METHODSThe rebound effect: An evolutionary perspective
Introduction
The rebound effect is portrayed by economic literature as a price-adjusting phenomenon that counterbalances, partially or thoroughly, the conservation effects expected by the adoption of a more efficient technology (Khazzoom,, 1980, Brookes, 1990, Saunders, 1992). It involves an upswing of new costs brought about by the introduction of new energy conversion technology. Economic theory, however, seems to focus solely on the problem of estimating the size of the effect, i.e. the percentage of savings offset by the increased demand (Geening, et al., 2000, Berkhout et al., 2000, Dimitropoulos, 2007). In the present analysis, we address the rebound effect from evolutionary and thermodynamic perspectives using an approach first envisioned by Alfred Lotka in the field of life sciences (Lotka, 1956). The aim of this analysis is therefore to provide unique insight on the issue in order to achieve a novel explanatory framework of the phenomenon. We seek this new framework due to our conviction that phenomena exhibiting significant analogies, albeit belonging to different fields, can be approached using a broader theory that is general in terms.
There are indeed similarities between economical and biological systems. One important analogy, particularly relevant to the present research, is the relationship between higher efficiency and higher energy level: systems with a better energy conversion rate also display a higher energy density rate. If the positive relationship between energy efficiency and energy density is a constant of thermodynamic evolutionary systems then the analogy can be broadened to a third feature: complexity (Odum, 1996). It is considered fundamental knowledge of evolutionary biology that greater complexity is linked to greater efficiency. Evolution thus awards the highest efficiency achieved to the highest structural complexity throughout the system (Alberts et al., 1994). Furthermore, it is well known that greater complexity entails higher energy costs and thus, the greater the complexity, the higher the energy density rate. It is therefore possible that the formation of a more complex structure may offset the conservation effect brought about by a more efficient technology. The explanation of the rebound effect would rest in the complexity leap of the system following the advent of new technology.
More specifically, the rebound effect addressed here is the macro rebound effect or wide-economy rebound effect, a specific type of rebound effect that is considered most crucial in determining energy consumptions (Sorrell and Dimitropoulos, 2007). The macro rebound effect underlies a long-term adjusting mechanism affecting factors markets. As stated by Sorrell and Dimitropoulos: “a fall in the real price of energy services [that] will reduce the price of intermediate and final goods throughout the economy, leading to a series of price and quantity adjustments, with energy-intensive goods and sectors gaining at the expense of less energy-intensive ones" (Sorrell and Dimitropoulos, 2006). This effect is considered by some authors to be the main cause of the constant growing trend in energy consumptions (Ayres and van den Bergh, 2005, Sorrell and Dimitropoulos, 2007). Nevertheless, the methodology and the extent of the impact on the economy energy path remain unanswered questions (Dimitropoulos, 2007). Here, the macro rebound effect is analyzed in the context of the productive structure's shape, as a result of the energy efficiency in the freight transport sector. This work will not tackle the question of the rebound size, nor the dispute over the proper econometric/theoretical approach. It will propose instead a novel view on the issue in order to create a bridge between different disciplines and deal with the broader question of the relationship between energy level, energy efficiency, and complexity.1 Although the analysis is qualitative, it provides a link between the dynamics of production patterns and the effect of efficiency increases, in the light of the macro-economic effects of increased energy demand.
Section snippets
The rebound effect in the freight transport system
In the aftermath of the first Oil Crisis (in the year of 1973), developed countries underwent a remodeling process in the fields of energy final use and energy production. Both in the EU and in the U.S., the road freight transport systems were subject to major changes. Some measures taken include: engines technology enhancement, aerodynamics, size and speed limits and market deregulation.2
The traffic density growth: Italy case study
The shift from a uni-located (fordian) productive chain to a pluri-located (postfordian) one, together with the integration of markets, placed an increasing burden on the road transport system. The fuel use in the freight transport sector grew not only because the mean distance of travels augmented, but also because the frequency changed. In other words, the outsourcing system of production is strictly connected with a more intensive and flexible transport system, which was provided solely by
The complexity leap
The underlying hypothesis of this work is that higher complexity counterbalances, on a global scale, the effects of higher efficiency on a process scale. So far, we have focused on demonstrating the actual extent of efficiency improvements and the timing with respect to the evolution of the productive structure. However, the fact that the productive structure, all through globalization, evolves towards higher complexity was taken for granted. We now want to stress the extent to which the
Conclusions
It has been shown by several qualitative approaches that the complexity of the productive system has grown in the last three decades. According to all but one (Fig. 3) of these approaches, complexity growth also caused the major circulation of materials, in terms of distance and frequency. Such a complexity leap was augmentative of the number, in time, of relationships among its parts and explained the growth in energy density rate thereafter. Figures (Table 4, Table 5) reveal that traffic
Acknowledgments
We would like to thank Professor Sergio Ulgiati and Professor Eric Weeks for their comments and advice. We are also very grateful to Farrah Elchahal for her kind and meticulous revision of the manuscript.
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