Integrated assessment models (IAMs) are mathematical models that integrate several different sectors of the society with the natural world to understand the future effects of human choices and development on the state of human society and the earth system. They started off as a combination between energy and economics models, however IAMs today have expanded to represent complex socio-economic and biophysical systems and are based upon cross-disciplinary knowledge. Their goal is to aid policy-making by studying “what if?” and “how to?” research questions. Their main structural elements are visible in Figure 1.

One important feature of IAMs is their modularity. An IAM is in fact composed of modules, which are the elements of the model to represent a specific sector of the society, or a topic. They can be used separately from the other parts of the model, provided the necessary inputs are introduced into the module.

Model inputs, such as data and assumptions, are fed into the model and undergo a lengthy process of calculations (often with the goal of optimising for minimal costs) and produce numerical results for all model variables. Although modular, the model structure often includes feedback links which enable one-way or two-way communication between the modules so that changes in one module will induce changes in another (e.g., changes in the energy system may reflect changes in land use and/or in the economy). This is particularly true for System Dynamics models, whose robustness relies in their capacity to represent complex feedback loops among modules. In figure 1 this is clearly visible in the green box, where the single modules can interchange information (or endogenous inputs) among them.

The outputs are basically the main interest of the user. They are the reason why a modeller wants to run the model. For example, in the context of climate change, the value of total emissions might be one of the most relevant outputs, without forgetting that several other variables are connected to that, such as the renewable energy deployment, the allocation of land to several uses etc. Here lies in fact the power of system dynamics models: a change in a variable will most probably induce a change to several other variables of the system.

The simulation of the model occurs with the definition of storylines and scenarios.

Storylines, also known as narratives, are general representations of future development based on a fundamental set of assumptions about the structure of environmental problems and the current socio-economic system. While storylines are of a general qualitative nature, scenarios represent possible quantifications of storylines. As such, scenarios have emerged as one of the key ways of dealing with uncertainty and performing projections of future developments as is the case with IPCC Assessment reports dealing with climate change, Millennium Ecosystem Assessment reports dealing with biodiversity, FAO Global Resources Assessment dealing with forests or more general UNEP Global Environmental Outlooks. In figure 1, the group of quantified inputs needed to run a simulation (including the set of policies) can represent a single scenario.

In each storyline a positive or negative outcome may occur in terms of fulfilment of the overall goals of the storyline. An overall goal can be defined as the objective of a specific storyline as a result of the policies put in place under that storyline. Model simulations allow examining the conditions under which climate and socio-economic goals may be reached within these narratives. For our purpose of simulating future projections of specific variables of our socio-economic system, we can identify two types of storylines: the baseline storylines which represent an interpretation of the continuation of current trends, and policy-action storylines, developed on top of baseline scenarios and include the additional or enhanced policy measures which, consistent with the policy-action storyline, allow for attaining the pre-defined overall goals. A policy can be defined as a set of ideas or a plan of what to do or how to act in particular situations that has been agreed officially by a group of people, a business organisation, a government, or a political party. Different types of policies can be defined, depending on their scope: Energy Policy, Economic Policy, Trade Policy, Health policy, Environmental policy, etc. A policy objective (or policy goal) is generally formulated as the desired outcome of a policy. Examples can be: decarbonization, transition to renewables, reduction of unemployment, reduction of inequalities, Gross Domestic Product per capita (GDPpc) growth, increasing exports, etc.

Storylines and scenarios are the basis of comparability studies, i.e., their value does not lie in the precise outputs obtained by one scenario but in the comparison between different storylines and policy options. The storylines presented here define future development based on a fundamental set of assumptions starting from the base year onward. It is important to note that these storylines are archetypical in the sense that they represent extremes rather than a likely development of reality, which may rather be a nuanced mix of these storylines. That is why a model does not give predictions, but only the results of applying a scenario (i.e. projections).

In RethinkAction, models that can represent local case studies are the main instruments to simulate scenarios with the so-called Land-use based Adaptation and Mitigation Solutions (LAMS). The simulations will help understand which types of policies will increase the chances to mitigate and adapt to climate change effects, taking into account the several trade-offs of applying policies in one specific sector and the effects on the other sectors.


Link to the webpage of the Group of energy, economics and system dynamics from the University of Valladolid

CarbonBrief: “Q&A: How ‘integrated assessment models’ are used to study climate change”