The Nexus between Energy, Food, Land Use, and Water
Application of a Multi-Scale Integrated Approach

Underlying concepts

  • The Nexus Assessment Project was commissioned by the Energy Team of the Climate, Energy and Tenure Division (NRC) of the UN Food and Agriculture Organisation (FAO)

    and sponsored by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).

Bioeconomics: The flow-fund model

The flow-fund conceptual model proposed by Nicholas Georgescu-Roegen lies at the basis of MuSIASEM given its usefulness in the characterization of the metabolic pattern of social systems. In MuSIASEM, fund elements are those elements of the observed system that are transformative agents expressing the functions required by society.  Funds are used but not consumed; they remain and should remain “the same” across the duration of the analysis. They represent “what the system is” and “what the system is made of”. Examples of fund elements are human beings, managed land uses, rivers, and technological capital. The idea of sustainability implies that these fund elements have to be maintained and reproduced in the metabolic process through the duration of the analysis. Fund elements correspond (to a certain extent) to production factors (labour, capital, land) in the economic narrative.  Flow elements, on the other hand, are those elements that appear or disappear (their attributes are changed) over the duration of the analysis, such as outputs that are generated or inputs that are consumed by the socio-economic process. The analysis of the transformation of flows tells us “what the system does” in relation to its context/environment (at the large scale) and with regard to its internal components (at the local scale). Examples of flow elements are consumption and production of food, exosomatic energy (fossil energy, electricity), water (for drinking, domestic use, irrigation, industrial processes) and other key materials.

In stark contrast to traditional input/output analysis (e.g., energy input per unit of output, water footprint per unit of crop produced, energy intensity of the economy), MuSIASEM always characterizes flows in relation to funds (e.g., energy input per hour of labour, water consumed per hectare of land in production, energy consumption per year per capita). This feature is essential because it allows us to account for the special nature and the size of the system under analysis.

For any metabolic system (e.g., a person, a society) we can define expected relations between specific flow and fund elements in both qualitative and quantitative terms.  Indeed, the very identity of a flow depends on its end-use and therefore a flow (e.g., energy carrier) is always fund-specific (e.g., horses eat hay while tractors ‘eat’ fuel). This qualitative relation determines what attributes should be used to characterize a given flow and hence which flows are admissible in the accounting. For example, drinking water (flow) for human beings (fund) must satisfy certain criteria (e.g., absence of toxic substances and harmful microorganisms) to qualify as such and so must irrigation water (flow) for cropland (fund) (e.g. salinity level).

As regards the quantitative aspects, the nature of metabolic systems allows us to define for the various fund elements (e.g., human beings, cropland, rivers) a range of admissible values for the ratio flow/fund that guarantees the survival and reproduction of these fund elements. For example, a human being must consume on average about 10 MJ of food per day, not much more and not much less; different crops require different quantities of irrigation water per hectare.  Thus, basing the analysis of metabolic patterns on the flow-fund model it becomes possible to integrate in a coherent way various pieces of quantitative information referring to different dimensions of analysis (biophysical, agronomic, economic, demographic, and ecological).

In particular, in order to bridge the socio-economic and the ecological view,  MuSIASEM uses simultaneously two complementing but non-equivalent definitions of fund elements, one relevant for socio-economic analysis (human activity and power capacity/technology) and one relevant for ecological analysis (land uses/land covers, water funds), at all the levels and scales considered (e.g., local crop field, watershed, whole country). In this way, MuSIASEM provides an integrated characterization of society’s metabolic pattern and its effect on the metabolism of the embedding ecosystems by combining non-equivalent systems of accounting.

Three conceptual tools derived from complexity theory

The system of accounting of MuSIASEM is further based on three conceptual tools derived from complex systems theory:

  1. Multi-level/Multi-scale accounting (T.F.H. Allen’s Hierarchy Theory): Society is viewed and analyzed as a nested hierarchical system using the concept of “holon” developed by Arthur Koestler. Each component of the system (e.g., the agricultural sector) is part of a larger whole (e.g. the paid work sector), which is in turn part of a still larger whole (e.g. the society) embedded in an even larger process determining boundary conditions (e.g. large-scale ecological processes).  At the same time, each part can be analyzed by looking at its lower-level components (the paid work sector is composed of the agricultural sector, energy sector, service sector, etc.), which in turn can be analyzed in still smaller parts. The definition of the identity of the various components at the different scales is based on the identification of a structural and functional relation (the holon) that can be seen (in different ways) from both the higher (as a function) and lower (as a structure) hierarchical level.
  2. Multi-purpose grammar (from R. Rosen’s modeling relation): A grammar is different from a model in the sense that it provides a description based on an expected set of relations over semantic categories and then it establishes an expected set of relations between semantic and formal categories (data and formal systems of inference).  For this reason a grammar is semantically open (e.g., “cheap labour” can be formalized in different ways depending on the year and type of society; the categories describing activities in the agricultural sector can be chosen using different criteria of accuracy).  A multi-purpose grammar defines the relevant characteristics of the system as depending on other characteristics and therefore can be tailored and calibrated to specific situations and adjusted to include new relevant qualities in the analysis.
  3. Impredicative loop analysis (from theoretical ecology – R. Ulanowicz): Unlike conventional (linear) deterministic models, MuSIASEM accommodates the chicken-egg predicament typically encountered in the description of complex systems. Having established a relation between the characteristics of the whole and those of the parts of the system in semantic terms, we formalize the grammar in quantitative terms (using proxy variables) by generating a set of forced relations of congruence between the characteristics of the parts and those of the whole.  These forced relations of congruence imply that the characteristics of the parts must be compatible with those of the whole and vice-versa, but they do not define a linear causal relation (hence the label “impredicative”).

The application of a multi-purpose grammar to perform an impredicative loop analysis across the nested hierarchical organization of the system makes it possible to construct a multi-level, multi dimensional matrix that shows strong similarities with the popular Sudoku game. Indeed, when discussing the option space (i.e., possible scenarios of change) of a system whose metabolic pattern has been characterized in this way, we can identify the existence of a series of congruence constraints across levels (characteristics of parts/characteristics of whole) and, at the same time, congruence constraints across dimensions (money flows, water flows, energy flows, technical requirements, labour requirements). The definition of these constraints is similar to the rules for a Sudoku grid.

Further reading

Georgescu-Roegen, N. (1971). The Entropy Law and the Economic Process. Cambridge, MA, Harvard University Press.

Ahl, V. and T.F.H. Allen (1996). Hierarchy Theory: A Vision, Vocabulary and Epistemology. New York,University of Columbia Press.

Rosen, R. (1985). Anticipatory Systems: Philosophical, Mathematical and Methodological Foundations. New York, Pergamon Press.

Ulanowicz, R.E. (1986). Growth and Development - Ecosystems phenomenology. New York, Springer.