Climatic Change

, Volume 38, Issue 2, pp 159–205 | Cite as

Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools?

  • Simon Shackley
  • Peter Young
  • Stuart Parkinson
  • Brian Wynne


In this paper we explore the dominant position of a particular style of scientific modelling in the provision of policy-relevant scientific knowledge on future climate change. We describe how the apical position of General Circulation Models (GCMs) appears to follow ‘logically’ both from conventional understandings of scientific representation and the use of knowledge, so acquired, in decision-making. We argue, however, that both of these particular understandings are contestable. In addition to questioning their current policy-usefulness, we draw upon existing analyses of GCMs which discuss model trade-offs, errors, and the effects of parameterisations, to raise questions about the validity of the conception of complexity in conventional accounts. An alternative approach to modelling, incorporating concepts of uncertainty, is discussed, and an illustrative example given for the case of the global carbon cycle. In then addressing the question of how GCMs have come to occupy their dominant position, we argue that the development of global climate change science and global environmental ‘management’ frameworks occurs concurrently and in a mutually supportive fashion, so uniting GCMs and environmental policy developments in certain industrialised nations and international organisations. The more basic questions about what kinds of commitments to theories of knowledge underpin different models of ‘complexity’ as a normative principle of ‘good science’ are concealed in this mutual reinforcement. Additionally, a rather technocratic policy orientation to climate change may be supported by such science, even though it involves political choices which deserve to be more widely debated.


Climate Change General Circulation Model Carbon Cycle Future Climate Change Policy Orientation 
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Notes and References

  1. 1.
    Levins, R.: 1966, ‘The Strategy of Model Building in Population Biology’, Amer. Sci. 54(4), 421–431.Google Scholar
  2. 2.
    Lindblom, C. and Cohen, D.: 1979, Usable Knowledge, Yale University Press, London, p. 129; Hogwood, B. and Gunn, L.: 1984, Policy Analysis For The Real World, Oxford University Press, Oxford, p. 289; Brunner, R.: 1996, ‘Policy and Global Change Research: A Modest Proposal’, Clim. Change 32 (2) 121–147.Google Scholar
  3. 3.
    See, however: Glantz, M.: 1979, ‘A Political View of CO2’, Nature 280, 189–190; Stewart, T. and Glantz, M.: 1985, ‘Expert Judgement and Climate Forecasting: A Methodological Critique of “Climate Change to the Year 2000”’, Clim. Change 7, 159–183; Schneider, S.: 1985, 'science by Consensus: The Case of The National Defense University Study “Climate Change to the Year 2000” – An Editorial’, ibid., pp. 153–157; Edwards, P.: 1996, ‘Global Comprehensive Models in Politics and Policymaking’, Clim. Change 32 (2), 149–161; Pielke, R. Jr.: ‘Usable Information for Policy: an Appraisal of the U.S. Global Change Research Program’, Policy Sci. 28, 39–77; Jamieson, D.: 1991, ‘The Epistemology of Climate Change: Some Morals for Managers’, Soc. Natural Res. 4, 319–329.Google Scholar
  4. 4.
    For example, Hajer, M.: 1995, The Politics of Environmental Discourse, Oxford University Press, Oxford, p. 332; Haas, P.: 1992, ‘Introduction: Epistemic Communities and International Policy Coordination’, Int. Organ. 46 (1), 1–35.Google Scholar
  5. 5.
    Grotch, S. and MacCracken, M.: 1991, ‘The Use of General Circulation Models to Predict Regional Climate Change’, J. Clim. 4, 286–303; von Storch, H. and Hasselmann, K.: 1995, Climate Variability and Change, Report no. 152, MaxPlanckInstitut für Meteorologie, Hamburg, p. 26.Google Scholar
  6. 6.
    Natural Science and Technology Council: 1996, Our Changing Planet: The FY 1996 U.S. Global Change Research Program, A report by the Subcommittee on Global Change Research, Committee of Environment and Natural Resources Research, A Supplement to the President's Fiscal Year 1996 Budget, Washington, D.C., p. 152.Google Scholar
  7. 7.
    Page 82, Shine, K. and Henderson Sellers, A.: 1983, ‘Modelling Climate and the Nature of Climate Models: A Review’, J. Climatol. 3, 81–94.Google Scholar
  8. 8.
