GX & Decarbonization Research

Nguyen Q.

This paper analyzes how data quality of Scope 3 emissions affects the prediction accuracy of machine learning models. It evaluates the impact of data variability on model performance, highlighting the potential and limitations of ML for cor…

R.Kaladevi, V.Umarani, V. N +1

This study builds an ML and explainable AI system to assess green loan eligibility. Using synthetic data, multiple models were evaluated, with Gradient Boosting achieving high accuracy. SHAP analysis identified credit score and energy effic…

Huilu Jiang, He-Liang Zhu, Zichen Xu +1

This paper examines the impact of China's green factory certification on corporate digital-green collaborative transformation using panel data of A-share listed firms from 2012-2022 and a multi-period DID model. Certification significantly …

Yukun Sang, Kannan Loganathan, Lu Lin

Using China's AI Pilot Zone policy as a quasi-natural experiment, this study employs a multi-period DID approach on listed firms (2014-2024) to show the policy significantly improves corporate sustainable development performance (SDP). Dyna…

Suganthi Pais

This study quantitatively examines the impact of AI-powered FinTech innovations on Circular Economy Transition (CET), with Sustainable Consumption Behavior (SCB) as a mediator. Using SEM on data from 384 respondents, it identifies AI capabi…

Li Zhang, Jinsong Deng

This study integrates geographical detector and machine learning models to analyze carbon emission scenarios in China's thermal power sector. It identifies key regional drivers and provides policy insights for emission reduction, exemplifyi…

Stephen Oko Gyan Torto, Rupendra Kumar Pachauri, Jai Govind Singh +4

This review systematically classifies transactive energy management (TEM) in multi-vector multi-agent energy systems, analyzing market topology, agent interaction, game-theoretic models, and deployment challenges. It introduces TE-RM, a hyb…

Hina Ahmad, Kinza Kamran, Fizzah Muhammad

This study analyzes the 'AI-climate nexus' reshaping international relations. Through document analysis of 127 sources and four case studies, it finds AI enhances emissions verification and grid optimization but introduces vulnerabilities l…

Xinxiu Liu

ESG ratings are crucial in capital markets, but the surge in disclosed data leads to transparency data bias, potentially compromising rating accountability. This interview-based study reveals awareness of this bias, its causes (data quality…

Farinu Uthman, Philip Edward, Adekola Priscilla

This study develops a hybrid AI model integrating random forest, LSTM, and gradient boosting to forecast carbon sequestration and financial returns of afforestation projects. Using satellite, soil, carbon price, climate, and cost data, it g…

Saeed Askary, Davood Askarany, Hassan Yazdifar +3

This study examines the interplay between AI-related environmental perceptions, ESG reporting timing, and CDP disclosure quality across 18 European countries. Mandatory reporting modestly improves disclosure quality, but effects on carbon i…

Arpit Darbari

Proposes a Unified Digital ESG Framework (UDEF) for manufacturing that integrates CSRD/ESRS compliance, GHG Protocol-based accounting, and SDG alignment. Leverages smart metering, IIoT, cloud EMS, and AI-assisted decision support for contin…

Nihad Isgandarov

This paper theorizes and measures systemic economic fragility arising from the simultaneous acceleration of AI adoption and decarbonization. It proposes the Isgandarov Adaptive Transition Theory (IATT) and develops the Isgandarov Transition…

Makhrus Ali, Amin Zaki, Zain Nizam

This paper examines green fintech at the intersection of financial technology and sustainability, analyzing applications such as digital payments, sustainable investment platforms, and climate-risk analytics. It shows that big data, AI, and…

(著者不明)

This paper addresses AI-based ESG evaluation and risk analysis. Although details are unavailable, it focuses on the intersection of AI and ESG, applicable to corporate sustainability assessment and climate risk quantification.

Hermanus, Davy Ronald

This study proposes an AI-driven situational awareness architecture for citywide energy governance, integrating digital twins and multi-drone verification. Simulations show reduced false anomaly confirmations from 16% to 6%, improved Situat…

Wang J.

This paper proposes a hybrid CEEMDAN-LSTM model to predict green bond index movements. By applying AI to ESG financial instruments, it offers insights for investment decisions and risk management in the green bond market.

He Jiang, Rongyu Cao

This paper proposes an explainable time-varying feature selection method for predicting EU ETS carbon prices. It uses AI/ML to dynamically identify key drivers, balancing predictive accuracy with interpretability. Empirical analysis on EU E…

Chao He, Yunjie Su, Sirui Zhang +2

This review systematically examines the use of large language models (LLMs) in low-carbon smart energy systems, focusing on cloud-edge power IoT. It analyzes five technical themes: task offloading, dynamic edge resource allocation, low-late…

Archie Nathanael Mulyawan, Nataniella Eva Kezia, Verena Jesslyn Utama +1

This qualitative study examines how accountants, as ESG analysts and strategic leaders, enhance sustainability report transparency using the Susteineri system integrating IoT and AI. Real-time data collection enables monitoring of carbon em…