Advertisement

Climatic Change

, Volume 109, Supplement 1, pp 21–42 | Cite as

Projecting long-run socioeconomic and demographic trends in California under the SRES A2 and B1 scenarios

  • Alan H. SanstadEmail author
  • Hans Johnson
  • Noah Goldstein
  • Guido Franco
Article

Abstract

The State of California is developing and implementing a new generation of environmental policies to transition to a low-carbon economy and energy system in order to reduce the risks of future damages from global climate change. At the same time, it is increasingly clear that climate change impacts are already occurring and that further effects cannot be completely avoided. Thus, anticipating and planning for emerging and potential future climate change impacts in California must complement the state’s greenhouse gas mitigation efforts. These impacts will depend substantially on the future evolution of the state’s social structure and economy. To support impact studies, this report describes socioeconomic storylines and key scenario elements for California that are broadly consistent with the global “A2” and “B1” storylines in the 2000 Special Report on Emissions Scenarios of the Intergovernmental Panel on Climate Change, including qualitative socioeconomic context as well as quantitative projections of key variables such as population, urbanization patterns, economic growth, and electricity prices.

Keywords

Electricity Price Urban Extent Capita Income Growth California Energy Commission Potential Climate Change Impact 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are indebted to Alexei Sankovski and William Pepper of ICF International, and Detlef Van Vuuren of the National Institute for Public Health and Environmental Hygiene (RIVM), The Netherlands, for making available detailed results from the SRES A2/ASF and B1/IMAGE scenarios, respectively; Hugh Pitcher of the Joint Global Change Research Institute (JGCRI) for his advice on the methodology and results of the SRES; and Martin Ross of the Research Triangle Institute for providing results from the ADAGE model analysis of S. 2191. We would like to thank Steven Smith of the JGCRI, Gary Yohe, and an anonymous referee for their invaluable comments and suggestions. Finally, we acknowledge and thank Mark Wilson for his outstanding editorial work.

