ECG 590 – Dynamic Environmental & Resource Management
Distance Learning Course at North Carolina State University
Instructor: Paul L. Fackler
This course examines inter-temporal problems in environmental and resource economics, including management of renewable and non-renewable resources, the conservation of endangered species, the determination of optimal harvesting levels and the setting of harvest regulations, the management of pollution and the control of invasive pests. All of these issues involve important trade-offs between current and future costs and benefits. These trade-offs are examined by casting the problems as dynamic optimization problems. Model solutions will then be obtained and interpreted. Also examined are some of the thorny issues involved in economic analysis of these topics, including model uncertainty and discounting. Students can expect to gain an appreciation for the economic issues involved in inter-temporal allocation problems and to be able to formulate and analyze dynamic optimization models. Prerequisites for this course include basic college mathematics (linear algebra is highly recommended) and either intermediate micro-economics or familiarity with resource modelling in forestry, ecology, wildlife management or a related field. The course will use the MATLAB based package MDPSolve to obtain model solutions; computer programming tools used will be covered in the course though familiarity with some programming language would be beneficial. This course is designed to be of use to economics majors interested in environmental and resource economics as well as to students in biology, forestry and wildlife and natural resource management interested in modeling and decision analysis.
The course is conducted on-line with class participation via the internet.
For more information contact the instructor at firstname.lastname@example.org.
NCSU, UNC and Duke students can register for ECG590-002 through the TRACS system.
Other students should contact the instructor.
What does this course address?
Making good decisions often involves balancing tradeoffs between current and future costs and benefits. Polluting may result in lower cost goods today but can lead to large future health and/or clean-up costs. Harvesting a resource now might lead to higher current consumption but may leave less of the resource for future generations. Dynamic optimization models are designed to help balance such tradeoffs in order to make better decisions. In addition the process of developing models helps clarify what issues are involved and makes decisions more transparent. This class will develop the modelling tools needed to implement and analyze natural resource management strategies. Particular attention shall be placed on how different kinds of uncertainty can be addressed. Topics will include the conservation of endangered species, the determination of optimal harvesting levels and the setting of harvest regulations for fish and game animals, habitat management, the management of pollution and the control of invasive pests.
How will the course be conducted?
The course will use a combination of written and video lecture materials to present key concepts and examples. Furthermore computer code to implement all examples will be provided. Class time will focus real time demonstrations, elaboration and clarification of key principles and on collaborative development of models to address management problems posed by the instructor. The course will use a graphical rapid prototyping approach to initial model development. The goal will be to develop simple models during class sessions which can then be further refined outside of class and discussed during the next class session.
Who should take this course?
This course is designed to be of use to economics majors interested in environmental and resource economics as well as to students in applied ecology, biology, forestry and wildlife and natural resource management interested in modeling and decision analysis. Mathematical prerequisites for the course are minimal (no calculus is used). Students would benefit from either intermediate micro-economics and/or familiarity with resource modelling in forestry, ecology, wildlife management or a related field. In addition familiarity with basic concepts of probability will be very useful. The course will use the MATLAB based package MDPSolve to obtain model solutions; computer programming tools used will be covered in the course though some familiarity with computer programming would be beneficial. Students who are not sure of their level of preparation for this course should consult the instructor.
This course will examine inter-temporal problems involving the management of renewable and non-renewable resources. Examples include the conservation of endangered species, the determination of optimal harvesting levels and the setting of harvest regulations, the management of pollution and the control of invasive pests. All of these issues involve important trade-offs between current and future costs and benefits. These trade-offs will be examined by casting the problems as dynamic optimization problems. Model solutions will then be obtained and interpreted. We will also examine some of the thorny issues involved in economic analysis of these topics, including model uncertainty and discounting. Students can expect to gain an appreciation of inter-temporal allocation problems and to be able to formulate and analyze dynamic optimization models. This course is designed to be of use to economics majors interested in environmental and resource economics as well as to students in biology, forestry and wildlife and natural resource management interested in modeling and decision analysis.
This course will be offered online. Readings and taped lectures will be available at the course website. The class will meet online once a week for discussions and demonstrations (participation in the class meeting is mandatory). We will use web-based conference software for audio and computer connection (this will require a headset for student audio participation). Details will be supplied prior to the first class meeting.
Student Learning Outcomes
By the end of this course, students will be able to:
…….identify the components of Markov Decision Problems (MDPs)
…….formulate environmental and resource management problems as MDPs
…….be familiar with a range of problems that can be addressed as MDPs
…….understand how alternative kinds of uncertainty can be addressed
……. utilize appropriate software for solving MDPs
…….analyze and interpret model results
…….describe management plans to stakeholders
The course will make extensive use of a set of MATLAB procedures called MDPSolve which can be downloaded from https://sites.google.com/site/mdpsolve/ (instruction instructions are in the README file). You will need to have MATLAB installed on your computer to run MDPSolve.
