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NIMBioS Working Group:

Ecosystem Federalism

Topic: Ecosystem Federalism: Building theoretical and data-analytic foundations for designing adaptive regulatory institutions for the provision of ecosystem services

Meeting dates: June 28-30, 2017

Organizers:

  • Julie Blackwood, Mathematics and Statistics, Williams College, Williamstown, MA
  • Charles Sims, Economics, Univ. of Tennessee; Howard H. Baker Jr. Center for Public Policy, Knoxville

Objectives: The characterization of problems and solutions in managed ecosystems is often guided by the current allocation of regulatory authority between different levels of government. For instance, in the United States, it is common for federal agencies to set minimum standards for ecosystem protection while states and local governments are charged with identifying the policy or set of policies that will best meet those standards. This group will explore mathematical, control/optimization, game theoretic, and econometric frameworks and tools that can be used to determine how regulatory authority over ecological outcomes should be allocated between a central (federal) government and local (state, municipal) governments. Our goal is to develop novel mathematical, statistical, and computational methods that incorporate optimal strategic, adaptive management of ecosystem services at multiple interacting levels of government. The group is co-sponsored and partially supported by the Howard Baker Center for Public Policy at the University of Tennessee.

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Meeting Summaries


Mtg # Dates Agenda Summary Photo Evaluation
1 Jun 28-30, 2017 pdf Link Link Report

Meeting 1 Summary. This working group spent the first day familiarizing the participants with the concept of environmental federalism that originated from the environmental economics literature. Through discussion of the pros and cons of centralized and local management, some terminology and motivation for various arguments were introduced. We also discussed how the environmental federalism could be expanded to consider the appropriate level of regulatory authority for the conservation of ecosystems and new arguments that may arise in the context of managing ecological systems that were absent in the existing literature on pollution control. Two short kickoff talks related to spatial-dynamic modeling and adaptive management were given to address two of the most prominent arguments for and against environmental management at the federal level. The group then formed two breakout groups to discuss the role of dispersal and learning (gaining knowledge of some parts of the system over time) in the environmental federalism debate. Simple mathematical models were developed to address these arguments as well as a series of modeling scenarios that could be used to analyze the validity of arguments for and against centralized environmental management. The meeting concluded with a discussion of possible case studies and modeling frameworks that would allow for more integration of data and empirical evidence.


Group photo.
Meeting 1 participants (L to R): Michael Neubert, Julie Blackwood, Abdul-Aziz Yakubu, Mona Papes, Ben Fitzpatrick, Suzanne Lenhart, David Kling, Jim Sanchirico, Bistra Dilkina, Paul Fackler, Charles Sims. Not pictured: Cynthia Lin Lawell, Katriona Shea, Michael Springborn

 

NIMBioS Working Groups are chosen to focus on major scientific questions at the interface between biology and mathematics. NIMBioS is particularly interested in questions that integrate diverse fields, require synthesis at multiple scales, and/or make use of or require development of new mathematical/computational approaches. NIMBioS Working Groups are relatively small (up to 10 participants), focus on a well-defined topic, and have well-defined goals and metrics of success. Working Groups will meet up to 3 times over a two-year period, with each meeting lasting up to 2.5 days.

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NSF logo. NIMBioS is sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
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