Selection of the most appropriate life cycle inventory dataset: new selection proxy methodology and case study application

Using generic or country-specific life cycle inventory (LCI) datasets for life cycle assessments (LCAs) with no site-specific LCI datasets can result in inaccurate LCA results, whereas comprehensive site-specific LCI datasets require considerable time and effort. We use the term site-specific as a broad term, the site denoting a site, region, or technology. We propose a methodology for systematic selection of the most appropriate proxy dataset for a specific site, from the available LCI datasets for a specific background process.

The “Selection Proxy” methodology applies rigorous mathematical and statistical techniques. The selection process is based on the concept of a descriptive characteristics space. Each potential proxy dataset and the missing dataset are assigned coordinates in the characteristics space. The proxy is selected based on the “distance” between candidate LCI datasets and the known coordinates of the target dataset in the characteristics space. The dataset with the minimum distance is the “Selection Proxy” dataset. The methodology is demonstrated on water supply systems using a harmonized set of 22 LCA studies, focusing on climate change, with analysis of four additional impact categories. The methodology is corroborated on climate change of coal-fired electric power stations using a harmonized set of 100 LCA studies.

The results that we have obtained for assessing climate change of water supply systems indicate the potentially high approximation power of our “Selection Proxy” methodology. The proposed method provides LCA impact scores that in most cases are considerably closer to “true values” at only a small fraction of the effort needed to create a comprehensive site-specific LCI dataset. The application of the methodology to the coal-fired power stations demonstrated the approximation power and cost-effectiveness of the methodology. The methodology incorporates a built-in improvement capability: every additional unique LCI dataset improves the accuracy of results.

The new methodology enables selection of a dataset that represents the missing dataset that leads in most cases to a much better approximation of environmental impacts than a dataset selected by default or by geographical proximity. The methodology is general and is applicable to various background processes. The models developed for water supply systems and coal-fired power stations are freely available upon request and can be used “as is” for LCAs in locations for which site-specific LCIs for these processes are not available.