This can only be achieved when disciplinary boundaries between natural sciences
and humanities are overcome. Consequently, integrative research tools can be
designed that are suitable not only to describe the interactions of variables within each
sub-system, but also the processes at the interface between them. Here, conceptual
and mathematical models can play an important role as a tool to contextualise and
organise knowledge and data. There are long traditions of environmental models
covering different aspects such as biomass productivity or climate dynamics as well
as of models for societal and economic systems. Early attempts to bring together both
worlds in a consistent framework were the studies from the Club of Rome dating back
to the 1970s (Meadows et al. 2004). Using coupled differential equations, the
World-3 model (Forrester 1971) was applied to analyse global limits of societal and
economic growth. It included sub-models for population development, food production and resource use, among others. Building on this pioneering work, Integrated
Assessment Models (IAMs) were developed (Rotmans and van Asselt 2001).
A prominent example is the IMAGE model (Stehfest et al. 2014) that couples relatively aggregated versions of process-based environmental models with econometric
approaches and allows exploring global feedbacks between these sub-systems.
Methodological challenges for model coupling include the often fundamentally
different character of the individual models as well as questions of temporal and
spatial scales. In respect to the climate change challenge, IAMs were playing a
prominent role in the Fifth IPCC Assessment Report (IPCC 2014), where they were
used to generate future trajectories of global greenhouse gas emissions based on
societal development trends and to analyse potential mitigation and adaptation
options (Van Vuuren et al. 2010). Closer to the spatial levels of actual societal
decision-making are models that operate on a regional or local scale. This includes
agent-based approaches of human decision-making coupled with environmental
models (e.g. An 2012) and land system models that combine societal drivers of
land-use change with environmental impact modelling (e.g. Brown et al. 2012). Here,
applications range from the analysis of deforestation processes (Lapola et al. 2011) to
the effects of agricultural intensification on pollination (Priess et al. 2007).
The second challenge might be even more demanding. In order to play an active
role in defining and implementing applicable climate change mitigation and
adaptation strategies, researchers need to reach out beyond their scientific communities and to establish transdisciplinary linkages, e.g. to societal actors and
political decision makers (e.g. Nicolescu 2002; Lang et al. 2012). This touches all
elements of scientific work: The definition of research questions, the selection of
suitable analytical tools and the design of experiments, and last but not least, the
communication of scientific findings and their transformation into practice. There is
a wide range of the literature that deals with the design of transdisciplinary research
projects in the context of climate change and sustainability studies and how scientific and non-scientific stakeholders can be involved in these projects (Hegger
et al. 2012; Brandt et al. 2013; Mauser et al. 2013). The aforementioned models
have proved to be valuable instruments also for transdisciplinary research, where
they are often used in combination with scenarios (Scholz et al. 2006). Scenarios
are defined as hypothetical but plausible sequences of future events considering the
Climate Change Challenge (3C) and Social-Economic-Ecological …
5
and humanities are overcome. Consequently, integrative research tools can be
designed that are suitable not only to describe the interactions of variables within each
sub-system, but also the processes at the interface between them. Here, conceptual
and mathematical models can play an important role as a tool to contextualise and
organise knowledge and data. There are long traditions of environmental models
covering different aspects such as biomass productivity or climate dynamics as well
as of models for societal and economic systems. Early attempts to bring together both
worlds in a consistent framework were the studies from the Club of Rome dating back
to the 1970s (Meadows et al. 2004). Using coupled differential equations, the
World-3 model (Forrester 1971) was applied to analyse global limits of societal and
economic growth. It included sub-models for population development, food production and resource use, among others. Building on this pioneering work, Integrated
Assessment Models (IAMs) were developed (Rotmans and van Asselt 2001).
A prominent example is the IMAGE model (Stehfest et al. 2014) that couples relatively aggregated versions of process-based environmental models with econometric
approaches and allows exploring global feedbacks between these sub-systems.
Methodological challenges for model coupling include the often fundamentally
different character of the individual models as well as questions of temporal and
spatial scales. In respect to the climate change challenge, IAMs were playing a
prominent role in the Fifth IPCC Assessment Report (IPCC 2014), where they were
used to generate future trajectories of global greenhouse gas emissions based on
societal development trends and to analyse potential mitigation and adaptation
options (Van Vuuren et al. 2010). Closer to the spatial levels of actual societal
decision-making are models that operate on a regional or local scale. This includes
agent-based approaches of human decision-making coupled with environmental
models (e.g. An 2012) and land system models that combine societal drivers of
land-use change with environmental impact modelling (e.g. Brown et al. 2012). Here,
applications range from the analysis of deforestation processes (Lapola et al. 2011) to
the effects of agricultural intensification on pollination (Priess et al. 2007).
The second challenge might be even more demanding. In order to play an active
role in defining and implementing applicable climate change mitigation and
adaptation strategies, researchers need to reach out beyond their scientific communities and to establish transdisciplinary linkages, e.g. to societal actors and
political decision makers (e.g. Nicolescu 2002; Lang et al. 2012). This touches all
elements of scientific work: The definition of research questions, the selection of
suitable analytical tools and the design of experiments, and last but not least, the
communication of scientific findings and their transformation into practice. There is
a wide range of the literature that deals with the design of transdisciplinary research
projects in the context of climate change and sustainability studies and how scientific and non-scientific stakeholders can be involved in these projects (Hegger
et al. 2012; Brandt et al. 2013; Mauser et al. 2013). The aforementioned models
have proved to be valuable instruments also for transdisciplinary research, where
they are often used in combination with scenarios (Scholz et al. 2006). Scenarios
are defined as hypothetical but plausible sequences of future events considering the
Climate Change Challenge (3C) and Social-Economic-Ecological …
5