CAREER: Computational Approaches for Life Cycle Inventory Database Development

1554349（Xu）这项研究旨在将目前开发生命周期清单（LCI）数据库的实践推进到一个更快，更便宜的过程中，仍然生成可靠的LCI数据。该研究将（1）创建建模和分析LCI网络的框架，（2）开发用于估计缺失LCI数据的计算模型，以及（3）应用这些模型评估LCI数据质量并预测新兴技术的LCI数据。该教育计划将（1）在项目过程中吸引不同的LCA从业人员，（2）为LCA从业人员提供开源软件附加组件，以轻松使用拟议研究中开发的计算模型，（3）开发一个教育理论基础课程模块，将研究成果纳入更广泛的传播，以及（4）通过让本科生和研究生参与研究计划和其他教育活动，培养他们在STEM领域具有不同背景的学生。本研究将开发计算方法，用于仅基于有限的已知数据来估计生命周期清单（LCI）数据库中的缺失数据，而不依赖于耗时，昂贵的经验数据收集。该方法将网络科学的最新知识转移到LCI数据库开发中。LCI数据库代表了单元过程和环境干预的相互依赖性。这种相互依存的整体特征是基础技术网络（或LCI网络）的结构。如果足够充分，观测到的LCI数据虽然有限，但可用于提取底层LCI网络的结构特征。这样的结构特征，反过来，可以用来预测LCI网络的未知区域的结构，这相当于估计LCI数据库中的未知数据。本研究将首先建立一个建模和分析LCI网络的框架。然后，该框架将用于开发和验证各种链接预测模型，以估计LCI数据库的缺失数据。最后，经验证的链接预测模型将用于评估LCI数据质量，并预测与利益攸关方协商选择的试验台数据库的新兴技术的LCI数据。

1554349 (Xu)This research aims to advance the current practice of developing Life Cycle Inventory (LCI) databases into a faster, less expensive process that still generates reliable LCI data. The research will (1) create a framework for modeling and analyzing LCI networks, (2) develop computational models for estimating missing LCI data, and (3) apply these models to evaluate LCI data quality and predict LCI data for emerging technologies. The education plan will (1) engage a diverse group of LCA practitioners during the course of the project, (2) deliver open source software add-ons for LCA practitioners to easily use the computational models developed in the proposed research, (3) develop an education theory grounded curriculum module incorporating research outcomes for broader dissemination, and (4) train undergraduate and graduate students with diverse background in STEM fields by engaging them in the research program and other education activities. This research will develop computational approaches for estimating missing data in Life Cycle Inventory (LCI) databases based solely on limited known data, without relying on time-consuming, expensive empirical data collection. The approach transfers the latest knowledge from network science to LCI database development. An LCI database represents the interdependence of unit processes and environmental interventions. The ensemble of such interdependence characterizes the structure of the underlying technology network (or LCI network). If sufficient enough, observed LCI data, although limited, can be used to extract structural features of the underlying LCI network. Such structural features, in turn, can be used to predict the structure of the unknown area of the LCI network, which is equivalent to estimating the unknown data in the LCI database. This research will first create a framework for modeling and analyzing LCI networks. This framework will then be used to develop and validate a variety of link prediction models to estimate missing data for LCI databases. Finally the validated link prediction models will be used to evaluate LCI data quality and predict LCI data for emerging technologies for testbed databases selected in consultation with stakeholders.