ASPECTS OF TECHNOLOGY ASSESSMENT IN A DEVELOPING PETROLEUM TRANSITION ERA

NOTES AND REFERENCES

1. Joseph E. Coates, ‘Technology assessment: the benefits, the costs, the consequences’, The Futurist, 5, 1971.

2. James B. Sullivan, ‘A public interest laundry list for technology assessment: two dozen eternal truths about people and technology’, Technological Forecasting and Social Change, 8, 1976, pp. 439–40.

3. Ida R. Hoos, ‘Societal aspects of technology assessment’, Technological Forecasting and Social Change, 13, 1979, p. 193.

4. Ida R. Hoos, ‘Social fallacies in futures research’, Technological Forecasting and Social Change, 10, 1977, p. 340.

5. Lynn White, ‘Technology assessment from the stance of a mediaeval historian’, Technological Forecasting and Social Change, 6, 1974, pp. 359–69.

6. Francois Hetman, ‘Society and the assessment of technology’, OECD, 1973; J. Nehnevajsa and J. Menkes, ‘Technology assessment and risk analysis’, Technological Forecasting and Social Change, 19, 1981, pp. 245–55. The evolutionary stages of TA are identified and, germane to this discussion, Lindlom's pragmatic approach is described as follows: A technology assessment does not divine the future, but is ideally a tool for contingency planning, crisis avoidance, and policy option analysis. It develops alternative strategies that are internally consistent and subject to explicit constraints. Policy choices must often be based on compromise and decision makers are responsible for assuring that the compromises are equitable and that costs and benefits are fully allocated — both in terms of dollars and social costs and benefits. The anticipated information of an assessment should be therefore:

7. Identify technological developments

8. Specifiy alternatives based on the distribution of a variety of costs and benefits among affected parties

9. Present social choice and policy options compatible with a wide spectrum of future environments.

10. Louis H. Mayo, ‘Thoughts on the adequacy of performance of technology assessments’, Technological Forecasting and Social Change, 22, 1982, p. 267.

11. See, for example, Theo van Dugteren (ed.) Oil and Australia's Future, Australian Institute of Political Science 45th Summer School, Hodder and Stoughton, 1980. Various publications of the National Energy Advisory Council, Canberra: No. 9 ‘Liquid fuels — longer term needs, prospects and issues’, 1979. No. 10. ‘Strategies for greater utilisation of Australian coal’, 1980. No. 11 ‘Natural gas: the key issues’, 1980. No. 12 ‘Alternative liquid fuels’, 1980. No. 14 ‘Australia's energy resources 1980‘, 1980. No. 18 ‘Petroleum products: demand and supply trends in Australia’, 1982.

12. Australian Energy Statistics, Department of National Development and Energy, Canberra, 1983; Forecasts of Energy Demand and Supply — Primary and Secondary Fuels, Australia, 1984–85 to 1993–94, Department of National Development and Energy, Canberra, 1983.

13. G.A. Stewart et al, The Potential for Liquid Fuels from Agriculture and Forestry in Australia, CSIRO, 1979.

14. Joseph P. Martino, Technological Forecasting for Decision Making, Elsevier, 1983.

15. Hoos, op.cit.

16. James Henry, ‘The future hustle’, The New Republic, 4 February 1978, pp. 16–20 quoted in Ida Hoos, op.cit., p. 195.

17. Economic forecasting is one of the hottest fields in the US economy, along with law, medicine, evangelical religion, astrology and pornography. In some ways it resembles these other industries. It has the pretension to technical rigor of legal analysis, the therapeutic swank of medicine, the irrational claims to authority of evangelical religion and, also, pornography's insatiable audience of subscribers.

18. N. Georgescu-Roegen, The Entropy Law and the Economic Process, Harvard University Press, Cambridge, Mass., 1972.

19. William Ascher, ‘Problems of forecasting and technology assessment’, Technological Forecasting and Social Change, 13, 1979, pp. 154–5.

20. Joseph E. Coates, ‘The role of formal models in technology assessment’, Technological Forecasting and Social Change, 9, 1976, pp. 139–90.

21. V. Caddy, ‘The development and use of energy models in the private sector: an example of motor gasoline demand modelling’ in National Energy Development and Demonstration Program, Energy Modelling in Australia, Workshop, Sydney, 1985, pp. 93–112; Paul Lopert and Suzanne Williamson, ‘Energy modelling by the energy authority of New South Wales’ in Energy Modelling in Australia, op.cit., pp. 71–92; W.A. Donnelly and M. Diesendorf, ‘Variable elasticity of demand models for electricity’, Energy Economics, July 1985.

