Systematic study of the bio-energy policies on agricultural production, land use, and biodiversity conservation in Sweden
Evaluating the effect of bioenergy policies and production in the Swedish agricultural sector
Sweden has the most ambitious climate and energy targets among the EU member states. The county targets on a 40% reduction of GHG emission by 2020 and net emission to zero by 2050, 50% of all energy produced as renewable energy by 2020, and for the Swedish car fleet to be independent of fossil fuels by 2030. To achieve this goal, a variety of policy instruments have been introduced to motivate production and consumption of bioenergy, including the ones in agricultural sector. Though only about 3% of Sweden’s agricultural land is currently devoted to biofuel production, it will contribute more with the technology development for second-generation of biofuel technology. Accroding to Tjus and Fortkamp (2011), about 24%, (650,000 out of 2,600,000 hectares) of agricultural land will be involved in bioenergy production (equivalent to 66% of the maximum potential from forest sector) by 2020. As such, it is a not surprising that the impact of bioenergy policies on agricultural sector is and will be substantial.
This project aims to evaluate the effect and effectiveness of bioenergy policies and production in agricultural sector. A comprehensive data set is being compiled to approximate bioenergy policies, farmers’ decision-making on agricultural practices and biodiversity or ecosystem quality. We will carry out analyses at different levels, including land parcel level, farm level, bioenergy firm level, and region/county level. We plan to use both conventional and advanced econometrics, such as Quasi-experimental methods and spatial econometrics in this study depending on the questions and contexts. For example, land use decision, crop choice, and biodiversity are essentially spatial problems as famers’ decision will be affected by what happens in their neighborhood. Biodiversity is also spatial corrected as fauna can migrate from one area to the others. We hence account for spatial corrections using both explicit measures and spatial econometrics (spatial lag and spatial error). Our empirical findings from this study will feed ecological models that predict changes in bird communities following from policy-induced changes in agricultural practices.