The overall strategy of CC-TAME is tailored towards aligning and further developing existing decision support tools in the land-use sector to respond to demands from the climate policy process. CC-TAME tools will be informed by and be informing policy makers interested in implementing changes in land-use practices under post-Kyoto and UNFCCC climate policy regimes. Policies to be analysed include those aimed at enhancing or preserving carbon stocks (national implementation of incentives provided by the Kyoto Protocol, for example), enhancing the use of bioenergy (Renewable Electricity Directive, Liquid Biofuels Directive, in the future a Renewable Heating and Cooling Directive), as well as policies aimed at reducing non-CO2 GHG emissions (CH4, N20) from agriculture. Analysis carried out with CC-TAME tools will also help the process of coordinating climate policies with the Common Agricultural Policy (CAP), Rural development Strategy, EU Forestry Strategy, Forest Action Plan and a number of EU environmental policies such the CAFÉ, Water directive, Soil thematic strategy and Biodiversity policy processes. Bottom-up type models as suggested under CC-TAME have proven to provide excellent quantitative platforms to foster policy coordination across different, but linked policy “sectors”.
The main coordination function of CC-TAME model cluster will be the generation of research results assessing and evaluating the impacts of alternative policy scenarios and estimating the associated costs and benefits of the policies. Thus, the overall modelling and policy relevant strategy of CC-TAME is strongly aligned with those models of the European Consortium for Modelling of Air Pollution and Climate Strategies to which the major CC-TAME models are linked and are actively participating. At the same time international modelling coordination is facilitated through joint development of major tools such as FASOM and GFM with US and Japanese modelling groups, which are used for policy support in the respective countries. Also strong participation in the Land-use group of the Energy modelling forum and the Forest and Agricultural Greenhouse Gas Modelling Forum will be conducive to international coordination of model based policy assessment.
By their very nature land use activities occupy space. Through the geographically and technologically explicit bottom-up approach the CC-TAME project integrates seamlessly regional climate models with biophysical models and full fledged regional and national economic sector models. This multifaceted approach bridges geographic and temporal scales and integrates all major land-use sectors. CC-TAME methodological approach combines explicit crop/trees growth models operating on the plot scale that have sufficient, sub-national spatial detail to estimate the responses and adaptation possibilities of crops and trees.
Regional climate model will deliver daily climate projections to allow for scenarios of extreme climate events and changes in weather patterns, including effects of biotic and abiotic disturbances. The methodological approach allows for consistent linkage to continental scale, which guarantees robustness and consistency in the assessment of sustainable and cost-effective GHG mitigating and adaptive management strategies and policies. CC-TAME employs a multitude of models on different scales which allow for model comparisons and thus model uncertainties can be quantified and assessed in a systematic manner. The issue of uncertainties in the LULUCF sector has been a major stumbling block in the international and European negotiations to include the sector in an efficient policy portfolio. Therefore, CC-TAME invests relatively large resources in building reliable and trustworthy data and assessment tools. The bottom-up approach on the one hand will inform more aggregate models and facilitate the validation of aggregate results and, on the other hand, will help illustrate behavioural change on the micro scale that economy-wide policies seek to influence. Thus, results from the modelling exercises will become more tangible also for non-expert users through visual demonstration cases on the plot scale.
The data and analysis tools of CC-TAME are employed to assess a wide range of environmental, agricultural, forest, and energy policy scenarios. Particular policies to be analysed include those aimed at enhancing or preserving carbon stocks (national implementation of incentives provided by the Kyoto Protocol, for example), enhancing the use of bioenergy (Renewable Electricity Directive, Liquid Biofuels Directive, in the future a Renewable Heating and Cooling Directive), as well as policies aimed at reducing non-CO2 GHG emissions (CH4, N20) from agriculture. Biodiversity enhancement and soil improvement / preservation also form important policy objectives. In terms of policy instruments used to achieve these policy goals, a differentiation can be made between upstream instruments that influence management decisions directly, and downstream instruments that focus on the outcomes of land management, such as emissions of GHGs, and their reduction, which could, for example, be included in the planned revisions of the EU Emissions Trading Scheme. Currently the EU ETS does not include any domestic offsets (except JI and CDM projects), but an initiative by some countries (e.g., France) is seeking to allow domestic offset projects that do not result in double counting of emission reductions.
Policies can be assessed individually or jointly. Heterogeneous qualities, multiple uses and physical limits are the reasons for complex impacts of land use policies. The integration and link between (a) site specific, biophysical modelling, (b) micro-economic farm level modelling, and (c) multi-sector, macro-economic modelling allows us to embody both heterogeneous natural conditions and market adjustments in a globalizing world with internationally connected agricultural, forest products and energy markets.
The figure below shows the flow of information through the modelling framework of CC-TAME in three steps.
Biophysical models use detailed soil and climate information (→ 3) in combination with regional data on existing land management technologies (→ 6, → 5a) to produce environmental impact data (→ 5b) for each relevant soil-climate-management combination. Note that these impacts are given per hectare. Regionally detailed farm and forestry models then combine the microeconomic data from regional farm and forest statistics (→ 8) and the simulated environmental impacts (→ 7a) to estimate regional emissions (→ 11). These estimations depend on macro-economic drivers (→ 10) and policy scenarios (→ 9). Insight from farm and forest models will be used to refine the set of management practices (→ 7b, → 5b). The macro-economic drivers are the results from price-endogenous market models (→ 15), which compute commodity market feedbacks (most important are price changes) and land use changes between agriculture, forestry and nature reserves. The macro-economic sector and multi-sector models use the same policy scenarios (→ 14) as do the regional models. In addition, needed macro-economic drivers (projections for GDP, population, and technological change) will be employed (→ 13). Land management technologies in sector and multi-sector models comprise aggregates of regionally specific technologies (→ 12). The explicit heterogeneity of regional farm and forest models will be approximated via non-linear cost and yield functions in aggregated models. The two directional link (→ 10,→ 12) between regional and macro-economic models may be executed in several iterations to achieve consistency before regional emission data from land use are statistically downscaled to the required grid resolution (→ 17).
The Regional Climate Model takes the baseline emission values for different policy scenarios (→ 2a) and re-computes the future path of important climate variables (→ 2b). The number of policy scenarios and the resolution of the regional climate model need to be determined at project start and are subject to computational requirements and available computational power. For each policy path, an independent climate path will be produced. Envisioned are different scopes and different levels of policy.
Once a climate baseline has been produced, step 1 is repeated. This implies that forest growth and agricultural crop yields are updated to recomputed climate conditions and that economic decisions will also be updated to account for changes in profitability. The aggregated impacts of these micro-economic changes on markets will be portrayed through iterative links between the regional and macro-economic models. Steps 2 and 3 could be repeated several times for each policy path until equilibrium or convergence is achieved.