22. David James, Integrated Energy — Economic Environment Modelling with Reference to Australia, Department of Home Affairs and Environment, AGPS, Canberra, 1983; A.R. de L. Musgrove, K.J. Stocks, P. Essam and J. Hoetzel, Exploring Some Australian Energy Alternatives Using MARKAL, CSIRO Division of Energy Technology Report TR-2, 1983; D.E. James, A.R. de L. Musgrove and K.J. Stocks, ‘The integration of MERG and MENSA for studies of the Australian energy system’ in Broadening Australia's Energy Perspective, Australian Institute of Energy Conference, 1984, Brisbane, pp. 197–203.

23. Federal Energy Administration, National Energy Outlook, US Government Printing Office, Washington, 1976.

24. K. C. Hoffman, ‘A unified framework for energy systems planning’ in M. Searl (ed.), Energy Modelling, Resources for the Future, Washington, 1973.

25. Musgrove et al, op.cit.

26. C.J. Hitch (ed.), Modelling Energy — Economy Interactions: Five Approaches, Resources for the Future, Washington, 1977.

27. A brief description of the technique is given by Pugh in Dynamo User's Manual, MIT Press, 1976: The modelling procedure consists of constructing sets of conservative subsystem components, each component being capable of portraying dynamic cause and effect relationships. All relationships can be expressed mathematically through the process of integration and by data initialisation. Inter-relationships are identified, and a network of positive and negative feedback loops identify the dynamics of the equilibrating processes (positive feedback loops, for example, indicate that an initial disturbance is reinforced by influences in the feedback).

28. Cross-impact analysis has not been included so far in the overall energy planning model although a methodology and procedure has been described by Mitchel F. Bloom, ‘Deterministic trend cross-impact forecasting’, Technological Forecasting and Social Change, 8, 1974, pp. 35–74.

29. Michael R. Chambers, unpublished work; J. D. Sterman, The Energy Transition and the Economy: A Systems Dynamics Approach, (PhD Dissertation, MIT, 1981). This work, concentrating on macroeconomic effects in the US, shows that economic consequences of depletion are most severe during the energy transition period. The effects include: reductions in economic growth, increased unemployment, inflationary stress, high real interest rates, and reduced consumption per capita. The forces likely to cause these effects are thought to be: depletion of indigenous oil will depress production and boost energy prices; less efficient energy substitutes will need greater inputs and will thereby deprive the non energy sector of productive resources; overshoot of unconventional energy prices will substantially worsen the impact of depletion.

30. Malcom Slesser, A.R. Gloyne and R.J. Peckham, Dynamic Energy Analysis of the EEC Energy Transition Programme, Energy Studies Unit Report, University of Strathclyde, Glasgow, 1976. This study demonstrated that there are limits to the rates of energy transitions if supply and economic disruptions are to be minimised. This was based on a ‘dynamic net energy analysis’ of various energy forms, of whatever quality, and their energy costs between source and consumer.

31. Michael R. Chambers, ‘A strategic planning framework for chemicals and fuels production. Part 2: economic and energy analytic complementarity’, Engineering Costs and Production Economics (in press). Under optimal resource allocation conditions, using a linear programming method, a close correspondence between monetary and energy analysis criteria was found. Some important consequences and implications of this are as follows. The mean value of chemicals and fuels in the market place can be predicted with reasonable accuracy using energy analysis parameters for a given policy option. Process susceptibilities to substitution, process technological limits and required levels of technological improvement can be ascertained. The value of both resources and products in the economy may be determined via imputed energy and monetary shadow prices.

32. Michael R. Chambers, ‘A strategic planning framework for chemicals and fuels production. Part 1: allocation model’ Engineering Costs and Production Economics (in press). The LP model allocates resources optimally and, additionally, provides opportunities for sensitivity analysis. Thus, from given resource availabilities, cost determinants, and technology status, it is possible to impute the market price for the products, the marginal value of the resources, and the opportunity costs of optimal process alterations.

33. Donald Mackay, ‘North Sea oil — past lessons and future prospects’, Chemistry and Industry, 5 August 1978, p. 544.

34. Synthetic Fuels, Report by the Sub-Committee on Synthetic Fuels by the Committee on the Budget, United States Senate, Washington DC, 1979.

35. M. Hammerli, ‘When will electrolytic hydrogen become competitive?’, International Journal of Hydrogen Energy, 9, 1984, pp. 25–51.

36. J. Mitchell, ‘Status of hydrogen development for aircraft in five countries: a Canadian perspective’, International Journal of Hydrogen Energy, 8, 1983, pp. 453–8; L. A. Slotin, ‘The hydrogen economy: future policy implications’, International Journal of Hydrogen Energy, 8, 1983, pp. 291–4.

37. S.C. Ballard and T.A. Hall, ‘Theory and practice of integrated impact assessment’, Technological Forecasting and Social Change, 25, 1984, pp. 37–48.

38. Nehnevajsa and Menkes, op.cit., p. 246.