensive efficiency evaluation system on WWTPs, including three inputs of operating cost, electricity consumption and labor, three desirable outputs of chemical oxygen demand (COD) removal rate, ammonia nitrogen (NH3–N) removal rate and reclaimed water yield, and one undesirable output of dry sludge yield. 861 WWTPs in China were assessed by a slacked-based DEA model based on cluster benchmarking. The technology gap ratio (TGR) confirmed that large WWTPs operated more efficiently than small ones. The WWTPs had an average efficiency score of 0.611. Among them, 170 samples were relatively efficient with a score of 1, which means these samples could be a benchmark for other inefficient samples. Different degrees of input excesses or output shortfalls existed in 691 inefficient samples and these samples should be the key objects to improve the operational efficiency. Furthermore, through the Kruskal-Wallis test, the influent COD concentration and capacity load rate showed significant effects on the WWTP performance. These findings, derived from a simple but effective framework, have potential value for managers to make decisions

Advancing efficiency analysis using data envelopment analysis: the case of German health care and higher education sectors

The main goal of this dissertation is to investigate the advancement of efficiency analysis through DEA. This is practically followed by the case of German health care and higher education organizations. Towards achieving the goal, this dissertation is driven by the following research questions: 1.How the quality of the different DEA models can be evaluated? 2.How can hospitals’ efficiency be reliably measured in light of the pitfalls of DEA applications? 3.In measuring teaching hospital efficiency, what should be considered? 4.At the crossroads of internationalization, how can we analyze university efficiency? Both the higher education and the health care industries are characterized by similar missions, organizational structures, and resource requirements. There has been increasing pressure on universities and health care delivery systems around the world to improve their performance during the past decade. That is, to bring costs under control while ensuring high-quality services and better public accessibility. Achieving superior performance in higher education and health care is a challenging and intractable issue. Although many statistical methods have been used, DEA is increasingly used by researchers to find best practices and evaluate inefficiencies in productivity. By comparing DMU behavior to actual behavior, DEA produces best practices frontier rather than central tendencies, that is, the best attainable results in practice. The dissertation primarily focuses on the advancement of DEA models primarily for use in hospitals and universities. In Section 1 of this dissertation, the significance of hospital and university efficiency measurement, as well as the fundamentals of DEA models, are thoroughly described. The main research questions that drive this dissertation are then outlined after a brief review of the considerations that must be taken into account when employing DEA. Section 2 consists of a summary of the four contributions. Each contribution is presented in its entirety in the appendices. According to these contributions, Section 3 answers and critically discusses the research questions posed. Using the Translog production function, a sophisticated data generation process is developed in the first contribution based on a Monte Carlo simulation. Thus, we can generate a wide range of diverse scenarios that behave under VRS. Using the artificially generated DMUs, different DEA models are used to calculate the DEA efficiency scores. The quality of efficiency estimates derived from DEA models is measured based on five performance indicators, which are then aggregated into two benchmark-value and benchmark-rank indicators. Several hypothesis tests are also conducted to analyze the distributions of the efficiency scores of each scenario. In this way, it is possible to make a general statement regarding the parameters that negatively or positively affect the quality of DEA estimations. In comparison with the most commonly used BCC model, AR and SBM DEA models perform much better under VRS. All DEA applications will be affected by this finding. In fact, the relevance of these results for university and health care DEA applications is evident in the answers to research questions 2 and 4, where the importance of using sophisticated models is stressed. To be able to handle violations of the assumptions in DEA, we need some complementary approaches when units operate in different environments. By combining complementary modeling techniques, Contribution 2 aims to develop and evaluate a framework for analyzing hospital performance. Machin learning techniques are developed to perform cluster analysis, heterogeneity, and best practice analyses. A large dataset consisting of more than 1,100 hospitals in Germany illustrates the applicability of the integrated framework. In addition to predicting the best performance, the framework can be used to determine whether differences in relative efficiency scores are due to heterogeneity in inputs and outputs. In this contribution, an approach to enhancing the reliability of DEA performance analyses of hospital markets is presented as part of the answer to research question 2. In real-world situations, integer-valued amounts and flexible measures pose two principal challenges. The traditional DEA models do not address either challenge. Contribution 3 proposes an extended SBM DEA model that accommodates such data irregularities and complexity. Further, an alternative DEA model is presented that calculates efficiency by directly addressing slacks. The proposed models are further applied to 28 universities hospitals in Germany. The majority of inefficiencies can be attributed to “third-party funding income” received by university hospitals from research-granting agencies. In light of the fact that most research-granting organizations prefer to support university hospitals with the greatest impact, it seems reasonable to conclude that targeting research missions may enhance the efficiency of German university hospitals. This finding contributes to answering research question 3. University missions are heavily influenced by internationalization, but the efficacy of this strategy and its relationship to overall university efficiency are largely unknown. Contribution 4 fills this gap by implementing a three-stage mathematical method to explore university internationalization and university business models. The approach is based on SBM DEA methods and regression/correlation analyses and is designed to determine the relative internationalization and relative efficiency of German universities and analyze the influence of environmental factors on them. The key question 4 posed can now be answered. It has been found that German universities are relatively efficient at both levels of analysis, but there is no direct correlation between them. In addition, the results show that certain locational factors do not significantly affect the university’s efficiency. For policymakers, it is important to point out that efficiency modeling methodology is highly contested and in its infancy. DEA efficiency results are affected by many technical judgments for which there is little guidance on best practices. In many cases, these judgments have more to do with political than technical aspects (such as output choices). This suggests a need for a discussion between analysts and policymakers. In a nutshell, there is no doubt that DEA models can contribute to any health care or university mission. Despite the limitations we have discussed previously to ensure that they are used appropriately, these methods still offer powerful insights into organizational performance. Even though these techniques are widely popular, they are seldom used in real clinical (rather than academic) settings. The only purpose of analytical tools such as DEA is to inform rather than determine regulatory judgments. They, therefore, have to be an essential part of any competent regulator’s analytical arsenal

