GX & Decarbonization Research

J. Taborda, Cesar Enrique Polo Castro, Alexander Armando Bustamante +1

This paper proposes a cybernetic governance framework integrating blockchain, IoT, and LCA for trustworthy monitoring of renewable energy impacts. Implemented in Colombia's wind and solar projects, it achieved high performance and participa…

Fnu Aishwariya Kannan R. Uma, Nandini Jagannarayan

This review examines the latest literature on Green FinTech from an economic perspective. It discusses how digital technologies such as AI, blockchain, and big data contribute to environmental risk assessment, carbon market transparency, an…

Yu Hu, Xiaofang Chen, Xin Chen +1

This paper empirically analyzes the impact of digital transformation (DX) on corporate carbon emission reduction using panel data from Chinese A-share listed industrial enterprises from 2010-2021. It finds that production efficiency improve…

Sabeeh Ullah, Md. Shabbir Alam

This study empirically examines the interrelationship among FinTech, institutional quality, and sustainable finance (SFI) in G20 economies using panel data from 2004-2023. FinTech enhances SFI, but its impact is moderated by institutional q…

Gikonyo Ndugu

This paper develops and demonstrates a forensic assurance algorithm to verify transaction-level evidence for IFRS S1/S2 disclosures. It proposes an adjusted Herfindahl-Hirschman Index with a gender-parity baseline and a five-tier Data Quali…

Abed Saif Ahmed Alghawli, Ali Raza, Suzan Hassan Bakhit +2

This study proposes a Q-learning-based routing and fault-recovery framework for CCS monitoring WSNs, integrating energy awareness. Evaluated on a 50-node HIL testbed, it reduced latency by 53.6%, improved recovery time by 65.9%, achieved 99…

Man Luo, Yixuan Zhao

Bike-sharing is key for low-carbon urban mobility, but expansion faces cold-start prediction and equitable allocation challenges. FairGIN, a fairness-aware GNN, integrates expansion-simulated training, attention-based knowledge transfer, an…

Ren-Jie Wu, Hesong Jin, Boyang Liu +6

This paper proposes a generative AI approach to automate the mix design of low-carbon, high-ductility engineered cementitious composites (ECC). The AI optimizes material performance and carbon emissions, contributing to decarbonization in c…

Yuxuan Wu, Qianqian Wang, Qihan Zhang +6

This study develops a data-driven framework for sustainable design of rare-earth permanent magnets, using 346 experimental samples and MLP models to predict magnetic properties with high accuracy. SHAP analysis identifies Nd as key, and mul…

Yu Tian, Bangjun Wang

This study analyzes the integration of Green Electricity Certificates (GEC) and Carbon Emissions Trading (CET) in China using AI methods (BERTopic, cosine similarity, Bayesian ERGMs) on 1,137 policy documents. It finds rapid central discour…

Simona Fioretto, Elio Masciari, Enea Vincenzo Napolitano

This paper applies AI techniques to institutional reports from the public sector to extract and analyze ESG information. It demonstrates the potential and challenges of AI-driven ESG assessment, offering new perspectives on utilizing disclo…

Avuzwa Lerotholi, Ibidun Christiana Obagbuwa, Olaperi Okuboyejo

This paper proposes a machine learning and explainable AI (XAI) approach to predict ESG risk scores, enhancing transparency in ESG assessments. It enables investors and companies to understand the drivers of ESG ratings. Originating from So…

Ruosong Hou, Wei Guo, Ziheng Zhao +2

This study proposes a data-model hybrid method for multi-timescale low-carbon economic dispatch of manufacturing-park virtual power plants. It integrates empirical uncertainty scenarios with a production-constrained stochastic program, achi…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper presents an experimental dataset of ultra low-carbon concrete designed for performance prediction and machine learning applications. It supports decarbonization in construction by enabling efficient material design and CO2 reduct…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper provides an experimental dataset of ultra low-carbon concrete for performance prediction and machine learning applications. The dataset supports decarbonization in the construction sector, enabling performance evaluation and opti…

Shuangyang Zhai, Zishan Zhang, Haoyu Hou +2

This study examines whether China's carbon emissions trading (CET) pilot policy affects auditor industry specialization using a staggered difference-in-differences design on A-share firms from 2010 to 2022. Results show that CET exposure in…

Yelmen B.

This study models energy use and GHG emissions in orange production in Turkey using artificial neural networks (ANN). It demonstrates AI application in agriculture for emission prediction and energy efficiency improvement, offering insights…

Shunhao Mai, Zenglu Zhang, Jie Zhu +2

This paper introduces the Disclosure-Emission Gap (DEG) Index, a novel metric to detect corporate carbon washing by quantifying the divergence between disclosed environmental commitments and verified emission performance. Using CarbonBERT-C…

Jinpeng Dai, Fanghui Lu, Riccardo Maddalena +2

This study uses CPO-XGBoost machine learning to predict compressive strength of limestone calcined clay cement (LC3) and optimize low-carbon mix designs. LC3 reduces CO2 emissions in cement production, contributing to construction decarboni…

Zeqi Zhang, Yingjie Li, Danhui Lai +2

This paper proposes a low-carbon operational decision-making method for computing-energy parks using large language models (LLMs) and multi-agent collaboration. A dynamic carbon emission factor (DCEF) captures time-varying carbon signals, a…