Multicriteria Analysis for Environmental Decision-Making (eBook)

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2019
138 Seiten
Anthem Press (Verlag)
978-1-78308-954-3 (ISBN)

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Multicriteria Analysis for Environmental Decision-Making - Davide Geneletti
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The book provides an introduction to key concepts of multicriteria analysis, and a series of case studies that illustrate its application to a variety of environmental decision-making problems, ranging from protected area zoning to landfill siting, and from forest restoration to environmental impact assessment of tourism infrastructures.


The aim of this book is to provide an overview of the principles of multicriteria analysis (MCA), and a series of case studies that illustrate its application to a variety of environmental decision-making problems, ranging from the siting of facilities with critical environmental effects to Natural Park planning, and from the prioritisation of environmental restoration interventions to the assessment of the impact of tourism infrastructures.



Chapter 1 introduces the principles of MCA and describes the main stages of a generalized MCA process, by providing details and references to support the implementation of each stage. Chapter 2 reviews the application of MCA for a specific field of environmental decision-making: nature conservation. The objective is twofold: to take stock of past experiences by investigating how key stages of the MCA process have been performed, and to compare findings with best practices in order to provide recommendations for successful applications.



In Chapter 3, a case study about landfill site selection is presented. The method is based on the combination of stakeholder analysis and spatial MCA to first design possible sites for a landfill, and then rank them according to their suitability. Chapter 4 presents an application of MCA to support protected area planning. The case study illustrates the process of proposing a zoning scheme for a natural park, by combining MCA and multiobjective evaluation. Chapter 5 addresses the problem of forest landscapes restoration. In this case study, spatial and non-spatial MCA are applied to first identify forest reforestation priority areas, and then design landscape-scale reforestation options aimed at improving both ecosystem quality and human living conditions. [NP] The last case study, described in Chapter 6, shows how MCA can be combined with GIS-based indicators to assess and compare the environmental impacts of proposed ski areas in a mountain watershed. Finally, Chapter 7 provides some conclusions about the potential of MCA to support environmental decision-making, and about the set of skills required for successful MCA applications.

lt;p>Davide Geneletti is associate professor of spatial planning at the University of Trento, Italy, specializing in impact assessment of projects, plans and policies; ecosystem services and urban green infrastructures; and multicriteria analysis. He is the editor-in-chief of the Journal of Environmental Assessment Policy and Management and deputy editor of the open-access journal, One Ecosystem.

Acknowledgements; Notes on Contributors; Introduction; Chapter 1. Principles of Multicriteria Analysis; Chapter 2. A Review of MCA Applications for Nature Conservation; Chapter 3. Landfill Site Selection: Combining Spatial MCA with Stakeholder Analysis; Chapter 4. Protected Area Zoning: Integrating Spatial MCA and Multi-objective Evaluation; Chapter 5. Forest Landscape Restoration: Applying MCA to Design and Compare Options; Chapter 6. Impact Assessment of Ski Areas: Combining GIS Indicators with MCA; Chapter 7. Conclusions; References; Annex 1; Index.

Erscheint lt. Verlag 30.5.2019
Reihe/Serie Anthem Environment and Sustainability Initiative
Anthem Environment and Sustainability Initiative (AESI)
Anthem Impact
Anthem Impact
Strategies for Sustainable Development Series
Strategies for Sustainable Development Series
Verlagsort London
Sprache englisch
Maße 150 x 150 mm
Gewicht 454 g
Themenwelt Technik Architektur
ISBN-10 1-78308-954-7 / 1783089547
ISBN-13 978-1-78308-954-3 / 9781783089543
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