Efficient distribution of carbon emissions reduction targets at the city level: A case of Yangtze River Delta region

The Chinese central government has released detailed carbon emissions abatement targets at the provincial level, but provides no specific emissions reduction targets at the city level. Most provincial governments simply allocate carbon emissions reduction tasks to their cities based on the GDP of their cities. Allocation approaches, however, should emphasize the most effective distribution to reach overall targets that reflect actual reduction capacities of cities. This paper proposes an allocation method at the city level by combining a data envelop analysis method, an entropy weight method and a clustering analysis method using the Yangtze River Delta region as a case study. Results of our analysis indicate that cities with higher carbon emissions abatement potentials, financial abilities, a larger number of above-scaled industrial enterprises and higher GDP are better positioned to reduce carbon emissions and should be assigned proportionately higher reduction targets. The merits and policy implications of the proposed approach are discussed in comparison to simply using GDP to allocate emission reduction targets

Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling

Thanks to information, communication and technological improvements in these days, data mining method are used to obtain significant results from very large data sets. In terms of businesses, decisionmaking in product design, placement, layout and so on issues are of vital importance. Association rules taking part in data mining topic is used so much especially in marketing research in the market basket. The Multi- Dimensional scaling (MDS) method is also frequently used for the positioning of products in the marketing field. MDS is measured similarities between products, units and so on according to the method of Euclidean space. Relations between products or units are visualized in two or three dimensions using MDS method according to the purpose. The aim of this study is to determine the product shelf layout using association rules according to the relationship map of the products generated by MDS. Together with the association rules (conviction ratios) used in data mining field, proximity coefficients between products were calculated and used in MDS analyze. Product groups were created by using MDS and proximity coefficient combinations made up between products. Shelf layout ensuring similar products in line with side by side was determined with the help of association rules. The applicability of the proposed method for products and alternative shelf layout was presented visually. 750 shopping and customers who purchase products in the same shelf made up the data of this study. In this study, placement of the products designed to maximize the benefit level for customers in terms of time and convenience