    Page 55, Henderson Sellers, A. and McGuffie, K.: 1987, A Climate Modelling Primer, John Wiley, Chichester, p. 217.Google Scholar
  9. 9.
    Wigley, T. and Raper, S.: 1992, ‘Implications for Climate and Sea Level of Revised IPCC Emissions Scenarios’, Nature 357, 293–300.Google Scholar
  10. 11.
    Page 17, Schneider, S.: 1992, ‘Introduction to ClimateModeling’, in Trenberth, K. (ed.), Climate System Modeling, Cambridge University Press, Cambridge, pp. 3–26.Google Scholar
  11. 12.
    In a further contribution to the book, Meehl extends the concept of a model hierarchy to coupled models, relating comprehensiveness to realism and reliability of model output. (Meehl, G.: 1992, ‘Global Coupled Models: Atmosphere, Ocean, Sea Ice’, in Trenberth, K. (ed.), Climate System Modeling, Cambridge University Press, Cambridge, pp. 555–581; Kiehl, J.: 1992, ‘Atmospheric General Circulation Modeling’, in Trenberth, K. (ed.), Climate System Modeling, Cambridge University Press, Cambridge, pp. 319–369.)Google Scholar
  12. 13.
    Page 760, Schlesinger, M. and Mitchell, J.: 1987, ‘Climate Model Simulations of the Equilibrium Climatic Response to Increased Carbon Dioxide’, Rev. Geophys. 25, 760–798. Similar sentiments are expressed in the IPCC 1995 report, e.g., see Gates, L. et al.: 1996, ‘Climate Models – Evaluation’, in Houghton, J. et al. (eds), Climate Change 1995: The Science of Climate Change, Cambridge University Press: Cambridge, pp. 572.Google Scholar
  13. 16.
    Personal communication to first author from a GCMer, 22 May 1995.Google Scholar
  14. 19.
    Interview with two climatologists by first author, 5 and 6 August 1992.Google Scholar
  15. 20.
    Ibid. Also, Land, K. C. and Schneider, S. H.: 1987, ‘Forecasting in the Social and Natural Sciences: An Overview and Analysis of Isomorphisms’, Clim. Change 11, 7–31. Our argument is an inevitable simplification of how GCMs are perceived and in reality perceptions of GCMs are often more finelytextured and undergoing change. For example, new relationships have been emerging between GCMers and other scientists, such as ecologists and hydrologists, in which GCMs are less predominant (e.g., Root, T. L. and Schneider, S. H.: 1995, ‘Ecology and Climate: Research Strategies and Implications’, Science 269, 334–341); and in the U.S.A. there are not only more research centres than in Europe, but greater diversity in the key issues pursued. However, GCMs still dominate in the U.S.A. when it comes to providing policyrelevant knowledge.Google Scholar
  16. 23.
    Kiehl, in [12]. See also Kiehl, J. and Williamson, D.: 1991, ‘Dependence of Cloud Amount on Horizontal Resolution in the National Center for Atmospheric Research Community Climate Model’, J. Geophys. Res. 96, 10955–10980; Slingo, T.: 1990, 'sensitivity of the Earth's Radiation Budget to Changes in Low Clouds’, Nature 243, 49–51; Randall, D., Harshvardhan, Dazlich, D., and Corsetti, T.: 1989, ‘Interactions among Radiation, Convection, and LargeScale Dynamics in a General Circulation Model’, J. Atmos. Sci. 46 (13), 1943–1970.Google Scholar
  17. 24.
    Personal communication to first author from a GCMer, 7 February 1995.Google Scholar
  18. 26.
    Stone, P. and Risbey, J.: 1990, ‘On the Limitations of General Circulation Climate Models’, Geophys. Res. Lett. 17(12), 2173–2176; Stone, P. and Yao, MS.: 1991, ‘Vertical Eddy Heat Fluxes from Model Simulations’, J. Clim. 4 (3), 304–317.Google Scholar
  19. 27.
