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Energy production from Municipal Solid Waste (MSW) has become one of the most prominent strategies in MSW management. In this study a multi-objective mathematical programming model is developed in order to provide the candidate (Pareto optimal) solutions for a MSW management system performing structural, design and operational optimization. Besides the economic criterion the Green House Gas (GHG) emissions are taken into account as a second optimization criterion. Therefore, we do not obtain just an optimal solution (i.e. least cost), but a set of Pareto optimal solutions that spread from minimum cost to minimum GHG emissions. Each Pareto optimal solution provides the corresponding technologies and the capacities that are associated with it. An innovative issue is that we incorporate the external costs/benefits associated with (a) atmospheric pollution impacts (b) impacts on soil and groundwater (c) impacts on quality of life (d) electricity use/displacement (e) fertilizer use reduction from compost. Using the external costs/benefits as an additional term in the cost objective significantly affects the results especially regarding the Waste-to-Energy options. This is clearly illustrated in the case study for the MSW management of the Athens region in Greece. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly affect countries in Southern Europe and the Mediterranean Basin, causing higher-than-average temperature increases, considerable reductions in rainfall and water runoff, and extreme events such as heatwaves. These pose severe threats to local energy systems, requiring a reliable and quantitative risk analysis. A methodological approach is thus proposed which covers both energy supply and demand, utilizing the latest climate projections under different greenhouse gas emissions scenarios and an appropriate scale for each energy form. For energy supply technologies, risks are assessed through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. To analyze climate risks for energy demand, bottom–up models were developed, integrating both behavioral and policy aspects which are often considered in a very limited way. The results show that climate change will mainly affect electricity generation from hydroelectric and thermal power plants, with variations depending on the plants' locations and uncertainties associated with precipitation and runoff changes. The climate risks for solar and wind energy were found to be low. Energy consumption will also be affected, but the range of risks depends on the ambition and the effectiveness of measures for upgrading the thermal performance of buildings and the intensity of climate change. [ABSTRACT FROM AUTHOR]

Highlights: [•] The multi-objective multi-period optimization model. [•] The solution approach for the generation of the Pareto front with mathematical programming. [•] The very detailed description of the model (decision variables, parameters, equations). [•] The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. [ABSTRACT FROM AUTHOR]

Although climate change risks have been studied for a number of economic sectors, banking has received relatively little attention. The paper proposes a methodology and an associated decision-support tool for quantifying, in monetary terms, the risks for banks from the exposure of their loan recipients and/or applicants to climate change. The framework and tool are applied to a case study based on input from a Greek bank; results indicate that climate change risks for banks are considerable, and thus decision makers need to estimate their magnitude and possibly consider these within the credit management process, and in environmental planning. [ABSTRACT FROM AUTHOR]