Identifying economic and financial incentives for forest and landscape restoration in Latin America using Natural Language Processing

World Resources Institute

112 followers.

Forest and landscape restoration is a cross-cutting agenda that traverses sectors such as agriculture, forestry, water and natural resources. While this cross-cutting nature makes restoration an attractive policy measure for carbon sequestration, mitigation, and adaptation, it complicates policy analysis. The sheer volume of text impedes researchers and decision makers from identifying misalignment and monitoring evolving policy and agenda shifts. Analyzing such a large corpus of documents exacerbates policy analysis’ transparency, objectivity, access, and scalability. Our proposal is to standardize and scale policy analysis, alignment, and agenda setting with natural language processing (NLP). A previous proof-of-concept we developed demonstrated the utility of NLP to quickly summarize agenda-specific information from policies. The aim of this project would be to identify financial and economic incentives to support enabling conditions for Nature Based Solutions.

Environment International development
check New check Scoping check Scoping QA check Staffing check In progress check Final QA done_all Completed
Volunteers are working on this project

Project scope (as of Aug. 11, 2020, 3:32 p.m.)

Project goal(s)

• Identify which policies relate to forest and landscape restoration

• Identify financial and economic incentives in policies

• Identify disincentives, misalignment and conflicts in policies (if possible)

• Create a heat map which determines the relevance of policies to forest and landscape restoration

Interventions and Actions

Support provided as needed.

Data

• Policy documents have been compiled by the team

• Most documentation is in Spanish therefore a Spanish speaker is needed

Analysis Needed

• Identify which policies relate to forest and landscape restoration

• Identify which policies identify incentives (in particular financial and economic incentives) for forest and landscape restoration

• Identify which polices identify disincentives for forest and landscape restoration

• Create a heat map to understand related polices

Validation Methodology

The team will provide guidance on which incentives we seek to understand as a guide. We will manually analyze policies for certain countries. This analysis can be used to compare with the machine learning results.

Implementation

This work will contribute to the policy accelerator which seeks to work with governments to identify tangible policy recommendations. If successful, it will act as a prototype for scaling in other languages.

The aim would be to create a tool that could be used to understand how new policy changes support or conflict for Nature Based Solutions (such as forest and landscape restoration). This would enable governments to easily create supporting policies, environmentalists to advocate for positive change and create greater transparency regarding creating enabling conditions for Nature Based Solutions at national and jurisdictional levels.

Scope version notes