Integration of Simulation and DEA to Determine the Most Efficient Patient Appointment Scheduling Model for a Specific Clinic Setting

This study develops a method to determine the most efficient scheduling model for a specific clinic setting. The appointment scheduling system assigns clinics' timeslots to incoming requests. There are three major scheduling models: centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM). In order to schedule multiple appointments, CSM involves one scheduler, DSM involves all the schedulers of individual clinics and HSM combines CSM and DSM. Clinic settings are different in terms of important factors such as randomness of appointment arrival and proportion of multiple appointments. Scheduling systems operate inefficiently if there is not an appropriate match between scheduling models and clinic settings to provide balance between indicators of efficiency. A procedure is developed to determine the most efficient scheduling model by the integrated contribution of simulation and Data Envelopment Analysis (DEA). A case study serves as a guide to use and as proof for the validity of the developed procedure

Homogeneity and best practice analyses in hospital performance management: an analytical framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals

Understanding the Evaluation Abilities of External Cluster Validity Indices to Internal Ones

Evaluating internal Cluster Validity Index (CVI) is a critical task in clustering research. Existing studies mainly employ the number of clusters (NC-based method) or external CVIs (external CVIs-based method) to evaluate internal CVIs, which are not always reasonable in all scenarios. Additionally, there is no guideline of choosing appropriate methods to evaluate internal CVIs in different cases. In this paper, we focus on the evaluation abilities of external CVIs to internal CVIs, and propose a novel approach, named external CVI\u27s evaluation Ability MEasurement approach through Ranking consistency (CAMER), to measure the evaluation abilities of external CVIs quantitatively, for assisting in selecting appropriate external CVIs to evaluate internal CVIs. Specifically, we formulate the evaluation ability measurement problem as a ranking consistency task, by measuring the consistency between the evaluation results of external CVIs to internal CVIs and the ground truth performance of internal CVIs. Then, the superiority of CAMER is validated through a real-world case. Moreover, the evaluation abilities of seven popular external CVIs to internal CVIs in six different scenarios are explored by CAMER. Finally, these explored evaluation abilities are validated on four real-world datasets, demonstrating the effectiveness of CAMER

Reward-Penalty Scheme for Power Distribution Companies

In this research, we propose three Reward-Penalty algorithms, to improve the reliability of power distribution companies. The significance of this research, lies in encouraging power distribution companies, to maintain, or even improve, customer service and satisfaction, by developing the Performance Regulatory Reward-Penalty models. These models are designed to place little administrative burden, on either the regulators, or the power distribution companies, whilst providing valuable information, which will prevent degradation of service reliability

Knowledge-Based Economy in Developing Countries: Measurements and Impacts

The traditional factors of production, such as land, labour, and capital, have typically determined a nation’s comparative advantage. However, in the context of a global Knowledge-Based Economy (KBE), a nation’s prosperity is now determined by its knowledge assets. This transition to a KBE offers endless advantages and is desirable for all countries. However, developing countries face significant challenges in adopting this new development paradigm, where knowledge is the key driver of economic growth. Yet, to effectively measure the extent to which a country is considered knowledge-based on the international level, a robust framework is needed. Although the burgeoning literature, existing KBE measurement frameworks have limitations and may not accurately reflect the progress and efficiency of the transition to a KBE, especially in developing countries. Consequently, relying on these frameworks can lead to misleading policy directions that hinder the necessary rapid transition in developing countries. This thesis aims to fill the gap in understanding the KBE within developing countries through an extensive analysis. To achieve this, the thesis begins by reviewing the conceptual and theoretical literature on the KBE. It then critically examines existing measurement frameworks and empirical studies related to the KBE, specifically evaluating their suitability for developing countries. In response to the limitations found, a new and more effective measurement framework is proposed. This framework focuses on input-output indicators across four dimensions of the KBE: acquisition, distribution/dissemination, production, and utilization. Notably, it utilizes a non-parametric approach known as Data Envelopment Analysis (DEA), which differs from conventional econometric analysis. The DEA empirical results are then compared with those obtained from other existing KBE measurement frameworks, allowing for a comprehensive assessment of the advantages offered by DEA. Based on the DEA empirical findings, knowledge production is identified as the weakest aspect, despite its utmost importance among the four KBE dimensions. As a result, this thesis places special emphasis on enhancing innovation development in selected developing countries through effective innovation policies tailored to their specific circumstances and utilizing country-specific innovation policy instruments

