The land-use sector is both a contributor to and a potential victim of climate change. Global historical emissions from land-use are estimated to exceed those from fossil fuels by some 25% and are currently considered to be the second largest sources of GHG emissions. In Europe, the agricultural sector is the third largest sector of greenhouse gas emissions, accounting for 9% of EU-25 emissions and 10% of EU-15 greenhouse gas emissions and sinks are believed to be a substantial. At the same time recent drought periods and other weather extremes are responsible for a significant share of crop outages in Europe and it is predicted that climate change will increase the share of agricultural losses due to weather or climate related extreme events.
Policy induced changes in land management carry a large potential to both increase the adaptive capacity of ecosystems as well as reduce the emission burden from the land-use sector. Policy coordination of EU climate mitigation and adaptation policies with the Common Agricultural Policy (CAP), Rural development Strategy, EU Forestry Strategy and Clean Air and Water Policies could potentially lead to a number of ancillary benefits and thereby reduce costs of compliance of any individual policy.
The main idea that led to this proposal is the vision of implementing a “policy-model-data fusion” concept which shall guarantee efficient and effective mitigation and adaptation in the land-use sector and maximize benefits from policy coordination with other EU policies. With respect to climate policy a few modelling teams, such as the POLES, PRIMES and IIASA RAINS/GAINS, embraced such a policy-model-data fusion concept. These models are regularly used for strategy building of future international climate policies of the European Union and are used to inform European policy makers for negotiations to implement European policies such as the European Emission Trading System and international negotiations at COPs. These models share the common feature of being data and technology rich bottom-up models. The land use sector is still poorly represented in these models and also lacks the “policy” component in the fusion concept. The CC-TAME project is designed to fill this gap by aligning and linking the currently leading and most suitable land-use models with other climate policy tools to quantify benefits from policy coordination and finally provide consistent policy analysis across sectors including the entire land-use sector. All policy models in CC-TAME are data and technology rich bottom-up models, which are fed by information from plot level simulation “experiments” which guarantees robustness of results and will illustrate the impacts and efficiency of policies on various levels of aggregation both in terms of economic impacts and on the concrete place specific concrete management practice.
The CC-TAME project’s prime objective is to live up to the criterion “policy relevant topic” of the call. The objective is to build a strong Science-Policy interface by delivering timely, relevant and understandable information from state-of-the-art policy impact assessments to the policy community.
The specific objectives are as follows:
The project’s expected impact is an assessment of the efficiency of current and future land use adaptation and mitigation processes and identification and quantification of the adaptation induced by policies. Thus, a scientific tool box needs to be built to quantify (scenario analysis), understand (attribute through modelling), predict and assess the impact of policies on the evolution of land use processes. This requires new scientific approaches and synthesis that bridge disciplinary boundaries and geographic scale, and place particular emphasis on the land-use sector as an integral part of the coupled biophysical-climate-human system. CC-TAME is trying to overcome this challenge by maximizing the use of the richness of place specific information and knowledge in aggregate policy analysis.