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arXiv · 2201.07105

Beyond modeling: NLP Pipeline for efficient environmental policy analysis

Jordi Planas·Daniel Firebanks-Quevedo·Galina Naydenova·Ramansh Sharma·Cristina Taylor·Kathleen Buckingham·Rong Fang
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Abstract

As we enter the UN Decade on Ecosystem Restoration, creating effective incentive structures for forest and landscape restoration has never been more critical. Policy analysis is necessary for policymakers to understand the actors and rules involved in restoration in order to shift economic and financial incentives to the right places. Classical policy analysis is resource-intensive and complex, lacks comprehensive central information sources, and is prone to overlapping jurisdictions. We propose a Knowledge Management Framework based on Natural Language Processing (NLP) techniques that would tackle these challenges and automate repetitive tasks, reducing the policy analysis process from weeks to minutes. Our framework was designed in collaboration with policy analysis experts and made to be platform-, language- and policy-agnostic. In this paper, we describe the design of the NLP pipeline, review the state-of-the-art methods for each of its components, and discuss the challenges that rise when building a framework oriented towards policy analysis.

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Jordi Planas, Daniel Firebanks-Quevedo, Galina Naydenova, Ramansh Sharma, Cristina Taylor, Kathleen Buckingham, Rong Fang. 2022-01-08. Beyond modeling: NLP Pipeline for efficient environmental policy analysis. https://arxiv.org/abs/2201.07105

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