Instructor biographical information
Paul L.Fackler is a professor of agricultural and resource economics and associate professor of applied ecology at North Carolina State University and an internationally recognized teacher and scholar in the areas of decision analysis and computational methods. He co-authored a widely used textbook on the use of computational methods (Applied Computational Economics and Finance) along with the CompEcon Toolbox, a package of computer programs used in both teaching and research. The main focus of his research currently is the application of dynamic optimization tools to problems involving the management of natural resources. He is also the developer of the MDPSolve package for solving dynamic optimization problems. Among his published work in the area of resource management are the following papers:
Katherine M. O’Donnell, Paul L. Fackler, Fred A. Johnson, Mathieu N. Bonneau, Julien Martin, Susan C. Walls. (2020). Category count models for adaptive management of metapopulations: Case study of an imperiled salamander. Conservation Science and Practice. https://doi.org/10.1111/csp2.180
Paul L. Fackler (2019). Gambling with extinction: Comments on Chauvenet et al. (2010). Ecological Applications. https://doi.org/10.1002/eap.1872
Jaime A. Collazo, Adam J. Terando, Augustin C. Engman, Paul F. Fackler & Thomas J. Kwak (2018) Toward a Resilience-Based Conservation Strategy for Wetlands in Puerto Rico: Meeting Challenges Posed by Environmental Change. Wetlands. https://doi.org/10.1007/s13157-018-1080-z
David R. Smith, Paul L. Fackler, Sheila M. Eyler, Laura Villegas Ortiz & Stuart A. Welsh. (2017) Optimization of Decision Rules for Hydroelectric Operation to Reduce Both Eel Mortality and Unnecessary Turbine Shutdown: A Search for a Win-Win Solution. River Research and Applications.
David Kling, James Sanchirico and Paul L. Fackler. (2017) Optimal monitoring and control under state uncertainty: application to lionfish management. Journal of Environmental Economics and Management.
Fred A. Johnson, Paul L. Fackler, G. Scott Boomer, Guthrie Zimmerman, Byron K. Williams, James D. Nichols and Robert M. Dorazio. (2016) State-Dependent Resource Harvesting with Lagged Information about System States. PLoS ONE.
Matthew J. MacLachlan, Michael R. Springborn and Paul L. Fackler. (2016) Learning about a moving target in resource management: Optimal Bayesian disease control. American Journal of Agricultural Economics.
Michele Baggio and Paul L. Fackler. (2016) Optimal management with reversible regime shifts. Journal of Economic Behavior and Organization.
Fackler PL, Pacifici K, Martin J, McIntyre C. (2014) Efficient Use of Information in Adaptive Management with an Application to Managing Recreation near Golden Eagle Nesting Sites. PLoS ONE.
Paul L. Fackler. (2014) Structural and Observational Uncertainty in Environmental and Natural Resource Management. International Review of Environmental and Resource Economics.
Fackler, Paul L. and Robert G. Haight. (2014) Monitoring as a Partially Observable Decision Problem. Resource and Energy Economics.
Paul L. Fackler and Krishna Pacifici. (2014) Addressing Structural and Observational Uncertainty in Resource Management. Journal of Environmental Management.
Lucile Marescot, Guillaume Chapron, Iadine Chadès, Paul L. Fackler, Christophe Duchamp, Eric Marboutin and Olivier Gimenez. (2013) Complex decisions made simple: a primer on stochastic dynamic programming. Methods in Ecology and Evolution.
Jaime A. Collazo, Paul L. Fackler, Krishna Pacifici, Thomas H. White Jr., Ivan Llerandi-Roman and Stephen J. Dinsmore. (2013) Optimal allocation of captive-reared Puerto Rican parrots: Decisions when divergent dynamics characterize managed populations. Journal of Wildlife Management.
Fackler, Paul L. (2012) Category count models for resource management. Methods in Ecology and Evolution.
Martin J, Fackler PL, Nichols JD, Lubow BL, Runge, MC, McIntyre CL, Lubow BL, McCluskie MC, Schmutz JA. (2011) Adaptive-Management Framework for Optimal Control of Hiking Near Golden Eagles Nests in Denali National Park. Conservation Biology.
Martin J, Nichols JD, Fackler PL, Lubow BL, Eaton MJ, Runge, MC, Stih BM, Langtimm CA. (2011) Structured decision making as a proactive approach to dealing with sea level rise in Florida. Climatic Change.