Purchase Print Copy

Add to Cart Paperback15 pages Free

The traditional framework for assessing alternative climate-changing policies, which shapes much climate-change policy research and informs the thinking of many of the most sophisticated policy-makers, rests on the assumption that we can predict the future. Climate change, however, presents a problem of deep uncertainty, where key aspects of the future remain unpredictable. Under such circumstances, policy-makers should seek strategies that are robust against a wide range of plausible scenarios. Such strategies are desirable because they would perform reasonably well, at least compared to the alternatives, even if confronted with surprises or catastrophes. Robust strategies may also provide a more solid basis for consensus on political action among stakeholders with different views of the future because it would provide reasonable outcomes no matter whose view proved correct. This paper describes novel analytic methods for finding robust strategies. These methods, called exploratory modeling, combine some of the best features of narrative scenario-based planning and quantitative decision analysis. The authors suggest that robust strategies for climate change are possible. In the near term, the key components of such strategies should include: establishing the physical and institutional capability to monitor the relevant climate and economic systems, establishing the capability to effectively regulate greenhouse gases, and encouraging the development and diffusion of new emissions-reducing technologies.

Originally published in: Climatic Change, v. 45, no. 1, April 2000, pp. 387-401.

This report is part of the RAND reprint series. The Reprint was a product of RAND from 1992 to 2011 that represented previously published journal articles, book chapters, and reports with the permission of the publisher. RAND reprints were formally reviewed in accordance with the publisher's editorial policy and compliant with RAND's rigorous quality assurance standards for quality and objectivity. For select current RAND journal articles, see External Publications.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.