Elsevier

Ecological Economics

Volume 73, 15 January 2012, Pages 158-167
Ecological Economics

Analysis
Global energy modelling — A biophysical approach (GEMBA) Part 2: Methodology

https://doi.org/10.1016/j.ecolecon.2011.10.028Get rights and content

Abstract

Economists, investors and policy makers need to understand the changing climate of energy systems and the potential for investment in both alternative energy supply and demand side efficiency and management technologies. Biophysical economics has contributed to conventional economics by incorporating thermodynamic and ecological principles and emphasising the importance of natural resources to the economic process. This paper is presented in two parts. Part 1 gives a historic review of biophysical economics and discusses some previous models of the energy-economy system built around the principles of biophysical economics. Part 2 presents the GEMBA model — a new modelling methodology in the biophysical economics tradition. The methodology proposes a new and important contribution to the field of biophysical economics; a lifetime evolving function for the dynamics of the energy return on investment (EROI). In the development stage of a new resource, EROI increases due to technological learning, market growth and capital investment. EROI then reaches a peak as diminishing returns are experienced on further technological innovation and capital investment. In the later stage EROI declines over time as the most accessible resources are developed first, resources become depleted, or scarcity develops for materials needed to extract, process or convert the energy for the market. EROI can also diminish over time as environmental restoration or emission reduction becomes required by the society. The dynamic EROI function was incorporated into a global energy model using a biophysical approach (GEMBA) and implemented in VenSim. The GEMBA model is calibrated using historical energy production data, i.e. training to historical data, then running the trained model to 2100 under a series of varying assumptions regarding availability of energy resources and corresponding EROIs. The main finding of the model is that growth of the renewable energy sector may impact investment in other areas of the economy and thereby stymie economic growth.

Introduction

This second part of this paper outlines the GEMBA modelling methodology. GEMBA is a system dynamics model based on the fundamental principles of net energy analysis and biophysical economics. The first part of this paper offers a review of the history of biophysical economics and previous models that have been developed using these principles. Costanza and Gottlieb, 1998, Costanza and Voinov, 2001 offers a treatment of modelling ecological and economic systems using dynamic modelling packages. The GEMBA model is programmed using the dynamic systems software package VenSim (Ventana 1988–2008).

Section snippets

System Parameter and Modelling Definition

The energy-economy system is considered as a dynamic system. Dynamic systems are characterised by their complex nature, with many interacting causal and feedback loops that must be analysed at the systems level; they cannot be decomposed into simpler independent elements or processes. Due to the existence of feedback loops, i.e. X(Y) and Y(X) or in logical terms X  Y, complicated dynamic systems cannot be studied analytically Bertalanffy, 1971, Forrester, 1972, Hall and Day, 1990. Hence these

Model Formulation

In order to analyse the model numerically, the relationships between system elements (hereafter referred to as “variables”) must be defined mathematically. The rate of annual production by the energy sector will depend both on the size of the energy sector capital stock, Capenergy [EJ], and the accessibility of the energy resource, EROIenergy [dmnl].1

Define Assumptions Explicitly

In order to engage in any modelling process, be it quantum mechanics, economics or psychology, it is necessary to make a number of (more or less) arbitrary assumptions from which the consequences of the model may flow. The most important process is to make those assumptions explicit and open to discussion. This section outlines the major assumptions of the model. Assessment of the validity of the assumptions is undertaken in the Discussion (Section 3).

Following the work of Bodger and Baines

Results and Discussion

As shown in Fig. 4, the TPES from the baseline run of the GEMBA model peaks in 2060 at 800 EJ/yr. From the perspective of standard energy models, such as MESSAGE, this behaviour may seem confusing, since no explicit conservation policy decision was modelled nor has the amount of energy produced from any of the energy sources (either cumulatively or annually) exceeded the resource base of that energy source. The decline in energy production occurs because the cost of energy production, in

Conclusion

The underlying motivation for developing energy models and the standard economic approach taken by the main contenders (MESSAGE, MARKAL and WEM) has been looked at. Some of the limitations of such an approach have been outlined and other physical resource-based models, seeking to obviate these problems, have been identified. The GEMBA model developed in this paper represents an extension on that alternative physical resource approach.

The GEMBA model may be used to explore the large-scale

Acknowledgements

The authors would like to thank the Keith Laugesen Trust and the Department of Mechanical Engineering at the University of Canterbury for their financial support whilst this work was undertaken. Thanks also to Adam Brandt at Stanford and Bob Lloyd at Otago University for their recommendations.

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