WP8: Exploring the sustainability of the “smart” bioeconomy
If the 2030 bioeconomy is to be “smart” then it has to be sustainable. Although there are many aspects of sustainability that could be examined in this WP we focus on three key issues. The first two concern the implications of agricultural land use changes on terrestrial biodiversity and GHG emissions. The third concerns how different approaches to bioeconomic development are likely to influence the energy footprint of the bioeconomy. How environmentally sustainable are the foresighted bioeconomic development scenarios likely to be?
Task 8a: Effects of changes in spatial distribution of agricultural production on greenhouse gas emissions.
This task addresses the question of how structural and spatial changes in agricultural production from the scenarios developed in the foresight analysis (WP2) will influence GHG emissions from agriculture. Method: The approach will be based on modelling GHG emissions and soil carbon change in six case studies representing different agroclimatic regions in Norway. For the case studies, we use the municipalities selected by Skjelvåg et al. (2012) as detailed data on climate and soil needed for the modelling are available. Based on the FA in WP2 and the spatial analyses in WPs 6 and 7, models will be developed to describe expected changes in GHG emissions and soil carbon balance given different scenarios. Data on present agricultural production on each farm will be retrieved from production registry data as a starting point for the modelling. The model will build on the existing HOLOS model adapted for Norwegian conditions (Bonesmo et al. 2013).
Output: An analysis of how GHG emissions are expected to change given different scenarios and in different regions of Norway.
Task 8b: Exploring the relationship between agricultural land-use and large-scale patterns in biodiversity.
In this task, the relationship between agricultural production and land-use intensity on one side and large-scale patterns in biodiversity on the other will be explored. By using large-scale nature monitoring data, we will specifically investigate change in agricultural land-use as a driver for change in biodiversity. Method: The relationship between agricultural production and land-use intensity on one side and large-scale patterns in biodiversity on the other will be explored using a correlative approach. Existing data on climate, landscape, natural resources, agricultural land-use and biodiversity will be mapped into a common hierarchical data structure. Some biodiversity indicators (response variables in the analysis) will be mapped into a 10x10km grid covering Norway. Other biodiversity indicators are only available at the municipality level. The geographical variation in agricultural production and land-use intensity will be described by compiling data from the registry of direct farm payments and other sources. The approach described by Erb al. (2013) will be used as a starting point for describing variation in land-use intensity. Large-scale patterns in biodiversity will be described using direct and indirect indicators of biodiversity. The distributions of a predefined set of plant species with a strong association with semi-natural habitats will be used as direct indicators of biodiversity in agricultural landscapes. Data on occurrence of species (so-called presence-only data) will be retrieved from the Global Biodiversity Facility. The relationship between the distribution of the model species and explanatory variables will be examined using the maximum entropy algorithm (Phillips et al. 2006). The models developed will be evaluated by setting aside parts of the original data set prior to the analysis (although ideally independent data would be used for model evaluation). Indirect indicators of biodiversity developed within the programme Nature Index for Norway will also be used as response variables but then at the level of municipalities (see Certain et al. 2011).
Output: An analysis of how changes in agricultural land use in the bioeconomic transition scenarios are likely to affect biodiversity.
WP8c: Applying a bio-energy approach to understanding energy efficiency in the bioeconomy.
The transformation to a bioeconomy will demand increasing the energy efficiency of processes that are used to generate desirable products (Philp et al, 2013). In addition, some of these products will have to be generated in completely new structures, to accommodate bioenergetic limitations (Marquardt et al., 2012). Method: This work package will explore a novel bio-energy approach to evaluate resource utilization and production streams based on bioenergetic considerations. For this an agent-based approach will be employed operating within an metabolically and energetically coupled metabolic environment (Ebrahim et al., 2013). While there are alternative approaches to understanding energy in the bioeconomy (e.g. life-cycle analysis) this experimental approach provides us with the possibility of synthesising new transdisciplinary theory between natural and social sciences.
Output: The identification of non-sustainable products and production networks and options for enhancing energy efficiency in the future bioeconomy.
WP leader: Dr Knut Anders, Bioforsk (WP8a & b), Dr Hohmann-Marriot, NTNU Department of Biotechnology (WP8c). Additional participants: CRR, CSAFE (NZ), James Hutton Institute (UK).