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Applying the nexus structuring space to characterize the EU food system

Applying the nexus structuring space to characterize the EU food system

Juan J. Cadillo Benalcazar & Ansel Renner

In the MAGIC project, an evaluative framework called quantitative story-telling (QST) was developed as a capable way of generating robust inputs on the science-policy interface. This article demonstrates the potential of that approach to characterize a flexible information space capable of supplying the structured quantitative data demanded by QST exercises. In this article, we focus on examples taken from an analysis of European Union (EU) agriculture.

In diagnostic mode, our analysis evaluated the current metabolic profile of the agriculture sectors of 29 European countries (the EU-27 plus the United Kingdom and Norway). In anticipation mode, our analysis then evaluated the possibility of a dramatic agricultural internalization for each of those 29 countries—what would be needed for near-complete self-sufficiency in foodstuffs, a crude look at downscaling planetary boundaries to the national level under the assumption that current imports become undependable. Across both analytical modes, a semantic interface referred to as the nexus structuring space was developed in which four lenses across four different descriptive domains were used. Fig. 1 summarizes the four lenses used.


Figure 1: Analytical representation of a modern agriculture sector, highlighting the macroscope (A), mesoscope (B and C) and microscope (D) lenses proposed by the nexus structuring space

When adopting a macroscope lens (symbol A in Fig. 1), multi-metric data concerning the absolute and relative sizes of the various societal sectors (the household sector, the manufacturing sector, the agriculture sector, etc.), as well as their respective metabolic characteristics, was generated. In our analysis, the macroscope gathered information on the end-uses of various foodstuffs and related those end-uses to more general societal consumption patterns. The mesoscope lens describes the dependence of the country under study on other social-economic systems. This dependence is evaluated in terms of how much of each agricultural commodity consumed is of local origin and how much is imported. In Fig. 1, two descriptive domains are identified for the mesoscope—symbol B describes the external dependency in terms of primary/secondary products while symbol C describes the external dependence in terms of live animals required to maintain animal production systems. The mesoscope thereby provides rich information relevant for discussions of food security and vulnerabilities to external factors. The microscope lens (symbol D in Fig. 1) describes the pressure exerted by local agricultural activities on the local ecosystem, differentiating between elements under human control (for example, fertilizers, human activity/labor, blue water) from those that are not (for example, green water, aquifers, soil). Finally, the virtualscope lens describes the characteristics of the “virtual” production processes that are required for the production of imported goods. The virtualscope is not visualized in Fig. 1 since, in practice, its characterization depends on the set of assumptions made. For example, the virtualscope can be understood from the anticipatory perspective of saving local biophysical resources (what would be needed for local self-sufficiency) or from the diagnostic perspective of pressure exerted on external social-ecological systems (outsourcing).

In diagnostic mode, the macroscope revealed substantial heterogeneity in the dietary profile of the EU countries, due mainly to a mix of cultural and environmental factors. In Portugal, for example, 21% of food consumed derives from animal products (in energy terms, fat products and marine/aquatic products not included). That same figure is 31% for Sweden. Similarly, 27% of the food consumed in Austria derives from grains, roots and tubers (in energy terms, again). On the other hand, grains, roots and tubers represent a full 46% of food consumed in Romania. The mesoscope suggests that when products are considered in terms of primary product equivalent, most of the countries assessed (20 out of 29) exceeded a 50% self-sufficiency level concerning plant products. That number of countries reduces by approximately half when analyzing animal products. When assessing animal feed (again, primary product equivalent), nearly all countries stand at less than 30% self-sufficiency. In anticipation mode, evaluating the possibility of a near-complete (90%) internalization of foodstuff imports by 2050—considering also population, diet and yield projections—the microscope and virtualscope lenses revealed that countries such as the Netherlands and Belgium would need to increase their agricultural area by 14x and 8x, respectively. In terms of NPK fertilizer usage, those same two countries would expect to increase application rates by approximately 90%. It should be stressed that these figures include in their consideration import for re-export, but also that the obverse (e.g. the elimination of high throughput agribusiness) would imply dramatic economic transformation in some countries.

