Rongyun Tang
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Postdoc
Also affiliated: Oak Ridge National Laboratory (2020–2021); Chinese Academy of Sciences (2018); Beijing Normal University (2018); Institute of Remote Sensing and Digital Earth (2018); State Key Laboratory of Remote Sensing Science (2018); University of Tennessee at Knoxville (2019–2024)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Rongyun Tang's research explores environmental regulations, corporate social responsibility, and their impact on manufacturing and supply chains. Tang has investigated the effects of environmental regulations on the green transformation of manufacturing enterprises, as well as decision-making models for closed-loop supply chains under conditions of corporate social responsibility and fairness concerns. Additionally, Tang's work has examined manufacturer decision-making in the context of carbon emission permits and capital constraints.
Further research by Tang has focused on the environmental impacts of urbanization and climate change. This includes studies on vegetation coverage in the Beijing–Tianjin–Hebei region, the spatiotemporal dynamics of ecosystem fires and associated carbon emissions in China, and the influence of urbanization on albedo in the same region. Tang has also contributed to understanding the interannual variability and climatic sensitivity of global wildfire activity.
Metrics
- h-index: 10
- Publications: 21
- Citations: 421
Positions
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Postdoc 2025–presentNorth Carolina State University Plant and Microbial Biology ORCID
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Postdoc publications 2024–2026University of Arkansas at Fayetteville ORCID
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Post Doctoral Fellow 2023–2025University of Arkansas at Fayetteville Biological and Agricultural Engineering ORCID
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Research Associate 2023University of Tennessee at Knoxville Institute for a Secure & Sustainable Environment ORCID
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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