The most interesting issues in environmental and resource economics have an explicit temporal dimension, since variables of interest such as pollutants, greenhouse gases, biomass of biological resources, or the stocks of fossil fuels accumulate in the ambient environment or are depleted through human actions and natural processes.The purpose of these lectures is to present the mathematical tools for analyzing environmental and resource management issues in a dynamic set-up.The lectures include a brief description of differential equations and then move on to describe methods of optimal control, dynamic programing, and differential games. The final chapters cover two novel topics: (1) environmental issues characterized by deep uncertainty, and aversion to ambiguity using robust control methods and formulations of precautionary policies; and (2) the study of pollution/resource management in space and time when the environmental variables evolve in time and diffuse in space.The lectures are a valuable tool for advanced graduate students in environmental and resource economics who are studying dynamic problems.

Mathematical analysis is key to the modeling and management of natural resources. By presenting required mathematical methods, classic dynamic models for non-renewable and renewable resources, and by exploring several contemporary problems, this text provides a foundation for advanced research. Topics include seminal models in fishery, forestry and non-renewable resource management, as well as an extensive collection of contemporary applications that include the optimal transition from fossil fuels to clean energy, the optimal timing of interventions to save endangered species, pest control and the optimal management of antibiotic resistance. Deterministic and stochastic models in both discrete and continuous time are covered. The book encourages students to pursue a deeper understanding of the analytics of resource problems and to deploy numerical methods when analytical results prove intractable. The combination of analysis, theory and applications will launch the next generation of resource economists, while serving as a useful reference for established researchers.

Mathematical Programming Models for Agriculture, Environmental, and Resource Economics provides a comprehensive overview of mathematical programming models and their applications to real world and important problems confronting agricultural, environmental, and resource economists. Unlike most mathematical programming books, the principal focus of this text is on applications of these techniques and models to the fields of agricultural, environmental, and resource economics. The three fundamental goals of the book are to provide the reader with: (1) a level of background sufficient to apply mathematical programming techniques to real world policy and business to conduct solid research and analysis, (2) a variety of applications of mathematical programming to important problems in the areas of agricultural, environmental, and resource economics, and (3) a firm foundation for preparation to more advanced, Ph.D. level books on linear and/or nonlinear programming. Despite its introductory nature, the text places significant emphasis on real world applications of mathematical programming to decision problems. A wide array of examples and case studies are used to convey the various programming techniques available to decision analysts.

Key Features:First book of its kind in the fieldExamines and analyzes how key tools are used to conduct theoretical and empirical research in natural resource and environmental economics in contemporary timesCompiles various articles and accounts concerned with the relevant pedagogical discussionWritten by recognized experts and prominent international researchers in the field.

Simulation methods are revolutionizing the practice of applied economic analysis. In this book, leading researchers from around the world discuss interpretation issues, similarities and differences across alternative models, and propose practical solutions for the choice of the model and programming. Case studies show the practical use and the results brought forth by the different methods.

This book presents the state of the art in the relatively new field of dynamic economic modelling with regime switches. The contributions, written by prominent scholars in the field, focus on dynamic decision problems with regime changes in underlying dynamics or objectives. Such changes can be externally driven or internally induced by decisions. Utilising the most advanced mathematical methods in optimal control and dynamic game theory, the authors address a broad range of topics, including capital accumulation, innovations, financial decisions, population economics, environmental and resource economics, institutional change and the dynamics of addiction. Given its scope, the book will appeal to all scholars interested in mathematical and quantitative economics.

Economic efficiency analysis has received considerable worldwide attention in the last few decades, with Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) establishing themselves as the two dominant approaches in the literature. This book, by combining cutting-edge theoretical research on DEA and SFA with attractive real-world applications, offers a valuable asset for professors, students, researchers, and professionals working in all branches of economic efficiency analysis, as well as those concerned with the corresponding economic policies. The book is divided into three parts, the first of which is devoted to basic concepts, making the content self-contained. The second is devoted to DEA, and the third to SFA. The topics covered in Part 2 range from stochastic DEA to multidirectional dynamic inefficiency analysis, including directional distance functions, the elimination and choice translating algorithm, benefit-of-the-doubt composite indicators, and internal benchmarking for efficiency evaluations. Part 3 also includes exciting and cutting-edge theoretical research on e.g. robustness, nonparametric stochastic frontier models, hierarchical panel data models, and estimation methods like corrected ordinary least squares and maximum entropy.