Forest fires and adaptation options in Europe

This paper presents a quantitative assessment of adaptation options in the context of forest fires in Europe under projected climate change. A standalone fire model (SFM) based on a state-of-the-art large-scale forest fire modelling algorithm is used to explore fuel removal through prescribed burnings and improved fire suppression as adaptation options. The climate change projections are provided by three climate models reflecting the SRES A2 scenario. The SFM’s modelled burned areas for selected test countries in Europe show satisfying agreement with observed data coming from two different sources (European Forest Fire Information System and Global Fire Emissions Database). Our estimation of the potential increase in burned areas in Europe under “no adaptation” scenario is about 200 % by 2090 (compared with 2000–2008). The application of prescribed burnings has the potential to keep that increase below 50 %. Improvements in fire suppression might reduce this impact even further, e.g. boosting the probability of putting out a fire within a day by 10 % would result in about a 30 % decrease in annual burned areas. By taking more adaptation options into consideration, such as using agricultural fields as fire breaks, behavioural changes, and long-term options, burned areas can be potentially reduced further than projected in our analysis.     

The sensitivity of the costs of reducing emissions from deforestation and degradation (REDD) to future socioeconomic drivers and its implications for mitigation policy design

Mitigation and Adaptation Strategies for Global Change - Climate change mitigation policies for the land use, land use change, and forestry (LULUCF) sector are commonly assessed based on marginal...     

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO₂ sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.     

The value of rapid damage assessment for efficient earthquake response

Every year earthquakes cause substantial economic losses and loss of life in many parts of the world. Earth observations may provide valuable information on spatial damage distribution and through that increase the efficiency of rapid response actions, finally resulting in reduction of earthquake induced losses. However, a methodology for quantitative assessment of the potential value of information in such a context has not yet been developed. That knowledge would be of high importance for planning and development of Earth observation systems on both regional and international levels when aiming at sustainable development goals. In this paper we suggest a stochastic modeling approach to assess the value of information for earthquake rapid response. Our analysis is focused on a rather short time interval after an earthquake occurrence (order of several hours) when there is a maximum need for urgent help to the earthquake victims. We use rescue efficiency as a performance measure of earthquake response actions; this efficiency is described in terms of timely arrival of rescue teams at places, where their help is mostly needed. We quantify the benefit of using spatial damage distribution information in terms of cost reduction/rescue efficiency gains. We intentionally conduct our analysis at a methodological level and do not go deeper into case studies to keep general conclusions traceable. The modeling exercise we present in the paper is a first step towards a more detailed and integrated approach that could contribute to better systematic understanding of earthquake response actions and ultimately improve their efficiency.