GX & Decarbonization Research

Runxin Hua

This paper proposes an end-to-end LSTM-Copula hybrid model for joint risk modeling in carbon-electricity markets. Using Chinese market data from 2021–2025, the model integrates LSTM-GARCH for marginal forecasts, EVT for tail risk, and time-…

Xi Zhang

Using panel data from 30 Chinese provinces (2005-2022), this study introduces an attention-mechanism-enhanced CNN-LSTM hybrid model to quantify the spatio-temporal differentiated contributions of infrastructure, R&D, and energy efficiency i…

Radosveta Krasteva-Hristova, Vanya Georgieva

This conceptual article examines how AI can support sustainability assurance under CSRD, ESRS, ISSA 5000, and the EU AI Act. It explores applications such as disclosure identification, ESRS mapping, anomaly detection, greenwashing risk scre…

Haoye Jiang

This paper proposes a carbon assessment framework for Rural Integrated Energy Systems (RIES) integrating deep reinforcement learning. It constructs dynamic models and carbon flow tracking, and introduces DRL algorithms (DDPG and SI-SAC) to …

Xiaobing Yu, Haitao Zhang

This paper proposes a UCB–Q-value assisted differential evolution algorithm for low-carbon microgrid scheduling with flexible loads. It optimizes operational plans to reduce carbon emissions while accommodating demand flexibility. Experimen…

(著者不明)

Using double machine learning on 266 Chinese cities (2010-2020), this study finds that the Low-carbon City Pilot and Innovative City Pilot both significantly enhance urban carbon neutrality performance, with notable synergistic effects. Mec…

Run Liu

This dataset provides multivariate time series data from three pilot carbon markets (Beijing, Hubei, Shenzhen) including a unified dataset with imputed missing values. Accompanying code includes cross-market correlation, nonlinear causality…

Mustafa Çağrı Peker

This study applies machine learning (Perceptron and Decision Tree) and the Multi-Level Perspective framework to model the energy transition in Türkiye's road transport sector. It identifies entrenched fossil fuel infrastructure, taxation, a…

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…