Despite such characteristics, these models can differ greatly in how detailed the aspects of the system are represented and in how the components interact. For instance, some IAMs place particular focus on macroeconomic feedbacks or particular sectors or technologies. Climate policy analysis often involves comparisons among results from several IAMs in order to provide more robust cost estimates and a clearer representation of uncertainties.

The use of IAMs for climate policy assessments has been framed in the context of solution-oriented assessments (Edenhofer and Kowarsch, 2015; Beck and Mahony, 2017). This approach emphasizes the exploratory nature of integrated assessment modeling to produce scenarios of internally consistent, goaloriented futures. They describe a range of pathways that achieve long-term policy goals, and at the same time highlight trade-offs and opportunities associated with different courses of action. However, future pathways cannot be completely isolated from society and decision making (Edenhofer and Kowarsch, 2015; Beck and Mahony, 2017). This suggests an interactive approach which engages societal values and user perspectives in the pathway production process. It also requires transparent documentation of IAM frameworks and applications to enable users to contextualize pathway results in the assessment process.

# **3. Main taxonomy of IAM models**

Different modeling frameworks were created for different problems, with each model design tailored to address a specific set of questions. The characteristics of the modeling framework as well as the primary questions that guided its designs must be kept in mind when comparing the model results.

Detailed, process-based IAMs include a diverse set of models ranging from partial equilibrium energy–land models to computable general equilibrium models of the global economy, from myopic to perfect foresight models, and from models with to models without endogenous technological change. IAMs cover most supply-side mitigation options on the process level, while many demand-side options are treated as part of underlying assumptions, which can be varied (Clarke _et al._ , 2014).

The literature review will take stock of the advantage and disadvantages of alternative approaches, by offering a taxonomy of the current modeling approach, focusing _inter alia_ on the characteristics of the models.

# **3.1 Main taxonomy according to modeling approaches**

As summarized in Figure 1, IAMs may be broadly grouped into general equilibrium and partial equilibrium models. General equilibrium models cover the full economy with a more or less detailed representation of specific economic sectors. General equilibrium models are top-down models that provide a highly aggregated representation of economic effects. They have a macroeconomic perspective and focus mainly on the relations between the energy sector and other sectors of the economy. General equilibrium models can use in turn a dynamic recursive approach or intertemporal optimization. Dynamic recursive computable general equilibrium models (CGE) are prominent examples of general equilibrium models, that identify market equilibria for each point in time. In doing so, they are inherently myopic and usually provide a detailed description of the sector composition of the economy. Intertemporal general equilibrium models focus on the intertemporal dynamics of investment in production capital under foresight about future production and consumption. They describe a closed economy but can usually only represent one to three aggregated economic sectors due to the computational burden of intertemporal optimization. IAMs based 4