GX & Decarbonization Research

Habes Ali Khawaldeh, Moath Bani Fayyad, Mohammad Al-Smairan, Wasseem Al Rousan and Omar Alnhoud

This paper designs a fixed-dome biogas plant for a 200-cow dairy farm in Jordan, performing techno-economic and environmental assessment with LSTM-based AI forecasting. Results show a 4-year payback, LCOE ~0.093 USD/kWh, and 28.46 tCO2/year…

Amir Ahmad Dar, Vanshita Arora, Harith Yas

This chapter examines the potential and challenges of AI in addressing the climate crisis. It explores applications in renewable energy, low-carbon cities, and biodiversity monitoring, while also critically discussing AI's energy footprint …

Huong Dinh Thi, Trang Trần Văn, Ngoc Anh Nguyen +2

This study examines how AI-driven CSR and green HRM enhance low-carbon behavior and financial performance in the hospitality sector, offering practical implications.

Huajian Jiang

This paper introduces ESG-DocQA, a benchmark dataset for evidence-grounded question answering over corporate ESG reports. It contains 300 samples with verification, comparison, and inference questions, validated by three reviewers achieving…

Edina K.D.

This paper explores the role of AI and automation in green investment platforms, framing next-generation ESG. Lacking empirical data, it contributes to conceptual discussion on technology-driven sustainability finance.

Lemuel Kenneth David

This paper explores the integration of AI with climate modeling to improve sustainability forecasting and economic decision-making under climate uncertainty. From Xi'an Jiaotong University, it suggests applications in scenario analysis and …

Huajian Jiang

ESG-DocQA is a 300-sample benchmark for evidence-grounded question answering over corporate ESG reports. Constructed with three-reviewer validation, it achieves substantial inter-reviewer reliability (Fleiss' kappa = 0.644). It includes ver…

Mareta Putri, I. Mutia, S. F. Kartasari

This paper conducts a systematic literature review on the role of AI in sustainability reporting, analyzing 30 relevant studies from 1,087 initial articles. It finds benefits in efficiency, accuracy, transparency, and big data management, w…

Namratha Bm

This paper examines how AI can enhance sustainable investment decisions. AI-enabled platforms analyze ESG data and use natural language processing to support investors, potentially accelerating the transition to a sustainable economy. Howev…

Sharif Mohd., Mohammad Fakhrul Islam, R. Ramachandran +1

This paper uses bibliometric analysis of SCOPUS data (2015-2024) to map AI/ML research in sustainable development. It finds growing applications in energy, emissions, environmental monitoring, climate mitigation, agriculture, and water mana…

Furong Cai, E. Bolisani, M. Nakash +1

This systematic review of 80 articles (2004-2025) explores how AI enhances knowledge management (KM) for sustainable business transformation in the context of Industry 5.0. It identifies KM, AI, and sustainability as core motor themes, with…

(著者不明)

This paper proposes an AI-enabled competency framework and development pathway for cultivating green marketing talent in vocational colleges, aiming to support the net-zero transition. It addresses the gap between education and practice by …

Ma W.

This paper explores the synergistic spillover effects of AI, cloud computing, and blockchain on green finance. It suggests that these technologies can enhance efficiency and transparency in environmental investments, but lacks specific empi…

(著者不明)

This paper systematically reviews the literature at the intersection of emerging technologies (AI, blockchain, etc.) and ESG, analyzing research trends across countries like China, USA, and Italy. It suggests that regulatory pressures and i…

Ibrokhimjon U. Tursunaliev, Abrorbek X. Kozimjonov, Jaloliddin U. Suyunov +2

This study explores how AI can enhance environmental auditing and green accounting to improve transparency and reliability of sustainability reporting. Using a mixed-methods approach, it finds that ESG reporting is widespread and AI adoptio…

Akey Sungheetha, Kayapati Rajagopal

This paper proposes an Energy-Aware Explainable AI (EA-XAI) framework for sustainable cloud computing that optimizes energy consumption and carbon footprint. It achieves 34.7% energy reduction, 2.1 tons/month CO2 reduction, and 41.6% cost s…

Hao Wang

This study uses data from 2,334 Chinese A-share listed manufacturing firms (2015-2024) and a double machine learning model to show that AI affordance (AIA) significantly promotes digital-green synergistic transformation (DGST). Technologica…

Ghaprial E.A.A.

This paper explores the integration of AI and digital tools to enhance ESG reporting and sustainable business practices. It covers automation of data collection, analysis, and disclosure, aiming to improve accuracy, reduce greenwashing, and…

Avtar Singh, Dalwinder Kaur Dhillon

This chapter explores the role of artificial intelligence (AI) in environmental governance. It discusses applications such as deforestation monitoring, greenhouse gas tracking, renewable energy optimization, precision agriculture, and extre…

Huajian Jiang

This dataset contains the complete submission package for the ESG-DocQA benchmark paper, including 300 validated samples, annotation guidelines, experimental results, and supplementary materials. It focuses on evidence-grounded question ans…