Compensation for wildlife damages: Habitat conversion, species preservation and local welfare

We develop a model of hunting, farming and defensive action to study the environmental and economic consequences of introducing a program to compensate peasants of a small economy for the damage caused by wildlife. We show that the widespread belief that compensation induces wildlife conservation may be erroneous. Compensation can lower the wildlife stock, and may result in a net welfare loss for local people.     

Energy consumption in the Norwegian fisheries

Energy use is of great concern within fisheries, due to both associated environmental effects and the cost of fuel to fishermen. This article explores the scale of energy consumed by most segments of the Norwegian fishing fleet for gadoid fish and for parts of the pelagic fleet for the period 1980–2005. Fuel use is assigned to the different species caught and different fishing gears using economic and mass-based allocation, where data permit. Correlations between variations in energy use and changing catch rates, quotas and oil prices are found. Inverse correlations are found between fuel consumption per kilogram of fish and catch rates on a yearly basis and between fuel consumption and oil prices on a longer term basis. A long term trend towards increased fuel consumption and reduced real prices is observed from the mid 1980s until 2000. This may indicate that low fuel prices do not motivate the development of energy efficient technology in the long run. Increased fuel use may further be used as an indication of over fishing as the correlation between low catch rates and increased fuel consumption is rather strong. Possible means of reducing energy use and emissions are discussed including changing operational strategies, hull forms and the use of alternative energy carriers. A comparison with measures taken in connection with the previous oil crisis around 1980 is done.     

The role of population in understanding Honduran land use patterns

Land use patterns are usually influenced by large variety of factors that act over a broad range of scales. Biophysical, climatic, and socioeconomic factors are important and need to be considered, when distribution of land use is to be understood. The main objective of this study is to test this hypothesis using a statistical analysis at 'supra-local' level. Regression analysis is used to describe land use patterns in Honduras, selected because of its rare combination in Latin America of high population growth and poor biophysical conditions. Furthermore, the aim of the analysis is to specifically highlight two aspects, the effect of spatial and temporal scale and the influence of population density: to determine the influence of spatial and temporal scale, six spatial resolutions at two points in time (1974 and 1993) were included. To determine the role of population density and population growth, this factor was singled out; an analysis of migration patterns was performed; and a measure for technological development was calculated. Multiple regression equations indicate the importance of soil-related, climatic and demographic factors for most of the land uses. Relations appear to be stable in space and time. Rural population density dominates as driver over the whole range of resolutions and for both years, especially for maize where it explains up to 80% of the variation. The strong constant relationship between population and agricultural area could be caused by a lack of technological development. An analysis of yield development confirms that for most annual crops yield increases lag behind area growth. Besides, the strong correlation could be explained by assuming rural population density to be a proxy for a range of other factors, like labour costs, or accessibility that are the direct drivers of land use change. In any case, this study suggests that for a specific--relatively coarse--window of temporal and spatial scale, land use patterns can be described with very simple relationships, with a strong contribution of population density. More local studies are needed to test the hypothesis that rural population density is a proxy for other variables.