    Interview with a climate modeller by first author, 10 April 1994. (Michaud, R. and Derome, J.: 1991, ‘On the Meridional Transport of Energy in the Atmosphere and Oceans as Derived from Six Years of ECMWF Analyses’, Tellus 43A, 1–14; Oort, A. H.: 1978, ‘On the Adequacy of the Rawinsonde Network for Global Circulation Studies Tested through Numerical Model Output’, Mon. Wea. Rev. 106, 174–195; Oort, A. H.: 1983, Global Atmospheric Circulation Statistics, 1958–1973, NOAA Professional Paper 14, U.S. Government Printing Office,Washington, D.C.; Keith, D.W.: 1995, ‘Meridional Energy Transport: Uncertainty in ZonalMeans’, Tellus 47A (1), 30–44; the ongoing debate is reflected in Trenberth, K. and Solomon, A.: 1994, ‘The Global Heat Balance: Heat Transports in the Atmosphere and Ocean’, Clim. Dyn. 10, 107–134). A major comparison of models with respect to their heat transport has now been conducted (Glecker, P. et al.: 1995, ‘Cloud Radiative Effects on Implied Oceanic Energy Transports as Simulated by Atmospheric General Circulation Models’, Geophys. Res. Lett. 22 (7), 791–794) which reveals the extent of the probable errors).Google Scholar
  20. 28.
    Pp. 2175 and 2176 (our italics) respectively, in [26].Google Scholar
  21. 29.
    Young, P., Parkinson, S., and Lees, M.: 1996, ‘Simplicity out of Complexity: Occam's Razor Revisited’, J. Appl. Statis. 23, 165–210.Google Scholar
  22. 30.
    Interview by first author with a GCM modeller, 11 March 1993.Google Scholar
  23. 31.
    Some evidence for this view comes from research with relatively simple models (e.g., Marotkze, J. and Stone, P.: 1995, ‘Atmospheric Transports, the Thermohaline Circulation, and Flux Adjustments in a Simple Coupled Model’, J. Phys. Ocean. 25, 1350–1364; Nakamura, M., Stone, P., and Marotzke, J.: 1994, ‘Destabilisation of the Thermohaline Circulation by Atmospheric Eddy Transport’, J. Clim. 7, 1870–1882). Note, however, that not all GCMers would accept this evidence since simple models were used in these experiments.Google Scholar
  24. 32.
    It has been suggested to us that the evaluation of climate models has become increasingly formalised since the early 1990s, with projects such as the Atmospheric Model Intercomparison Project (AMIP) and its offshoots (Mike Hulme, personal communication, February 1997). An interesting question is the extent to which such formalisation is related to the perceived policy requirement for validation in a context of uncertain science for policy, rather than such a process being driven solely by the climate modelling community. There are generic similarities to the strategies of molecular biologists and particle physicists (Knorr-Cetina, K.: 1991, ‘Epistemic Cultures: Forms of Reason in Science’, Hist. Political Econ. 23, 105–122).Google Scholar
  25. 33.
    A discussion and classification of the limitations and uncertainties of climate and integrated assessment models can be found in van der Sluijs, J.: 1997, Anchoring Amid Uncertainty: On the Management of Uncertainties in Risk Assessment of Anthropogenic Climate Change, Ludy Feyen, Leiden, p. 260.Google Scholar
  26. 35.
    Personal communication by first author with Dr. James Risbey, 6 July 1994.Google Scholar
  27. 36.
    Hempel, C.G.: 1966, Philosophy of Natural Science, Prentice-Hall, Englewood Cliffs, NJ, p. 116; Hesse, M.: 1974, The Structure of Scientific Inference, Macmillan, London, p. 309; Popper, K.: 1959, The Logic of Scientific Discovery, Hutchinson, London, p. 480.Google Scholar
  28. 37.
    P. 234 in Hesse [36].Google Scholar
  29. 38.
    P. 44 in Popper, K.: 1982, The Open Universe, Hutchinson, London, p. 185; Tennekes, H.: 1992, ‘Karl Popper and the Accountability of Numerical Weather Forecasting’, Weather 47, 343–346.Google Scholar
  30. 39.
    Gieryn, T.: 1995, ‘Boundaries of Science’, in Jasanoff, S. et al. (eds.), Handbook of Science and Technology Studies, Sage, London, pp. 393–443; Collins, H.: 1992 (1985), Changing Order: Replication and Induction in Scientific Practice, Chicago University Press, London, p. 187.Google Scholar
  31. 40.
    Shackley, S. and Wynne, B.: 1995, ‘Integrating Knowledges for Climate Change: Pyramids, Nets and Uncertainties’, Global Environ. Change 5(2), 113–126.Google Scholar
  32. 41.
