GX & Decarbonization Research

Kaiwen Zhao, Bharathan Balaji, Stephen Lee

This paper proposes an LLM-based question-answering framework to extract product carbon footprint information from PDF sustainability reports. It introduces CarbonPDF-QA, an open-source dataset of 1,735 reports with human-annotated Q&A pair…

Heedo Choi, Jee Soo Park, Chul-Hee Lim

This paper proposes an explainable graph neural network (GNN) for urban heat risk assessment under the IPCC AR6 framework. It incorporates spatiotemporal attention to identify vulnerable areas from a climate justice perspective. The work me…

(著者不明)

This paper discusses the use of AI and digital twin technologies for achieving carbon neutrality. Specific methods or case studies are not available from the title alone, but it suggests potential applications.

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This paper proposes using large language models (LLMs) to assess corporate Scope 3 greenhouse gas emissions, focusing on Category 11 (use of sold products). It demonstrates the effectiveness and challenges of automated LLM-based estimation,…

Lin Sun, Jun Zeng

This study examines how carbon transition risk affects green debt financing spreads for Chinese high-energy-consuming firms. Using panel fixed-effects models, event studies, and machine learning (random forest, XGBoost, neural networks), it…

Liu X.

This study proposes an adaptive recurrent neural network model to predict risks in carbon emission trading markets. The model aims to improve prediction accuracy for market volatility and price fluctuations.

Vidura perera

This paper develops the Adaptive Green Finance Effectiveness Theory (AGFET), an integrated framework explaining how green finance effectiveness is conditional on policy uncertainty, governance, institutional quality, and technological capab…

Ritik Raj, Survi Sinha, Atreyi Pramanik +1

This paper reviews how AI enhances efficiency, sustainability, and system integration across the hydrogen value chain. AI modeling and optimization improve environmental impact assessment of hydrogen production routes like waste polymer and…

Lakshmi Narasimha Prasad Nagaragere, Dr.S. Prabakar

Proposes a vernacular ESG transformation model rooted in South Asian institutional cultures. Using AI-based environmental impact assessment and ESG reporting tools, the study demonstrates through pre/post case studies in family-owned busine…

Fuoli M.

This paper uses corpus-assisted analysis to evaluate the substance of corporate net-zero pledges, applying NLP methods to quantify commitment specificity and target rigor, revealing significant variation in pledge quality and potential gree…

Singh S.K.

This paper proposes policy frameworks that leverage AI technologies to accelerate the transition to a net-zero economy. It explores how data-driven decision-making and predictive models can support the design and evaluation of effective dec…

Khara S.

This paper explores the pathways to achieve net-zero energy using artificial intelligence, discussing how AI can optimize energy efficiency and integrate renewable energy sources.

Qian He, Ying Jin, Chen Chen +1

This study examines China from 1990-2023 using machine learning methods (LASSO, causal forest, Gaussian process) to analyze how trade openness, structural transformation, and government effectiveness affect coal usage and energy costs, ulti…

Fei Wang, Lei Jiao, Konglin Zhu +4

This paper addresses scheduling of cloud-edge federated learning under carbon neutrality constraints, incorporating demand response. It proposes methods to optimize the trade-off between learning performance and carbon emissions reduction.

Mykhailo Prazian

This paper explores how digital technologies, including AI and IoT, can be integrated into supply chains to reduce Scope 3 emissions and enhance cyber-physical resilience. It highlights the potential for improved emissions visibility and ri…

Shadrach Kukuchuku, Rachael Dickson, Tamunotonye Sotonye Ibanibo

This paper proposes a Digital Asset Management Framework integrating IoT monitoring, Asset Health Index modelling, ML-based predictive maintenance (Random Forest, ANN, SVM), and lifecycle optimization (Genetic Algorithm, PSO) for renewable …

Kuang Y.

This study uses double machine learning to estimate the causal impact of green finance on urban energy efficiency, providing insights for policy and investment decisions.

Xu P.

This paper evaluates the causal effect of China's emissions trading scheme (ETS) using high-frequency trading data and double machine learning (DML). It estimates the impact of policy intervention on carbon prices and trading volumes, provi…

Jakkaraju V.T.D.

This paper proposes Predictive Green FinOps, a method for jointly optimizing cost, carbon, and reliability in AI-intensive cloud environments. It uses predictive analytics to dynamically adjust resource allocation, reducing carbon footprint…

Singh V.

This paper applies FinBERT, a financial domain-specific BERT model, to conduct sentiment analysis on ESG disclosures of Indian mutual funds using a pillar-based classification approach. The study demonstrates the effectiveness of NLP techni…