Dataset Information

Global Sensitivity Analysis of Background Life Cycle Inventories.

ABSTRACT: In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

SUBMITTER: Kim A

PROVIDER: S-EPMC9069693 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Global Sensitivity Analysis of Background Life Cycle Inventories.

Kim Aleksandra A Mutel Christopher L CL Froemelt Andreas A Hellweg Stefanie S

Environmental science & technology 20220412 9

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lac ...[more]

PMID: 35413184

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Project description:An accurate and objective evaluation of the carbon footprint of rice production is crucial for mitigating greenhouse gas (GHG) emissions from global food production. Sensitivity and uncertainty analysis of the carbon footprint evaluation model can help improve the efficiency and credibility of the evaluation. In this study, we combined a farm-scaled model consisting of widely used carbon footprint evaluation methods with a typical East Asian rice production system comprising two fertilization strategies. Furthermore, we used Morris and Sobol' global sensitivity analysis methods to evaluate the sensitivity and uncertainty of the carbon footprint model. Results showed that the carbon footprint evaluation model exhibits a certain nonlinearity, and it is the most sensitive to model parameters related to CH4 emission estimation, including EFc (baseline emission factor for continuously flooded fields without organic amendments), SFw (scaling factor to account for the differences in water regime during the cultivation period), and t (cultivation period of rice), but is not sensitive to activity data and its emission factors. The main sensitivity parameters of the model obtained using the two global sensitivity methods were essentially identical. Uncertainty analysis showed that the carbon footprint of organic rice production was 1271.7 ± 388.5 kg CO2eq t-1 year-1 (95% confidence interval was 663.9-2175.8 kg CO2eq t-1 year-1), which was significantly higher than that of conventional rice production (926.0 ± 213.6 kg CO2eq t-1 year-1, 95% confidence interval 582.5-1429.7 kg CO2eq t-1 year-1) (p<0.0001). The carbon footprint for organic rice had a wider range and greater uncertainty, mainly due to the greater impact of CH4 emissions (79.8% for organic rice versus 53.8% for conventional rice). EFc , t, Y, and SFw contributed the most to the uncertainty of carbon footprint of the two rice production modes, wherein their correlation coefficients were between 0.34 and 0.55 (p<0.01). The analytical framework presented in this study provides insights into future on-farm advice related to GHG mitigation of rice production.

Dataset Information

Global Sensitivity Analysis of Background Life Cycle Inventories.

Publications

Global Sensitivity Analysis of Background Life Cycle Inventories.

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