Opportunities for adaptation-mitigation synergies in geothermal energy utilization- Initial conceptual frameworks

In this article, the role of geothermal energy in mitigation and potential role in adaptation are discussed, and synergies between them developed. The article creates the Geothermal Adaptation-Mitigation (Geo-AdaM) conceptual frameworks that can be used in combining mitigation and adaptation in geothermal projects, e.g. by introducing adaptation additionality in Clean Development Mechanism or mitigation projects, using geothermal energy in climate vulnerable sectors, combining geothermal development with carbon forestry to improve recharge of geothermal systems in water stress areas, displacing fossil fuels in heating and cooling, and use of geothermal heat in raising tree seedlings in cold regions, and in greenhouses to create carbon sinks and green areas. The conceptual frameworks created in this research can cut across most regions, and types of utilization schemes with mitigation/adaptation co-benefits. The resulting co-benefits come with net positive environmental, economic and social impact. However, the co-benefits cannot be homogenous across all projects and regions. Tradeoffs may occur when using geothermal energy in adaptation projects, whose upstream activities are carbon intensive, or in adaptation and mitigation projects that have the potential of increasing vulnerability. The foreseen limitations of creating the synergies include; inadequate research on geothermal energy and adaptation, nature and scale of adaptation, involvement of different institutions and actors, access to finance and other resources especially in developing countries and lack of clear legal framework. Without proper legislation, fiscal incentives, to attract investment in adaptation aspects of geothermal energy, and to guard against tradeoffs, the interelationships between the two will remain a pipe dream.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).