The results obtained in our application of the nexus structuring space to agriculture in the EU illustrate—across a wide set of biophysical indicators—that the import of low added value agricultural products is an essential lifeline for the EU's contemporary agribusiness model. Our examples prove highly relevant when considering aspects such as the expected dramatic increase in global food demand by 2050 (putting strain on imports), the major agricultural demands being placed on EU agriculture by the European Green Deal, ongoing revision efforts related to the Common Agricultural Policy (CAP) and the uncomfortable fact that the CAP’s nine primary objectives currently imply several mutually antagonistic actions. The objective of "increasing competitiveness", for example, may likely lead to increased biophysical stress, which is antagonistic to the objective of "preserving landscapes and biodiversity". Our approach facilitates the integration of diverse perspectives by researchers and the development of policy-relevant indicators capable of informing the discussion between what is wanted and what can be done. More information can be found in Cadillo-Benalcazar et al. (2020) and Renner et al. (forthcoming).


Cadillo-Benalcazar JJ, Renner A, Giampietro M (2020) A multiscale integrated analysis of the factors characterizing the sustainability of food systems in Europe. J Environ Manage in press: https://doi.org/10.1016/j.jenvman.2020.110944

Renner A, Cadillo-Benalcazar JJ, Benini L, Giampietro M (forthcoming) Environmental pressure of the European agricultural system: An exercise in biophysical anticipation. Ecosyst Serv.

Modelling energy systems as multi-scale systems

Modelling energy systems as multi-scale systems

Louisa Jane Di Felice

One of the main goals of the MAGIC project has been that of modelling the interactions between energy, food and water, taking a perspective that is grounded in complexity. Most systems in the world can be broken down into components: cities are made of neighbourhoods; molecules are made of atoms; societies are made of people. Nexus interactions span through systems across different scales, with each scale affecting one another. For example, a coal power plant may affect its local embedding environment by polluting a nearby water source, while also generating global greenhouse gas emissions which, in turn, alter its local environment.

Our approach to modelling nexus interactions has been to focus on this multi-scale perspective, by using different information to describe nexus patterns at different scales of analysis. These types of information cannot be reduced to a single metric, and each description may be more or less useful depending on the goal of the analysis. This is why in MAGIC we do not rely on single indicators, such as efficiency or energy intensity, to measure the performance of the energy system.

The way we have broken down the energy system across different scales has not been in purely material forms – e.g., breaking down power plants into their components. Instead, we have focused on the distinction between function and structure of the energy system, taking inspiration from biology. For the case of energy, this means considering the different functions played by energy technologies – e.g., providing heating, or fuels, or baseload electricity.


Figure 1. A multi-scale description of Spain’s energy sector (for the year 2018)


Figure 1 shows an example of this, mapping Spain’s energy sector as a multi-scale network. The main node, “Energy sector”, is split into a fuels and an electricity component (since Spain does not have a heating sector). Electricity and fuels are then split hierarchically into further sub-sectors. Additional functional layers could be added depending on the goal of the analysis. Electricity, for example, could be split into baseload, peak and intermittent electricity. Each node in the network represents a processor, i.e., each node is associated with a set of nexus inputs and outputs (water, GHG emissions, labour, land, etc.). Further information on how elements of the energy systems can be described as processors can be found in Di Felice et al. (2019) (see the link to the open-access article at the bottom of this page). While intermediate levels in the network are functional, at the lowest level these functional layers are mapped onto their structures, i.e. the technologies fulfilling different purposes.

Here, the network in Figure 1 shows a distinction between blue and red nodes. Blue nodes are local ones. They are the processes taking place within the geographic boundaries of Spain. This includes most power plants and most refineries. Red nodes, instead, are those connected to Spain’s energy system, but which take place elsewhere (what we refer to as externalised processes). These include the extraction processes tied to Spain’s direct and indirect imports, for example. Mapping the energy sector across these different functional layers, associating each node with a set of nexus inputs and outputs, and making the distinction between local and externalised processes allows us to tap into questions that are relevant to the multi-level governance of sustainability, including:

  • Which functions of the energy sector emit most greenhouse gases? How can these functions be reduced or substituted?
  • What would happen to nexus elements across different scales, if the energy sector were to be gradually electrified?
  • How would the pattern of local and global environmental effects shift, if Spain decided to produce all of its energy locally?

We are currently working on this application, providing a multi-scale network description of the energy sector of the EU as a whole, and relating it to pressing policy questions. Follow us on twitter at @MAGIC_NEXUS to find out when the article is out!



Di Felice, L. J., Ripa, M., & Giampietro, M. (2019). An alternative to market-oriented energy models: Nexus patterns across hierarchical levels. Energy Policy, 126, 431-443. https://doi.org/10.1016/j.enpol.2018.11.002