Economic risks of climate change an american prospectus

Foreword by Michael R Bloomberg, Henry M Paulson, and Thomas F Steyer ECONOMIC R SKS OF CL MATE CHANGE An American Prospectus TR E V O R H O U S E R, S O L O M O N H S I A N G, R O B E RT K O P P, A N D K ATE L A R S E N Contributions by Karen Fisher-Vanden, Michael Greenstone, Geoffrey Heal, Michael Oppenheimer, Nicholas Stern, and Bob Ward ECONOMIC RISKS OF CLIMATE CHANGE ECONOMIC RISKS OF CLIMATE CHANGE AN AMERICAN PROSPECTUS TREVOR HOUSER • SOLOMON HSIANG • ROBERT KOPP KATE LARSEN • MICHAEL DELGADO • AMIR JINA MICHAEL MASTRANDREA • SHASHANK MOHAN ROBERT MUIR-WOOD • D J RASMUSSEN JAMES RISING • PAUL WILSON With contributions from Karen Fisher-Vanden, Michael Greenstone, Geoffrey Heal, Michael Oppenheimer, Nicholas Stern, and Bob Ward AND A FOREWORD BY MICHAEL R BLOOMBERG, HENRY M PAULSON JR., AND THOMAS F STEYER Columbia University Press New York Columbia University Press Publishers Since 1893 New York Chichester, West Sussex cup.columbia.edu Copyright © 2015 Solomon Hsiang, Robert Kopp, and Rhodium Group All rights reserved Library of Congress Cataloging-in-Publication Data Houser, Trevor Economic risks of climate change : an American prospectus / Trevor Houser, Solomon Hsiang, Kate Larsen, Robert Kopp, D J Rasmussen, Michael Mastrandrea, Robert Muir-Wood, Paul Wilson, Amir Jina, James Rising, Michael Delgado, Shashank Mohan With contributions from Karen Fisher-Vanden, Michael Greenstone, Geoffrey Heal, Michael Oppenheimer, Nicholas Stern, and Bob Ward And a foreword by Michael R Bloomberg, Henry Paulson, and Tom Steyer pages cm Includes bibliographical references and index ISBN 978-0-231-17456-5 (cloth : alk paper) — ISBN 978-0-231-53955-5 (e-book) Climatic changes—Economic aspects—United States Climatic changes—Risk management—United States I Title QC903.2.U6 H68 2015 363.738'74—dc23 2014045703 Columbia University Press books are printed on permanent and durable acid-free paper This book is printed on paper with recycled content Printed in the United States of America c 10 Cover Design: Noah Arlow References to websites (URLs) were accurate at the time of writing Neither the author nor Columbia University Press is responsible for URLs that may have expired or changed since the manuscript was prepared CONTENTS PART PRICING CLIMATE RISK  119 Foreword vii Preface ix Acknowledgments xvii Opening Commentary by Geoffrey Heal INTRODUCTION  1 12 FROM IMPACTS TO ECONOMICS 13 DIRECT COSTS AND BENEFITS PART 1. AMERICA’S CLIMATE FUTURE 14 MACROECONOMIC EFFECTS  125  127  149 15 VALUING RISK AND INEQUALITY OF DAMAGES  153 Opening Commentary by Michael Oppenheimer WHAT WE KNOW PART UNQUANTIFIED IMPACTS  13 WHAT COMES NEXT  17 U.S CLIMATE PROJECTIONS Opening Commentary by Nicholas Stern and Bob Ward  23 16 WHAT WE MISS PART ASSESSING THE IMPACT OF AMERICA’S CHANGING CLIMATE  39 Opening Commentary by Michael Greenstone AN EVIDENCE-BASED APPROACH AGRICULTURE LABOR CRIME 17 WATER  165  171 18 FORESTS  177 19 TOURISM  183 20 NATIONAL SECURITY  45 MANAGEMENT  75  195 Opening Commentary by Karen Fisher-Vanden  85 10 ENERGY 21 MITIGATION  95 11 COASTAL COMMUNITIES  189 PART INSIGHTS FOR CLIMATE-RISK  51  67 HEALTH  159  105 22 ADAPTATION  201  209 VI CONTENTS TECHNICAL APPENDIXES Appendix A Physical Climate Projections  219 Appendix B Climate Impacts  249 Appendix C Detailed Sectoral Models  281 Appendix D Integrated Economic Analysis  295 Appendix E Valuing Risk and Unequal Impacts  327 References 329 About the Authors Index 351 349 FOREWORD MICHAEL R BLOOMBERG, HENRY M PAULSON JR., AND THOMAS F STEYER COCH A I RS , RI S K Y B U S I N E S S P RO J E CT H much economic risk does the United States face from climate change? The answer has profound implications for the future of our economy and the American way of life But until recently there was no systematic, analytically rigorous effort to identify, measure, and communicate these risks It was the looming, unknown scale of these risks that led us to launch the Risky Business Project in summer 2013 and to commission the research that became the American Climate Prospectus report, published here in its entirety as Economic Risks of Climate Change: An American Prospectus Our aim is to quantify the economic risks of climate change to the U.S economy and then communicate these risks to the business sector In applying a standard risk-assessment approach to future climate impacts, this research provides specific, local, and actionable data for businesses and investors in both the public and private sectors We hope its findings help spur an active, rigorous conversation among economists, business executives, investors, and public-policy makers about how best to manage these risks, including taking prudent action to prevent them from spiraling out of control OW Over the years, the scientific data have made it increasingly clear that a changing climate, driven by carbon pollution from human activities, will lead to overall global warming These rising temperatures in turn lead to specific and measurable impacts such as sea-level rise, melting ice and glaciers, and more observable weather events such as droughts, wildfires, coastal and inland floods, and storms But, until recently, scant analytical work has been done to connect these broad climate changes to the daily workings of our economy In our view, the significant and persistent gap between the fields of climate science and economics makes businesses, investors, and public-sector decision makers dangerously vulnerable to long-term and unmanageable risks How can we make wise financial decisions without understanding our exposure to such risks as severe floods or prolonged drought or storm surge? How can we plan for and build new, more resilient infrastructure and manage our limited public resources responsibly without taking into account the probable changes to our coastlines, our agricultural lands, and our major population centers? These were the questions that led to the formation of the Risky Business Project We knew from the outset that, to VIII FOREWORD be effective, the project must be grounded in the same sort of rigorous analytical framework typically used by investors and business leaders in other areas of risk management The American business community has been slow to assess and address climate risk in part because of a lack of actionable data Without these data, businesses cannot create risk-assessment models that effectively capture the potential impact of climate change So it’s no surprise that most corporate risk committees, even in industries and sectors at significant risk of climate-driven disruption, not consistently include climate risk in their disclosures to investors or overall management priorities The success of our efforts was dependent on our ability to point business leaders toward exactly the kind of pathbreaking analysis contained in this book To be credible, the research had to be methodologically unassailable and strictly independent To be useful, the data it produced had to be detailed, relevant, and highly localized—what climate modelers call “downscaled”—in a way that would allow businesses to incorporate it into their existing riskmanagement protocols and strategies The Risky Business Project and this book are critical first steps toward this goal The study does not tackle the entire U.S economy but instead focuses on a few important sectors (agriculture, energy demand, coastal property, health, and labor) In examining how climate change will introduce new risks to these sectors, this research builds on the best available climate science and econometric research, reviewed by a panel of world-class scholars This work is also unusual—and unusually relevant to the business sector—in its level of detail and specificity to particular geographic regions In the following chapters, readers will find a nearly unprecedented level of geographic granularity Probable climate impacts have been modeled down to the county level, which is the scale at which many business decisions—such as crop planting and harvesting and real estate development—are actually made This level of geographic detail also underscores the broad regional disparities we can expect from climate change In a country as large and diverse as the United States, not all states or even counties will face the same type or level of risk Economy-wide studies, focused on Gross Domestic Product impact or national productivity, completely mask these disparities When we undertook this project, it was clear that simply quantifying the economic risks of climate change would not be enough The data needed to take a form that was meaningful within companies’ existing risk-assessment frameworks Thus, while this report is in many ways novel and groundbreaking, it’s also notable in that it makes use of the same risk-assessment approach that businesses and investors use on a daily basis In the wake of Hurricane Sandy, New York City created a comprehensive resilience blueprint  that measures climate risk across all major vulnerable areas, from the power grid to hospitals to the coastline We should not wait for a national disaster to create the same blueprint for the U.S economy as a whole We hope that this analysis is useful not only for the data it provides but also as a framework for a more effective dialogue among scientists, economists, and the business community—one that will provide decision makers with the information they need to decide how much climate risk they are comfortable taking on As we said in the October 2013 Washington Post op-ed that launched this entire effort: We believe the Risky