GX & Decarbonization Research

Assunta Di Vaio, Elisa Van Engelenhoven, Anum Zaffar +1

This study analyzes sustainability disclosures of two European ports (Rotterdam and Hamburg) that have implemented digital twin (DT) technology. Using automated thematic extraction via Leximancer and manual coding of 36 documents (sustainab…

Aversa D.

This study empirically analyzes the relationship between banks' sustainability disclosures, climate change, and central banks using NLP on unstructured data. It evaluates disclosure quality and climate risk management, offering insights for…

Leong W.Y.

This study leverages AI to optimize renewable energy deployment and operation, proposing pathways for emission reduction. Machine learning models predict energy supply-demand and formulate cost-effective decarbonization strategies.

Si Zheng, Yifan Duan, Chao Xue +1

This paper proposes Scope3Trace, a framework that extracts Scope 3 GHG emissions from sustainability reports using LLMs with evidence grounding. It integrates PDF parsing, OCR, table reconstruction, and hybrid rule-LLM extraction to obtain …

Xinyu Tang, Mingzhu Tang, Na Li +1

This paper proposes ConvTimeXer, a hybrid model combining bidirectional temporal convolution and TimeXer for carbon price forecasting. Experiments on China's carbon market data over three years demonstrate high accuracy and robustness, effe…

Yue Wang, Waya Zhao, Wenli Ye +2

This study uses spatial DID and double machine learning on 30 Chinese provinces to examine how digital government development and regional e-commerce ecosystem competitiveness drive the low-carbon energy transition. Digital government has l…

Weixiang Xu, Yi-Fan Shi, Jinhui Zheng

This study uses panel data from cities in the Yangtze River Economic Belt (2011-2021) to empirically analyze AI's impact on urban green and low-carbon development. Applying dual machine learning and spatial Durbin models, it finds that AI p…

Thi Huong Dinh, Nhung Hong Nguyen, Ngoc Quang Nguyen +1

This paper examines how AI and green transformational leadership affect low-carbon practices in the Vietnamese hospitality industry using PLS-SEM. It finds that AI positively influences green innovation culture and low-carbon behavior, medi…

Ángeles Montserrat Govea Franco, Saúl Domínguez Casasola, Heriberto Salazar-Soto

This study uses machine learning (k-prototypes clustering and ANN) to analyze the effectiveness of emissions trading systems (ETSs) across 53 countries. It classifies 58 ETSs into four archetypes and identifies renewable energy consumption …

Artem SHAPOSHNIKOV, Svetlana RATNER, Inna Choban de Sousa Paiva +1

This paper conducts a meta-analysis of 76 empirical studies (2009-2025) on the effect of AI and digital technologies (DT) adoption on corporate greenwashing (ESG disclosure-performance gap). AI/DT implementation is associated with a statist…

Sala D, Polyanska A, Psyuk V

This paper examines the evolution of energy transition research through a bibliometric co-occurrence analysis of keywords from 146 publications. Recent research (2022-2024) focuses on renewable energy, sustainable development, and intellige…

Kokubu, Hiroyuki

A quarterly snapshot of GX disclosure metrics machine-extracted from integrated reports of TSE Prime-listed companies using AI. Covers Scope 1/2/3, SBT, TCFD, CDP, renewable ratio, internal carbon price, and purchased carbon credits. This v…

Xinyi Mu, Dujuan Yang, Qi Han

This study proposes a Bayesian learning agent-based model to analyze heating system decarbonization decisions in homeowner associations. It models the learning process of members and evaluates how policy and economic factors affect the adop…

Rai A.

This study combines techno-economic assessment with deep learning methods to evaluate wind-driven green hydrogen fuel production in Scandinavia. It uses deep learning models to predict wind output and hydrogen production costs, assessing fe…

Leng J.

This study applies data analytics and machine learning to site screening for CO2 geological storage in depleted oil and gas reservoirs in the Gulf of Mexico. It proposes a method to improve the accuracy and speed of storage site evaluation,…

Lin K.

This review comprehensively examines the application of machine learning (ML) to CO2 geological storage. It covers predictive modeling, site selection, monitoring, and risk assessment, highlighting how ML enhances storage efficiency and saf…

Kumar R.

This paper reviews recent trends and future prospects of AI and sustainable finance using Natural Language Processing (NLP) models. It focuses on applications in ESG assessment and greenwashing detection, discussing how AI can enhance decis…

Zhipeng Han, Liguo Wang, Yongling Wang

Using an interpretable machine learning framework (LASSO, random forest, SHAP) on 7,791 firm-year observations of Chinese A-share industrial firms (2016-2022), this study reveals a threshold-dependent ESG-carbon total factor productivity (C…

Mira Kopp

This study develops a typology of European cities for climate mitigation and adaptation using automated text extraction and clustering. It integrates a systematic review to link city types with tailored strategies, supporting evidence-based…

Šribar, Jernej, Mohorcic, Mihael, Čampa, Andrej

This paper proposes CoMAD V2G, a multi-agent deep reinforcement learning framework for coordinated management of V2G-enabled EVs and shared energy storage in Positive Energy Districts. Validated with real-world datasets, it reduces grid rel…