Prospects for sustainable intensification of smallholder farming systems in Ethiopian highlands

2017 Summer.Includes bibliographical references.This dissertation examines the prospects of sustainable agricultural intensification by rural farming households in Ethiopia. Although widely accepted as the new paradigm for agricultural development in sub-Saharan Africa, several research and empirical questions still surround the concept of sustainable intensification, particularly its operationalization. Efforts to promote, measure and monitor progress towards sustainable intensification are hampered by the lack of quantifiable indicators at the farm level, as well as the uncertainty over the relationship between intensification and sustainability. This dissertation contributes to this knowledge gap by examining the relationship between agricultural intensification and sustainability, with a view to determine if sustainable paths of agricultural intensification are possible within the smallholder farming systems of Ethiopian highlands. To help better execute the research inquiry, and achieve the main goal of this study, the themes of this dissertation are addressed through three separate but interrelated essays, on top of the introductory and conclusion chapters. The first essay, presented in chapter two, examines the drivers and processes shaping agricultural intensification by smallholder farmers. This chapter contributes to the literature by providing evidence of how agricultural intensification depends on a wide range of factors, whose complex interactions give rise to different intensification pathways. The implication is that, even in a region that is undergoing the process of agricultural intensification, households are likely to respond differently to intensification incentives and production constraints, and thus pursue different paths of agricultural intensification. The second essay, chapter three, develops a methodological framework for defining elements of sustainability based on observed, context-specific priorities and technologies. Farm-level indicators of agricultural sustainability are developed using insights drawn from literature, and adapted to the Ethiopian context through consultations with agricultural experts and key stakeholders in the agricultural sector. A Data Envelopment Analysis (DEA) framework is applied to synthesize the selected indicators into a relative farm sustainability index, thus reducing subjectivity in the sustainability index. A generalized linear regression model applied on the computed sustainability scores shows that farm size, market access, access to off farm income, agricultural loans, access to agricultural extension and demonstration plots are key drivers of agricultural sustainability at the farm level. Despite being applied to the Ethiopian context; the methodology has broader policy implications and can be applied in many contexts. The third essay, chapter four, examines the relationship between agricultural intensification and relative farm sustainability, and identifies four clusters of farmers depending on their relative levels of intensification and sustainability. The main thrust of this essay is to examine whether farmers who are highly productive are also sustainable, and whether systems that are relatively more sustainable are mostly on the highly productive farms. The results show that of the farms that are relatively most intensive, in terms of the gross value of crop output per hectare, only 27 percent are relatively more sustainable. Of the farms that are relatively most sustainable, about 60 percent are more intensive. Overall, only 10 percent of the farms were both highly intensive and relatively more sustainable. In order to understand the typology of farmers that are likely to embark on sustainable paths of agricultural intensification, multivariate methods of Principal Components Analysis (PCA) and Cluster Analysis (CA) were used to cluster farmers according to their common characteristics. Multinomial Logit (MNL) regression models were used to model the probability of cluster membership as well as the likelihood of farmers embarking on different intensification trajectories. is used to analyze the odds of embarking on a sustainable intensification path. The results suggest that increasing farmers' access to technical information through demonstration plots and government extension services, addressing farm liquidity constraints, improving market access, as well fostering crop-livestock interactions, significantly increases the likelihood of sustainable intensification