Business Project and this book bring a critical missing piece to the national dialogue about climate change while helping business leaders and investors make smart, wellinformed, financially responsible decisions Ignoring the potential costs could be catastrophic—and that’s a risk we cannot afford to take PREFACE ROBERT KOPP, SOLOMON HSIANG, KATE LARSEN, AND TREVOR HOUSER H UMAN civilization is reshaping Earth’s surface, atmosphere, oceans—and climate In May 2013, at the peak of its seasonal cycle, the concentration of carbon dioxide (CO2) in the atmosphere spiked above 400 parts per million (ppm) for the first time in more than 800,000 years; within the next couple of years, it will exceed 400 ppm year-round This elevated CO2 concentration is the result of human activities—primarily the combustion of coal, oil, and natural gas and, secondarily, deforestation The physics linking increased concentrations of greenhouse gases like CO2 to higher global average temperatures has been known since the work of Joseph Fourier and Svante Arrhenius in the nineteenth century And as early as 1938, Guy Stewart Callendar provided evidence that an elevated CO2 concentration was, in fact, warming the planet By the early twenty-first century, the scientific evidence of human-caused warming (briefly summarized in chapter 2) was unequivocal It is equally certain that climate change will affect the economy and human well-being Quantifying these impacts and the value of avoiding them has, however, been a major challenge, because the climate, the economy, and their interface are all highly complex Modern economic analyses of climate change date to the pioneering works of William Nordhaus, William Cline, Samuel Fankhauser, and others in the early 1990s One central insight from this early work was that investing in heavy-emissions mitigation too early can carry substantial opportunity costs because investments elsewhere in the economy may yield larger returns However, subsequent work showed that accounting for uncertainty in climate damage could, when combined with risk aversion, motivate more rapid mitigation In 2007, Lord Nicholas Stern (co-commentator for part 4) led a groundbreaking analysis of the macroeconomic costs and benefits of climate-change policies The Stern Review and the dialogue it triggered clarified the critical role of social discount rates in economic evaluations of climate-change policies In 2010, the U.S government attempted to quantify the economic cost of climate change and benefits of mitigation In that year, a working group cochaired by Michael Greenstone (commentator for part 2) issued the U.S government’s first estimates of the social cost of carbon, which are used to integrate REFERENCES from Projected Climate Change Retrieved from www.energy ca.gov/2012publications/CEC-500–2012–057/CEC-500 –2012–057.pdf Sathaye, J A., Dale, L.  L., Larsen, P.  H., Fitts, G A., Koy, K., Lewis, S. M & de Lucena, A. F. P (2013) Estimating impacts of warming temperatures on California’s electricity system Global Environmental Change, 23(2), 499–511 doi:10.1016 /j.gloenvcha.2012.12.005 Schlenker, W & Roberts, M.  J (2009) Nonlinear temperature effects indicate severe damages to U.S crop yields under climate change APPENDIX Proceedings of the National Academy of Sciences USA, 106(37), 15594–98 doi:10.1073/pnas.0906865106 Schlenker, W., Roberts, M.  J & Lobell, D.  B (2013) Transformation is adaptation: US maize adaptability Nature Climate Change 3(8), 690–91 doi:10.1038/nclimate1959 Schneider, P & Hook, S. J (2010) Space observations of inland water bodies show rapid surface warming since 1985 Geophysical Research Letters, 37, doi:10.1029/2010GL045059 Scholes, R., Settele, J., Betts, R., Bunn, S., Leadley, P., Nepstad, D., . .  Taboada, M. A (2014) Terrestrial and inland water systems In C. B Field, V Barros, D. J Dokken, K. J Mach, M. D Mastrandrea, T. E Bilir, . .  L. L White (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability Contribution of Working Group II to the IPCC Fifth Assessment Report (p 153) Cambridge: Cambridge University Press Scott, D., Amelung, B., Becken, S., Ceron, J.-P., Dubois, G., Gossling, S., . .  Simpson, M. C (2008) Change and Tourism: Responding to Global Challenges (p 256) Madrid: World Tourism Organization Scott, D., McBoyle, G & Schwartzentruber, M (2004) Climate change and the distribution of climatic resources for tourism in North America 27, 105–117 Scott, D., McBoyle, G., Minogue, A & Mills, B (2006) Climate change and the sustainability of ski-based tourism in eastern North America: A reassessment Journal of Sustainable Tourism, 14(4), 376–98 doi:10.2167/jost550.0 SDG&E (2007) SDG&E Completes Service Restoration to All Fire-Impacted Customers San Diego: San Diego Gas & Electric Seager, R., Ting, M., Li, C., Naik, N., Cook, B., Nakamura, J & Liu, H (2013) Projections of declining surface-water availability for the southwestern United States Nature Climate Change, 3, 482–86 doi:10.1038/nclimate1787 Seki, O., Foster, G. L., Schmidt, D. N., Mackensen, A., Kawamura, K & Pancost, R. D (2010) Alkenone and boron-based Pliocene pCO2 records Earth and Planetary Science Letters, 292(1–2), 201–11 doi:10.1016/j.epsl.2010.01.037 Semenza, J.  C., McCullough, J.  E., Flanders, W.  D., McGeehin, M.  A & Lumpkin, J.  R (1999) Excess hospital admissions during the July 1995 heat wave in Chicago American Journal of Preventive Medicine, 16, 269–77 doi:10.1016/S0749 -3797(99)00025-2 343 Seneviratne, S.  I., Nicholls, N., Easterling, D., Goodess, C.  M., Kanae, S., Kossin, J., . .  M Reichstein, A Sorteberg, C Vera, & X Zhang (2012) Changes in climate extremes and their impacts on the natural physical environment In C.  B Field, V Barros, T. F Stocker, D Qin, D. J Dokken, K. L Ebi, . .  P M. M. M Tignor (Eds.), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) (pp 109–230) Cambridge: Cambridge University Press Seppanen, O., Fisk, W & Lei, Q (2006) Effect of Temperature on Task Performance in Office Environment Retrieved from http://escholarship.org/uc/item/45g4n3rv.pdf Shepherd, A., Ivins, E.  R., Geruo, A., Barletta, V.  R., Bentley, M. J., Bettadpur, S., . .  Zwally, H. J (2012) A reconciled estimate of ice-sheet mass balance Science, 338, 1183–89 doi:10.1126 /science.1228102 Sherwood, S. C & Huber, M (2010) An adaptability limit to climate change due to heat stress Proceedings of the National Academy of Sciences USA, 107(21), 9552–55 doi:10.1073/pnas.0913352107 Skaggs, R., Janetos, T., Hibbard, K & Rice, J (2012) Climate and Energy-Water-Land System Interactions Alexandia, Va.: National Technical Information Service Spracklen, D.  V., Mickley, L.  J., Logan, J A., Hudman, R.  C., Yevich, R., Flannigan, M.  D & Westerling, A L (2009) Impacts of climate change from 2000 to 2050 on wildfire activity and carbonaceous aerosol concentrations in the western United States Journal of Geophysical Research, 114(D20), D20301 doi:10.1029/2008JD010966 Stabenau, B. E., Engel, V., Sadle, J & Pearlstine, L (2011) Sealevel rise: Observations, impacts, and proactive measures in Everglades National Park Park Science, 28(2), 26–30 Stern, N (2013) The structure of economic modeling of the potential impacts of climate change: grafting gross underestimation of risk onto already narrow science models Journal of Economic Literature, 51(3), 838–59 St-Pierre, N., Cobanov, B & Schnitkey, G (2003) Economic losses from heat stress by US livestock industries Journal of Dairy Science, 86(31), E52–E77 doi:10.3168/jds.S0022-0302(03)74040-5 Strategic Environmental Research and Development Program (2013) Assessing impacts of climate change on coastal military installations: Policy implications (p 36) Retrieved from www.serdp.org/Featured-Initiatives/Climate-Change-and -Impacts-of-Sea-Level-Rise Strauss, B.  H., Ziemlinski, R., Weiss, J.  L & Overpeck, J.  T (2012) Tidally adjusted estimates of topographic vulnerability to sea level rise and flooding for the contiguous United States Environmental Research Letters 7, 014033 doi:10.1088/1748-9326/7/1/014033 Street, I., Bin, O., Poulter, B & Whitehead, J (2007) Measuring the Impacts of Climate Change on North Carolina 344 REFERENCES Coastal Resources Washington, D.C.: National Commission on Energy Policy Stroeve, J., Holland, M. M., Meier, W., Scambos, T & Serreze, M (2007) Arctic sea ice decline: Faster than forecast Geophysical Research Letters, 34, L09501 doi:10.1029/2007GL029703 Stroeve, J.  C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland, M & Meier, W.  N (2012) Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations Geophysical Research Letters, 39, L16502 doi:10.1029/2012GL052676 Tagaris, E., Manomaiphiboon, K., Liao, K.-J., Leung, L. R., Woo, J.-H., He, S., . .  Russell, A. G (2007) Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States Journal of Geophysical Research 112, D14312 doi:10.1029/2006JD008262 Tai, A P. K., Mickley, L. J & Jacob, D. J (2012) Impact of 2000– 2050 climate change on fine particulate matter (PM2.5) air quality inferred from a multi-model analysis of meteorological modes Atmospheric Chemistry and Physics, 12(23), 11329–37 doi:10.5194/acp-12–11329–2012 Taylor, K.  E., Stouffer, R.  J & Meehl, G.  