A GIS-based approach for optimizing the development of collective biogas plants

Agriculture substantially contributes to anthropogenic emissions of both N2O and CH4 as well as ammonia (NH3). As signatories to international conventions, EU Member States must reduce their emissions. Moreover, the European Council (December 12, 2008) defined the energy-climate package and implemented a target called "3 X 20". The aim of this target is to reduce GHG emissions by 20% by 2020 compared to 1990 levels. It also aims to bring to a 20% share of renewable energy in the final consumption and to increase energetic efficiency by 20%. In response to these commitments, anaerobic digestion of livestock wastes is expected to expand in France in the coming years. The objectives of economic performance lead to a particular interest in centralized treatment plants involving other wastes, specifically wastes with high potential for energy production (agri-food waste, crop residues, etc.) which may be collected over long distances. However, such development is complex and requires the awareness of social and technical constraints (heat recovery, access to bio-resources...) as well as adhering to legal restrictions in the concerned areas. In this context of potential development of collective biogas plants in France, the use of Geographic Information Systems (GIS) in order to georeference the bio-resources and then to locate the optimal sites appears very interesting and useful tools. Such investigations have been carried out on both national and regional scales (Batzias, 2005; Dagnall, 2010) but need to be adapted for local diagnosis. For this purpose, a research project is devoted to the development of such methodologies, then applied in the “Pays de Fougères”, a 1000 km² wide rural area located in the north-eastern Brittany in France. Firstly, a bio-resource mapping is drawn by applying a calculation method specific for each substrate. A derived layer, the energy potential grid (EPG), is calculated as the sum of the energy potential at any point in the area (100 m resolution per pixel) considering for each substrate a maximum distance proportional to the energetic potential of the substrate. Next, sensitive areas (wetlands, distances from housing…) are identified as areas where the development of biogas plants is restricted, resulting in a constraint map (CM). A final suitability map is constructed by combining the CM and the EPG, synthesized in the form of a raster GIS file. To go further on this issue, the network analysis capability of GIS is used, in order to take into account the actual transport route and competitive access to bio-resources. As a result, it allows refining the diagnosis of candidate sites. This study initiates the construction of a GIS model to determine optimal sites for collective biogas plants. The specificity of the approach is that methodologies are implemented to reach a very fine level of spatialization. The precise geolocation of farms is successfully obtained through the analysis of aerial photographs and Landsat imagery is used to help the identification of crop residues. However, some improvements could be implemented in the future, such as assigning a weight factor to the bio-resources reflecting their relative importance (liquid/solid form, no additional N content in the digestate, etc.). Although this mapping provides a basis for discussion in the context of decision support, it does not allow itself to make a choice on a future location, particularly since in the investigated area, the availability of bio-resources is not a major barrier (range from 6800 to 9300 ton of oil equivalent (toe)/year). Complementary parameters could be considered like social acceptance and environmental concerns generated by such a collective biogas plant. To explore this aspect in depth, the question of local impacts like eutrophication should be taken into consideration. Thus, beyond this GIS-based model, in order to test different scenarios for the development of collective biogas plants, a Life Cycle Assessment (LCA) is realised, assessing the environmental impacts of three scenarios as described in the paper of Aissani (2012). Future work will be to link more closely the GIS in LCA studies through the integration of spatial data and the spatial differentiation of local environmental impacts, the ultimate goal being to build a generic model for maximizing energy recovery from bio-resources in conjunction with a low environmental impact.