GX & Decarbonization Research

Zhouyi

This paper presents a protocol for a PRISMA-ScR scoping review that audits validation and reporting practices in LLM-assisted ESG disclosure coding against emerging general-purpose standards (GUIDE-LLM, TRIPOD-LLM). It also synthesizes ESG-…

Güven Korkut, Murat Emeç, Muzaffer Ertürk

This study develops an AI-driven framework integrating K-Means, PCA, and Random Forest applied to the IFCMA Climate Policy Database. It compares policy portfolios of nine emerging and thirty-two developed economies, revealing structural und…

Saleh Mohammed Baqader

This paper proposes ESG-C, a bilingual (English-Arabic) NLP framework to measure ESG disclosure quality in Saudi Tadawul annual reports. It uses a weighted index of verifiability, sector specificity, quantitative clarity, and standards alig…

Huijuan Sun, Ning Bai

This study uses double machine learning to identify the causal impact of energy transition on corporate ESG performance, revealing mechanisms and providing implications for corporate sustainability strategies.

Metin Katlav, Kazim Turk

This paper presents an explainable stacked ensemble learning and multi-objective optimization framework for the intelligent design of green and low-carbon geopolymer concrete. It enables efficient exploration of mix proportions that balance…

Wanqiao Che, Weimin Zhuang

This paper proposes an AI-enabled pre-design programming method for low-carbon buildings, where spatial form is optimized based on carbon flow analysis. It introduces a novel approach to building design that integrates AI to automate carbon…

Md Qamruzzaman, Abdulrahman Alomair, Mohammed Alomair

This paper examines the interconnections among FinTech adoption, intellectual capital, ESG disclosure, and dividend policy on firm value in financial institutions from an emerging economy (Bangladesh). Using fixed-effects, GMM, CS-ARDL, qua…

Marcin Trybulec, Agnieszka Karman, Anna Sedlak +1

This paper presents a dataset of ESG reports, providing a benchmark for natural language processing and machine learning methods applied to ESG evaluation and analysis. It enables applications such as automated extraction of disclosure cont…

Badescu, Luca Arthur

This paper examines AI's potential for assessing urban business sustainability using a literature review and empirical data from S&P Global CSA 2024 (≈13,000 companies). AI adoption significantly improves ESG performance, especially environ…

S. Saranya, K. Chandrasekar

This review systematically examines how AI technologies (ML, predictive analytics, IoT) enhance supply chain sustainability (carbon reduction, waste minimization) and operational efficiency. It synthesizes cases in demand forecasting, inven…

Dileep G. Menon, Sangeetha Gunasekar, Badri Narayanan Gopalakrishnan

This paper uses text mining to analyze ESG disclosure of Indian hospitality firms and examines its relationship with financial performance. Findings likely indicate a positive correlation between ESG disclosure and financial metrics.

Chenyao Duan, Yuanfang Chen, Junlin He +1

This study applies a hybrid CNN-LSTM model to forecast carbon prices in China's largest carbon market, Guangdong. It demonstrates the model's superiority over standalone LSTM and finds that lagged intraday OHLC prices are the most critical …

Fengsheng Chien, Muhammad Sadiq, Atif Aziz +1

This study examines whether AI acts as a sustainability capability to accelerate progress toward SDG 7 (clean energy) and SDG 9 (industry/innovation) in China's low-carbon transition. Using panel data from listed enterprises (2007-2022), it…

HONGZHI LU, Mohamad Anuar Kamaruddin

This paper proposes an evaluation model for siting AI data centers that considers both carbon and water impacts. Using the concept of digital carbon-water arbitrage, it explores optimal locations to minimize environmental footprint.

Marwane Boussetta, Mawouto HOEDEGBE, Mohamed Amine ERROCHDI

This bibliometric study analyzes the integration of AI into carbon accounting using Scopus and VOSviewer, covering publications from 2000 to 2023. It reveals accelerating research trends driven by stricter environmental regulations and corp…

Qingwen Zeng, Hanlin Xu, N. Xu +5

This systematic review synthesizes engineering practices for carbon credits using AI, focusing on carbon price prediction and corporate emission prediction algorithms. It highlights how these data-driven solutions enhance transparency, miti…

Hui Li, Mingyue Chen, Xin Kang

This paper presents a physics-guided generative inverse design method for optimizing low-carbon concrete mixtures. Using multi-objective latent space optimization, it achieves strength targets while reducing CO2 emissions.

Chen Wang, Songyuan Liu, Guoqing Li +5

This study proposes a framework that decomposes power grid projects into 11 standard modules and uses an improved PSO-DRESN model to determine modular carbon quotas with high precision. Empirical analysis of a 500kV PGP reveals a lifecycle …

Solomon Oisasoje Ayo-Odifiri, Andrew Ebekozien, Clinton Aigbavboa +1

This study explores the integration of AI technologies to enhance urban green infrastructure (UGI) for carbon neutrality in smart buildings. Through interviews with 30 stakeholders in three Nigerian cities, it finds that AI effectively pred…

Sara; id_orcid 0000-0003-1053-1641 Tavakoli, Pedro Sampaio, Ali Hassanzadeh

This study develops a three-level modeling framework operationalizing intersectionality to capture how combinations of socio-economic characteristics interact to produce regional energy poverty. Using statistical and machine learning method…