A (2012) An overview of CMIP5 and the experiment design Bulletin of the American Meteorological Society, 93(4), 485–98 doi:10.1175 /BAMS-D-11-00094.1 Tebaldi, C & Knutti, R (2007) The use of the multi-model ensemble in probabilistic climate projections Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857), 2053–75 doi:10.1098/rsta.2007.2076 Tebaldi, C., Strauss, B. H & Zervas, C. E (2012) Modelling sea level rise impacts on storm surges along US coasts Environmental Research Letters, 7(1), 014032 doi:10.1088/1748–9326/7/1/014032 Titus, J & Richman, C (2001) Maps of lands vulnerable to sea level rise: Modeled elevations along the US Atlantic and Gulf coasts Climate Research 18(3), 205–28 doi:10.3354/cr018205 Tol, R. S. J (1995) The damage costs of climate change: Toward more comprehensive calculations Environmental and Resource Economics, 5(4), 353–74 doi:10.1007/BF00691574 Trenberth, K. E & Fasullo, J. T (2013) An apparent hiatus in global warming? Earth’s Future 1, 19–32 doi:10.1002/2013EF000165 Tu, J (2009) Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA Journal of Hydrology, 379(3–4), 268–83 doi:10.1016 /j.jhydrol.2009.10.009 Turney, C. S. M & Jones, R. T (2010) Does the Agulhas Current amplify global temperatures during super-interglacials? Journal of Quaternary Science, 25(6), 839–43 doi:10.1002/jqs.1423 University of New Orleans & LSU (2009) Louisiana Tourism Forecast: 2009–2013 U.S Bureau of Economic Statistics (2013) US Travel and Tourism Satellite Accounts for 2009–2012 Retrieved from http://www bea.gov/scb/pdf/2013/06%20June/0613_travel_and_tourism _text.pdf U.S Department of Agriculture (2010) 2007 Census of Agriculture: Farm and Ranch Irrigation Survey (2008) (vol 3, p 201) U.S Department of Agriculture (2013a) Crop Production 2012 Summary Retrieved from http://usda.mannlib.cornell.edu/usda /nass/CropProdSu//2010s/2013/CropProdSu-01-11-2013.pdf U.S Department of Agriculture (2013b) US Forest Products Retrieved from www.fs.fed.us/research/forest-products U.S Department of Defense (2010) Quadrennial Defense Review Report 2010 Retrieved from www.defense.gov/qdr/qdr as of 29jan10 1600.PDF U.S Department of Defense (2012) Base Structure Report Fiscal Year 2012 Baseline Washington, D.C U.S Department of Defense (2014) Quadrennial Defense Review Report 2014 Washington, D.C U.S Department of Defense Science Board (2011) Trends and Implications of Climate Change for National and International Security Retrieved from www.acq.osd.mil/dsb/reports /ADA552760.pdf U.S Department of Energy (2012a) Hurricane Sandy situation report #5, October 30, 2012 JAMA Neurology, 71 doi:10.1001 /jamaneurol.2013.4131 U.S Department of Energy (2012b) Petroleum Reserves Retrieved from http://energy.gov/fe/services/petroleum-reserves Washington, D.C U.S Department of Energy (2013a) Comparing the Impacts of Northeast Hurricanes on Energy Infrastructure Washington, D.C U.S Department of Energy (2013b) US Energy Sector Vulnerabilities to Climate Change and Extreme Weather Washington, D.C U.S Department of Homeland Security (2009) National Infrastructure Protection Plan: Partnering to Enhance Protection and Resiliency Washington, D.C U.S Department of the Interior (2012) Colorado River Basin Water Supply and Demand Study Washington, D.C U.S Energy Information Administration (2013) State Energy Data System 1960–2011 Retrieved from www.eia.gov/state/seds /seds-data-complete.cfm U.S Environmental Protection Agency (2008a) Clean Watersheds Needs Survey 2008 Report to Congress, 22–23 U.S Environmental Protection Agency (2008b) Review of the Impacts of Climate Variability and Change on Aeroallergens and Their Associated Effects Washington, D.C U.S Environmental Protection Agency (2009) Drinking Water Infrastructure Needs Survey and Assessment: Fourth Report to Congress Washington, D.C U.S Environmental Protection Agency (2010) Guidelines for Preparing Economic Analyses (vol 2010) Washington, D.C U.S Environmental Protection Agency (2012) Climate Change Indicators in the United States, 2012 Washington, D.C U.S Environmental Protection Agency (2013) Integrated Science Assessment for Ozone and Related Photochemical Oxidants Washington, D.C REFERENCES U.S Global Change Research Program (2009) Global Climate Change Impacts in the United States, ed T. R Karl, J. M Melilo & T. C Peterson New York: Cambridge University Press U.S Government Accountability Office (2006) Hurricane Katrina: Better Plans and Exercises Needed to Guide the Military’s Response to Catastrophic Natural Disasters Washington, D.C U.S National Research Council Committee on National Security Implications of Climate Change for Naval Forces (2011) National Security Implications of Climate Change for US Naval Forces Naval Forces National Research Council U.S Navy (2010) Navy Climate Change Roadmap Retrieved from www.navy.mil/navydata/documents/CCR.pdf U.S Nuclear Regulatory Commission (2012) Power Reactor Status Report for 2012 Retrieved from www.nrc.gov/reading-rm /doc-collections/event-status/reactor-status/2012/#August Van Mantgem, P. J., Stephenson, N. L., Byrne, J. C., Daniels, L. D., Franklin, J. F., Fulé, P. Z., . .  Veblen, T. T (2009) Widespread increase of tree mortality rates in the western United States Science, 323(5913), 521–24 doi:10.1126/science.1165000 Van Vliet, M. T. H., Ludwig, F., Zwolsman, J. J. G., Weedon, G. P & Kabat, P (2011) Global river temperatures and sensitivity to atmospheric warming and changes in river flow Water Resources Research, 47, 19 doi:10.1029/2010WR009198 Van Vliet, M.  T.  H., Yearsley, J.  R., Ludwig, F., Vögele, S., Lettenmaier, D. P & Kabat, P (2012) Vulnerability of US and European electricity supply to climate change Nature Climate Change, 2(9), 676–81 doi:10.1038/nclimate1546 Van Vuuren, D P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K.,      & Rose, S K (2011) The representative concentration pathways: an overview Climatic Change, 109, 5–31 doi:10.1007/s10584-011-0148-z Vecchi, G & Knutson, T (2008) On estimates of historical North Atlantic tropical cyclone activity Journal of Climate, 21(14), 3580–3600 doi:10.1175/2008JCLI2178.1 Vecchi, G & Knutson, T (2011) Estimating annual numbers of Atlantic hurricanes missing from the HURDAT database (1878–1965) using ship track density Journal of Climate, 24(6), 1736–46 doi:10.1175/2010JCLI3810.1 Vecchi, G. A & Soden, B. J (2007) Increased tropical Atlantic wind shear in model projections of global warming Geophysical Research Letters 34, L08702 doi:10.1029/2006GL028905 Villarini, G., Smith, J. A., Baeck, M. L., Vitolo, R., Stephenson, D.  B & Krajewski, W.  F (2011) On the frequency of heavy rainfall for the Midwest of the United States Journal of Hydrology, 400, 103–20 doi:10.1016/j.jhydrol.2011.01.027 Vine, E (2008) Adaptation of California’s Electricity Sector to Climate Change San Francisco: Public Policy Institute of California Viscusi, W. K & Aldy, J. E (2003) The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World (No 9487) Cambridge, Mass 345 Vissing-Jørgensen, A & Attanasio, O.  P (2003) Stock-market participation, intertemporal substitution, and risk-aversion American Economic Review, 93(2), 383–91 Vose, J., Peterson, D & Patel-Weynand, T (2012) Effects of Climatic Variability and Change on Forest Ecosystems: A Comprehensive Science Synthesis for the US Forest Sector (vol PNW-GTR-87, p 282) Portland, Ore Vose, R. S., Applequist, S., Menne, M. J., Williams, C. N & Thorne, P (2012) An intercomparison of temperature trends in the US Historical Climatology Network and recent atmospheric reanalyses Geophysical Research Letters, 39, doi:10.1029/2012GL051387 Vrij, A., Van der Steen, J & Koppelaar, L (1994) Aggression of police officers as a function of temperature: An experiment with the fire arms training system Journal of Community & Applied Social Psychology, 4, 365–370 Wada, Y., Wisser, D., Eisner, S., Flörke, M., Gerten, D., Haddeland, I., . .  Schewe, J (2013) Multimodel projections and uncertainties of irrigation water demand under climate change Geophysical Research Letters, 40, 1–7 doi:10.1002/grl.50686 Walsh, J., Wuebbles, D., Hayhoe, K., Kossin, J., Kunkel, K., Stephens, G., . .  Somerville, R (2014) Our changing climate In J. M Melillo, T C Richmond & G. W Yohe (Eds.), Climate Change Impacts in the United States: The Third National Climate Assessment (pp 19–67) Washington, D.C.: US Global Change Research Program Retrieved from http://nca2014.globalchange.gov/report/our-changing-climate/introduction Walthall, C. L., Hatfield, J., Backlund, P., Lengnick, L., Marshall, E., Walsh, M.,… Ziska, L.  H (2012) Climate Change and Agriculture in the United States: Effects and Adaptation Washington, D.C Retrieved from www.ars.usda.gov/is/br /CCAgricultureReport02-04-2013.pdf Walthall, C. L., Hatfield, J., Backlund, P., Lengnick, L., Marshall, E., Walsh, M., . .  L. H. Z (2013) Climate Change and Agriculture in the United States: Effects and Adaptation USDA Technical Bulletin, 1935, 186 Waring, K.  M., Reboletti, D.  M., Mork, L A., Huang, C.-H., Hofstetter, R. W., Garcia, A M., . .  Davis, T. S (2009) Modeling the impacts of two bark beetle species under a warming climate in the southwestern USA: Ecological and economic consequences Environmental Management, 44(4), 824–35 doi:10.1007/s00267-009-9342-4 Warren, R (2011) The role of interactions in a world implementing adaptation and mitigation solutions to climate change Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1934), 217–41 doi:10.1098 /rsta.2010.0271 Wayne, P., Foster, S., Connolly, J., Bazzaz, F & Epstein, P (2002) Production of allergenic pollen by ragweed (Ambrosia artemisiifolia L.) is increased in CO2-enriched atmospheres Annals of Allergy, Asthma & Immunology, 88(3), 279–82 doi:10.1016 /S1081-1206(10)62009-1 346 REFERENCES Wehner, M.  F (2012) Very extreme seasonal precipitation in the NARCCAP ensemble: Model performance and projections Climate Dynamics, 40, 59–80 doi:10.1007/s00382 -012-1393-1 Wehner, M., Easterling, D.  R., Lawrimore, J.  H., Heim, R.  