Show Summary Details

Page of

Printed from Oxford Research Encyclopedias, Environmental Science. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 26 July 2021

Environmental Economics and Uncertainty: Review and a Machine Learning Outlooklocked

Environmental Economics and Uncertainty: Review and a Machine Learning Outlooklocked

  • Ruda Zhang, Ruda ZhangUniversity of Southern California Viterbi School of Engineering Ringgold standard institution - Department of Civil and Environmental Engineering
  • Patrick Wingo, Patrick WingoNational Energy Technology Laboratory Albany, Oregon
  • Rodrigo Duran, Rodrigo DuranNational Energy Technology Laboratory and Theiss Research
  • Kelly Rose, Kelly RoseNational Energy Technology Laboratory Albany, Oregon
  • Jennifer BauerJennifer BauerNational Energy Technology Laboratory Albany, Oregon
  •  and Roger GhanemRoger GhanemUniversity of Southern California Viterbi School of Engineering Sonny Astani Department of Civil and Environmental Engineering

Summary

Economic assessment in environmental science means measuring and evaluating environmental impacts, adaptation, and vulnerability. Integrated assessment modeling (IAM) is a unifying framework of environmental economics, which attempts to combine key elements of physical, ecological, and socioeconomic systems. The first part of this article reviews the literature on the IAM framework: its components, relations between the components, and examples.

For such models to inform environmental decision-making, they must quantify the uncertainties associated with their estimates. Uncertainty characterization in integrated assessment varies by component models: uncertainties associated with mechanistic physical models are often assessed with an ensemble of simulations or Monte Carlo sampling, while uncertainties associated with impact models are evaluated by conjecture or econometric analysis. The second part of this article reviews the literature on uncertainty in integrated assessment, by type and by component.

Probabilistic learning on manifolds (PLoM) is a machine learning technique that constructs a joint probability model of all relevant variables, which may be concentrated on a low-dimensional geometric structure. Compared to traditional density estimation methods, PLoM is more efficient especially when the data are generated by a few latent variables. With the manifold-constrained joint probability model learned by PLoM from a small, initial sample, manifold sampling creates new samples for evaluating converged statistics, which helps answer policy-making questions from prediction, to response, and prevention. As a concrete example, this article reviews IAMs of offshore oil spills—which integrate environmental models, transport models, spill scenarios, and exposure metrics—and demonstrates the use of manifold sampling in assessing the risk of drilling in the Gulf of Mexico.

Subjects

  • Environmental Issues and Problems
  • Quantitative Analysis and Tools
  • Environmental Economics

You do not currently have access to this article

Login

Please login to access the full content.

Subscribe

Access to the full content requires a subscription