The “budgeting for SDGs”–B4SDGs–paradigm seeks to coordinate the budgeting process of the fiscal cycle with the Sustainable Development Goals (SDGs) set by the United Nations. Integrating the goals into public financial management systems is crucial for an effective alignment of national development priorities with the objectives set in the 2030 Agenda. Within the dynamic process defined in the B4SDGs framework, the step of SDG budget tagging represents a precondition for subsequent budget diagnostics. However, developing a national SDG taxonomy requires substantial investment in terms of time, human, and administrative resources. Such costs are exacerbated in least developed countries, which are often characterized by a constrained institutional capacity. The automation of SDG budget tagging could represent a cost-effective solution.
In this project, we employ Natural Language Processing techniques to explore the scope and scalability of automatic labelling budget programs within the B4SDGs framework, to understand if and how these methods can support policymakers in integrating the goals into their budgetary procedures. The final aim is to incorporate such algorithms into a web application. This open platform will allow any potential users to upload data on budget lines and get their classification into the SDGs, pushing forward the B4SDGs agenda.
- Guariso, D., Guerrero, O. A., & Castañeda, G. (2023). Automatic SDG Budget Tagging: Building Public Financial Management Capacity through Natural Language Processing. SSRN Electronic Journal. Paper
Online repository (upcoming)