What are the latest trends in LCA data quality and uncertainty research?

1 Data quality indicators

Data quality indicators (DQIs) are numerical or qualitative measures that describe the characteristics and limitations of data sources used in LCA. DQIs can help practitioners and users to evaluate and compare the quality of different data sets and identify potential sources of error or bias. Some of the common DQIs used in LCA are age, geographical representativeness, technological representativeness, completeness, consistency, and reliability. However, there is no universal agreement on how to define, measure, and report DQIs, which can lead to inconsistent and incomparable results. Therefore, researchers are developing and testing new frameworks and guidelines for standardizing and harmonizing DQIs across different LCA databases and studies.

2 Uncertainty propagation methods

Uncertainty propagation methods are techniques that estimate how the uncertainty in input data affects the uncertainty in output results in LCA. Uncertainty propagation methods can help practitioners and users to understand the variability and sensitivity of LCA results and to communicate the level of confidence and robustness of their findings. Some of the common uncertainty propagation methods used in LCA are Monte Carlo simulation, analytical propagation, and interval analysis. However, these methods have different assumptions, limitations, and computational requirements, which can affect their suitability and accuracy for different LCA applications. Therefore, researchers are exploring and comparing new and existing uncertainty propagation methods and developing best practices and recommendations for their selection and implementation.

3 Data quality and uncertainty integration

Data quality and uncertainty integration is the process of combining data quality indicators and uncertainty propagation methods in a consistent and transparent way in LCA. Data quality and uncertainty integration can help practitioners and users to link the quality of input data to the uncertainty of output results and to identify the most influential data quality aspects and uncertainty sources in LCA. Data quality and uncertainty integration can also support the improvement of data quality and uncertainty management in LCA by providing feedback and guidance on data collection, validation, and documentation. However, there are few examples and methods for integrating data quality indicators and uncertainty propagation methods in LCA, which can limit their applicability and effectiveness. Therefore, researchers are developing and demonstrating new and innovative approaches for integrating data quality and uncertainty in LCA.

5 Data quality and uncertainty applications

Data quality and uncertainty applications are the uses and benefits of data quality indicators and uncertainty propagation methods for different purposes and contexts in LCA. Data quality and uncertainty applications can help practitioners and users to improve the quality and reliability of LCA results and to support the decision-making and policy-making processes based on LCA. Data quality and uncertainty applications can also foster the innovation and development of new products, processes, and systems that have lower environmental impacts and higher sustainability performance. However, there are many gaps and opportunities for applying data quality and uncertainty analysis in LCA, such as the integration of data quality and uncertainty with other LCA methods and tools, the adaptation of data quality and uncertainty to different sectors and domains, and the evaluation of the impacts and outcomes of data quality and uncertainty on LCA results and decisions. Therefore, researchers are exploring and demonstrating new and relevant applications of data quality and uncertainty in LCA.