Sustainable Governance of Natural Resources : Uncovering Success Patterns with Machine Learning

A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management.

What can be done to ensure natural resources aren''t exploited? Is it possible to determine how to sustainably manage them? What makes some systems successful? In Sustainable Governance of Natural Resources, Ulrich Frey delves deep into unanswered questions like these about resource management. The book explains the current state of biological cooperation mechanisms, case studies in the field, findings from economic-behavioral experiments, common-pool resource dilemmas, and how these are all relevant to these questions surrounding the best way to sustainably manage natural resources. There are many case studies within the field of social-ecological systems, but there are few large-N studies conducted in a methodologically rigorous manner. Frey does just this and takes readers step-by-step through the preparation of datasets like the CPR, NIIS, and IFRI. He also grounds his research through the development of an indicator system which operationalizes 24 individually-synthesized success factors that influence the management of natural resources. The book reveals the practical and operational uses of measuring ecological success in this way, showcasing various statistical and machine learning methods to develop highly predictive, robust, and empirically-sound models. Three different methods, multivariate linear regressions, random forests, and artificial neural networks are compared to achieve robust results. The book sheds new light on factors that have previously been investigated, allowing readers to build off of Frey''s system and use his methods to determine whether or not their way of managing natural resources will yield ecological success in practice.