Principles of Prospective Models of Socioeconomic Metabolism

Overview and General Principles

Prospective models of society's metabolism require certain features to be fit for purpose. They need to follow an interdisciplinary systems approach, as explained above, and in Table 2.1, we list salient features of the systems approach and mention briefly how they are commonly implemented.

(1) Dynamic models have an explicit time dimension and contain mechanisms to generate the future state of the system from its past and from additional exogenous information. Dynamic models link different time scales with each other, which is necessary to study how changes on short time scales affect the long-term dynamics of the system. For example, large-scale substitution of materials today will alter the recycling system in the future. Combining dynamic models of SEM with other models with an explicit time line, like climate models, allows us to study the interaction between socioeconomic metabolism and the environment over time. Dynamic models allow for flexible handling of time discounting, e.g., an artificial time horizon to calculate the global warming potential is not needed in models where time is explicit. Finally, dynamic models enable researchers to study changes that happen gradually over time, like the introduction of new tech-

Table 2.1 Salient features of prospective models of socioeconomic metabolism and common ways of implementing them

Feature

Common implementation

Ability to capture different spatial, organizational, and temporal scales

(1) Dynamic models

(2) Assessment at full scale

Capability to produce results that are relevant for different scientific disciplines

(3a) Multilayer modeling

(3b) Satellite accounts

Ability to determine future consequences of decisions in the past

(1) Dynamic models

Ability to deal with the indeterminacy (“uncertainty”) of future development

(4) Scenario modeling with exogenous model parameters

Adherence to generally accepted scientific principles such as mass and energy conservation or economic balances

(3c) Balancing constraints for processes and regions

The numbers refer to the paragraphs below, where more detailed explanation is provided

nologies and the transformation of in-use stocks that leads to new recycling opportunities, resource depletion, and declining ore grades.

(2) Assessment at full scale: The ultimate goal of the coming transformation is to rescale human activity to a level that can be sustained by nature in the long run and that allows for future human development at the same time. Identifying the appropriate scale of human activity requires us to study socioeconomic metabolism on the global level, which was not necessary to understand the previous socioeconomic transitions.

Socioeconomic metabolism is a nonlinear system, which means that the impact of upscaling small modifications to the system is in general not proportional to the scaling factor. The upscaling of certain sustainable development strategies is subject to local and global constraints for, e.g., land, water, or mineral resources. Moreover, large-scale implementation of certain strategies feeds back into the system and causes structural change. Examples include changing recycling systems, technology learning, or rebound and spillover effects (Hertwich 2005). The total system-wide impact of the strategies' potential effect can therefore only be reliably assessed if the latter are studied at full scale, so that constraints and feedbacks can be included in the assessment.

(3a–c) Multilayer modeling, satellite accounts, and balancing constraints allow scientists from different disciplines, like industrial ecologists and economists, to use a consistent framework to describe society's metabolism and to address a variety of research questions. In multilayer modeling, the physical and economic properties of objects are quantified in consistent parallel frameworks (Pauliuk et al. 2015; Schmidt et al. 2012). Satellite accounts, like emissions to nature or labor requirements, contain additional information about how society's metabolism is connected to the environment and to human agents. They form the interface between models of SEM and those from other scientific disciplines, like climate models or environmental impact assessment. Balancing constraints for the physical and monetary layers, like industry or market balances, is the most fundamental way to check the validity of a prospective system description. Prospective models should always respect these fundamental balances.

(4) Scenariomodeling with exogenous parameters acknowledges the indeterminacy of future development and reduces system complexity to a manageable level. Only with scenario modeling one can build scientifically credible prospective models of complex indeterminate systems like socioeconomic metabolism. This central aspect of prospective modeling needs some more elaboration.

Socioeconomic metabolism is a non-isolated and non-deterministic complex system. It is not isolated, because it exchanges energy and matter with the inner of the Earth and with space. SEM is non-deterministic, because it is controlled by human agents that use their environmental literacy to intervene and divert the system from its current trajectory in a non-predictable manner. For such a system, there is no deterministic model that can predict its future development. Instead, scientists use prospective models of socioeconomic metabolism to compute future trajectories of the system that are considered possible but not necessarily likely continuations of historic development. Such possible future trajectories are called scenarios. Prospective models use a trick to compute future scenarios for an indeterminate system: First, a number of exogenous parameters, assumptions, and model drivers are defined, and then these are fed into a dynamic model of socioeconomic metabolism that is deterministic relative to the exogenous parameters. Parameters like fertility or efficiency improvement rates, model drivers like GDP or population trajectories, and assumptions like “ceteris paribus” or “business as usual” describe the possible future development of certain indicators and system properties on the macro-scale. In a second step, the prospective model applies the exogenous assumptions to the system description and generates a detailed scenario for society's future metabolism. Specification of exogenous parameters not only eliminates indeterminacy from the model, it also reduces the complexity of the system description by fixing those system variables that one else would have to determine by modeling poorly understood feedback mechanisms or those where sufficient empirical data are not available. Scenario analysis is therefore an important way to handle our ignorance of human-environment systems in a productive and transparent way.

 
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