GX & Decarbonization Research

(著者不明)

This paper proposes a method to evaluate residential energy performance using feature importance analysis based on HEMS (Home Energy Management System) data. By analyzing energy consumption patterns, it identifies effective energy-saving me…

Verma J.

This paper provides a bibliometric overview of artificial intelligence in business management and its impact on sustainable finance. It maps the research landscape from past to present trends and identifies future directions, highlighting t…

Singh J.P.

This paper examines the role of higher education institutions in integrating green finance and artificial intelligence to build a sustainable future. It explores how universities can promote AI-driven green finance, develop educational prog…

Woon Leong Lin, A. Ignasiak-Szulc

This systematic review (2013–2024) examines AI integration into CSR, identifying five empirical clusters and three generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighti…

Haoying Niu, Qinzi Xiao, Mingyun Gao

This study uses text from annual reports of Chinese A-share listed green companies (2015-2024) to construct a climate resilience indicator via word frequency and sentiment analysis. It compares four ensemble learning models, finding that XG…

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…