Quantification and valuation of ecosystem services in life cycle assessment: Application of the cascade framework to rice farming systems.

The integration of ecosystem service (ES) assessment with life cycle assessment (LCA) is important for developing decision support tools for environmental sustainability. A prequel study has proposed a 4-step methodology that integrates the ES cascade framework within the cause-effect chain of life cycle impact assessment (LCIA) to characterize the physical and monetary impacts on ES provisioning due to human interventions. We here follow the suggested steps in the abovementioned study, to demonstrate the first application of the integrated ES-LCIA methodology and the added value for LCA studies, using a case study of rice farming in the United States, China, and India. Four ES are considered, namely carbon sequestration, water provisioning, air quality regulation, and water quality regulation. The analysis found a net negative impact for rice farming systems in all three rice producing countries, meaning the detrimental impacts of rice farming on ES being greater than the induced benefits on ES. Compared to the price of rice sold in the market, the negative impacts represent around 2%, 6%, and 4% of the cost of 1 kg of rice from China, India, and the United States, respectively. From this case study, research gaps were identified in order to develop a fully operationalized ES-LCIA integration. With such a framework and guidance in place, practitioners can more comprehensively assess the impacts of life cycle activities on relevant ES provisioning, in both physical and monetary terms. This may in turn affect stakeholders' availability to receive such benefits from ecosystems in the long run.

Overview and recommendations for regionalized life cycle impact assessment.

PURPOSE: Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still faces multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provides an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software. METHOD: A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method spatial resolution and scale, the spatial resolution of input parameters, choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors. RESULTS AND DISCUSSION: Survey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modelled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated models CFs are not easily available, archetype models are sometimes developed. CONCLUSIONS: Regionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided, and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency and robustness in the development and application of regionalized LCIA methods.