GX & Decarbonization Research

Kyung Bae Jang, Tae Ho Woo

This study uses a system dynamics approach to analyze how generative AI optimizes nuclear energy's contribution and mitigates vulnerabilities of carbon-intensive sources. Test 1 (low initial value) outperformed others, with the highest sens…

Ummesara ., Chaitanya Kumar Sahu, R Ranjith +5

This review integrates trends in AI-enabled regenerative agriculture (RA) to promote climate resilience. AI tools (ML, remote sensing, IoT) enable dynamic monitoring of soil/crop health, carbon accounting, and adaptive farm management, acce…

Pieter de Jong, Inga Timmerman, Alona Bilokha

This study introduces the Claim-Reality Divergence (CRD) index, combining satellite methane data (Sentinel-5P TROPOMI) with NLP analysis of SEC filings and earnings calls for 233 US heavy emitters (2018-2023). A one standard deviation incre…

Nguyen V.

This paper proposes a method to learn domain-specific embeddings for ESG question answering. It uses a retrieval-augmented generation framework to adapt embeddings to the ESG domain, enabling accurate extraction of answers from ESG document…

D. R, Santhosh Kumar A G

This secondary data analysis (2020-2025 sources including Bloomberg, MSCI, World Bank) examines AI's role in ESG integration and sustainable finance. It finds that NLP and ML improve ESG data coverage by up to 40% and enhance climate risk p…

Murali Krishna Pasupuleti

This paper proposes the Generative Climate-Aware Deep Hedging (G-CADH) framework, which combines deep hedging with generative market models to manage portfolio risk and financial resilience under climate change. It integrates a conditional …

Anuj Pal

This study evaluates the ability of large language models (LLMs) to distinguish between symbolic and substantive sustainability communication in ESG disclosures. It constructs a dataset from annual and sustainability reports of publicly lis…

‬‬‬Mohammad Khaled al-Bashiti, M.Z. Naser

This paper presents an autonomous ML approach using Bayesian optimization and NSGA-II/III to optimize green concrete mixtures. Analyzing over 2,300 real mixes, it achieves an average 40% reduction in CO2 emissions and 30% reduction in mater…

Trésor Ndengo Tietia, Rostin Mabela Matendo Makengo, Pierre Kasengedia Motumbe +4

This paper proposes a smart network resource management model using AI (DQN, Q-Learning, PSO, ACO) to optimize energy consumption of IT infrastructures in the Democratic Republic of Congo. Simulations show up to 32% energy reduction and 21.…

Saher Elsayed

This paper presents the first phase-resolved carbon footprint characterization of RLHF, finding that the PPO phase accounts for 59-64% of total training emissions. It introduces CarbonAware-RLHF, a scheduling framework that adaptively shift…

Q Zhang, 胜生 余, Tongna Liu

This reproducibility artifact provides portable Python code, derived inputs, frozen results, and tests for a carbon-water trade-off analysis in geo-distributed AI inference allocation under water-stress screening. It enables verification of…

Niko Firzi Anansyah, Ratu Mutiara Siregar, Andi Prayogi +1

This study develops an automated ESG reporting system for the palm oil industry using a dual-LLM architecture (Groq Llama 3 primary, Gemini backup). It introduces a programmatic constraint that caps the ESG score at 50 if Ganoderma infectio…

Krisztina Finta, Csaba Dezső Dér, Klaudia Gabriella Horváth +1

This study investigates the sustainability paradox in AI-driven short-form video marketing, revealing that video generation consumes ~30x more energy than image creation. It models that a mid-sized team producing 2,500 AI videos annually em…

Saheed A. Adeniyi, Adeyemi A. Adefisan-Williams, Rukayat Abisola Olawale, Daniel O. Olokpo and Elizabeth I. Igbodor

This study develops a digital twin coupling a physics-based drying-kinetics engine with an autoencoder-driven predictive maintenance engine for a hybrid solar-electric dehydrator in tropical Nigeria. It predicted moisture ratio with RMSE 0.…

Damla Yalçıner Çal, Ecir Uğur Küçüksille

This study uses the EDGAR dataset (1970-2023) to forecast GHG emissions with LSTM and Transformer models. While LSTM captures long-term trends, it underperforms a naive benchmark under regime changes. Transformer shows improvement for the l…

Jiachen Shou, Waner Li, Hui Li +5

This study integrates Random Forest and DEMATEL to identify key drivers of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Findings show that R&D expenditure, registered scientific achievements, and energy con…

Shimei Li, Yuqing Li, Jinman Luo +1

This paper proposes a dynamic optimization strategy for carbon emissions in an electrocarbon coupling system using reinforcement learning. The approach enables real-time emission reduction, outperforming static methods. It contributes to sy…

Omar Nassar, Fahimeh Jafari, David King

This paper presents KG-ESG, an AI-driven proof-of-concept that converts public ESG news into structured, queryable knowledge graphs. It processes 1,098 news articles for 450 FTSE 100/250 and ASX 200 companies using NER, sentiment analysis, …

Mai-Phuong Nguyen, Minh Thi Ngoc Trieu

Using a panel of Vietnamese manufacturing firms from 2018-2024, this study finds that digital transformation positively impacts ESG performance, but the effect is weaker for state-owned enterprises. The analysis employs text-mining methods …

Cande Lian, Wentao Zeng, Jiabin Wu +10

This paper proposes FGDSE, an interpretable causal-ensemble framework that predicts EV charging station fault risk under climate stress (extreme heat, heavy precipitation, humidity). Using 25 months of data from 13 stations, it outperforms …