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Social and Behavioral Sciences Commons

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Articles 1 - 4 of 4

Full-Text Articles in Social and Behavioral Sciences

Economics Of Afforestation: A Global Leadership Opportunity For Efd, Jeffrey R. Vincent Nov 2019

Economics Of Afforestation: A Global Leadership Opportunity For Efd, Jeffrey R. Vincent

Forest Collaborative Research

Slides from a presentation that examines the economics of afforestation and forest restoration in light of climatic changes, rising CO2 levels, carbon sequestration and other factors. Provides directions for further research, including retrospective analysis of previous afforestation projects, and targeted analysis of impediments to institutional investment in afforestation.


Forest And Health: China Case, Shilei Liu, Jintao Xu Nov 2019

Forest And Health: China Case, Shilei Liu, Jintao Xu

Forest Collaborative Research

Slides from a presentation that examines the relationship between ecosystem change and human health in China. The authors reviewed data from the Chinese Center for Disease Control and Prevention and other sources to search for links between afforestation, forest protection and human health.


The Nepal Community Forestry Program And Member Mental Health - June 2019, Randall Bluffstone Jun 2019

The Nepal Community Forestry Program And Member Mental Health - June 2019, Randall Bluffstone

Forest Collaborative Research

This presentation asks - Do Community Forestry's (CFs) and better forest quality yield mental health benefits?


Asset, Property Rights And Forest Dependency: Evidence From Machine Learning Analysis - June 2019, Dambala Gelo, Daniela Lamparelli Jun 2019

Asset, Property Rights And Forest Dependency: Evidence From Machine Learning Analysis - June 2019, Dambala Gelo, Daniela Lamparelli

Forest Collaborative Research

In many poor regions, the poor heavily depend on the income derived from the natural resource base. This presentation tests the forest-dependency-asset poverty hypotheses; looks at the impacts of credit constraint on forest dependency; and using machine learning approach resolves the problems of model selection uncertainty and structural parameters identification.