Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Rana, Pushpendra; Varshney, Lav R.

doi:10.3389/ffgc.2020.587178

Cited by 5 publications

(2 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies have attempted to identify areas where tree planting is more likely to produce livelihood benefits alongside environmental objectives (Brancalion et al 2019, Brancalion and Holl 2020, Di Sacco et al 2021, Rana and Varshney 2020. This work recognizes that extending forest cover without addressing local needs risks negative economic consequences for millions of forest-dependent people and compromises restoration efficacy (Erbaugh et al 2020, Scheidel and Gingrich 2020, Pichler et al 2021, Löfqvist et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to uncover synergies between forest restoration and livelihood support in the Himalayas

Rana,

Fischer,

Coleman

et al. 2024

E&S

View full text Add to dashboard Cite

In recent years, governments and international organizations have initiated numerous large-scale tree planting projects with the dual goals of restoring landscapes and supporting rural livelihoods. However, there remains a need for greater knowledge of drivers and conditions that enable positive social and environmental outcomes over the long term. In this study, we used interpretable machine learning (IML) to explore win-win and win-lose outcomes between livelihood benefits and forest cover using four decades of tree plantation data from northern India. Our results indicated that, in areas with a larger population of socioeconomically marginalized groups, moderate levels of education, and existing histories of community collective action, there is a higher probability of achieving joint positive outcomes. We also found that joint positive outcomes are more common within a consolidated local institutional space, suggesting that decentralized governance structures with cross-sectoral duties and functions may be better equipped to mediate conflicts between intersecting forest and land use challenges. Finally, our findings showed that non-forestry and anti-poverty interventions such as universal labor generation programs and universal education are associated with improved forest cover alongside livelihood benefits from plantations. Whereas contemporary policy discussions have given substantial attention to tree plantation schemes, our work suggests that effective restoration requires much more than planting alone. A broad mixture of socioeconomic, institutional, and policy interventions is needed to create favorable conditions for long-term success. In particular, anti-poverty programs may serve as important indirect policy pathways for ensuring restoration gains.

show abstract

Section: Introductionmentioning

confidence: 99%

Using machine learning to uncover synergies between forest restoration and livelihood support in the Himalayas

Rana,

Fischer,

Coleman

et al. 2024

E&S

View full text Add to dashboard Cite

show abstract

“…Recent improvements in Natural Language Processing (nlp) technologies and Machine Learning (ml) algorithms have allowed solving an ample variety of problems such as summarization (Trappey et al, 2020;Gambhir & Gupta, 2017), user profiling (Tellez et al, 2018;Flores et al, 2022) and decision making (Trappey et al, 2020;Rana & Varshney, 2021). They have jointly contributed to intelligent conversational assistants (Rustamov et al, 2021;Hasal et al, 2021) and sentiment and emotion analysis systems (Kastrati et al, 2021;Tao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%