    Cartwright, N.: 1994, ‘Fundamentalism vs. the Patchwork of Laws’, Proc. Aristotelian Soc. 94, 279–292; Hacking, I.: 1992, ‘The SelfVindication of the Laboratory Sciences’, in Pickering, A. (ed.), Science as Practice and Culture, Chicago University Press, Chicago, p. 474.Google Scholar
  33. 42.
    Interview by first author with a GCMer, 10 September 1992.Google Scholar
  34. 44.
    Palmer, T. N.: 1993, ‘A Nonlinear Dynamical Perspective on Climate Change’, Weather 48(10), 314–326.Google Scholar
  35. 45.
    Ibid.: 324.Google Scholar
  36. 48.
    Cf. Ascher, W.: 1981, ‘The Forecasting Potential of Complex Models’, Policy Sci. 13, 247–267.Google Scholar
  37. 49.
    Enting, I. G. and Pearman, G. I.: 1987, ‘Description of a One-Dimensional Carbon Cycle Model Calibrated Using Techniques of Constrained Inversion’, Tellus 39B, 459–476; Wigley and Raper [9]; Enting, I. and Lassey, K.: 1993, Projections of Future CO2, Division Of Atmospheric Research, Technical Paper 27, CSIRO, Australia, p. 42; Schimel, D., Enting, I. G., Heimann, M., Wigley, T. M. L., Raynaud, D., Alves, D., and Siegenthaler, U.: 1995, ‘CO2 and the Carbon Cycle’, in Houghton, J., Meira Filho, L., Bruce, J., Lee, H., Callander, B., Haites, E., Harris, N., and Maskell, K.: 1995, Climate Change 1994: Radiative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios, Cambridge University Press, Cambridge, p. 339.Google Scholar
  38. 50.
    Gardner, R. H. and Trabalka, J. R.: 1985, Methods of Uncertainty Analysis for a Global Carbon Dioxide Model. Publication DOE/OR/214004. U.S. Dept. of Energy, Washington D.C., p. 41.Google Scholar
  39. 51.
    Schneider, E.: 1996, ‘Flux Correction and the Simulation of Changing Climate’, Annales Geophysicae 14, 336–341.Google Scholar
  40. 52.
    Enting and Lassey [49].Google Scholar
  41. 53.
    Further details can be found in Parkinson, S.: 1995, The Application of Stochastic Modelling Techniques to Global Climate Change, Ph.D. Thesis, Lancaster University, U.K., p. 263; Young et al., 1996 [29]; Parkinson, S. and Young, P.: 1997, ‘Uncertainty and Sensitivity in Global Carbon Cycle Modelling’, submitted manuscript.Google Scholar
  42. 54.
    Huggett, R.: 1993, Modelling the Human Impact on Nature: Systems Analysis of Environmental Problems, Oxford University Press, Oxford, p. 192.Google Scholar
  43. 55.
    See e.g., Scavia, D.: 1993, ‘Lake EcosystemModelling’, inYoung, P. (ed.), Concise Encyclopedia of Environmental Systems, Pergamon Press, Oxford, pp. 318–320.Google Scholar
  44. 56.
    One GCM group recently wrote a paper entitled: ‘Monte Carlo climate change forecasts with a global coupled oceanatmosphere model’ (Cubasch, U., Sausen, R., Maier Reimer, E., and Voss, R.: 1994, ‘Monte Carlo Climate Change Forecasts with a Global Coupled Ocean-Atmosphere Model’, Clim. Dyn. 10, 1–19). In this study they ran the AOGCM model only four times, which according to the standards of many modellers would not count as MCS at all! Nevertheless the significance of the model's initial conditions was clearly indicated, so it had some positive contribution.Google Scholar
  45. 58.
  46. 60.
  47. 64.
    Schimel et al. [49].Google Scholar
  48. 66.
    Young, P.: 1984, Recursive Estimation and TimeSeriesAnalysis, SpringerVerlag, Berlin, p. 300; Young, P.: 1985, ‘The Instrumental Variable Method: A Practical Approach to Identification and System Parameter Estimation’, in Barker, H. and Young, P. (eds.), Identification and System Parameter Estimation: Vol. 1 and 2, Pergamon Press, Oxford, pp. 1–16.Google Scholar
  49. 69.
    These values come from the continuoustime reduced order model reported in Young and Parkinson (1996),which explains the ELmodel data a little better than the discretetimemodel describedpreviously in Young et al. (1996) [29]. However, the time constants of both models are quite similar and the mechanistic interpretations are the same. (Young, P. and Parkinson, S.: 1996, Simplicity out of Complexity in Forecasting Climate Change, Technical Note Centre for Research on Environmental Systems and Statistics, Lancaster University, p. 37.)Google Scholar
  50. 71.
