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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.
We are on the verge of the United Nations Decade for Ecosystem Restoration. The Decade starts in 2021 and ushers in a global effort to drive ecosystem restoration to support climate mitigation and adaptation, water and food security, biodiversity conservation and livelihood development. In order to prepare for the decade, we must understand the enabling environment. However, to understand policies involves reading and analyzing thousands of pages of documentation across multiple sectors. Using NLP to mine policy documents, would promote knowledge sharing between stakeholders and enable rapid identification of incentives, disincentives, perverse incentives and misalignment between policies. If a lack of incentives or disincentives were discovered, this would provide an opportunity to advocate for positive change. Creating a systematic analysis tool using NLP would enable a standardized approach to generate data that can support evidence-based change.
The viability of Nature Based Solutions projects is often impeded by the lack of positive incentives to adopt practices that conserve or restore land. Perverse incentives also encourage business-as-usual practices that have a heavy carbon footprint, degrade ecosystems, exploit workers or fail to generate decent livelihoods for rural communities.
Shifting incentives in a specific jurisdiction begins with a diagnosis of the country’s existing regulations, incentives and mandates across agencies. The aim is to gain a thorough understanding of current regulations and incentives that are relevant to forest and landscape restoration, the reality of how they are applied in practice and the degree of alignment or conflict across ministries and different levels of government. Shifting incentives at international level, may require such diagnostics across multiple countries, or voluntary standards and business practices. For this purpose, natural language processing technologies are needed to expedite systematic review of the legal and policy context in the relevant jurisdictions, as well as examples of innovative incentives from other contexts.
The initial focus is in Latin America, therefore native or fluent Spanish speakers are required to lead the project. If volunteers are interested in other country contexts, please contact us and we will assess data availability, but Latin America is a priority focus in the first instance.
Success will be achieved as governments or market platforms create aligned incentives across sectoral silos, remove administrative bottlenecks, or reorient incentives in line with recommendations. To advocate for change, a systematic process of analyzing incentives is needed beyond manual policy analysis. Currently manual policy analysis is the only method utilized to understand incentives. This is inadequate when considering the scale of the task.
Global Restoration Initiative and Forest teams. Supporting the work of the Policy Accelerator
Kathleen Buckingham; Research Manager
Rene Zamora-Cristales; Forest Economist & Senior Associate, Latin America Research Coordinator