Skip to main content

Forecasting for Environmental Decision Making

  • Chapter
Tools to Aid Environmental Decision Making

Abstract

Those making environmental decisions must not only characterize the present, they must also forecast the future. They must do so for at least two reasons. First, if a no-action alternative is pursued, they must consider whether current trends will be favorable or unfavorable in the future. Second, if an intervention is pursued instead, they must evaluate both its probable success given future trends and its impacts on the human and natural environment. Forecasting, by which I mean explicit processes for determining what is likely to happen in the future, can help address each of these areas.

“The Ford engineering staff, although mindful that automobile engines provide exhaust gases, feels that these waste vapors are dissipated in the atmosphere quickly and do not present an air pollution problem.” Official spokesperson for the Ford Motor Company in 1953 in response to a letter from the Los Angeles county supervisor Cerf and Navasky, 1984, p. 38.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Adya, M., Armstrong, J.S., Collopy, F., and Kennedy, M. 1998. Automatic identification of time series features for rule-based forecasting. (Working paper), Cleveland, OH: The Weatherhead School, Case Western Reserve University.

    Google Scholar 

  • Anderson, C.A. 1983. Abstract and concrete data in the perseverance of social theories: when weak data lead to unshakeable beliefs. Journal of Experimental Social Psychology 19:93–108.

    Article  Google Scholar 

  • Armstrong, J.S. and Overton, T. 1977. Estimating nonresponse bias in mail surveys. Journal of Marketing Research 14:396–402.

    Article  Google Scholar 

  • Armstrong, J.S. 1984. Forecasting by extrapolation: Conclusions from 25 years of research. Interfaces 14(Nov./Dec.):52–66.

    Article  Google Scholar 

  • Armstrong, J.S. 1985. Long-range Forecasting 2nd ed. New York: John Wiley and Sons.

    Google Scholar 

  • Armstrong, J.S. 1987. Forecasting methods for conflict situations. In: G. Wright and P. Ayton (Eds.). Judgmental Forecasting. Chichester, England: John Wiley and Sons. Pp. 157–176.

    Google Scholar 

  • Armstrong, J.S. and Collopy, F. 1993. Causal forces: Structuring knowledge for time series extrapolation. Journal of Forecasting. 12:103–115.

    Article  Google Scholar 

  • Armstrong, J.S. and Collopy, F. 1998. Integration of statistical methods and judgment for time series forecasting: Principles from empirical research. In: G. Wright and P. Goodwin. Forecasting with Judgment. Chichester, England: John Wiley and Sons. Pp. 269–293.

    Google Scholar 

  • Armstrong, J.S. and Hutcherson, P. 1989. Predicting the outcome of marketing negotiations: Role-playing versus unaided opinions. International Journal of Research in Marketing 6:227–239.

    Article  Google Scholar 

  • Ascher, W. 1978. Forecasting: An Appraisal for Policy Makers and Planners. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Ashton, A.H. 1985. Does consensus imply accuracy in accounting studies of decision making? Accounting Review 60:173–185.

    Google Scholar 

  • Ashton, A.H. and Ashton, R.H. 1985. Aggregating subjective forecasts: Some empirical results. Management Science 31:1499–1508.

    Article  Google Scholar 

  • Baker, E.J. 1979. Predicting responses to hurricane warnings: A reanalysis of data from four studies. Mass Emergencies 4:9–24.

    Google Scholar 

  • Baker, E.J. et al. 1980. Impact of offshore nuclear power plants: Forecasting visits to nearby beaches. Environment and Behavior 12:367–407.

    Article  Google Scholar 

  • Blattberg, R.C. and Hoch, S.J. 1990. Database models and managerial intuition: 50 percent model + 50 percent manager. Management Science 36:887–899.

    Article  Google Scholar 

  • Brenner, L.A., Koehler, D.J., and Tversky, A. 1996. On the evaluation of one-sided evidence. Journal of Behavioral Decision Making 9:59–70.

    Article  Google Scholar 

  • Bretschneider, S.I. et al. 1989. Political and organizational influences on the accuracy of forecasting state government revenues. International Journal of Forecasting 5:307–319.

    Article  Google Scholar 

  • Brown, L.R., Kane, H., and Roodman, D.M. 1994. Vital Signs 1994. New York: Norton.

    Google Scholar 

  • Buchannan, W. 1986. Election predictions: An empirical assessment. Public Opinion Quarterly 50:222–227.

    Article  Google Scholar 

  • Cerf, C. and Navasky, V. 1984. The Experts Speak. New York: Pantheon.

    Google Scholar 

  • Chatfield, C. 1995. Positive or negative? International Journal of Forecasting 11:501–502.

    Article  Google Scholar 

  • Clernen, R. 1989. Combining forecasts: A review and annotated bibliography. International Journal of Forecasting 5:559–583.