    Enting and Lassey, Enting and Pearman [49].Google Scholar
  51. 72.
    The IPCC 1990 report itself stated that: ‘Because running coupled oceanatmosphere GCMs is expensive and timeconsuming, many of our conclusions about global trends in future climates are based upon simplified models’ (Houghton, J., Jenkins, G., and Ephraums, J.: 1990, Climate Change: The IPCC Scientific Assessment, Cambridge University Press, Cambridge, p. 365). HendersonSellers has presented similar arguments to ours. (HendersonSellers, A.: 1996, ‘Climate Modelling, Uncertainty and Responses to Predictions of Change’, Mitigat. Adapt. Strat. Global Change 1, 1–21; and ‘Bridging the Climate Gap’, in Giambelluca, T. and HendersonSellers, A. (eds.) 1996, Climate Change: Developing Southern Hemisphere Perspectives, John Wiley, Chichester, pp. 35–60.)Google Scholar
  52. 74.
    Interview by first author with a government official, 23 February 1993.Google Scholar
  53. 76.
    Polanyi, K.: 1958, Personal Knowledge, Routledge and Kegan Paul, London, p. 428.Google Scholar
  54. 78.
    Interview by first author with a GCMer, 11 April 1994. The episode is mentioned in Gore, A.: 1992, Earth in the Balance, Houghton Miflin Company, New York, p. 407. A similar point is made by Parsons, E.: 1995, ‘Integrated Assessment and Environmental Policy Making: In Pursuit of Usefulness’, Energy Policy 23, 463–476.Google Scholar
  55. 79.
    Technology Review: 1992, ‘The Political Pleasures of Engineering: An Interview with John Sununu’, Technol. Rev. 95, August/September, 22–28.Google Scholar
  56. 80.
    Research results do not clearly indicate the effects of changes in the THC on the takeup of heat, and subsequent feedbacks upon surface temperature and precipitation. See, e.g., Harvey, L. D. Danny: 1994, ‘Transient Temperature and Sea Level Response of a Two Dimensional Ocean-Climate Model to Greenhouse Gas Increases’, J. Geophys. Res. 99(C9), 18,447–18,466.Google Scholar
  57. 81.
    Afurther example is the argument by Schlesinger and Jiang that a ten year delay in greenhouse gas emission reductions would have a minimal effect on potential warming. Schlesinger and Jiang were criticised by Risbey, Handel and Stone for basing strong policy conclusions on simple climate models which failed to take account of nonlinear, possibly abrupt climate change, as well as probably underrecognising the extent of regional climate change. The debate between Schlesinger and Jiang and Risbey, Handel and Stone subsequently revolved, however, around different and conflicting interpretations of the same GCM model runs, indicating that it was, in practice, difficult to divorce the evaluation of the simple models from more complex ones, and vice versa. A further difference between Schlesinger and Jiang and Risbey, Handel and Stone was the degree to which models, whether simple or GCMs, were held to be sufficiently robust to act as the basis of policy decisions to delay action. Schlesinger and Jiang defended the use of both types of models for this purpose, whilst Risbey, Handel and Stone implied that neither simple models nor GCMs were currently adequate to act as the basis for such decisions. (Schlesinger, M. and Jiang, X.: 1991, ‘Revised Projection of Future Greenhouse Warming’, Nature 350, 219; Schlesinger, M. and Jiang, X.: 1991, ‘A PhasedIn Approach to GreenhouseGasInduced Climatic Change and Climatic Response to Increasing Greenhouse Gases’, Eos Trans. A.G.U. 72 (53), 596–597; Risbey, J., Handel, M., and Stone, P.: 1991, 'should We Delay Responses to the Greenhouse Issue?’ and ‘DoWe KnowWhat Difference a Delay Makes?’, Eos Trans. A.G.U. 72 (53), 593.)Google Scholar
  58. 82.
    In this section we draw upon theories developed in the field of social studies of science. See, for example, Callon, M.: 1986, ‘Some Elements of a Sociology of Translation: Domestication of the Scallops and of the Fisherman of St. Brieuc Bay’, in Law, J. (ed.), Power, Action and Belief,Methuen, London, pp. 196–233; Latour, B.: 1988, The Pasteurisation of France, Harvard University Press, London, p. 273; Latour, B.: 1992 (1987), Science in Action, Harvard University Press, London, p. 274.Google Scholar
  59. 83.
