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Updated: Mar 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Model confirmation in climate economics.

Antony Millner1, Thomas K J McDermott2

  • 1Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science, London WC2A 2AE, United Kingdom; a.millner@lse.ac.uk.

Proceedings of the National Academy of Sciences of the United States of America
|July 20, 2016
PubMed
Summary

Benefit-cost integrated assessment models (BC-IAMs) are crucial for climate policy but their economic assumptions need empirical validation. A hindcasting experiment revealed that a leading BC-IAM

Keywords:
climate policyeconomic growthintegrated assessmentmodel confirmationstructural uncertainty

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Area of Science:

  • Climate Science
  • Environmental Economics
  • Integrated Assessment Modeling

Background:

  • Benefit-cost integrated assessment models (BC-IAMs) couple climate and economic systems to evaluate greenhouse gas abatement strategies.
  • These models provide qualitative insights but are increasingly used for real-world policy selection, necessitating confidence in their quantitative outputs.
  • Confidence in climate models stems from empirical testing and confirmed physical principles, unlike the often untested economic components of BC-IAMs.

Purpose of the Study:

  • To assess the empirical validity of economic components within BC-IAMs by applying methods similar to climate science model confirmation.
  • To investigate the potential benefits of model confirmation exercises for enhancing the reliability of BC-IAMs for policy applications.

Main Methods:

  • Conducted a long-run hindcasting experiment using a leading BC-IAM.
  • Focused on validating the model's representation of long-run economic growth, a critical component.

Main Results:

  • The hindcasting experiment demonstrated that the BC-IAM's model of long-run economic growth exhibited questionable predictive power over the 20th century.
  • This highlights a potential weakness in the empirical validity of key economic assumptions within BC-IAMs.

Conclusions:

  • Model confirmation exercises, particularly hindcasting, are valuable for building confidence in the economic components of BC-IAMs.
  • Refinement of economic components in BC-IAMs is necessary to improve their reliability for informing climate policy selection.