Spatial targeting of multi-purpose forestry
The UK Forestry Commission seeks to expand forestry and provide more non-market benefits. It must therefore identify areas where afforestation will bring a high amount of particular benefits at acceptable costs. This thesis aims to develop a systematic framework to identify the best locations for specific forestry benefits and demonstrate its use in the existing policy context. Literature shows that economic instruments are being adapted to capture some of the spatial variability of the costs and benefits of land use change, but that a systematic framework for assessing this variability for policy purposes is still lacking. Such a framework is developed in this thesis, based on the mapping of potential benefits of afforestation. The framework compares the spatial distribution of costs and benefits and identifies optimal target areas and subsidy levels within these. New prototype methods are developed for mapping visual amenity and biodiversity, the main benefits of farm woodlands. The framework is applied to the evaluation of the Farm Woodland Premium Scheme in NE Scotland, showing that woodlands are not sufficiently planted in locations where visual amenity and biodiversity are high. Calculations suggest that the targeting of areas of high benefit with increased incentive payments can provide more benefits at lower overall costs. Attention is also drawn to the use of potential value maps to improve Indicative Forestry Strategies (IFS) which are currently based on constraints rather than opportunities. The thesis demonstrates that current forestry policy, both at the strategic (IFS) and the applied level (incentive schemes), is economically inefficient because it is insufficiently spatially explicit and benefit specific. This underlines the policy relevance of the framework as an additional appraisal and evaluation tool. Further work is recommended to include spatially explicit behavioural models, GIS-supported public consultation and dynamic models for competitive bidding.