    Interviews by first author with global vegetation dynamics modellers, 15 December 1993 and 16 September 1993.Google Scholar
  60. 84.
    Compare, Perry, J.: 1992, The United States Global Change Research Program: Early Achievements and Future Directions, National Academy Press, Washington D.C., p. 20.Google Scholar
  61. 86.
    Interview by first author with a GCM research manager 5 April 1993.Google Scholar
  62. 87.
    Interview by first author with an observational climatologist, 10 February 1993.Google Scholar
  63. 89.
    Parry, M.: 1990, Climate Change and World Agriculture, Earthscan, London, p. 157; Carter, T., Holopainen, E., and Kanninen, M. (eds): 1993, ‘Techniques for Developing Regional Climatic Scenarios for Finland’, Publications of the Academy of Finland 2/93, Painatuskeskus, Helsinki, p. 63; Viner, D. and Hulme, M.: Climate Change Scenarios for Impact Studies in the U.K., Climatic Research Unit, University of East Anglia, p. 70.Google Scholar
  64. 90.
    Admittedly, economists also make extensive use of simple models. Nordhaus, W. D.: 1994, Managing the Global Commons: The Economics of Climate Change, MIT Press, London, p. 213.Google Scholar
  65. 91.
    It might be objected that this is a circular argument, since the status of GCMs in policy derives itself in part from the prominence and credibility of GCMs within science. However, what we are proposing is a process of mutual reinforcement of status in which both processes occur concurrently (Shackley, S. and Wynne, B.: 1995, ‘Global Climate Change: The Mutual Construction of an Emergent Science Policy Domain’, Sci. Publ. Pol. 22(4), 218–230.)Google Scholar
  66. 92.
    It could be countered that versions of the science emphasising the climate system's chaotic nature might act to strengthen the rationale for global environmental policy action, especially if the precautionary principle is accepted. Note, however, that a dominant response to the suggestion that the climate systemmight face abrupt, chaotic and unexpected changes, has been modelbased analyses of whether this feature affects our ability to find an optimal solution to the problem of managing climate change. Hence it attempts to reimpose a control and management ethos at a subsequent level. (e.g., see: Lempert, R., Schlesinger, M., and Hammitt, J.: 1994, ‘The Impact of Potential Abrupt Climate Changes on NearTerm Policy Choices’, Clim. Change 26, 351–376). Nevertheless, as James Risbey points out, Palmer's analysis may point to a happy marriage between chaos and GCMs, since the latter are needed to represent regime structure, identify climate attractors and perform ensemble climate forecasts. A similar point has been made by a GCM modeller, who noted that: ‘GCMs provide the most practical means of investigating instability, given that the details of the “mean” climate or attractor determine the nature of the instability.... to get the right answer the detailed shape of the attractor may be important’ (personal communication, November 1995).Google Scholar
  67. 95.
    Young et al. [29].Google Scholar
  68. 96.
    Van Asselt, M.: 1994, Global Integrated Assessment Models as Policy Support Tools, Masters Thesis, University of Twente, Netherlands, p. 94.Google Scholar
  69. 97.
    For example, there is good reason to believe that the overwhelmingly technical framing of the climate change issue structures the policy agenda in a way which fails to engage with the diverse constituencies (such as local government, industry and lay members of the public) whose commitment would be necessary for any serious policy on global climate change. (Macnaghten, P., GroveWhite, R., Wynne, B., and Jacobs, M.: 1995, Public Perceptions and Sustainability in Lancashire: Indicators, Institutions and Participation, Lancashire County Council, Preston, p. 96; Macnaghten, P. and Jacobs, M.: 1997, ‘Public Identificationwith SustainableDevelopment: Investigating Cultural Barriers to Participation’, Global Environ. Change 7 (1), 5–24.)Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Simon Shackley
    • 1
  • Peter Young
    • 2
  • Stuart Parkinson
    • 2
  • Brian Wynne
    • 1
  1. 1.Centre for the Study of Environmental Change (CSEC) Bowland Tower EastLancaster UniversityLancasterU.K
  2. 2.Centre for Research into Environmental System (CRES), Institute of Environmental and Biological SciencesLancaster UniversityU.K

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