References

  1. Abrishami M, Bender S, Lewis KC, Movassagh N, Puglia P, Sharp G, Sullivan K, Tian M, Valencia B, Vidivar D (2005) Energy Demand Forecast Methods Report: Companion Report to the California Energy Demand 2006–2016 Staff Energy Demand Forecast Report. Staff Report CEC-400-2005-036Google Scholar
  2. Arthur D Little, Inc (2002) Benefits of reducing demand for gasoline and diesel—vol. 3, Task 1 Report. Joint Report to the California Air Resources Board and the California Energy Commission, March 28Google Scholar
  3. Bauer PW, Lee Y (2006) Estimating GSP and labor productivity by state. Federal Reserve Bank of Cleveland Policy Discussion Paper Number 16, MarchGoogle Scholar
  4. California Air Resources Board (2007) California 1990 Greenhouse Gas Emissions Level and 2020 Emissions Limit. Staff Report, Jamesine Rogers, Primary Author, November, 16Google Scholar
  5. California Air Resources Board (2008) Climate change draft scoping plan: a framework for change. June 2008 Discussion DraftGoogle Scholar
  6. Clarke KC, Gaydos LJ (1998) Loose-coupling a cellular automaton model and GIS—long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12(7):699–714CrossRefGoogle Scholar
  7. Clarke L, Edmonds J, Jacoby H, Pitcher H, Reilly J, Richels R (2007) Scenarios of greenhouse gas emissions and atmospheric concentrations. Sub-report 2.1A of Synthesis and Assessment Product 2.1 by the U. S. Climate Change Science Program and the Subcommittee on Global Change Research. Department of Energy, Office of Biological & Environmental Research, Washington, D.C. 154 ppGoogle Scholar
  8. Cox W (2008) Demographia World Urban Areas (World Agglomerations). Wendel Cox Consultancy, Illinois, 91 ppGoogle Scholar
  9. Davis SC, Diegel SW (2007) Transportation Energy Data Book—Edition 26. ORNL—6978 (Edition 26 of ORNL-5198); report prepared for the Office of Planning, Budget Formulation, and Analysis, Energy Efficiency and Renewable Energy, U. S. Department of Energy by the Oak Ridge National LaboratoryGoogle Scholar
  10. ESRI (2007) U.S. Census Urbanized Areas Metadata. www.esri.com/data/community_data/census/overview.html
  11. Farrell AE, Brandt AR (2006) Risks of the oil transition. Environ Res Lett 1:014004CrossRefGoogle Scholar
  12. Franco G, Cayan D, Moser S, Hanemann M, Jones M (2011) Second California Assessment: Integrated Climate Change Impacts Assessment of Natural and Managed Systems. Climatic ChangeGoogle Scholar
  13. Fischer C, Morgenstern RD (2006) Carbon abatement costs: why the wide range of estimates? Energy J 27(2):73–86Google Scholar
  14. IPCC (2000) Special Report on Emissions Scenarios. Working Group III of the Intergovernmental Panel on Climate Change. N. Nakicenovic, Lead Author; N. Nakicenovic and R. Swart, Report Editors. Cambridge University PressGoogle Scholar
  15. Landis J (2001) CUF, CUF II, and CURBA: a family of spatially explicit urban growth and land-use policy simulation models. In: Brail RK, Klosterman RE (eds) Planning support systems: integrating geographic information systems, models and visualization tools. ESRI Press, Redlands, California, pp 157–200Google Scholar
  16. Landis J, Reilly M (2003) How we will grow: baseline projections of California’s urban footprint through 2100. Department of City and Regional Planning Institute of Urban and Regional Development, University of California at Berkeley, October, www.energy.ca.gov/reports/2003-10-31_500-03-058CF_A03.PDF
  17. McPherson T, Brown M (2003) U.S. Day and Night Population Database (Revision 2.0)—Description of Methodology. LA-UR-03-8389. Los Alamos National Laboratory, 30 ppGoogle Scholar
  18. Marshall L, Gorin T, principal authors (2007) California Energy Demand 2008–2018. Staff Revised Forecast. California Energy Commission Staff Final Report CEC-200-2007-015-SF2, NovemberGoogle Scholar
  19. Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z-C (2007) Global Climate Projections. Chapter 10 in Climate Change 2007: The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New YorkGoogle Scholar
  20. National Research Council (NRC) 2010 Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop. Panel on Socioeconomic Scenarios for Climate Change Research and Assessment. Committee on the Human Dimensions of Global Change, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies PressGoogle Scholar
  21. Parker L, Yacobucci B (2008) Climate change: costs and benefits of S. 2191. Congressional Research Service Report for Congress. Order Code RL34489, May 15Google Scholar
  22. Pendleton L, King P, Mohn C, Webster DG, Vaughn RK (2008) Estimating the potential economic impacts of climate change on Southern California Beaches. California Energy Commission draft paperGoogle Scholar
  23. Price S (2008) Electricity & natural gas GHG modeling: revised results and sensitivities. Energy and Environmental Economics, Inc., May, 13Google Scholar
  24. Ross M (2008) Personal communication, AugustGoogle Scholar
  25. Sanstad AH, Johnson H, Goldstein N, Franco G (2009) Long-run socioeconomic and demographic scenarios for California. California Energy Commission Final Paper CEC-500-2009-013F, AugustGoogle Scholar
  26. Smith SJ (2008) Personal communication, DecemberGoogle Scholar
  27. State of California, Department of Finance (Undated) E-4 Population Estimates for California Cities and Counties, January 1, 1981 to January 1, 1990. Sacramento, CaliforniaGoogle Scholar
  28. State of California, Department of Finance (2007) E-4 Historical Population Estimates for City, County, and the State, 1991–2000, with 1990 and 2000 Census Counts. Sacramento, California, AugustGoogle Scholar
  29. State of California, Department of Finance (2008) E-4 Population Estimates for Cities, Counties, and the State, 2001–2008, with 2000 Benchmark. Sacramento, California, MayGoogle Scholar
  30. State of California, Employment Development Department (2005) Historical Civilian Labor Force [Not Seasonally-Adjusted] 1976 to 1989; March 2004 Benchmark. Sacramento, California, AprilGoogle Scholar
  31. State of California, Employment Development Department (2007) Population projections for California and its counties 2000–2050, by age gender and race/ethnicity, Sacramento, California, JulyGoogle Scholar
  32. State of California, Employment Development Department (2009) Historical Civilian Labor Force [Not Seasonally-Adjusted]; March 2007 Benchmark. Sacramento, California, JanuaryGoogle Scholar
  33. United Nations (2006) World Urbanization Prospects: The 2005 Revision. Population Division of the Department of Economic and Social Affairs of the United Nations SecretariatGoogle Scholar
  34. United Nations (2007) World Population Prospects: The 2006 Revision. Population Division of the Department of Economic and Social Affairs of the United Nations SecretariatGoogle Scholar
  35. United States President (2008) Economic Report of the President—Transmitted to the Congress February 2008, together with the Annual Report of the Council of Economic Advisers. Washington, D.C.: U. S. Government Printing Office, FebruaryGoogle Scholar
  36. U. S. Bureau of Economic Analysis (2008) National Income and Product AccountsGoogle Scholar
  37. U. S. Census Bureau (2000) Table NP-T1 in Annual Projections of the Total Resident Population as of July 1: Middle, Lowest, Highest, and Zero International Migration Series, 1999–2100Google Scholar
  38. U. S. Energy Information Administration (2007) Annual Energy Review. Report No. DOE/EIA-0384(2007)Google Scholar
  39. U. S. Energy Information (2008a) State Energy Data System. February releaseGoogle Scholar
  40. U. S. Energy Information Administration (2008b) Annual Energy Outlook 2008. Report #DOE/EIA-0383(2008), JuneGoogle Scholar
  41. U.S. Environmental Protection Agency (2008) EPA Analysis of the Lieberman-Warner Climate Security Act of 2008—S. 2191 in 100th Congress. U. S. EPA Office of Atmospheric Programs, March 14Google Scholar
  42. United States President (2008b) Economic Report of the President Transmitted to the Congress together with The Annual Report of the Council of Economic Advisers. United States Government Printing Office, Washington, DCGoogle Scholar
  43. Wilson D (2002) Productivity in the Twelfth District. FRBSF [Federal Reserve Bank of San Francisco] Economic Letter Number 2002–33, November 8Google Scholar
  44. Yi Z, Land KC, Wang Z, Gu D (2006) U. S. family household momentum and dynamics: An extension and application of the ProFamy method. Popul Res Policy Rev 25:1–41CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Alan H. Sanstad
    • 1
    Email author
  • Hans Johnson
    • 2
  • Noah Goldstein
    • 3
  • Guido Franco
    • 4
  1. 1.Lawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Public Policy Institute of CaliforniaSacramentoUSA
  3. 3.Lawrence Livermore National LaboratoryLivermoreUSA
  4. 4.California Energy CommissionSacramentoUSA

Personalised recommendations