    Article  Google Scholar 

  • Collopy, F. and Armstrong, J.S. 1992a. Rule-based forecasting: Development and validation of an expert systems approach to combining time series extrapolations. Management Science 38:1394–1414.

    Article  Google Scholar 

  • Collopy, F. and Armstrong, J.S. 1992b. Expert opinions about extrapolation and the mystery of the overlooked discontinuities. International Journal of Forecasting 8:575–582.

    Article  Google Scholar 

  • Cosier, R.A. 1978. The effects of three potential aids for making strategic decisions on prediction accuracy. Organizational Behavior and Human Performance 22:295–306.

    Article  Google Scholar 

  • Dalrymple, D.J. 1987. Sales forecasting practices: Results from a United States survey. International Journal of Forecasting 3:379–391.

    Article  Google Scholar 

  • Dielman, T.E. 1986. A comparison of forecasts from least absolute value and least squares regression. Journal of Forecasting 5:189–195.

    Article  Google Scholar 

  • Fildes, R. 1985. The state of the art: Econometric models. Journal of the Operational Research Society 36:549–586.

    Google Scholar 

  • Fildes, R. and Hastings, R. 1994. The organization and improvement of market forecasting. Journal of the Operational Research Society 45:1–16.

    Google Scholar 

  • Fischhoff, B. and MacGregor, D. 1982. Subjective confidence in forecasts. Journal of Forecasting 1:155–172.

    Article  Google Scholar 

  • Fullerton, D. and Kinnaman, T.C. 1996. Household responses to pricing garbage by the bag. American Economic Review 86:971–984.

    Google Scholar 

  • Gardner, E.S. 1984. The strange case of the lagging forecasts. Interfaces 14:47–50.

    Article  Google Scholar 

  • Gardner, E.S. 1985. Exponential smoothing: The state of the art (with commentary). Journal of Forecasting 4:1–38.

    Article  Google Scholar 

  • Glantz, M.H. 1982. Consequences and responsibilities in drought forecasting: The case of Yakima, 1977. Water Resources Research 18:3–13.

    Article  Google Scholar 

  • Greenwald, A.G. et al. 1987. Increasing voting behavior by asking people if they expect to vote. Journal of Applied Psychology 72:315–318.

    Article  Google Scholar 

  • Gregory, W.L., Cialdini, R.B., and Carpenter, K. 1982. Self-relevant scenarios as mediators of likelihood estimates and compliance: Does imagining make it so? Journal of Personality and Social Psychology 43:88–99.

    Article  Google Scholar 

  • Griffith, J.R. and Wellman, B.T. 1979. Forecasting bed needs and recommending facilities plans for community hospitals: A review of past performance. Medical Care 17:293–303.

    Article  CAS  Google Scholar 

  • Hoch, S.J. 1985. Counterfactual reasoning and accuracy in predicting personal events. Journal of Experimental Psychology: Learning, Memory, and Cognition 11:719–731.

    Article  Google Scholar 

  • Hogarth, R.M. 1978. A note on aggregating opinions. Organizational Behavior and Human Performance 21:40–46.

    Article  Google Scholar 

  • Koriat, A., Lichtenstein, S., and Fischhoff, B. 1980. Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory 6:107–118.

    Article  Google Scholar 

  • Larreche, J. and Moinpour, R. 1983. Managerial judgment in marketing: The concept of expertise. Journal of Marketing Research 20:110–121.

    Article  Google Scholar 

  • Lau, R.R. 1994. An analysis of the accuracy of “trial heat” polls during the 1992 presidential election. Public Opinion Quarterly 58:2–20.

    Article  Google Scholar 

  • Lemert, J.B. 1986. Picking the winners: Politician vs. voter predictions of two controversial ballot measures. Public Opinion Quarterly 50:208–221.

    Article  Google Scholar 

  • Libby, R. and Blashfield, R.K. 1978. Performance of a composite as a function of the number of judges. Organizational Behavior and Human Performance 21:121–129.

    Article  Google Scholar 

  • Lowenstein, G. and Frederick, S. 1997. Predicting reactions to environmental change. In: M.H. Bazerman et al. (Eds.). Environment, Ethics, and Behavior: The Psychology of Environmental Valuation and Degradation. San Francisco: New Lexington Press. Pp. 52–72.

    Google Scholar 

  • MacGregor, D.G. 1999. Decomposition for judgmental forecasting and estimation. In: J.S. Armstrong (Ed.). Principles of Forecasting: A Handbook for Researchers and Practioners. Norwell, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Makridakis, S. et al. 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting 1:111–153.

    Article  Google Scholar 

  • Makridakis, S. et al. 1993. The M2-Competition: A real-time judgmentally based forecasting study. International Journal of Forecasting 9:5–22.

