Assessing land use efficiencies and land quality impacts of renewable transportation energy systems for passenger cars using the LANCA method

Abstract

Targets to reduce global warming impacts of the transportation sector may lead to increased land use and negative land quality changes. The aim of this paper is to implement the Land Use Indicator Calculation in Life Cycle Assessment (LANCA®) model to assess land quality impacts and land use efficiencies (concerning occupation and transformation) of different example renewable transport energy systems for passenger cars. In addition, the land use impacts are normalized according to the Soil Quality Index building on LANCA® and included in the environmental footprint. The assessment is based on information from GaBi life cycle assessment software databases and on literature. Functional unit of the model is to provide annual drive of 18,600 km for a passenger car in the EU. The analysis includes examples of biomass, electricity, electricity to fuels and fossil-based energy systems. Our findings confirm previous research that biomass-based transport energy systems have risks to lead to significantly higher land occupation and transformation impacts than do fossil oil or electricity-based ones. According to the LANCA® model, methane from Finnish wood and German corn has the highest impacts on filtration and the physicochemical filtration reduction potential. Sugarcane ethanol and palm oil diesel systems, on the other hand, lead to the highest erosion potential. Electricity-based transportation energy systems appear to be superior to biomass-based ones from the perspectives of land occupation, land transformation, and soil quality impacts for the selected examples. Land quality impacts should be taken into account when developing and expanding renewable transportation energy systems. The paper shows that the LANCA® method is applicable for the assessment of transport systems in order to provide extended information on environmental sustainability, which should be included more often in future analysis. However, it can be challenging to interpret underlaying assumptions, especially when aggregated information is used from databases.