Abstract
Metabolism refers to the process of energy and material flows required to sustain the structure of an organism, ecosystem, or socioeconomic system (such as an urban area). The study of energy metabolism of an economy is insightful on both a local scale (city, region, or country) and on a global scale (world economy). A key feature contributing to the complexity of socioecologic systems is feedback, manifest in the presence of cycles. Material cycles in ecological systems are closed: mass is conserved throughout all cyclic paths. Furthermore, the incoming solar energy is maximally dissipated throughout cycles. Ecological systems have developed intricate couplings in order to reduce or eliminate energy or material waste, in juxtaposition to economic systems. What makes then an economy so inefficient compared to nature? On a local scale, the study of metabolism indicates that cities or countries are not a self-sustaining systems: they draw materials, energy, and information from the surrounding ecological and economic environment. Cyclic metabolic paths in the world economy are typically strictly (anti)correlated to oil price. As showed in this chapter, the percentage of cycled material in trade was negatively correlated to oil price; this anti(correlation) scoring from 85 to 62% between 1960 and 2011. This shows that world metabolism is remarkably connected to the price of oil. In the long run, world metabolism is correlated to oil price because of the architecture of trading relationships. With low oil prices, the productive chain tends to unfold across countries, whereas with high oil prices the productive chain tends to shrink. Constraints and impediments to the complete success of renewable energy sources (RES) over fossil fuels are therefore based on certain factors which can be determined from a metabolic analysis of the economy: (1) energy source intensity, (2) the nonfungibility of oil in the transport sector, and (3) scale of production. Each factor raises particular questions which will be answered in this chapter. For example: Is the scale of the present economy/society (cities, countries, or world) strictly dependent on the intensity of fossil fuels? Can these scales of processes be sustained with energy sources at a lower intensity? What is the appropriate feedback between the scale of ecosystem services and scale of governance? Is circular economy attainable at the scale of the present global economy? These questions will be addressed in the light of energy metabolism.
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Notes
- 1.
A complex adaptive system (CAS) is a complex macroscopic collection of connected components organized in order to adapt to the changing environment. With the words of Holland: CAS are systems that have numerous components, often called agents that interact and adapt or learn (Holland 2006).
- 2.
Classic textbooks by Wallace et al. (1981) and Raven and Johnson (1989) do not include a straightforward definition, nor do they have “Life” listed in the glossary. A book by Wilson et al. (1978) has this line, “All living creatures must metabolize, grow and reproduce, protect themselves and their offspring, and evolve in response to long-term changes in their environment.” (p. 6).
- 3.
Producers are photosynthetic organisms, first consumers are species that feed directly from producers (such as herbivores) and second consumers are predators.
- 4.
It is worth noting that at the scale of biosphere only a source (short wave radiation) and a sink (long wave radiation) of energy is needed, being matter almost completely recycled (almost, because you still have a sequestration and de-sequestration of undecomposed biomass, which can become coal and hydrocarbons and varies according to climatic factors).
- 5.
In ecological networks, when we want to assess the amount of mass that is conveyed through one species (prey) to the other (predator) at every step of the food chain, from primary producers (grass) to the last predators (and decomposers), we cannot tag every atom of the organism and map every passage. We can only weigh the body mass of organisms through the food chain. If we know that the species A feeds 50% on the species B and 50% on the species C, then we know that the atoms of the species A have 0.5 probability of coming from B and 0.5 of coming from C. We can do this for all the species of the food chain and represent this by a continuous, steady food flow. If in the previous example the species C feeds on the species E for 50%, the species A has 0.25 probabilities of having atoms from species E, even if it does not directly feed on species E. Upon this, we can calculate the probabilities of an atom to go from one species to the other through all the possible direct and indirect paths. This is referred to as transition matrix, and in the transition matrix, we can calculate the share of atoms that make a cycle, i.e., that start from species A and come back to species A along all the possible paths (i.e., not only directly via the species B and C, but also indirectly along the species E). Now, suppose we are not talking about atoms, but value of a product. If, for example, Italy sells cars to USA, where the engines of the Italian cars are produced, the share of value of car relative to engine is cyclical with USA. Suppose now that the USA buys iron from China and that Italy sells cars to China. Even if Italy does not buy iron directly from China, the share of the value of iron in the engine of the car is cyclical.
- 6.
The formal definition of this index is complex. For a detailed definition see Picciolo et al. (2017).
- 7.
It is also worth noting, that by measuring the share of embodied value in traded goods, we also provide an indication of the embodied emissions in producing them. This issue concerning the increasing share of embodied emissions in traded goods have prompted many scientists to switch from a production-based to a consumption-based accounting of global emissions.
- 8.
Almost all empirical studies have found the largest impacts of oil prices on economic growth in the third and fourth quarters lag, with further negative effects in even later quarters (Donald et al. 2004).
- 9.
Naccache showed that the highest negative correlation results by defining price variation in terms of second derivative, that is, as price accelerations (Naccache 2010).
- 10.
According to Hooker there is no evidence of correlation between 1973 and 1994; for Mork and Alvarez-Ramirez, it weakened, but persisted while for Hamilton it is still statistically significant, albeit in a nonlinear analytical form (Hooker 1996; Mork 1989; Alvarez-Ramirez et al. 2010; Hamilton 2010). Did this tie weaken with time or disappear since the time of oil shocks? Recent studies, with more refined statistical tools and price specifications, have accomplished in restoring a stable relationship between the oil price and the economic activity beyond the 1986, the negative oil shock, when oil prices reached a minimum (Hamilton 2010; Naccache 2010; Papapetrou 2001; Oladosu 2009; Cologni and Manera 2009). According to these studies, there is evidence of a negative correlation, in some cases, between oil price and economy up to 70%, depending on the economic indicator (like industrial production or inflation) or the wavelet component of economic output.
- 11.
With the words of Jones, “Development of policies to deal with oil price shocks, other than broad monetary and fiscal policies and holding strategic crude oil stocks, if any satisfactory ones are to be found, awaits firmer and more detailed understanding of the mechanisms by which those shocks work their impacts” (Donald et al. 2004).
- 12.
The temporal scales of ecosystems vary greatlly, from geological, with the order of magnitude of 105 years to that of population growth rates, with the OM 100−101 years.
- 13.
The issue of the constrain on the path toward a law carbon economy posed by the energy intensity of fossil fuels compared to RES is addressed in Chap. 3.
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Ruzzenenti, F., Fath, B.D. (2017). Present Energy Metabolism and the Future of Renewables. In: Labanca, N. (eds) Complex Systems and Social Practices in Energy Transitions. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-33753-1_4
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