Global S&T Development Trend Analysis Platform of Resources and Environment
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, Pushpendra; Miller, Daniel C. | |
2019-02-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS
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ISSN | 1748-9326 |
出版年 | 2019 |
卷号 | 14期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
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. |
英文关键词 | forest policy community forest management forest livelihoods deforestation machine learning impact evaluation |
领域 | 气候变化 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000457539400002 |
WOS关键词 | GOVERNANCE ; CONSERVATION ; FRAMEWORK ; BENEFITS ; POVERTY ; COMMONS |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/15013 |
专题 | 气候变化 |
作者单位 | Univ Illinois, Dept Nat Resources & Environm Sci, Urbana, IL 61801 USA |
推荐引用方式 GB/T 7714 | Rana, Pushpendra,Miller, Daniel C.. Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the Indian Himalaya[J]. ENVIRONMENTAL RESEARCH LETTERS,2019,14(2). |
APA | Rana, Pushpendra,&Miller, Daniel 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, Pushpendra,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). |
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