David Keith

David Keith has worked near the interface between climate science, energy technology, and public policy for twenty years. He is currently the Gordon McKay Professor of Applied Physics in the School of Engineering and Applied Sciences (SEAS) at Harvard University and Professor of Public Policy at the Harvard Kennedy School.

  • A Case for Climate Engineering

    A Case for Climate Engineering

    David Keith

    A leading scientist argues that we must consider deploying climate engineering technology to slow the pace of global warming.

    Climate engineering—which could slow the pace of global warming by injecting reflective particles into the upper atmosphere—has emerged in recent years as an extremely controversial technology. And for good reason: it carries unknown risks and it may undermine commitments to conserving energy. Some critics also view it as an immoral human breach of the natural world. The latter objection, David Keith argues in A Scientist's Case for Climate Engineering, is groundless; we have been using technology to alter our environment for years. But he agrees that there are large issues at stake.

    A leading scientist long concerned about climate change, Keith offers no naïve proposal for an easy fix to what is perhaps the most challenging question of our time; climate engineering is no silver bullet. But he argues that after decades during which very little progress has been made in reducing carbon emissions we must put this technology on the table and consider it responsibly. That doesn't mean we will deploy it, and it doesn't mean that we can abandon efforts to reduce greenhouse gas emissions. But we must understand fully what research needs to be done and how the technology might be designed and used. This book provides a clear and accessible overview of what the costs and risks might be, and how climate engineering might fit into a larger program for managing climate change.


  • Analytical Methods for Dynamic Modelers

    Analytical Methods for Dynamic Modelers

    Hazhir Rahmandad, Rogelio Oliva, and Nathaniel D. Osgood

    A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples.

    Simulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox.

    The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises.

    Contributors Wenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gönenç Yücel