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

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 quasiexperimental 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.

The Digital Forest: Mapping a Decade of Knowledge on Technological Applications for Forest Ecosystems

“…Community forestry, more generally referring to the governance of forests by local communities across a broad spectrum of land ownership models (Charnley & Poe, 2007), also seems underrepresented in our findings. Only a handful of studies on community-based forest monitoring and management were identified during the screening process, the majority of which made use of smartphones and other mobile devices: Wang et al (2016) explored the potential for community monitoring of forest pests using mobile phones; Rana and Miller (2019) used machine learning techniques to assess the impacts of different community forest management policies; Pratihast et al (2016) combined community data acquired through mobile devices with satellite imagery to develop a near real-time forest monitoring system; and Ferster et al (2013) adopted a public participation approach to wildfire management in the wildland-urban interface using a smartphone application. Given the importance of traditional knowledge and the contributions of local and Indigenous communities to sustainable forest management around the world (FAO & FILAC, 2021;Lawler & Bullock, 2017), along with historical precedents for community displacement and resettlement due to technological change (United Nations Department of Economic and Social Affairs, n.d.), it would be worthwhile for research programs to study the range of potential impacts of new technologies on traditional knowledge systems and management practices.…”

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

“…They should provide a detailed account of origins and use of training and test data, choice of models and other components used in their research so that users keep these facts in mind when judging the suitability of these algorithms for forest decision-making (Whittaker et al, 2018;Mueller et al, 2019). Moreover, they should consider the data-generating process and should increasingly use theories to guide the choice of variables and other regularization parameters to enhance user confidence in algorithmic decision-making (Rana and Miller, 2018). Even organizations that create algorithms should bear some responsibility for algorithmic decision-making and associated risks (Martin, 2019).…”

Forest-linked livelihoods in a globalized world

“…Generating more robust and comprehensive causal analyses will require: (i) a better balance between hypothesis generation and hypothesis testing, including a stronger integration of research methods (for example, between qualitative and quantitative methods to generate mixed methodologies), and use of both classic qualitative studies as well as emerging tools and approaches not widely used in the forest-linked livelihoods field, including novel tools for systematic qualitative analyses 64 , machine learning 65 , modelling approaches 66 and randomized control trials 67 ; (ii) strengthening existing data platforms (for example, TRASE (https://trase.earth) and the World Bank Microdata Library 68 ), and better integrating secondary socio-economic and biophysical datasets to assess joint livelihood and forest outcomes (for example, ref. 58 ); (iii) a more careful design of quantitative and qualitative primary data collection efforts that can be combined with existing datasets; and (iv) closer partnerships among different stakeholders to ensure that research can be co-produced and leveraged in advocacy strategies.…”