Rongyun Tang
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Postdoc
Also affiliated: Oak Ridge National Laboratory (2020–2021); Beijing Normal University (2018); University of Tennessee System (2020–2022); Laboratoire des Sciences du Climat et de l'Environnement (2020); Institute of Remote Sensing and Digital Earth (2018); State Key Laboratory of Remote Sensing Science (2018); University of Tennessee at Knoxville (2019–2024)
Formerly Arkansas Affiliated with University of Arkansas through 2024.
Postdoc Researcher
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Rongyun Tang's research focuses on the intersection of e-commerce, supply chain management, and sustainable development, with a particular emphasis on carbon emission strategies and decision-making models. Tang has investigated manufacturer decision-making under carbon emission permit repurchase strategies and capital constraints, as well as the coordination of e-commerce supply chains with logistics outsourcing and altruistic preferences. Additionally, Tang's work has explored global wildfire activity, analyzing interannual variability, climatic sensitivity, and modeling wildfire drivers and predictability using machine learning and satellite observations. This research extends to quantifying wildfire drivers in boreal peatlands and evaluating the effects of heatwaves on hydrological processes. Tang has 18 publications with an h-index of 10 and 411 citations, and has collaborated with researchers including Benjamin R. K. Runkle and Beatriz E. Moreno-García.
Metrics
- h-index: 10
- Publications: 18
- Citations: 411
Selected Publications
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Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems (2026)
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Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 (2024)
Collaboration Network
Top Collaborators
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
- Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems
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