R., Vose, R. S & Santer, B. D (2011) Projections of future drought in the continental United States and Mexico Journal of Hydrometeorology, 12, 1359–77 doi:10.1175/2011JHM1351.1 Weitzman, M L (2009) On modeling and interpreting the economics of catastrophic climate change Review of Economics and Statistics, 91(1), 1–19 Westerling, A & Swetnam, T.  W (2003) Interannual to decadal drought and wildfire in the western United States Eos, Transactions American Geophysical Union, 84(49), 545 doi:10.1029/2003EO490001 Westerling, A. L & Bryant, B. P (2008) Climate change and wildfire in California Climatic Change, 87(S1), 231–49 doi:10.1007 /s10584-007-9363-z Westerling, A., Hidalgo, H. G., Cayan, D. R & Swetnam, T. W (2006) Warming and earlier spring increase western US forest wildfire activity Science, 313(5789), 940–43 doi:10.1126 /science.1128834 Westerling, A., Turner, M.  G., Smithwick, E A H., Romme, W. H & Ryan, M. G (2011) Continued warming could transform Greater Yellowstone fire regimes by mid-21st century Proceedings of the National Academy of Sciences USA, 108(32), 13165–70 doi:10.1073/pnas.1110199108 Weyant, J & Kriegler, E (2014) Preface and introduction to EMF 27 Climatic Change, 123(3–4) White, M.  C., Etzel, R.  A., Wilcox, W.  D & Lloyd, C (1994) Exacerbations of childhood asthma and ozone pollution in Atlanta Environmental Research, 65, 56–68 doi:S0013S9351(84)71021-8 [pii]\n10.1006/enrs.1994.1021 Wilbanks, T., Bilello, D., Schmalzer, D & Scott, M (2012a) Climate Change and Energy Supply and Use Washington, D.C.: U.S Department of Energy Wilbanks, T., Fernandez, S., Backus, G., Garcia, P., Jonietz, K., Savonis, M., . .  Toole, L (2012b) Climate Change and Infrastructure, Urban Systems, and Vulnerabilities Washington, D.C.: U.S Department of Energy Williams, A.  P., Allen, C.  D., Millar, C.  I., Swetnam, T.  W., Michaelsen, J., Still, C.  J & Leavitt, S.  W (2010) Forest responses to increasing aridity and warmth in the southwestern United States Proceedings of the National Academy of Sciences USA, 107(50), 21289–94 doi:10.1073/pnas.0914211107 Wilson, C. O & Weng, Q (2011) Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River watershed, Chicago Metropolitan Statistical Area, Illinois Science of the Total Environment, 409, 4387–4405 doi:10.1016/j.scitotenv.2011.07.001 Wood, A.  W., Maurer, E.  P., Kumar, A & Lettenmaier, D.  P (2002) Long-range experimental hydrologic forecasting for the eastern United States Journal of Geophysical Research, 107(D20), 15 doi:10.1029/2001JD000659 Wright, J. D & Schaller, M. F (2013) Evidence for a rapid release of carbon at the Paleocene-Eocene thermal maximum Proceedings of the National Academy of Sciences USA, 110(40), 15908–13 doi:10.1073/pnas.1309188110 Wu, X., Brady, J. E., Rosenberg, H & Li, G (2014) Emergency department visits for heat stroke in the United States, 2009 and 2010 Injury Epidemiology, 1(8), 1–8 doi:10.1186/2197-1714-1-8 Wuebbles, D., Meehl, G., Hayhoe, K., Karl, T.  R., Kunkel, K., Santer, B.,      Sun, L (2013) CMIP5 climate model analyses: Climate extremes in the United States Bulletin of the American Meteorological Society 95, 571–83 doi:10.1175 /BAMS-D-12-00172.1 Wyon, D (2000) Thermal effects on performance In J Spengler, J. M Samet & J. F McCarthy (Eds.), Indoor Air Quality Handbook (pp 16.11–16.16) New York: McGraw-Hill Yancheva, G., Nowaczyk, N. R., Mingram, J., Dulski, P., Schettler, G., Negendank, J. F. W., . .  Haug, G. H (2007) Influence of the intertropical convergence zone on the East Asian monsoon Nature, 445(7123), 74–77 doi:10.1038/nature05431 Yin, J & Goddard, P. B (2013) Oceanic control of sea level rise patterns along the East Coast of the United States Geophysical Research Letters, 40, 5514–20 doi:10.1002/2013GL057992 Yin, J., Schlesinger, M. E & Stouffer, R. J (2009) Model projections of rapid sea-level rise on the northeast coast of the United States Nature Geoscience, 2, 262–66 doi:10.1038/ngeo462 Yohe, G., Neumann, J., Marshall, P & Ameden, H (1996) The economic cost of greenhouse-induced sea-level rise for developed property in the United States Climatic Change 32(4), 387–410 doi:10.1007/BF00140353 Yohe, G & Tirpak, D (2008) A research agenda to improve economic estimates of the benefits of climate change policies Integrated Assessment Journal, 8(1), 1–17 Yohe, G. W (1990) The cost of not holding back the sea: Toward a national sample of economic vulnerability Coastal Management, 18, 403–31 Yohe, G.  W & Schlesinger, M.  E (1998) Sea-level change: The expected economic cost of protection or abandonment in the United States Climatic Change, 38, 447–72 doi: 10.1023 /A:1005338413531 Youngblut, C (2009) Climate Change Effects: Issues for International and US National Security Retrieved from www.dtic.mil /cgi-bin/GetTRDoc?AD=ADA527880 Zachos, J.  C., Dickens, G.  R & Zeebe, R.  E (2008) An early Cenozoic perspective on greenhouse warming and carbon -cycle dynamics Nature, 451(7176), 279–83 doi:10.1038 /nature06588 REFERENCES Zanobetti, A., O’Neill, M. S., Gronlund, C. J & Schwartz, J. D (2012) Summer temperature variability and long-term survival among elderly people with chronic disease Proceedings of the National Academy of Sciences USA, 109(17), 6608–13 doi:10.1073 /pnas.1113070109 Zhang, D. D., Jim, C. Y., Lin, G. C.-S., He, Y.-Q., Wang, J. J & Lee, H.  F (2006) Climatic change, wars and dynastic cycles in China over the last millennium Climatic Change, 76(3–4), 459–77 doi:10.1007/s10584-005-9024-z Zhang, F & Georgakakos, A.  P (2012) Joint variable spatial downscaling Climatic Change, 111, 945–72 doi:10.1007 /s10584-011-0167-9 Ziska, L.  H (2008) Climate change, aerobiology, and public health in the Northeast United States Mitigation and Adaptation Strategies for Global Change, 13, 607–13 Ziska, L.  H (2010) Global climate change and carbon dioxide: Assessing weed biology and management In C and 347 D Rosenzweig & Hillel (Eds.), Handbook of Climate Change and Agro-Ecosystems: Impacts, Adaptation and Mitigation (pp 191–208) Hackensack, N.J.: World Scientific Publishing Ziska, L. H., Teasdale, J. R & Bunce, J. A (1999) Future atmospheric carbon dioxide may increase tolerance to glyphosate Weed Science, 608–615 doi:10.2307/4046118 Ziska, L., Knowlton, K., Rogers, C., Dalan, D., Tierney, N., Elder M.A., Filley, W., . . .& Frenz, D (2011) Recent warming by latitude associated with increased length of ragweed pollen season in central North America Proceedings of the National Academy of Sciences 108(10), 4248–4251 Zumbach, B., Misztal, I., Tsuruta, S., Holl, J., Herring, W., & Long, T (2007) Genetic correlations between two strains of Durocs and crossbreds from differing production environments for slaughter traits Journal of Animal Science, 85(4), 901–8 ABOUT THE AUTHORS PHYSICAL CLIMATE PROJECTIONS ECONOMETRIC RESEARCH Dr Robert Kopp is an associate professor of earth and planetary sciences and associate director of the Rutgers Energy Institute at Rutgers University His research focuses on understanding uncertainty in past and future climate change D J Rasmussen is a scientific programmer at the Rhodium Group with expertise in weather, climate, and air pollution Dr Michael Mastrandrea is an assistant consulting professor at the Stanford University Woods Institute for the Environment and Co-Director of Science for the Intergovernmental Panel on Climate Change (IPCC) Working Group II Technical Support Unit Dr Solomon Hsiang is an assistant professor of public policy at the University of California Berkeley’s Goldman School of Public Policy and a faculty research fellow at the National Bureau of Economic Research Dr Hsiang is at the forefront of using econometrics to understand the social impact of climate change Amir Jina is a postdoctoral scholar in Economics at the University of Chicago working on development and environmental economics James Rising is a doctoral candidate in sustainable development and modeling at Columbia University DETAILED SECTORAL MODELS INTEGRATED ECONOMIC ANALYSIS Dr Robert Muir-Wood is the chief research officer of the catastrophe risk management firm Risk Management Solutions, Inc Dr Paul Wilson is a senior director at Risk Management Solutions, Inc He is in the Risk Management Solutions (RMS) model development team, leading the ongoing development of the RMS North Atlantic Hurricane and Storm Surge Models Michael Delgado is a research analyst on the energy and natural resources team at the Rhodium Group Shashank Mohan is a director at the Rhodium Group and leads the development and management of the company’s suite of economic models and other quantitative tools 350 ABOUT THE AUTHORS PROJECT MANAGEMENT Kate Larsen is a director at the Rhodium Group and manages the firm’s work on domestic and international climate-change issues Trevor Houser is partner at the Rhodium Group, leading the firm’s energy and natural resources work and is a visiting fellow at the Peterson Institute for International Economics COMMENTATORS Dr Karen Fisher-Vanden is professor of environmental and resource economics at the Penn State College of Agricultural Sciences Dr Michael Greenstone is the Milton Friedman Professor of Economics at the University of Chicago and director of the interdisciplinary Energy Policy Institute at Chicago (EPIC) His other current positions and affiliations include elected member of the American Academy of Arts and Sciences, editor of the Journal of Political Economy, faculty director of the E2e Project, head of the JPAL Environment and Energy Program, co-director of the International Growth Centre’s Energy Research Programme, and nonresident senior fellow in economic studies at the Brookings Institution.  