Food market analysis: a case study of the Glocal methodology in the Dominican Republic

The present paper provides an insight into the food value chain of three specific sectors (fruit and vegetables, poultry and rice) in the Dominican Republic. The Glocal methodology used for the study combines a global view with local conditions and thus it can be applied to food markets. Each of these food chains is analyzed by following traditional industrial organization theory, based on structure, conduct and performance. Regarding the specific case of the Dominican Republic, different sources of information are used to analyze the weaknesses of the studied chains, including direct interviews. The food value chains of fruit and vegetables, poultry and rice in the Dominican Republic show a lack of structure and they are undergoing changes; however, they also have great opportunities to improve efficiency by making some changes.

A methodology to estimate uncertainty for emission projections through sensitivity analysis

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the “with measures” scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. Implications: A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.