气候变化领域集成服务门户(CCPortal): Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya

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DOI	10.1088/1748-9326/aafa8f
	Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya
	Rana P.; Miller D.C.
发表日期	2019
ISSN	17489318
卷号	14 期号:2
英文摘要	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. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	community forest management; deforestation, machine learning; forest livelihoods; forest policy; impact evaluation
语种	英语
scopus关键词	Deforestation; Ecology; Machine learning; Natural resources; Community partnerships; forest livelihoods; Forest policy; Impact evaluation; Joint forest managements; Machine learning approaches; Machine learning methods; Social-ecological systems; Economic and social effects; deforestation; ecological impact; forest management; forest resource; forestry policy; heterogeneity; machine learning; natural resource; Himalayas; India
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154704
作者单位	Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
推荐引用方式 GB/T 7714	Rana P.,Miller D.C.. Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya[J],2019,14(2).
APA	Rana P.,&Miller D.C..(2019).Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya.Environmental Research Letters,14(2).
MLA	Rana P.,et al."Machine learning to analyze the social-ecological impacts of natural resource policy: Insights from community forest management in the Indian Himalaya".Environmental Research Letters 14.2(2019).