Dr Geoffrey Heal is the Donald C Waite III Professor of Social Enterprise at Columbia Business School He is a fellow of the Econometric Society, past president of the Association of Environmental and Resource Economists, recipient of its prize for publications of enduring quality and a life fellow, a director of the Union of Concerned Scientists, and a founder and director of the Coalition for Rainforest Nations Dr Michael Oppenheimer is the Albert G Milbank Professor of Geosciences and International Affairs in the Woodrow Wilson School and the Department of Geosciences at Princeton University He is the director of the Program in Science, Technology, and Environmental Policy (STEP) at the Woodrow Wilson School and faculty associate of the Atmospheric and Ocean Sciences Program, Princeton Environmental Institute, and the Princeton Institute for International and Regional Studies He is a member of the National Academies’ Board on Energy and Environmental Studies Lord Nicholas Stern is the IG Patel Professor of Economics and Government and chair of the Grantham Institute for Climate Change and the Environment at the London School of Economics He was knighted for services to economics in 2004 and became the 29th president of the British Academy in July 2013 Dr Bob Ward is the policy and communications director at the Grantham Institute for Climate Change and the Environment at the London School of Economics He is a fellow of the Geological Society FOREWORD AUTHORS Michael R Bloomberg is the founder of the global financialdata services and media company Bloomberg LP Between 2002 and 2013, he served as mayor of New York City and reduced the city’s carbon footprint by 19 percent, revitalized the waterfront, implemented ambitious public-health and antipoverty programs, expanded support for arts and culture, and increased graduation rates and private-sector job numbers to record highs As a philanthropist, Bloomberg has given more than $3.3 billion in support of education, public health, government innovation, the arts, and the environment In 2014, United Nations Secretary-General Ban Ki-Moon appointed Bloomberg special envoy for cities and climate change In that role, Bloomberg works to highlight the climate work cities are doing and the critical role mayors can play in helping nations create ambitious carbon-reduction commitments This builds on his role as president of the board of the C40 Climate Leadership Group, a network of megacities working to reduce global greenhouse-gas emissions Bloomberg also serves as a cochair of Risky Business, an organization that is quantifying the economic risks American businesses face from climate change Henry M Paulson Jr is a businessman, China expert, conservationist, and author He is chairman of the Paulson Institute, which works to strengthen U.S.-China relations, and cochairman of the Risky Business Project and the Latin American Conservation Council of the Nature Conservancy Paulson served as the 74th secretary of the Treasury under President George W Bush and, previously, was chairman and chief executive officer of Goldman Sachs He graduated from Harvard Business School and Dartmouth College Thomas F Steyer is an investor, philanthropist, and advancedenergy advocate Before retiring from the private sector, Steyer founded and was the senior managing member of Farallon Capital Management Steyer also founded several organizations aimed to accelerate the transition to an advanced-energy future, including Advanced Energy Economy, Center for the Next Generation, NextGen Climate, and Beneficial State Bank Steyer serves on Stanford’s board of trustees, where he and his wife founded the TomKat Center for Sustainable Energy and the Steyer-Taylor Center for Energy Policy and Finance INDEX Abler, David, 126 ACP See American Climate Prospectus adaptation: of agriculture, 54, 55, 210–11, 212, 266, 266; analysis shortcomings, 197–98; background on, 209–10; of coastal communities, 116, 214–15; costs of, 210; crime reduction by, 88, 213–14, 214, 267, 270, 271–73; daily condition importance for, 47–48; of energy, 214; evidence-based approach and, 50; health and mortality, 79, 212–13, 213, 267, 268–70; impact functions for, 264–70, 266–73; labor productivity, 69, 212; livestock, 64–65; models, 198–99, 199; overview, xv, 50, 209–15; strategy types, 198, 199; technological development and, 308; water-related costs of, 175–76 age, mortality by, 79, 84, 260, 261, 314 agriculture: adaptation of, 54, 55, 210–11, 212, 265, 266, 266; air quality and, 62; background, 52–54; direct costs and benefits for, xiv, 128–29, 129, 130, 296; disease and, 62, 64, 84; drought and, 34–35, 174; economic importance of, 51–52; fertilization and, 56, 56–58, 57, 59–61; floods and, 53, 174; food prices and, 63–64, 122; impact categories in, 7; impact function extrapolation of, 276; IMPLAN subdivisions of, 314; imports, 63–64; inequality premiums for, 155; irrigation of, 61–62, 266; livestock and, 64–65; macroeconomic effects analysis of, 150–52, 151; mitigation benefits for, 203, 203; pests and, 62, 64, 84; RHGMUSE model on, 308; risk-assessment approach to, 54–58; risk premiums for, 154–55, 155; sectoral aggregation of, 314; storage, 256; unquantified impacts of, 61–65; USDA, 35, 54, 177; water resources for, 53–54, 61–62, 174; weeds and, 62, 64 agriculture yields: CO2 increase and, 54, 55–58, 56, 57–61, 62, 260, 261; by doseresponse function, 55, 260; frequency change in, 60; global, 63–64; growing season increase and, 52; historical trends in, 52–54; impact function aggregation, 274, 274–75, 277; impact function application on, 259–62, 260, 261; impact projections, 54–58, 56–57, 59–61, 203; micro-founding impact function data on, 254, 255–56, 257; positive outcomes in, 52, 56; precipitation and, 52–54, 55, 55, 255, 256, 260, 261; by region, xiv, 58, 59, 130, 203; technological development and, 51, 54; temperature and, 52–54, 55, 55, 255, 256, 260, 261 air-conditioning: energy demand and, 96, 198, 214; mortality and, 78, 212–13 air quality: agriculture and, 62; health, mortality and, 82–84, 163 Alabama: coastal damage projections in, 107, 109, 113; direct costs and benefits in, 129–48 Alaska: Arctic sea ice, 21, 191; humidity projections for, 232–33; oil and gas production in, 101; precipitation projections for, 229, 231–32; sea-level rise projections for, 36, 246; temperature projections for, 228; tree die-off in, 180 Aldy, J E., 132 allergens, 83–84 ambiguity aversion, 157 See also uncertainty amenity value, 122 American Climate Prospectus (ACP), x–xv, 298 See also risk-assessment approach 352 animals: fishing industry, 187; livestock, 64–65 Antarctic: temperature and CO2 trends, 14, 15; WAIS, 22, 35, 36, 107, 247 AR5 See Fifth Assessment Report Arctic: oil and gas production, 101; sea ice, 21, 191 See also Alaska Arizona: California compared to, 41; direct costs and benefits in, 129–48; energy supply in, 103; wildfires in, 103, 178 Arkansas: direct costs and benefits in, 129–48; vulnerability of, 42 Aroonruengsawat, A., 96 asthma, 83–84 Atlantic coast: energy supply from, 103; North Atlantic Hurricane Model, 4, 49, 106, 289–92; sea-level rise projections, 35–36, 37; storm projections, 37 Auffhammer, Max, 96 autumn, precipitation projections for, 34, 232 aversion See inequality aversion; risk aversion Barreca, A., 77–78, 258 Bayesian modeling, 250, 250–51 BEA See Bureau of Economic Analysis benefit-cost analysis See direct costs and benefits bin models, 253 Bloomberg, Michael R., vii–viii, x body temperature: humidity measurements and, 25; livestock, 64 Box, George, 169 Bureau of Economic Analysis (BEA), U.S., 296 Bureau of Reclamation, U.S., 173 California: agriculture in, 54, 174; Arizona compared to, 41; coastal damage projections in, 107, 109; direct costs and benefits in, 129–48; drought in, 174; energy demand econometrics in, 96; energy supply problems in, 102, 103; heat wave, 76; hydroelectric generation in, 102; Los Angeles, ozone pollution in, 82; sea-level rise projections for, 245; wildfires in, 83, 178, 181 Cambodia, 46 carbon dioxide (CO2): agriculture yields and, 54, 55–58, 56, 57–61, 62, 260, 261; current levels of, ix, 14, 15; equilibrium INDEX climate sensitivity and, 19, 220; ozone interaction with, 62; RCPs of, xii, 18, 18, 201–2; SCC shortcomings on, 42–43; transient climate response and, 19; trends in, 14, 15 cardiovascular problems, 82–83 Caribbean, 187 catastrophe property loss modeling See Risk Management Solutions CDR See consumption discount rate Centers for Disease Control and Prevention (CDC), 68, 77, 264 CGE See computable general equilibrium model Chinese dynasties, 46 Clay, K., 258 climate: amenity value of, 122; inertia, 202; response, transient, 19; sensitivity, equilibrium, 19, 220, 297, 306; tourist attraction to, 183 climate change: assessing risk of, vii–viii, ix–xv, 1–8; certainty of, ix, 13–14, 14, 15; global implications on U.S., 124; global physical uncertainty of, 19; historical trends in, 14, 15, 45–46; national security threat of, 189–93; natural variability in, 13, 14, 20–21; regional physical uncertainty of, 19–20; regional relevance of, 41–42; socioeconomic uncertainty of, 17–18, 18; tipping points of, 2, 21–22, 162 See also economics of climate change climate change impacts See impacts climate projections: drought, 34–35, 53; humidity, 25, 30, 31–32, 32, 33; humidity modeling, 219, 227, 232–33; overview, xii– xiv; precipitation, 33–34, 34, 229, 231–32, 237–39; precipitation modeling, 219–29, 221, 223–26; RCPs for, xii, 18, 18, 201–2; SEAGLAS component of, 2, 3, 4; sealevel rise, 35–36, 36, 37, 242–46; sea-level rise modeling, 106, 240, 240–41, 241, 247; storm, 36–38; temperature, 23, 24, 25, 26–29, 77, 228, 230, 