  In the evolving landscape of Environmental, Social, and Corporate Governance
(ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG
impact types. To address this challenge, we present a comprehensive system
leveraging ensemble learning techniques, capitalizing on early and late fusion
approaches. Our approach employs four distinct models: mBERT, FlauBERT-base,
ALBERT-base-v2, and a Multi-Layer Perceptron (MLP) incorporating Latent
Semantic Analysis (LSA) and Term Frequency-Inverse Document Frequency (TF-IDF)
features. Through extensive experimentation, we find that our early fusion
ensemble approach, featuring the integration of LSA, TF-IDF, mBERT,
FlauBERT-base, and ALBERT-base-v2, delivers the best performance. Our system
offers a comprehensive ESG impact type identification solution, contributing to
the responsible and sustainable decision-making processes vital in today's
financial and corporate governance landscape.