    Article  Google Scholar 

  • McCloskey, D.N. and Ziliak, S.T. 1996. The standard error of regressions. Journal of Economic Literature 34:97–114.

    Google Scholar 

  • McNees, S.K. 1992. The uses and abuses of consensus forecasts. Journal of Forecasting 11:703–710.

    Article  Google Scholar 

  • O’Connor, M. and Lawrence, M. 1989. An examination of the accuracy of judgmental confidence intervals in time series forecasting. Journal of Forecasting 8:141–155.

    Article  Google Scholar 

  • Perry, P. 1979. Certain problems with election survey methodology. Public Opinion Quarterly 43:312–325.

    Article  Google Scholar 

  • Plous, S. 1993. The Psychology of Judgment and Decision Making. New York: McGraw-Hill.

    Google Scholar 

  • Rausser, G.C. and Oliveira, R.A. 1976. An econometric analysis of wilderness areause. Journal of the American Statistical Association 71:276–285.

    Article  Google Scholar 

  • Read, S.J. 1983. Once is enough: Causal reasoning from a single instance. Journal of Personality and Social Psychology 45:323–334.

    Article  Google Scholar 

  • Rowe, G., Wright, G., and Bolger, F. 1991. The Delphi technique: A re-evaluation of research and theory. Technological Forecasting and Social Change 39(3):235–251.

    Article  Google Scholar 

  • Rush, H. and Page, W. 1979. Long-term metals forecasting: The track record: 19101964. Futures 11:321–337.

    Article  Google Scholar 

  • Shamir, J. 1986. Pre-election polls in Israel: Structural constraints on accuracy. Public Opinion Quarterly 50:62–75.

    Article  Google Scholar 

  • Squire, P. 1988. Why the 1936 Literary Digest poll failed. Public Opinion Quarterly 52:125–133.

    Article  Google Scholar 

  • Stewart, T.R. 1987. The Delphi technique and judgmental forecasting. Climatic Change 11:97–113.

    Article  Google Scholar 

  • Stewart, T.R. and Glantz, M.H. 1985. Expert judgment and climate forecasting: A methodological critique of “Climate Change to the Year 2000.” Climatic Change 7:159–183.

    Article  Google Scholar 

  • Stewart, T.R. and Leschine, T.M. 1986. Judgment and analysis in oil spill risk assessment. Risk Analysis 6(3):305–315.

    Article  Google Scholar 

  • Sudman, S. and Bradburn, N.M. 1982. Asking Questions: A Practical Guide to Questionnaire Design. Jossey-Bass, San Francisco.

    Google Scholar 

  • Tierney, J. 1990. Betting the planet. New York Times Magazine p. 52, December 2.

    Google Scholar 

  • Timmers, H. and Wagenaar, W. 1977. Inverse statistics and the misperception of exponential growth. Perception and Psychophysics 21:558–562.

    Article  Google Scholar 

  • Turner, R.S. et al. 1992. Sensitivity to change for low-ANC eastern U.S. lakes and streams and brook trout populations under alternate sulfate deposition scenarios. Environmental Pollution 77:269–277.

    Article  CAS  Google Scholar 

  • Wagenaar, W.A. and Sagaria, S.D. 1975. Misperception of exponential growth. Perception and Psychophysics 18:416–422.

    Article  Google Scholar 

  • Wagenaar, W.A., Schreuder, R., and van der Heijden, A.H.C. 1985. Do TV pictures help people to remember the weather forecast? Ergonomics 28:765–772.

    Article  CAS  Google Scholar 

  • Wagenaar, W.A. and Timmers, H. 1978. Extrapolation of exponential time series is not enhanced by having more data points. Perception and Psychophysics 24:182–184.

    Article  Google Scholar 

  • Wagenaar, W.A. and Timmers, H. 1979. The pond-and-duckweed problem: Three experiments in the misperception of exponential growth. Acta Psychologica 43:239–251.

    Article  Google Scholar 

  • Weimann, G. 1990. The obsession to forecast: Pre-election polls in the Israeli press. Public Opinion Quarterly 54:396–408.

    Article  Google Scholar 

  • Winston, C. 1993. Economic deregulation: Days of reckoning for microeconomists. Journal of Economic Literature 31:1263–1289.

    Google Scholar 

  • Yokum, J.T. and Armstrong, J.S. 1995. Beyond accuracy: Comparison of criteria used to select forecasting methods. International Journal of Forecasting 11:591–597.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer Science+Business Media New York

About this chapter

Cite this chapter

Armstrong, J.S., Trevarthen, J.A. (1999). Forecasting for Environmental Decision Making. In: Dale, V.H., English, M.R. (eds) Tools to Aid Environmental Decision Making. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1418-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-1418-2_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98556-5

  • Online ISBN: 978-1-4612-1418-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics