Exploring climate mitigation and low-carbon transitions: new challenges for model integration
There are various visions of our future, but most policy-makers and scientists agree that life will be substantially different in the post-fossil era. The cheap and abundant supply of fossil energy has led to unprecedented population growth and to staggering levels of consumption of natural resources, undermining the carrying capacity of nature. Eroding ecosystems, the end of cheap oil and climate change call for new policies to support societal transformations toward low-carbon alternative futures. This understanding has already been expressed in recent EU legislation, which requires that domestic GHG emissions be cut by 80% between 1990 and 2050. Energy is a major driver of change and an important ‘currency’ that runs economic and social systems and influences environmental systems. Being so used to the abundant and uninterrupted supply of fossil energy, we tend to forget the important role that it plays in our everyday lives. Non-marginal, abrupt changes, such as during the Oil Crisis of the 1970s or the sudden sharp rise in oil prices in 2008 remind us how vulnerable societies are with respect to energy. Future transitions and climate induced changes are also unlikely to be smooth and require new modeling paradigms and methods that can handle step-change dynamics and work across a wide range of spatio-temporal scales, integrating the knowledge of many stakeholder communities.
Here we are operating in a generalized ‘socio-environmental model space’, which includes empirical models, conceptual stakeholder models, complex computer simulations, and data sets, and which can be characterized in several dimensions, such as model complexity, spatial and temporal resolution, disciplinary coverage, bias and focus, sensitivity and uncertainty, usability and relevance. In this space we need a ‘model calculus’ – a set of relationships and operations that can apply to individual models and groups of models. Model integration across disciplinary boundaries faces two big challenges. First we need to learn to deal with a variety of modeling paradigms and techniques, allowing different types of models to exchange information in a meaningful way (agent based models talk to systems dynamics, to computed global equilibrium models, to empirical models, etc.). Secondly, we need to provide integration techniques and tools that bring qualitative, conceptual, mental models of stakeholders together with the quantitative simulation models.
Greater transparency and accessibility can be achieved through enhancing documentation and communication of model functioning and strengths and limitations of various models and approaches. This extensive model documentation following improved and enhanced meta model standards is an important first step that makes sure that models (both qualitative and conceptual) ‘talk the same language’ and can exchange information and knowledge at various stages of research. This also helps us create the ontology, which can be further used for computer aided semantic mediation of models. This semantic mediation should include such functionality as consistency checks (checking for units, concepts, spatio-temporal resolution, etc.). This should also help to explore the different models along the complexity continuum to understand how information from more aggregated qualitative models can be transmitted to more elaborated and detailed quantitative simulations, and vice versa. This bears the promise of insight on the complex behavior of non-linear systems where regime shifts and non-equilibrium dynamics is usually better understood with simple models, while the more complicated models are easier to parametrize with data and can take into account more detailed information about particular systems and situations.