Pierre Beaucoral

PhD Candidate

CERDI UCA CNRS IRD

Biography

This is the official website of Pierre Beaucoral, a PhD student specializing in development economics at CERDI. My research focuses on climate finance, development finance, and aid allocation and effectiveness in relation to environmental issues.

Interests

Development economics
Environment
Development finance

Education

PhD in Climate finance, 2023
Clermont Auvergne University
Master degree in development economics, with honours, 2021
Clermont Auvergne University
Bachelor degree in development economics, with honours, 2018
Clermont Auvergne University

Skills

Technical

Econometrics

Python

Hobbies

Music

Sports

Hiking

Experience

PhD student

CERDI CNRS UCA IRD

October 2023 – Present Clermont-Ferrand

Responsibilities include:

Analysing
Modelling
Teaching

Research assistant

Ferdi

October 2022 – September 2023 Clermont-Ferrand, France

Development economics, international development finance.

Accomplishments

Master degree in development economics

Clermont Auvergne University Sep 2019 – Sep 2021

Bachelor in economics

Clermont Auvergne University Sep 2016 – Sep 2018

Teachings

Teaching experiences

Teaching assistant: Introduction to economics, bachelor level, Autumn 2023.

R programming: Introduction to R for statistical analysis, Economic Policy Management (GPE) training programme, master level, Autumn 2024.

Projects

Pierre Beaucoral, Michael Goujon, Sebastien Marchand

Jan 1, 2025

No plan, No Aid? The effects of National Adaptation Plan implementation on received Adaptation Aid

This paper examines the relationship between changes in adaptation aid and the implementation of National Adaptation Plans (NAPs) in recipient countries, using a staggered difference-in-differences (DiD) framework. Adaptation aid, a critical component of climate finance, aims to bolster the resilience of vulnerable nations to climate change impacts. However, empirical evidence on its efficacy in driving policy implementation remains limited. Analyzing adaptation aid registered in the OECD CRS dataset and detailed information on NAP implementation stages across multiple recipient countries, we exploit temporal and cross-country variation in aid to identify signal effects of NAPs. The staggered DiD approach accounts for the heterogeneous timing of aid receipt and of NAP implementation, allowing us to isolate the effects of NAPs while addressing concerns of endogeneity and omitted variable bias. Our findings reveal that NAPs adoption tends to reduce received adaptation aid. These controversial results underscore the importance of recipient strategies and well-targeted aid in achieving global climate adaptation goals and provide critical insights for policymakers and donors seeking to enhance the effectiveness of climate finance mechanisms.

Sep 6, 2024

Under the Green Canopy: bringing up to date public climate finance determinants analysis with AI

Climate finance is critical for addressing the multifaceted challenges of climate change, encompassing mitigation, adaptation, and environmental sustainability. This study aims to renew the analysis of a critical part of climate finance determinants’ allocation across these dimensions and accurately estimate bilateral public climate finance flows using an advanced machine learning approach. ClimateFinanceBERT (Bidirectional Encoder Representations from Transformers) is employed to classify development finance projects, distinguishing those that contribute to climate mitigation, adaptation, and environmental objectives. By examining a comprehensive dataset of development finance projects (OECD CRS) and replicating a recent research on climate public aid determinants, this study identifies key factors influencing the allocation of climate finance. This work updates significant patterns in climate finance distribution. This research contributes to the growing field of climate finance by offering a robust analytical framework for assessing the determinants of climate finance and proposing a scalable solution for monitoring financial flows aimed at addressing climate change in its entirety. The insights gained have important implications for policymakers and stakeholders striving to understand and optimize the allocation of climate finance to support global sustainability and resilience goals.

Mar 22, 2024

Cracking the Code: Enhancing Development Project Classification with NLP on OECD CRS data.

Jean-Michel Severino

Apr 27, 2016

Millions for billions: Accelerating African entrepreneurial emergence for accelerated, sustainable and job-rich growth

Publication from Jean-Michel Severino (https://ferdi.fr/biographies/jean-michel-severino) where I provided an appendix aiming to reveal the actual support provided by development finance institutions to the African private sector. The article below argues for the need to strongly accelerate public involvement in support of entrepreneurial emergence in poor and fragile countries. After mentioning the economic and employment issue, it explains how this priority has long disappeared from the international agenda as well as from domestic public policies, particularly in Africa. Efforts to promote the private sector have in practice focused on foreign direct investment and the largest companies. Middle- and emerging income countries, and a limited number of sectors and financial instruments, such as debt, have been valued. The article evokes the gradual change of perception on this subject from the beginning of the century and the emergence of new so- called impact actors focused particularly on SMEs in poor countries, accompanied by some public private sector financing institutions (DFIs), development agencies or foundations..

Featured Publications

Pierre Beaucoral

February, 2025 Cracking the Code: Enhancing Development finance understanding with artificial intelligence

Cracking the Code: Enhancing Development finance understanding with artificial intelligence

Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.