Natural Language Processing (NLP)

As we enter the United Nations Decade on Ecosystem Restoration, the importance of restoring degraded ecosystems from anthropological activities is at the forefront of our minds. It is essential that we act quickly to prevent catastrophic collapses in the face of rising temperatures and changes in rainfall patterns.

Economic incentives have proven to be among the most effective tools for promoting healthy farming and returning agricultural lands to their original ecosystems. These incentives can come in the form of subsidies, tax reductions, direct payments, technical support, credits, and more. They provide affected farmers with the necessary resources to restore degraded lands and promote sustainable practices.

Initiative 20x20, a coalition of 18 Latin American countries, has committed to restoring 500 million hectares under restoration and conservation by 2030. This ambitious target emphasizes the urgent need for restoration efforts to combat the effects of climate change and biodiversity loss.

To achieve these restoration goals, stakeholders must have access to information on public policy incentives and their effectiveness in implementing restoration projects. However, retrieving information from policy documents can be time- and labor-intensive. Human performance in accuracy and completeness is also dependent on their expertise and energy level.

This is where natural language processing (NLP), a branch of Artificial Intelligence, comes into play. Our goal is to utilize NLP to create an auto policy assistant that can speed up or replace the manual process of information retrieval. NLP aims to train computers to understand human language and complete text, verbal, or other language-related tasks. By automating the policy retrieval process, we can provide stakeholders with the necessary information to make informed decisions about restoration projects more efficiently and accurately. Please find the public blog from the WRI's Insights and conference paper for more details.