234–36; temperature modeling, 219–29, 221, 223–26 Climate Risk Committee, x Cline, William, 125 cloud computing, CMIP5 See Coupled Model Intercomparison Project CO2 See carbon dioxide coal-fired power plants, 99–102, 174 coastal communities: adaptation of, 116, 214–15; background, 105–6; damage direct costs and benefits for, xiii–xiv, 138, 140, 140–41, 142, 143, 297; damage projections from SLR, 107–10, 107–12, 207; damage projections from storms, 110–16, 113–17, 207, 286–92; damage projections modeling, 105, 106, 286–92; econometric research on, 4, 49, 106; economic importance of, 105; energy supply from, 102–3; floods, 38; hurricane frequency and intensity and, 21, 36–38, 116, 117, 292; impact categories for, 7; macroeconomic effects analysis of, 150–52, 151; migration away from, 193; mitigation benefits for, 205–7, 207; North Atlantic Hurricane Model on, 4, 49, 106, 289–92; RHG-MUSE on, 310–12; riskassessment approach to, 4, 49, 106; sealevel rise and, 35–36, 37, 107–10, 107–12, 207; tourism future in, 187–88 cold days, extreme, 25, 29, 77 cold-related mortality: adaptation for, 267, 268–70; projections, 79, 80, 81 Colorado: direct costs and benefits in, 129–48; tourism in, 184 Colorado River, 173 computable general equilibrium (CGE) models, 297–98 See also Rhodium Group Model of the U.S Economy Connecticut: coastal damage projections in, 113; direct costs and benefits in, 129–48; nuclear power station in, 100 consumer surplus, 122 consumption discount rate (CDR), 123–24 coral reefs, 187–88 corn yields See maize yields cost-benefit analysis See direct costs and benefits costs: of adaptations, 210; of coastal damage, by SLR, 107, 107–8, 108, 109, 288–89; of coastal damage, by storms, 110–16, 113, 114, 116, 288–89; consumer surplus and, 122; of electricity transmission losses, 101; of emissions and health, 163; of flooding, 175; food prices, 63–64, 122; SCC, 42–43; of tree die-off, 181; underestimated, 122–24; unquantifiable, 121; of water challenges in agriculture, 174; water-related INDEX adaptation, 175–76; of wildfires, 178–79, 181 See also direct costs and benefits cotton yields: CO2 increase and, 56, 56–58, 57, 59–61, 260, 261; impact function aggregation, 275; impact function application on, 260, 261, 261; microfounding impact function data on, 255, 256; precipitation and, 260, 261; temperature and, 260, 261 Coupled Model Intercomparison Project (CMIP5): about, 20, 219; drought projections from, 34–35; models used, 221, 241; outputs, 220; pattern fitting by, 222, 223–26; precipitation projections from, 237–39; probability weighting, 222–27, 223–26; sea-level rise projections from, 240, 241; temperature projections from, 234–36 crime: adaptation to reduce, 88, 213–14, 214, 267, 270, 271–73; aggressive behavior and, 86, 87; background, 86–87; causes of, 85; direct costs and benefits for, xiv, 132, 136, 136, 137, 138, 297; dose-response for, 87, 260; economic importance of, 85–86; forward displacement of, 88; frequency change in, 93; heat stress evidence for, 86–87; impact function aggregation of, 275, 277; impact function application on, 260, 261, 262; impact function extrapolation of, 276, 278; impact projections, 88–93, 89–93, 205, 206; international civil conflict and, 190–91; macroeconomic effects analysis of, 150– 52, 151; micro-founding impact function data on, 255, 256–57; mitigation benefits for, 205, 206; opportunity costs of, 85; precipitation and, 87, 260, 261; prevention and persecution, 85, 92–93; rates, 136, 206; regional distribution of, xiv, 89–90, 91–92; retaliatory, 86; risk-assessment approach to, 87–93; temperature and, 86–87, 87, 89–92, 260, 261 daily conditions: annual averages compared to, 47; importance of, 47–48 daily temperature: extremes, projections of, 25, 27, 29; modeling values for, 227–28 dairy production, 64–65 death See mortality Delaware: coastal damage projections in, 107, 109, 111; direct costs and benefits in, 129–48; sea-level rise projections for, 243 Department of Defense (DOD), U.S., 163, 168, 189–90, 191–92 Department of Homeland Security, U.S., 192 Department of the Interior, U.S., 178 Deschênes, Olivier, 77–78, 96, 258 detailed sectoral models, 3, 4, 49, 281–92 direct costs and benefits: for agriculture, xiv, 128–29, 129, 130, 296; assumptions and inputs, 127–28, 295–97; for coastal communities, xiii–xiv, 138, 140, 140–41, 142, 143, 297; for crime, xiv, 132, 136, 136, 137, 138, 297; discount rate of, 123; for energy, xiii, 138, 138, 139, 297; IAMs for, 125–26; integrated economic analysis of, 125–26, 295–97, 314; for labor, xiii, 130–32, 131, 133, 296; for mortality, xiii, 132, 134–35, 146–48, 296; overview by impact category, xii–xiv; as share of economic output, 145; as share of GDP, xii–xiv, 141, 144; by state, 129–48; summary, 141, 144–48 discount rate, 123 discrete-discrete probability models, 251 disease: agriculture and, 62, 64, 84; cardiovascular, 82–83; labor and, 72–74; mortality and, 84; precipitation and, 64, 84; respiratory, 72, 82–83; tree die-off from, 180–81; vector-borne, 73–74, 84, 185; waterborne, 84 Distributed Meta-Analysis System (DMAS), xi, xv, 251, 252, 253 DOD See Department of Defense dose-response function: for agriculture yields, 55, 260; all results of, 260; for crime, 87, 260; for energy demand, 96; importance and use of, 47–48; for labor productivity, 69, 260; for mortality, 77, 260; out-of-sample extrapolation of, 168 downscaling models, 20 drought: agriculture and, 34–35, 174; energy supply outage from, 102; extinction from, 46; historical, 35, 46, 51, 53; projections, 34–35, 53; wildfires from, 103 Dust Bowl, 51 Earth’s orbit, 14, 36 econometric research: on coastal communities, 4, 49, 106; dose-response function in, 47–48; on energy, 49, 95–97; error potential and, 46–47; existing 353 sectoral models of, 3, 4, 49; impacts without, 49–50; limitations of, 49; overview, 3, 4; studies chosen for, 48–49 economic importance: of agriculture, 51–52; of coastal communities, 105; of crime, 85–86; of energy, 95; of health, 75–76; of labor productivity, 67 economics of climate change: current shortcomings of, 42–43; empirical estimates of, 46–49; historical development of, ix–x, 42–43, 125–26; impact categories on, xi, 7, 167; knowledge gap in, x, 11, 121–24; tipping points of, 2, 21–22, 162; underestimates in, 122–24, 162–63; unquantifiable nature of, 121 See also costs; direct costs and benefits economy modeling See Rhodium Group Model of the U.S Economy EIA See Energy Information Administration electricity: hydroelectric generation, 99–102, 174–75; production, 99; temperature and demand for, 96, 260, 261; transmission loss, 101 See also energy; energy demand; energy supply Electric Power Research Institute, 102 Emanuel, Kerry, 37, 38, 106, 116 emissions: greenhouse effect, 13, 14; methane destabilization, 22; ozone pollution, 62, 82–83; RCPs of, xii, 18, 18, 201–2; underestimation of, 163; wildfire, 83 See also carbon dioxide emissions reduction: opportunity costs of, ix; SCC shortcomings for, 42–43; socioeconomic uncertainty of, 17–18, 42–43; strategies, 198, 199 See also mitigation energy: adaptation for, 214; Annual Energy Outlook, 4, 49, 96–97, 282; background, 95; coal-fired, 99–101, 102; cost projections, 96, 99, 100; economic importance of, 95; IMPLAN subdivisions of, 315; macroeconomic effects analysis of, 150–52, 151; mitigation benefits for, 205, 206; natural gas, 99, 102; nuclear, 99–102; risk-assessment approach to, 4, 49, 95–99; socioeconomic uncertainty in, 17–18; supply demand conversion flow, 282; types assessed, 96 See also Rhodium Group National Energy Modeling System 354 energy demand: air-conditioning and, 96, 198, 214; direct costs and benefits of, xiii, 138, 138, 139, 297; dose-response for, 96; econometric research on, 49, 95–97; flow with supply and conversion, 282; impact projections, 97, 97–99, 98, 205, 206; models, 4, 49, 96–97, 281–86; national changes in, 97; regional distribution of, 97, 98, 138, 139, 140, 205, 206, 284, 285; RHG-MUSE for, 310 Energy Information Administration (EIA), U.S., 4, 49, 96–97, 282 energy supply: flow with demand and conversion, 282; impact projections, 99–103; oil and gas production, 101, 102; positive outcomes in, 101; power outages, 101, 102, 103; regional distribution of, 102–3; sea-level rise and, 102–3; storms and, 102–3; thermal generation efficiency and, 99–102, 174–75; transmission loss, 101; water for, 99–102, 174–75; wildfires and, 103 Environmental Protection Agency (EPA), xiii, 82, 132 equilibrium climate sensitivity, 19, 220; analysis, 297, 306 See also Rhodium Group Model of the U.S Economy equity premium, 123, 327–28 Escherichia coli, 84 Everglades, 187 evidence-based approach: adaptation from, 50; empirical estimates for, 46–49; paleoclimatic history and, 45–46 expected utility paradigm, 157 extinction: historical, 46; rate, 21; unquantifiable nature of, 121 extreme weather events See hurricanes; storms; wildfires fall, precipitation projections for, 34, 232 Fankhauser, Samuel, 125 Federal Bureau of Investigation (FBI), U.S., 85, 138, 257 fertilization, 56, 56–58, 57, 59–61 Fifth Assessment Report (AR5), 4, 18, 220, 220, 240 fires, 83, 103, 178–80, 181 Fisher-Vanden, Karen, 126 fishing industry, 187 floods: agriculture and, 53, 174; coastal damage projections from, 110–11, 114, 115, INDEX 116; energy supply and, 103; frequency, severity and cost of, 175; historical, 84; mortality from, 175; storm projections and, 38; urbanization and, 175; waterborne disease from, 84 See also sea-level rise Florida: coastal damage costs and benefits, 140, 140–41; coastal damage projections in, 107, 107–8, 109, 110, 111, 112, 113; direct costs and benefits in, 129–48; Everglades, 