The goal of this research is to expand and enhance Life Cycle Assessment (LCA) theory and practice by coupling LCA tools with the spatial analytical functions of geographic information systems (GIS). The project will apply these coupled LCA-GIS tools to model inventory flows and assess impacts on biodiversity of land use dynamics associated with biofuel crop production. Combining LCA and GIS creates the potential to develop indicators that are meaningful for biodiversity and that can account for the spatially-dependent and non-linear consequences of land use change. This project aims to develop the tools to make this coupling of software systems feasible and practical for all LCA studies of product systems that have large land use requirements. GIS tools will be developed to generate inventory flows from land use in Life Cycle Inventory. Flow data will be integrated with ecological databases to create and compare various biodiversity characterization models and impact indicators. Additionally, a multidisciplinary graduate seminar in environmental sustainability and impact assessment with LCA and GIS will be conducted to train a cadre of industrial ecology graduate students in this integration of technologies.
Life Cycle Assessment (LCA) quantifies potential environmental impacts of products and services across their entire life cycles from production and assembly through use and end-of-life management. In the last ten years the use of LCA by industry, governmental and non-governmental organizations has increased dramatically in the U.S.. One of the shortcomings of current LCA practice is that it ignores the specific spatial context in which the assessed industrial activities take place. Yet location matters. Agricultural and photovoltaic yields, for example, are functions of location, and so is the environmental impact of emissions. The overarching goal of this project was to expand and enhance LCA theory and practice by coupling it with the spatial analytical functions of geographic information systems (GIS). In this project, we not only developed a specific methodology for including space into LCA, namely sparse grid based data cubes, but we also applied it to two important case studies: Spatially-explicit LCA of sun-to-wheels transportation pathways in the U.S. Spatially-explicit LCA of air emissions in the U.S. toxic release inventory The two case studies clearly demonstrate that spatially-explicit LCA is feasible and is able to capture significant spatial variability that is ignored in conventional LCA. Spatially-explicit LCA thus has the potential to dramatically enhance LCA as an environmental decision support tool. The case studies were not just proofs of concept but generated important results in their own right. The sun-to-wheels assessment makes the case that biofuels are essentially a way to harvest sunlight and thus need to be compared, for each location, with alternative technologies, such as photovoltaics. It clearly shows that photosynthesis does not compare very favorably with photovoltaics. The TRI assessment generated a large spatial database of potential human health impacts due to the air emissions recorded in the TRI. It shows that non-spatial assessments underestimate those impacts by up to a factor of one thousand. The reason for this is that emissions are strongly correlated with population density, which is captured by spatially-explicit assessments but lost in non-spatial averages. The database can now be further mined for insights, such as issues of environmental justice. Yet the intellectual merit of this project goes far beyond the specific results of the case studies. They also show that field based spatial approaches, such as sparse grids, are an efficient way to achieve spatially-explicit LCAs with high spatial resolution and generate spatial data layers that are easy to exchange and reuse. While previous efforts to regionalize LCA exist, our approach of using uniform high spatial resolution is novel and potentially transformative. We essentially propose that computational power and storage capacity of even desktops have reached a point where the LCA community should can and should embrace big data. The potential broader impacts of this cannot be underestimated. LCA is emerging as one of the prominent environmental assessment methods for economic and industrial activity. As a quantitative method, it requires data that accurately describe these activities. Today’s decisions about how this kind of data is collected and stored will greatly impact how and when LCA can be used to support environmental decision making. There is a unique opportunity right now for the scientific communities that use LCA and those that use GIS to join forces and think big in terms of how environmentally relevant spatial data should be collected, stored, and used. This project makes a small, but hopefully significant, contribution to this movement by training faculty and students in combining LCA and GIS and disseminating our results and experiences through publications and presentations to technical and non-technical audiences.
