Reviving wood-pastures for biodiversity and people: A case study from western Estonia

Wood-pastures are associated with high cultural and biodiversity values in Europe. However, due to their relatively low productivity, large areas of wood-pastures have been lost over the last century. In some areas, incentive schemes have been developed to revive wood-pastures. We investigated the effects of one such scheme in western Estonia. We compared the structure of grazed wood-pastures (old and restored) to those of abandoned wood-pastures and ungrazed forest stands to explore the effects of management, and conducted interviews with 24 farmers to investigate their motivations to carry out the management. We found a positive influence of active management on the semi-open structure of wood-pastures. Financial support was vital for management, but personal values related to tradition also played an important role. The interviewees differed widely in their range of motivations, suggesting that other strategies in addition to financial incentives would further improve the management of wood-pastures in the region.

Electronic supplementary material

The online version of this article (doi:10.1007/s13280-015-0719-8) contains supplementary material, which is available to authorized users.     

How should support for climate-friendly technologies be designed?

Stabilizing global greenhouse gas concentrations at levels to avoid significant climate risks will require massive "decarbonization" of all the major economies over the next few decades, in addition to the reduced emissions from other GHGs and carbon sequestration. Achieving the necessary scale of emissions reductions will require a multifaceted policy effort to support a broad array of technological and behavioral changes. Change on this scale will require sound, well-thought-out strategies. In this article, we outline some core principles, drawn from recent social science research, for guiding the design of clean technology policies, with a focus on energy. The market should be encouraged to make good choices: pricing carbon emissions and other environmental damage, removing distorting subsidies and barriers to competition, and supporting RD&D broadly. More specific policies are required to address particular market failures and barriers. For those technologies identified as being particularly desirable, some narrower RD&D policies are available.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.