On the relationship between energy efficiency and complexity: Insight on the causality chain

The relationship between the energy efficiency, energy density and complexity level of the system is here addressed from both thermodynamic and evolutionary perspectives. A case study from economic systems is presented to show that, contrary to widespread opinion, energy efficiency is responsible for energy growth and the complexity leap. This article further examines to what extent complexity, on a historical time scale, may evolve to counterbalance conservative effects brought about by energy efficiency. We analyze structural complexity growth by four different paradigms. An evolutionary pattern is then proposed that may encompass the broad dynamics underlying complexity growth. This evolutionary pattern rests on the hypothesis that thermodynamic evolutionary systems are featured from an ever growing influx of energy driven into the system by self-catalytic processes, which must find its way through the constrains of the system. The system initially disposes of the energy by expanding, in extent and in number of components, up to saturation due to inner or outer constraints. The two counteractive forces, constraints and growing energy flux, expose the systems to new gradients. Every new gradient upon the system represents a symmetry rupture in components' space. By exploring a new gradient, the system imposes further restrictions on its components and increases its overall degree of freedom. © 2008 WIT Press

Spatial effects in real networks: measures, null models, and applications

Spatially embedded networks are shaped by a combination of purely topological (space-independent) and space-dependent formation rules. While it is quite easy to artificially generate networks where the relative importance of these two factors can be varied arbitrarily, it is much more difficult to disentangle these two architectural effects in real networks. Here we propose a solution to the problem by introducing global and local measures of spatial effects that, through a comparison with adequate null models, effectively filter out the spurious contribution of non-spatial constraints. Our filtering allows us to consistently compare different embedded networks or different historical snapshots of the same network. As a challenging application we analyse the World Trade Web, whose topology is expected to depend on geographic distances but is also strongly determined by non-spatial constraints (degree sequence or GDP). Remarkably, we are able to detect weak but significant spatial effects both locally and globally in the network, showing that our method succeeds in retrieving spatial information even when non-spatial factors dominate. We finally relate our results to the economic literature on gravity models and trade globalization

Cycling and reciprocity in weighted food webs and economic networks

Networks of mass flows describe the basic structure of ecosystems as food webs, and of economy as input–output tables. Matter leaving a node in these networks can return to it immediately as part of a reciprocal flow, or completing a longer, multi-node cycle. Previous research comparing cycling of matter in ecosystems and economy was limited by relying on unweighted or few networks. Overcoming this limitation, we study mass cycling in large datasets of weighted real-world networks: 169 mostly aquatic food webs and 155 economic networks. We quantify cycling as the portion of all flows that is due to cycles, known as the Finn Cycling Index (FCI). We find no correlation between FCI and the largest eigenvalues of unweighted adjacency matrices used as a cycling proxy in the past. Unweighted networks ignore the actual flow values that in reality can differ by even 10 orders of magnitude. FCI can be decomposed into a sum of contributions of individual nodes. This enables us to quantify how organisms recycling dead organic matter dominate mass cycling in weighted food webs. FCI of food webs has a geometric mean of 5%. We observe lower average mass cycling in the economic networks. The global production network had an FCI of 3.7% in 2011. Cycling in economic networks (input–output tables and trade relationships) and food webs strongly correlates with reciprocity. Encouraging reciprocity could enhance cycling in the economy by acting locally, without the need to perfectly know its global structure

Jan Tinbergen’s Legacy for Economic Networks: From the Gravity Model to Quantum Statistics

Jan Tinbergen, the first recipient of the Nobel Memorial Prize in Economics in 1969, obtained his PhD in physics at the University of Leiden under the supervision of Paul Ehrenfest in 1929. Among many achievements as an economist after his training as a physicist, Tinbergen proposed the so-called Gravity Model of international trade. The model predicts that the intensity of trade between two countries is described by a formula similar to Newton's law of gravitation, where mass is replaced by Gross Domestic Product. Since Tinbergen's proposal, the Gravity Model has become the standard model of non-zero trade flows in macroeconomics. However, its intrinsic limitation is the prediction of a completely connected network, which fails to explain the observed intricate topology of international trade. Recent network models overcome this limitation by describing the real network as a member of a maximum-entropy statistical ensemble. The resulting expressions are formally analogous to quantum statistics: the international trade network is found to closely follow the Fermi-Dirac statistics in its purely binary topology, and the recently proposed mixed Bose-Fermi statistics in its full (binary plus weighted) structure. This seemingly esoteric result is actually a simple effect of the heterogeneity of world countries, that imposes strong structural constraints on the network. Our discussion highlights similarities and differences between macroeconomics and statistical-physics approaches to economic networks

Energy conservation more effective with rebound policy

This article sketches the problem of indirect energy use effects, also known as rebound, of energy conservation. There is widespread support for energy conservation, especially when it is voluntary, as this seems a cheap way to realize environmental and energy-climate goals. However, this overlooks the phenomenon of rebound. The topic of energy rebound has mainly attracted attention from energy analysts, but has been surprisingly neglected in environmental economics, even though economists generally are concerned with indirect or economy-wide impacts of technical change and policies. This paper presents definitions and interpretations of energy and environmental rebound, as well as four fundamental reasons for the existence of the rebound phenomenon. It further offers the most complete list of rebound pathways or mechanisms available in the literature. In addition, it discusses empirical estimates of rebound and addresses the implications of uncertainties and difficulties in assessing rebound. Suggestions are offered for strategies and public policies to contain rebound. It is advised that rebound evaluation is an essential part of environmental policy and project assessments. As opposed to earlier studies, this paper stresses the relevance of the distinction between energy conservation resulting from autonomous demand changes and from efficiency improvements in technology/equipment. In addition, it argues that rebound is especially relevant for developing countries. © 2010 The Author(s)

Hierarchies, power and the problem of governing complex systems

The concept of hierarchy is central to thermodynamics. Energy processes can be evaluated in terms of entropy content and the higher the entropy the lower they are positioned in the hierarchy of irreversibility. Hence, a Joule of heat at 500 K has a higher quality that the same amount of heat at 400 K. Introducing irreversibility into the Carnot machinery—the intellectual device by which we have historically developed the concept of efficiency, leads to the concept of maximum power output at suboptimal efficiency level. Introducing irreversibility—the hierarchal criterion for thermodynamics, means that time becomes a binding variable in thermal machines. Interestingly and perhaps not surprisingly, hierarchy is also a key concept of complexity. Along the line of an increasing hierarchical complexity, economic progress and evolution have been rewarding larger organizations or organisms throughout sentient or accidental selection. From microbes to whales, from villages to nations, from family firms to international corporations, the scaling up of the system has been achieved at the expenses of a growing complexity and hierarchy. To sustain the increasing complexity, processes have been increasing their power capacity thorough evolution and economic history. Is this intriguing parallel important to understand the fate of renewable energy? In this chapter I will try to expand upon the ideas of hierarchical scaling and power maximization to the problem of governing RES, with insights from finite-time thermodynamics, algometric scaling and complex science