Endogenous Risks and Learning in Climate Change Decision Analysis

We analyze the effects of risks and learning on climate change decisions. A two-stage, dynamic, climate change stabilization problem is formulated. The explicit incorporation of ex-post learning induces risk aversion among ex-ante decisions, which is characterized in linear models by VaR- and CVaR-type risk measures. Combined with explicit introduction of "safety" constraints, it creates a "hit-or-miss" type decision-making situation and shows that, even in linear models, learning may lead to either less-or more restrictive ex-ante emission reductions. We analyze stylized elements of the model in order to identify the key factors driving outcomes, in particular, the critical role of quantiles of probability distributions characterizing key uncertainties

The great shift: The non-indigenous species Ammonia confertitesta (Foraminifera, Rhizaria) outcompetes indigenous Ammonia species in the Gironde estuary (France)

International audienc

IMA genome‑F17

ISSN:2210-6340ISSN:2210-635

Fluorescence based fibre optic pH sensor for the pH 10–13 range suitable for corrosion monitoring in concrete structures

The design, development and evaluation of an optical fibre pH sensor for monitoring pH in the alkaline region are discussed in detail in this paper. The design of this specific pH sensor is based on the pH induced change in fluorescence intensity of a coumarin imidazole dye which is covalently attached to a polymer network and then fixed to the distal end of an optical fibre. The sensor provides a response over a pH range of 10.0 – 13.2 with an acceptable response rate of around 50 minutes, having shown a very good stability over a period of longer than 20 months thus far. The sensor has also demonstrated little cross-sensitivity to ionic strength (IS) and also excellent photostability through a series of laboratory tests. These features make this type of sensor potentially well suited for in-situ long term monitoring of pH in concrete structures, to enhance structural monitoring in the civil engineering sector