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 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