Official Development Assistance directed towards green jobs and skills: An analysis of trends among G7 member contributions from 2016 to 2022

The International Labour Organization (ILO) Employment, Labour Markets and Youth Branch (EMPLAB) hosted a webinar to present the results of an analysis of ODA trends pertaining to G7 countries. The event is part of a pilot initiative implemented by the ILO and supported by the German Federal Ministry for Economic Cooperation and Development (BMZ) through the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) to improve the effectiveness of development assistance in “green jobs and skills”.

The webinar featured presentations by researchers from the University of Glasgow and the Kiel Institute for the World Economy. It brought together representatives from G7 countries involved in funding and supporting development policies and assistance aimed at strengthening employment and skills promotion in the context of the transition to environmentally sustainable economies and societies.

Background

In a global context of accelerating climate and environmental change, the concept of a just transition has emerged as process that maximizes the social and economic opportunities of climate action, while minimizing and carefully managing any challenges. The imperative of a just transition was cemented in the Paris Agreement of 2015.

As part of their commitment towards accelerating progress towards a just transition, the Leaders of the G7 in 2022 affirmed their intent to “shape the transition towards net-zero, nature positive economies and societies in a manner which contributes to our objectives of achieving decent work for all, social inclusion, the eradication of poverty, and ensuring that no one is left behind”. At this time, G7 Leaders committed to increase, by 2025, the share of their country’s official development assistance (ODA) employment and skills promotion programmes that is directed specifically towards green sectors and greening traditional sectors in alignment with emerging and developing partner countries’ strategies, and subject to G7 budgetary processes.

Following from these declarations, G7 development ministers requested the ILO to support the process of tracking this commitment including developing criteria for employment-oriented private sector and skills development that contribute to a green economy. A tracking mechanism for G7 Official Development Assistance (ODA) commitments on ‘green jobs and skills’ was developed in 2023 that relies on the use of ODA statistics compiled via the OECD’s Development Assistance Committee (DAC) Creditor Reporting System (CRS).

An analysis was then conducted in 2024, on the basis of the established approach. An additional approach was examined, supported by Large Language Model (LLM) analysis to gauge the effectiveness of the approach to support validation and offer additional insights into the results of the analysis provided by CSR codes. The objective of running this approach in parallel on the same data set is to inform ILO and G7 partners as to how to leverage LLM approaches in future for quick processing and analysis of textual data and allowing for a comprehensive and timely understanding of ODA trends.

The report of the combined analysis will be available as a publication in October 2024.

Objectives

1. Present the results of the analysis based on data reported under the Creditor Reporting Systems (CRS) codes used in the OECD-DAC ODA database.
2. Showcase the results of a complementary or alternative approach supported by Large Language Model (LLM) analysis.
3. Discuss perspectives for future reporting on ODA data trends and G7 commitments.

Agenda

Moderator: Gaëla Roudy Fraser, CTA & project manager, EMPLAB, ILO

Opening remarks: Sara Elder, Head of Analysis Unit, EMPLAB, ILO

Presentation

• Dr. Bernhard Reinsberg, UKRI Future Leaders Fellow, School of Social and Political Sciences, University of Glasgow: Results of analysis based on the Creditor Reporting Systems codes and green marker.
• Dr. Pietro Bomprezzi, Post-Doc, Kiel Institute for the World Economy: Comparative analysis using Large-Language Model (LLM) applications.

Q&A

Participants shared their questions and perspectives during Q&A.