Methods for Measuring Aggregate Costs of Conflict

This paper reviews the methods for measuring the economic cost of conflict. Estimating the economic costs of conflict requires a counterfactual calculation, which makes this a very difficult task. Social researchers have resorted to different estimation methods depending on the particular effect in question. The method used in each case depends on the units being analyzed (firms, sectors, regions or countries), the outcome variable under study (aggregate output, market valuation of firms, market shares, etc.) and data availability (a single cross-section, time series or panel data). This paper reviews existing methods used in the literature to assess the economic impact of conflict: cost accounting, cross-section methods, time series methods, panel data methods, gravity models, event studies, natural experiments and comparative case studies. The paper ends with a discussion of cost estimates and directions for further research.

Dynamic Inefficiency in an Overlapping Generation Economy with Pollution and Health Costs

Revised 2008-08.-- Published as an article in: Journal of Public Economic Theory (2008), 10(4), 563-594.

Linking Reduced Deforestation and a Global Carbon Market: Impacts on Costs, Financial Flows, and Technological Innovation

25 p.

The Costs of Ecosystem Adaptation: Methodology and Estimates for Indian Forests

34 p.

Long Run Trends in Energy-Related External Costs

30 p.

Regional IAM: analysis of risk-adjusted costs and benefits of climate policies

23 p.

The economics of hydro-meteorological disasters: approaching the estimation of the total costs

21 p.

The Costs of Drought: the Exceptional 2007-2008 Case of Barcelona

35 p.

Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms

This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.

Analysis of the housing market in the autonomous community of the Basque country: the adjustment after the burst of the housing bubble

In this project study the characteristic and dynamics of the residential housing market in the Basque country. When strong expansion and colapsing emerged in 2007,studied the differents adjustment.