187; sea-level rise projections for, 107, 244, 245 food prices, 63–64, 122 forests, 177–81; disturbance pattern change, 178; health factors of, 177–78; overview, 161; tree die-off, 22, 161, 180–81; unquantified impacts for, 177–82; value of, 177; wildfires, 83, 103, 178–80, 181 forward displacement: of crime, 88; of mortality, 68, 78 fruit crops, 54 global physical uncertainty, 19 global trade, 63–64, 168 GMSL See global mean sea-level Graff Zivin, Joshua, 68–70, 212, 258–59 grain yields See maize yields; wheat yields gravitational field, 35 greenhouse effect, 13, 14 greenhouse gases (GHGs) See emissions Greenland ice sheets, 35–36, 240 Greenstone, Michael, 77–78, 96, 258 Gross Domestic Product (GDP): from agriculture, 51; damage functions with, 125; direct costs and benefits as share of, xii–xiv, 141, 144; emissions and health costs by, 163; indicator quality of, 123; nonmarket impacts and, 168; RHGMUSE on trade and, 302, 303, 304, 305; SCC shortcomings on, 43 Gulf coast: energy supply from, 102–3; North Atlantic Hurricane Model of, 291 GAMS See general algebraic modeling system gas production, 101, 102 GCMs See global climate models GDP See Gross Domestic Product general algebraic modeling system (GAMS), 298, 299 geopolitical security, 168, 190–91 Georgia: coastal damage projections in, 107, 109, 113; direct costs and benefits in, 129–48; humidity projections in, 230; ozone pollution in, 83; sea-level rise projections for, 244 GHCN See Global Historical Climatology Network GHGs (greenhouse gases) See emissions Giardia, 84 glacial cycles, 14, 15 See also ice sheets global agriculture yields, 63–64 global average temperature: historical, 13–14, 14, 45–46; projection modeling, 219–20; projections, 23, 24, 25 global climate models (GCMs): history of, 19–20; limitations of, 162–63 See also Coupled Model Intercomparison Project Global Historical Climatology Network (GHCN), 220, 222 global mean sea-level (GMSL), 35–36, 36, 240, 241, 242, 247 harvesting See forward displacement Hawaii: coral reefs in, 188; humidity projections for, 232–33; precipitation projections for, 229, 231–32; sea-level rise projections for, 36, 246; temperature projections for, 228 hay fever, 83–84 health: adaptation, 79, 212–13, 213, 267, 268–70; air quality and, 82–84, 163; allergens and, 83–84; background, 76–77; cardiovascular problems, 82–83; economic importance of, 75–76; heat stress problems with, 76; impact categories, 7; impact projections, 77–82, 79–82, 204, 205; improvements in, 75; macroeconomic effects analysis of, 150–52, 151; mitigation benefits for, 204, 205; ozone pollution and, 82–83; particulate matter and, 83; positive outcomes in, 75, 77; risk-assessment approach to, 77–82; unquantified impacts on, 82–84, 167; vulnerability factors, 76, 84 See also disease heat-island effect, 76 heat-related mortality: adaptation for, 267, 268–70; air-conditioning and, 78, 212–13; historical events of, 76; labor and, 68; projections, 79, 80, 81 heat stress: crime evidence from, 86–87; health problems from, 76; labor and risk of, 68 INDEX heat stroke: cause of, 25; Humid Heat Stroke Index, 25, 30, 30, 232–33; labor and, 68; rate of, 76 herbicides, 62 high-risk labor: change in, 70, 70–72, 202; low-risk compared to, 68, 69, 260, 261; share of state employment in, 131; types of, 68 Hope, Chris, 125 hot days, extreme: historical heat waves, 76, 101; power outages on, 101; temperature projections, 25, 27, 77 Hsiang, Solomon, x Humid Heat Stroke Index: categories, 25, 30, 30; projections using, 232–33 humidity: category characteristics, 30; labor and, 258–59; measuring, 25; projection, 25, 30, 31–32, 32, 33; projection modeling, 219, 227, 230, 232–33 hurricanes: Andrew, 191; coastal damage from, 116, 116, 117, 207, 289–91; frequency and intensity of, 21, 36–38, 116, 117, 292; impact projections for, 116, 116, 117, 147, 148, 207; Ivan, 191; Katrina, 184, 191, 192, 291; mitigation benefits for, 205–6, 207; mortality costs of, 147, 148; nationwide damage from, 141, 143, 146–48; natural variability and increase in, 21; North Atlantic Hurricane Model, 4, 49, 106, 289–92; Sandy, 102; VSL mortality cost of, 147, 148 hydroelectric generation, 99–102, 174–75 hydrological droughts, 35 IAMs See integrated assessment models ice sheets: Antarctic, 22, 35, 36, 107, 247; behavior uncertainty of, 35–36, 107; glacial cycles, 14, 15; Greenland, 35–36, 240; North American, 36; tipping point, 22 Idaho: direct costs and benefits in, 129–48; Yellowstone, 181 Illinois: Chicago heat wave, 76; direct costs and benefits in, 129–48; drought in, 35; power outage in, 102 impact functions: for adaptation, 264–70, 266–73; aggregation of, 274–76, 277; application of, 259–64; introduction, 249; linear extrapolation assumption of, 276, 278; meta-analysis approach to, 250, 250–51, 252, 253; micro-founding, 253–59; return intervals of, 270, 274 See also dose-response function impact projections: agriculture yields, 54–58, 56–57, 59–61, 203; coastal damage, from SLR, 107–10, 107–12, 207; coastal damage, from storms, 110–16, 113–17, 207, 286–92; crime, 88–93, 89–93, 205, 206; energy cost, 96, 99, 100; energy demand, 97, 97–99, 98, 205, 206; energy supply, 99–103; flood frequency and severity, 175; health and mortality, 77–82, 79–82, 204, 205; hurricane, 116, 116, 117, 147, 148, 207; labor productivity, 70, 70–72, 71, 73, 74, 202, 203–4, 204; RCPs for, xii, 18, 18, 201–2; tourism, 184–88; tree die-off, 180–81; water adaptation, 175–76; water demand, 172; water supply, 173; wildfire, 179–80 impacts: adaptation overview on, xv, 50, 209–15; categories of, xi, 7, 167; climate projection overview on, xii–xiv; direct costs and benefits of, as share of economic output, 145; direct costs and benefits of, as share of GDP, xii–xiv, 141, 144; direct costs and benefits of, by mortality and hurricanes, 146–48; direct costs and benefits of, inputs for, 295–97; macroeconomic effects methodology on, 149–50, 297–98; macroeconomic effects results on, 150–52, 151; risk and inequality aversion to, 123, 153–57, 155, 327–28; uncertainty of, xii, xiv, 2, 6, 17, 123–24 impacts, unquantified: chart, 166; without econometrics, 49–50; forests, 177–82; international, 168, 191–93; introduction to, 161–63; market, 166–67; mitigation of, 207; national security, 168, 189–93; nonmarket, 167–68; out-of-sample extrapolation of, 168; structural changes, 168–69; tourism, 183–88; water, 171–76 IMPLAN: agriculture subdivisions of, 314; data incorporation of, 298–99; energy subdivisions of, 315; indoor services subdivisions of, 322–25; infrastructure subdivisions of, 315; manufacturing subdivisions of, 315–22; mining subdivisions of, 315; outdoor services subdivisions of, 322; real estate subdivisions of, 322; sectors overview, 299; service industry subdivisions of, 355 322–25; transportation subdivisions of, 315 imports, 63–64 income: consumption discount rate and, 123–24; inequality premium, 156, 327–28; lifetime labor, 132; mortality and, 43, 84; SCC shortcoming and, 43; tourist activity, 186 Indiana, direct costs and benefits in, 129–48 indoor services, 322–25 Industrial Economics, Inc., 215 inequality aversion: defining, 153–54; measuring, 155–56, 327–28; premiums on agriculture, 155; premiums on mortality, 155, 156; risk combined with, 155, 156–57; uncertainty in, 123, 153, 157 infrastructure: coastal adaptation of, 215; IMPLAN subdivisions of, 315; national security concern for, 192; transportation, 192; water-resource adaptation, 175–76 insurance industry, catastrophe, 49, 286, 288–89 integrated assessment models (IAMs), 125–26, 198–99 integrated economic analysis: direct costs and benefits, 125–26, 295–97, 314; historical development of, 125–26; IAMs for, 125–26, 198–99; introduction, 4–5, 295; macroeconomic effects modeling in, 149–52, 151, 297–312; in SEAGLAS, 3, 4–5 Intergovernmental Panel on Climate Change (IPCC): AR5 of, 4, 18, 220, 220, 240; food price report by, 63–64; Working Group of, 202 international security, 168, 191–93 international trade, 63–64, 168 intertemporal optimization, 298 investment: RHG-MUSE on, 303–4, 304, 306; risk aversion measurements on, 154 Iowa: agriculture background in, 53; direct costs and benefits in, 129–48 IPCC See Intergovernmental Panel on Climate Change irrigation, 61–62, 172, 173, 266 Jacob, B., 87, 88, 256–57 Jorgenson, Dale, 126 Kansas, direct costs and benefits in, 129–48 Kentucky, direct costs and benefits in, 129–48 356 Knight, Frank, 123 Knutson, T R., 37, 38, 106, 116, 292 Kopp, Robert, x labor categories See IMPLAN labor productivity: adaptation of, 69, 212; background, 67–68; coping strategies and, 68; direct costs and benefits for, xiii, 130–32, 131, 133, 296; disease and, 72–74; dose-response for, 69, 260; economic importance of, 67; frequency change in, 74; heat stress risk and, 68; historical baseline and projections in, 48, 48; humidity and, 258–59; impact categories of, 7; impact function aggregation of, 275, 277; impact function application on, 260, 261, 264; impact function extrapolation of, 276, 278; impact projections, 70, 70–72, 71, 73, 74, 202, 203–4, 204; lifetime labor income and, 132; macroeconomic effects analysis of, 150–52, 151; microfounding impact function data on, 255, 258–59; migration of, 307; mitigation for, 203–4, 204; mortality and, 68, 72–74, 132, 134, 135, 296; occupational thresholds, 68; population and, 307; by region, 72, 73, 130–32, 131, 133; RHG-MUSE model on, 308–10, 310; risk-assessment approach to, 68–72; schedule changes for, 68; storms and, 74; temperature and factors of, 68; temperature correlation to, 69, 260, 261; unaffected, 68; unquantified impacts on, 72–74 land ice See ice sheets law enforcement, 85, 86, 92–93, 213 