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This article examines the concept of eco-efficiency at a regional level as an approach to promote the sustainable transformation of regions, using the regions of Poland as an example. The data envelopment analysis (DEA) method--the input-oriented Charnes, Cooper, and Rhodes (CCR) model--was chosen as the eco-efficiency analysis tool because of its high capability to measure the regional eco-efficiency. The research process was divided into two stages. First, the chosen instruments of mathematical statistics (e.g., Hellwig's method and coefficient of determination) were applied to ensure an appropriate combination of environmental and economic indicators of the eco-efficiency equation. Next, the CCR model was used to calculate the eco-efficiency scores. The results of the study have revealed that the regions of Lubuskie, Mazowieckie, Śląskie, Warmińsko-Maurskie, and Wielkopolskie are relatively eco-efficient, whereas the remaining regions use too many environmental resources in relation to the produced value of goods and services. Six of the eleven eco-inefficient regions in Poland have increasing returns to scale, that is, the usage of natural resources connected with the negative impact upon the environment rises slower than the values of goods and services. Notwithstanding, it is beneficiary from the perspective of sustainability. The obtained research results are a valuable source of management information for the creation of regional environmental protection strategies and a basis for searching for the causes of eco-inefficiency.
Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries—the world’s largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development. The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions. In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors. Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA Explores new statistical modeling strategies and explores their economic and business implications Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is must-reading for researchers, academics, graduate students, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution.
A central asset of eco-efficiency analysis is that it does not depend on a specific evaluation of environmental impacts against economic effects. Several evaluation methods may be used, including those based on willingness-to-pay, panel procedures, and public statements on policy goals. This volume covers all aspects of eco-efficiency analysis and offers a global perspective on the subject.
With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network. This book is a concise and self-contained work with enormous amount of information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.
The growing complexity and intertwining of different socio-economic issues both in individual countries and internationally mean that single-theme analyses do not consider all the relationships and thus have cognitive limitations. Therefore, studies that combine several research areas are increasingly common in the literature to clarify the connections and relationships. In this study, considering the sequential nature of the stages, a combined analysis of eco-efficiency, eco-innovation, and Sustainable Development Goals (SDGs) was performed. The analysis was carried out for 27 European Union countries in 2017-2019. Dynamic Network SBM and Dynamic Divisional Malmquist Index were used for the study. The research results show that the EU countries achieve relatively higher efficiency results in eco-innovation and SDG than ecoefficiency. The average overall efficiency level for all EU countries was only 0.63. The change in productivity was influenced by both the frontier shift and catch-up effect, but only with regard to eco-efficiency and eco-innovation. At the same time, the frontier-shift effect did not affect the change in SDG productivity.
Data Envelopment Analysis (DEA) represents a milestone in the progression of a continuously advancing methodology for data analysis, which finds extensive use in industry, society and even in education. This book is a handy encyclopedia for researchers, students and practitioners looking for the latest and most comprehensive references in DEA. J.K. Mantri has specifically selected 22 research papers where DEA is applied in different fields so that the techniques discussed in this book can be used for various applications. In A Bibliography of Data Envelopment Analysis (1978-2001), Gabriel Tavares states that DEA is a mathematical programme for measuring performance efficiency of organizations popularly named as decision-making units (DMU). The DMU can be of any kind such as manufacturing units, a number of schools, banks, hospitals, police stations, firms, etc. DEA measures the performance efficiency of these kinds of DMUs, which share a common characteristic: they have a non-profit organization where measurement is difficult. DEA assumes the performance of the DMU using the concepts of efficiency and productivity, which are measured as the ratio of total outputs to total inputs. The efficiencies estimated are relative to the best performing DMU, which is given a score of 100%. The performance of other DMUs varies between 0% and 100%.
Environmental cost-benefit analysis (ECBA) refers to social evaluation of investment projects and policies that involve significant environmental impacts. Valuation of the environmental impacts in monetary terms forms one of the critical steps in ECBA. We propose a new approach for environmental valuation within ECBA framework that is based on data envelopment analysis (DEA) and does not demand any price estimation for environmental impacts using traditional revealed or stated preference methods. We show that DEA can be modified to the context of CBA by using absolute shadow prices instead of traditionally used relative prices. We also discuss how the approach can be used for sensitive analysis which is an important part of ECBA. We illustrate the application of the DEA approach to ECBA by means of a hypothetical numerical example where a household considers investment to a new sport utility vehicle.
There is broad consensus among economists that regions' competitiveness heavily relies on their ability to produce innovative goods and services (Baumol 1967, Romer 1990, Grossman and Helpman 1991, Barro and Sala-i-Martin 1997, Los and Verspagen 2006). Main drivers of innovation include, but are not limited to, human and cognitive capital (Quelle), R & D expenditures (Quelle), industrial clusters and structure (Quelle) and foreign direct investments (Quelle). Most empirical studies confirm the presumed positive correlation of these inputs and regional innovativeness, measured for example by patent applications. At the same time, regions operating at similar input level show significant differences in the degree of innovativeness. These differences can, to some extent, be explained by the regions efficiency in using their available input factors (Quelle). The presented paper aims, in a first step, to identify this efficiency by using an outlier robust enhancement of the data envelopment analysis (DEA), the so-called order-α-frontier analysis (Daouia and Simar 2005, Daraio and Simar 2006), for a sample of more than 200 EU regions (NUTS 2). The findings of this model suggest that the regions' efficiency is partly affected by a spatial factor. Therefore, the study foresees to decompose regional efficiency into a spatial and non-spatial part by introducing a geoadditive regression analysis based on markov fields. The spatial part reveals differences of the efficiency for greater areas. Regions located in efficient areas, for example, are likely to be efficient as well, since they benefit by the efficiency of neighboring regions. In contrast, the non-spatial effect gives an idea on a region's efficiency compared to the neighboring and nearby regions.