Further issues in forecasting primary energy consumption

The series of papers by Marchetti [8, 9, 10] and Graham and Senge [4] (published in this journal between 1977 and 1980) represents a useful alternative approach to the problems of energy demand forecasting and primary energy substitution. The learning system approach seems a most appropriate investigative framework, given the aggregated treatment of the energy supply-demand system. It is also interesting for its attempt to establish a link between the structure of the primary energy markets and the 50–60 year cycles of basic innovations.This paper seeks to illustrate further conclusions from the reported research findings, by considering additional energetic criteria. These additional criteria—energy concentration (or quality) and energy accessibility—appear to reinforce the linkage between innovation waves and primary energy substitution patterns, though we modify the interpretation placed on this connection. Furthermore, the additional criteria lead to alternative conclusions about future energy supply, namely that previously discarded energy sources may be revisited and may retain some of their earlier market share.     

Assessing techno-economic alternatives for rural development in Northeast Mexico

In the arid zone of Northeast Mexico are located some of the most socially deprived and economically marginal communities in the nation. This paper analyzes the current status of their social and economic organization and suggests alternative techno-economic subsistence activities that could improve productive capacity while preserving the fragile ecological balance between communities and exploitable natural resources. It shows how recent advances in computer mapping, linear programming, and techno-social modelling (SOPA technique) can assist decision makers and planners in choosing the best mix of social, economic, and technical advantages from an array of scenarios of change. A major finding is that the use of cooperatives as a managerial format is not always the best one to utilize, given the variability of community cohesiveness and varying organizational requirements of different techniques of exploiting animal and plant resources.     

Limits and possibilities of large-scale long-range societal models

It is argued that the appropriate criteria for evaluating large-scale long-range societal (or “world”) models differ significantly from the criteria that are appropriate for short-range econometric models or for small “toy” models. World models are designed to elucidate major societal problems arising from the interaction of many loosely connected factors—demographic, economic, technological, and environmental—that can be safely neglected in short-range extrapolative models. World models are also intended to explore the implications of long-range policy alternatives. Consequently, it is important that world models be phenomenological, rather than statistical, and they must “capture” the essence of some inherently nonlinear phenomena. This implies achieving qualitative realism without imposing an unwarranted assumption of causal determinism. Unfortunately, the mathematical characteristics of large nonlinear systems may essentially preclude meaningful long-run forecasts.     

Incorporating Delphi results in system dynamics models: A case of Indian tea industry

The results of a Delphi study on the future of the Indian tea industry are presented. Most of the information obtained for the study helps in formulating and validating a system dynamics model. The model also considers the occurrence of new events as visualized by the panelists. It is shown, however, that the policies recommended by the panelists yield poor industry behavior when Delphi-predicted new events operate in the environment. This may be due to lack of comprehension of the complexity of interaction between policies proposed and new events visualized by the panelists. On the basis of this investigation, it is proposed that Delphi and system dynamics studies should complement each other in arriving at viable policy decisions.