Lefgren, Lars, 87, 256–57 livestock, 64–65 Lobell, David B., 56, 256 local sea-level (LSL), 35–36, 37, 240 loss amplification, 288–89 Louisiana: coastal damage projections in, 107, 107, 108, 109, 113; direct costs and benefits in, 129–48; Hurricane Katrina, 184, 191, 192, 291; sea-level rise projections for, 245; tourism in, 184 low-risk labor: change in, 48, 70–72, 71; high-risk compared to, 68, 69, 260, 261; types of, 68 macroeconomic effects: CGE modeling of, 297–98; methodology, 149–50, 297–98; INDEX results, 150–52, 151; RHG-MUSE model for, 149–52, 151, 298–312; sectoral aggregation scheme in, 299, 314–25 MAGICC (physical climate model), 19, 20, 219–20, 223, 225, 227 Maine: coastal damage projections in, 113; direct costs and benefits in, 129–48; sealevel rise projections for, 242 maize yields: adaptation for, 212, 266, 266; CO2 increase and, 56, 56–58, 57, 59–61, 260, 261; dose-response for, 55, 260; drought projections and, 35; impact function aggregation, 274, 274; impact function application on, 260, 261, 262; micro-founding impact function data on, 254, 255, 256, 257; mitigation benefits for, 203; precipitation, temperature and, 55, 55, 260, 261 manufacturing, labor subdivisions of, 315–22 market impacts, unquantified, 166–67; nonmarket, 167–68 Maryland: coastal damage projections in, 107, 108, 109, 113; direct costs and benefits in, 129–48; sea-level rise projections for, 243 Massachusetts: Boston, sea-level rise projections in, 242; direct costs and benefits in, 129–48 mathematical programming system for general equilibrium (MPSGE), 298, 299, 301 Mayan civilization, 46 McGrath, Justin M., 56, 256 MCP See mixed complementarity problem meta-analysis approach: DMAS for, 251, 252, 253; hierarchical Bayesian modeling, 250, 250–51 meteorological droughts, 34 methane destabilization, 22 Mexico: migration from, 193; tourism in, 185 Michigan, direct costs and benefits in, 129–48 micro-founding impact functions: for agriculture, 254, 255–56, 257; for crime, 255, 256–57; data criteria for, 253–54; for health and mortality, 255, 258; for labor, 255, 258–59 migration: direct costs and benefits inputs and, 127; factors and patterns, 193; of labor, 307 military See national security mining, labor subdivisions of, 315 Minnesota: direct costs and benefits in, 129–48; power plants in, 102 See also IMPLAN Mississippi: coastal damage costs and benefits, 140, 140; coastal damage projections in, 107, 109, 113; direct costs and benefits in, 129–48 Mississippi River, 102, 174 Missouri: direct costs and benefits in, 129–48; heat wave, 101 mitigation: for agriculture, 203, 203; analysis shortcomings, 197–98; background, 201–2; benefits timing, 202–3; for crime, 205, 206; for energy, 205, 206; for health and mortality, 204, 205; for labor productivity, 203–4, 204; of unquantified impacts, 207 See also adaptation mixed complementarity problem (MCP), 297, 298 models: adaptation, 198–99, 199; Bayesian, 250, 250–51; bin, 253; CGE, 297–98; CMIP5, 221, 241; for coastal damages, 105, 106, 286–92; detailed sectoral, 3, 4, 49, 281–92; discrete-discrete probability, 251; DMAS, xi, xv, 251, 252, 253; downscaling, 20; for energy demand, 4, 49, 96–97, 281–86; global climate, 19–20, 162–63; for humidity projection, 240, 240–41, 241, 242–46, 247; IAMs, 125–26, 198–99; macroeconomic effects, 149–52, 151, 297–312; MAGICC, 19, 20, 219–20, 223, 225, 227; metaanalysis, 250, 250–51, 252, 253; National Coastal Property, 215; North Atlantic Hurricane, 4, 49, 106, 289–92; overview of, 3, 4; for precipitation projection, 219–29, 221, 223–26; for property loss, 286–89; regional physical uncertainty and, 19–20; RHG-MUSE, 150–52, 151, 298–312; RHG-NEMS, 4, 49, 214, 281, 282, 282–84, 283, 284, 285, 286; RMS, 4, 49, 106, 286–92, 287; for sea-level rise projection, 106, 240, 240–41, 241, 247; for social cost of carbon, 42–43; spline, 251; for temperature projection, 219–29, 221, 223–26; underestimation of, 162–63 Montana: direct costs and benefits in, 129–48; Yellowstone, 181 INDEX Moretti, E., 87, 256–57 mortality: adaptation to reduce, 79, 212–13, 213, 267, 268–70; by age, 79, 84, 260, 261, 314; air quality and, 82–84, 163; causes of, xiii; direct costs and benefits for, xiii, 132, 134–35, 146–48, 296; disease and, 84; dose-response function for, 77, 260; firefighter, 178; from flooding, 175; forward displacement of, 78; frequency change in, 82; impact function aggregation of, 275, 277; impact function application on, 260, 261, 262, 264; impact function extrapolation of, 276, 278; impact projections, 77–82, 79–82, 204, 205; income inequality and, 43, 84; inequality premiums for, 155, 156; inequality premiums on, 155, 156; labor and, 68, 72–74, 132, 134, 135, 296; micro-founding impact function data on, 255, 258; mitigation benefits for, 204, 205; nationwide hurricane damage by, 146–48; ozone pollution and, 82–83; particulate matter and, 83; race and, 84; rates of, xiii, 205, 314; regional distribution of, xiii, 79, 81; RHGMUSE model on, 312; risk-assessment approach to, 77–82; risk premiums for, 155; storms and, 84; temperature and, 77, 260; tree, 22, 161, 180–81; unquantified impacts on, 82–84 See also value of a statistical life MPSGE See mathematical programming system for general equilibrium NARR See North American Regional Reanalysis National Climate Assessment (NCA): focus of, 4; on forests, 178, 179, 180; on health, 75; regions defined by, 128 National Coastal Property Model (NCPM), 105, 215 National Energy Modeling System (NEMS) See Rhodium Group National Energy Modeling System National Intelligence Assessment, 189–93 National Oceanic and Atmospheric Administration (NOAA), 103 National Park Service, 186, 187 national security: domestic impacts on, 190–91; international impacts on, 168, 191–93; panels, 189–90 natural climate variability: history of, 14, 15, 45–46; trends compared to, 13–14, 14; uncertainty, 20–21 natural gas, 99, 102 NCA See National Climate Assessment NCPM See National Coastal Property Model Nebraska: agriculture in, 35, 53, 129, 130; direct costs and benefits in, 129–48 Neidell, Matthew, 68–70, 212, 258–59 NEMS (National Energy Modeling System), 4, 49, 96–97, 281, 282 See also Rhodium Group National Energy Modeling System Neumann, Jim, 106 Nevada, direct costs and benefits in, 129–48 New Hampshire: coastal damage projections in, 113; direct costs and benefits in, 129–48 New Jersey: coastal damage projections in, 107, 109, 111, 113; direct costs and benefits in, 129–48; sea-level rise projections for, 243 New Mexico: direct costs and benefits in, 129–48; energy supply and wildfires in, 103 New York: coastal damage projections in, 107, 109, 111, 112, 113; direct costs and benefits in, 129–48; heat wave, 101 New York City: flood projection for, 115; sea-level rise projection for, 35–36, 242, 247; temperature projection for, 222, 230 New York City Panel on Climate Change, 106 nitrogen oxides (NOx), 62, 82, 83 NOAA See National Oceanic and Atmospheric Administration Nordhaus, William, 125 North American ice sheets, 36 North American Regional Reanalysis (NARR), 227, 229 North Atlantic Hurricane Model, 4, 49, 106; coverage, 289–90; modifications to, 292; modules of, 290–91 North Carolina: coastal damage projections in, 107, 109, 113; coastal tourism in, 187; direct costs and benefits in, 129–48; sealevel rise projections for, 244 North Dakota, direct costs and benefits in, 129–48 NOx See nitrogen oxides 357 nuclear power plants, 99–102, 174 nut crops, 54 Ohio, direct costs and benefits in, 129–48 oil production, 101, 102 oilseed yields, 56, 56–58, 57, 59–61 Oklahoma, direct costs and benefits in, 129–48 orbit: natural climate variability and, 14; sea-level change regions and, 36 Oregon: direct costs and benefits in, 129–48; sea-level rise projections for, 246 outdoor services, 322 out-of-sample extrapolation, 168 ozone pollution: agriculture and, 62; mortality, health and, 82–83 Pacific coast: energy supply, 103; sea-level rise projections, 36, 37 Paleocene-Eocene Thermal Maximum (PETM), 22, 46 Paleoclimatic evidence, 45–46 particulate matter, 83 Paulson, Henry M., Jr “Hank,” vii–viii, x Pennsylvania: coastal damage projections in, 113; direct costs and benefits in, 129–48; sea-level rise projections for, 243 Pentagon, 189 pests: agriculture and, 62, 64, 84; forests and, 177, 180–81 PETM See Paleocene-Eocene Thermal Maximum physical uncertainty, 19–20 pollution See emissions; ozone pollution population: coastal, 105; labor, 307; migration and cost-benefit redistribution, 127 power outages, 101, 102, 103 precipitation: agriculture yields and, 52–54, 55, 55, 255, 256, 260, 261; crime and, 87, 260, 261; disease and, 64, 84; drought projections and, 34–35; energy supply water and, 102; projection modeling, 219–29, 222, 223–26; projections, 33–34, 34, 229, 231–32, 237–39; water management and, 172–73 probability: baseline relativity to, 48, 48; climate projections weighting, 222–27, 223–26; distribution with risk and inequality aversion, 123, 153–57, 155, 327–28; language of, 5–6; regional modeling and, 20; tail risks, 5, 11–12 .. .ECONOMIC RISKS OF CLIMATE CHANGE ECONOMIC RISKS OF CLIMATE CHANGE AN AMERICAN PROSPECTUS TREVOR HOUSER • SOLOMON HSIANG • ROBERT KOPP KATE LARSEN • MICHAEL DELGADO • AMIR JINA MICHAEL MASTRANDREA • SHASHANK... and to commission the research that became the American Climate Prospectus report, published here in its entirety as Economic Risks of Climate Change: An American Prospectus Our aim is to quantify... welfare Climate change could result in a significant decline in biodiversity, lead to the extinction of entire species of plants and animals, and permanently alter the appearance and utility of national