AnalysisGlobal energy modelling — A biophysical approach (GEMBA) Part 2: Methodology
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.
References (42)
- et al.
The use of growth-curves in energy studies
Energy
(1992) - et al.
Trend extrapolation in long-term forecasting: an investigation using New Zealand electricity consumption data
Technological Forecasting and Social Change
(1986) - et al.
Energy budgets. 2. Energy cost of fuels
Energy Policy
(1974) Net energy from the extraction of oil and gas in the United States
Energy
(2005)- et al.
Aggregation and the role of energy in the economy
Ecological Economics
(2000) - et al.
Modelling ecological and economic systems with STELLA: Part II
Ecological Modelling
(1998) - et al.
Modeling ecological and economic systems with STELLA: Part III
Ecological Modelling
(2001) - et al.
Energy scenarios: toward a new energy paradigm
Futures
(2008) - et al.
A simple substitution model of technological change*
Technological Forecasting and Social Change
(1972) - et al.
Global energy perspectives: a summary of the joint study by the International Institute for Applied Systems Analysis and World Energy Council
Technological Forecasting and Social Change
(1996)
Physical energy cost serves as the “invisible hand” governing economic valuation: direct evidence from biogeochemical data and the U.S. metal market
Ecological Economics
Technological growth curves: a competition of forecasting models
Technological Forecasting and Social Change
Reflections on Howard T. Odum's paper: Energy, Ecology and Economics, Ambio, 1973
Ecological Modelling
The Lancaster-Betz limit (energy conversion efficiency factor for windmills)
Journal of Energy
General System Theory: Foundations, Development, Applications
Dynamics of an energy-economic system subject to an energy substitution sequence
Energy Systems and Policy; (USA)
Statistical Review of World Energy
Energy inputs and outputs of nuclear power
Energy and the U.S. economy: a biophysical perspective
Science
Ultimate Recoverable Hydrocarbons in Louisiana: A Net Energy Approach
Global Energy Modelling: a Biophysical Approach (GEMBA). University of Canterbury. Mechanical Engineering
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