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An increase in Short Rotation Forestry (SRF) in Ireland is expected in response to the increasing demand for fibre products and wood energy. SRF is the practice of cultivating fast growing tree species mainly for the production of biomass, through a rotation length of less than 20 years in Ireland. Although forest policy is promoting SRF, currently establishment is limited. This thesis aims to investigate the potential financial value of practicing SRF and evaluate whether SRF should be financially managed to allocate a range of assortments to diverse markets or supply a single market only. The work included: 1) Quantification of above ground biomass by dry matter content, merchantable volume and energy content from four SRF stands, two of Eucalyptus spp. and two of Populus spp.; 2) A market survey identifying pallet producers and the wood energy sector as most willing to use SRF material; 3) Benchmarking conditions that facilitated market development in Oregon with current conditions in Ireland, identifying: large scale plantations; high value product market development; and, investment in research, education and marketing, as key conditions; 4) Development of a unit conversion tool to quantify and value wood resources by volume, weight and energy parameters for market segment comparison and 5) Determination, from the landowner’s perspective, of the optimal financial value of SRF and finding maximum returns were for eucalyptus stands used for diverse markets, mainly small sawlog for pallet production. Contrary to policy objectives, SRF will not contribute significant biomass to fibre and fuel markets, under current financial and market conditions. Policy promoting SRF must be long term, and supported by industry, research and education to gain landowners acceptance. Further research on SRF wood properties and yield models will be necessary to underpin SRF development in Ireland.

This paper investigates the effect of the conventional and unconventional (e.g. Quantitative Easing - QE) monetary policy intervention on the insurance industry. We first analyze the impact on the stock performances of 166 (re)insurers from the last QE programme launched by the European Central Bank (ECB) by constructing an event study around the announcement date. Then we enlarge the scope by looking at the monetary policy surprise effects on the same sample of (re)insurers over a timeframe of 12 years, also extending the analysis to the Credit Default Swaps (CDS) market. In the second part of the paper by building a set of balance sheet-based indices, we identify the characteristics of (re)insurers that determine sensitivity to monetary policy actions. Our evidences suggest that a single intervention extrapolated from the comprehensive strategy cannot be utilized to estimate the effect of monetary policy intervention on the market. With respect to the impact of monetary policies, we show how the effect of interventions changes over time. Expansionary monetary policy interventions, when generating an instantaneous reduction of interest rates, generated movement in stock prices in the same direction till September 2010. This effect turned positive during the European sovereign debt crisis. However, the effect faded away in 2014-2015. The pattern is confirmed by the impact on the CDS market. With regard to the determinants of these effects, our analysis suggests that sensitivity is mainly driven by asset allocation and in particular by exposure to fixed income assets.

During the last IAIS Global Seminar in June 2017, IAIS disclosed the agenda for a gradual shift in the systemic risk assessment methodology from the current Entity Based Approach (EBA) to a new Activity Based Approach(ABA). The EBA, which was developed in the aftermath of the 2008/2009 financial crisis, defines a list of Global Systemically Important Insurers (G-SIIs) based on a pre-defined set of criteria related to the size of the institution. These G-SIIs are subject to additional regulatory requirements since their distress or disorderly failure would potentially cause significant disruption to the global financial system and economic activity. Even if size is still a needed element of a systemic risk assessment, the strong emphasis put on the too-big-to-fail approach in insurance, i.e. EBA, might be partially missing the underlying nature of systemic risk in insurance. Not only certain activities, including insurance activities such as life or non-life lines of business, but also common exposures or certain managerial practices such as leverage or funding structures, tend to contribute to systemic risk of insurers but are not covered by the current EBA (Berdin and Sottocornola, 2015). Therefore, we very much welcome the general development of the systemic risk assessment methodology, even if several important questions still need to be answered.

Forest models are increasingly being used to study ecosystem functioning, through simulation of carbon fluxes and productivity in different biomes and plant functional types all over the world. Several forest models based on the concept of Light Use Efficiency (LUE) rely mostly on a simplified mathematical structure and empirical parameters, require little amount of data to be run, and their computations are usually fast. However, possible calibration issues must be investigated in order to ensure reliable results. Here we addressed the important issue of delayed convergence when calibrating LUE models, characterized by a multiplicative structure, with a Bayesian approach. We tested two models (Prelued and the Horn and Schulz (2011a) model), applying three Markov Chain Monte Carlo-based algorithms with different number of iterations, and different sets of prior parameter distributions with increasing information content. The results showed that recently proposed algorithms for adaptive calibration did not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest models used here, and that a high number of iterations is required to stabilize in the convergence region. This can be partly explained by the multiplicative mathematical structure of the models, with high correlations between parameters, and by the use of empirical parameters with neither ecological nor physiological meaning. The information content of the prior distributions of the parameters did not play a major role in reaching convergence with a lower number of iterations. We conclude that there is a need for a more careful approach to calibration to solve potential problems when applying models characterized by a multiplicative mathematical structure. Moreover, the calibration proved time consuming and mathematically difficult, so advantages of using a computationally fast and user-friendly model were lost due to the calibration process needed to obtain reliable results.