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This paper illustrates the main results of an expert elicitation survey on advanced (second and third generation) biofuel technologies. The survey focuses on eliciting probabilistic information on the future costs of advanced biofuels and on the potential role of Research, Development and Demonstration (RD&D) efforts in reducing these costs and in supporting the deployment of biofuels in Organisation for Economic Co-operation and Development (OECD) and non-OECD countries. Fifteen leading experts from different EU member states provide insights on the future potential of advanced biofuel technologies both in terms of costs and diffusion. This information results in a number of policy recommendations with respect to public RD&D strategies and is an important contribution to the integrated assessment modelling community.

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project). J.F. acknowledges nancial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project). The ESCENA project (200800050084265) of the Spanish \Strategic action on energy and climate change" provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.

Abstract The climate change impacts on sea level rise and coastal disasters, and the possible adaptation responses have been studied using very different approaches, such as very detailed site-specific engineering studies and global macroeconomic assessments of costal zone vulnerability. This paper reviews the methodologies and the modeling practices used by the sea level rise literature. It points at the strengths and weaknesses of each approach, motivating differences in results and in policy implications. Based on the studies surveyed, this paper also identifies potential directions for future research.

Deciding upon optimum planning actions in terms of sustainable urban planning involves the consideration of multiple environmental and socio-economic criteria. The transformation of natural landscapes to urban areas affects energy and material fluxes. An important aspect of the urban environment is the urban metabolism, and changes in such metabolism need to be considered for sustainable planning decisions. A spatial Decision Support System (DSS) prototyped within the European FP7-funded project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism), enables accounting for the urban metabolism of planning actions, by exploiting the current knowledge and technology of biophysical sciences. The main aim of the BRIDGE project was to bridge the knowledge and communication gap between urban planners and environmental scientists and to illustrate the advantages of considering detailed environmental information in urban planning processes. The developed DSS prototype integrates biophysical observations and simulation techniques with socio-economic aspects in five European cities, selected as case studies for the pilot application of the tool. This paper describes the design and implementation of the BRIDGE DSS prototype, illustrates some examples of use, and highlights the need for further research and development in the field.

As an EU policy agenda, the “knowledge-based bio-economy” (KBBE) emphasizes bio-technoscience as the means to reconcile environmental and economic sustainability. This frames the sustainability problem as an inefficiency to be overcome through a techno-knowledge fix. Here ecological sustainability means a benign eco-efficient productivity using resources which are renewable, reproducible and therefore sustainable. The KBBE narrative has been elaborated by European Technology Platforms in the agri-food-forestry-biofuels sectors, whose proposals shape research priorities. These inform policy agendas for the neoliberalization of both nature and knowledge, especially through intellectual property. In these ways, the KBBE can be understood as a new political-economic strategy for sustainable capital. This strategy invests great expectations for unlocking the productive potential of natural resources through a techno-knowledge fix. Although eco-efficiency is sometimes equated with biological productivity, commercial success will be dependent upon new combinations of “living” and “dead” labour.

We present an empirical analysis of the more than 250 space heating-focused energy efficiency policies that have been in force at the EU and national levels in the period 1990–2010. This analysis looks at the EU-14 residential sector (Pre-2004 EU-15, excluding Luxembourg) using a panel data regression analysis on unit consumption of energy for space heating (kWh/m2/year). The policies are represented as a regression variable using a semi-quantitative impact estimation obtained from the MURE Policy Database. The impacts of the policies as a whole, and subdivided into financial, regulatory, and informative policies, are examined. The correlation between the actual reductions in demand and the estimated impact of regulatory policies is found to be stronger than the corresponding correlations with the respective impacts of financial policies and informative polices. Together with the well-known market barriers to energy efficiency that exist in the residential sector, these findings suggest that regulatory policy measures be given a high priority in the design of an effective pathway towards the EU-wide goals for space heating energy.