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Yi Fan

Researcher at Michigan State University

Publications -  7
Citations -  712

Yi Fan is an academic researcher from Michigan State University. The author has contributed to research in topics: Structural equation modeling & Impact assessment. The author has an hindex of 6, co-authored 7 publications receiving 359 citations.

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Applications of structural equation modeling (SEM) in ecological studies: an updated review

TL;DR: The essential components and variants of structural equation modeling (SEM) are introduced, the common issues in SEM applications are synthesized, and the views on SEM’s future in ecological research are shared.
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Policy shifts influence the functional changes of the CNH systems on the Mongolian plateau

TL;DR: In this article, the authors compared spatiotemporal changes in livestock (LSK), land cover, and ecosystem production to understand the relative roles that natural and social driving forces have on CNH dynamics on the Mongolia plateau.
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Agent-Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

TL;DR: A general concept to integrate ABM into current building life cycle assessment standards is proposed and simulation results from the agent†based model confirm that there are temporal and spatial variations caused by behavioral dynamics.
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Neonatal mortality in East Africa and West Africa: a geographic analysis of district-level demographic and health survey data

TL;DR: Neonatal mortality was significantly associated with home births, mothers without an education and mothers whose husbands decided on contraceptive practices in East Africa and West Africa, and future SDG-interventions may target these dimensions of need in priority high-risk districts and countries, to further reduce the burden of neonatal mortality in Africa.
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Causality in social life cycle impact assessment (SLCIA)

TL;DR: In this article, the authors used structural equation modeling (SEM) to identify the impact pathways for type II characterization models, therefore resolving the issues of unobservability and unvalidatibility in type II models.