Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya

Pushpendra Rana, Daniel Charles Miller

Research output: Contribution to journalArticle

Abstract

Machine learning promises to advance analysis of the social and ecological impacts of forest and other natural resource policies around the world. However, realizing this promise requires addressing a number of challenges characteristic of the forest sector. Forests are complex social-ecological systems (SESs) with myriad interactions and feedbacks potentially linked to policy impacts. This complexity makes it hard for machine learning methods to distinguish between significant causal relationships and random fluctuations due to noise. In this context, SES frameworks together with quasi-experimental impact evaluation approaches can facilitate the use of machine learning by providing guidance on the choice of variables while reducing bias in estimated effects. Here we combine an SES framework, optimal matching, and Causal Tree-based algorithms to examine causal impacts of two community forest management policies (forest cooperatives and joint state-community partnerships) on vegetation growth in the Indian Himalaya. We find that neither policy had a major impact on average, but there was important heterogeneity in effects conditional on local contextual conditions. For joint forest management, a set of biophysical and climate factors shaped differential policy impacts across the study region. By contrast, cooperative forest management performed much better in locations where existing grazing-based livelihoods were safeguarded. Stronger local institutions and secure tenure under cooperative management explain the difference in outcomes between the two policies. Despite their potential, machine learning approaches do have limitations, including absence of valid precision estimates for heterogeneity estimates and issues of estimate stability. Therefore, they should be viewed as a complement to impact evaluation approaches that, among other potential uses, can uncover key drivers of heterogeneity and generate new questions and hypotheses to improve knowledge and policy relating to forest and other natural resource governance challenges.

Original languageEnglish (US)
Article number024008
JournalEnvironmental Research Letters
Volume14
Issue number2
DOIs
StatePublished - Feb 1 2019

Fingerprint

Forestry
ecological impact
Natural resources
forest management
Learning systems
natural resource
Ecosystem
Optimal systems
Joints
Feedback
social impact
machine learning
policy
Machine Learning
Natural Resources
Forests
Climate
grazing
Noise
vegetation

Keywords

  • community forest management
  • deforestation, machine learning
  • forest livelihoods
  • forest policy
  • impact evaluation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Public Health, Environmental and Occupational Health

Cite this

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abstract = "Machine learning promises to advance analysis of the social and ecological impacts of forest and other natural resource policies around the world. However, realizing this promise requires addressing a number of challenges characteristic of the forest sector. Forests are complex social-ecological systems (SESs) with myriad interactions and feedbacks potentially linked to policy impacts. This complexity makes it hard for machine learning methods to distinguish between significant causal relationships and random fluctuations due to noise. In this context, SES frameworks together with quasi-experimental impact evaluation approaches can facilitate the use of machine learning by providing guidance on the choice of variables while reducing bias in estimated effects. Here we combine an SES framework, optimal matching, and Causal Tree-based algorithms to examine causal impacts of two community forest management policies (forest cooperatives and joint state-community partnerships) on vegetation growth in the Indian Himalaya. We find that neither policy had a major impact on average, but there was important heterogeneity in effects conditional on local contextual conditions. For joint forest management, a set of biophysical and climate factors shaped differential policy impacts across the study region. By contrast, cooperative forest management performed much better in locations where existing grazing-based livelihoods were safeguarded. Stronger local institutions and secure tenure under cooperative management explain the difference in outcomes between the two policies. Despite their potential, machine learning approaches do have limitations, including absence of valid precision estimates for heterogeneity estimates and issues of estimate stability. Therefore, they should be viewed as a complement to impact evaluation approaches that, among other potential uses, can uncover key drivers of heterogeneity and generate new questions and hypotheses to improve knowledge and policy relating to forest and other natural resource governance challenges.",
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