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Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.
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This series of four presentations sought to examine case studies of Greek-Russian relations in the early modern and, mostly, modern period. In the choice of topics, I was guided by two considerations: first, I sought to highlight topics that, to my mind, have not yet attracted the attention they deserve in historiography. For example, although alms collections in the Russian Empire in the early modern period have been discussed repeatedly, this has not been the case until quite recently for t...
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This paper examines the effect of market-entry timing on a firm’s speed and cost of entry in a setting where a firm needs to build a plant for market entry. Based on our developed analytical model, we provide seven scenarios of the market-entry timing effect on a firm’s entry speed and cost. We test hypotheses in the liquefied natural gas (LNG) industry. We use Wooldridge’s three-step instrumental variable (IV) approach to account for endogeneity bias. We find that a late entrant has (1) a shorter time-to-build and (2) a higher cost-to-build relative to an early entrant. Further, (3) the late entrant positively moderates the negative relationship of time-to-build and cost-to-build (i.e., the negative relationship of time-to build and cost-to-build becomes less negative for the late entrant). These empirical results are consistent with the prediction of when both revenue effect (i.e., revenue curve shift) and cost effect (i.e., cost curve leftward shift) exist.
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