GX & Decarbonization Research

(著者不明)

This paper proposes a methodology for automating the analysis of ESG disclosures in the oil and gas sector. It demonstrates scalability and adaptability for extracting insights from large-scale disclosure documents, highlighting potential f…

(著者不明)

This paper proposes a method to detect greenwashing by leveraging LLMs to analyze the gap between ESG disclosure and performance scores. Using large language models for text analysis of corporate sustainability reports, it identifies false …

Peng Gao, Kunpeng Zhang, Zongchuan Liu

This paper uses double machine learning to measure urban energy rebound effects (ERE) in China and evaluates the impact of dual-pilot policies (National Big Data Comprehensive Experimental Zones and New Energy Demonstration Cities). Results…

O. Issah, Mutala Zubeiru, Samuel Anaba

This study uses machine learning (Random Forest, XGBoost) on a global sample of 5,000 firms across 50 countries to predict ESG reporting quality. XGBoost achieves R² of 0.78 vs 0.62 for panel regression. SHAP analysis identifies firm size, …

Xiaonan Dong, sungjin son

This study examines how AI adoption affects corporate carbon neutrality performance using Resource Orchestration Theory. Analyzing panel data of Chinese A-share listed manufacturing firms (2018-2023), it finds that AI significantly enhances…

A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani

This study examines how AI, big data analytics, and FinTech innovations affect ESG disclosure quality and corporate transparency, using survey data from 312 professionals in India, UAE, and UK, analyzed via PLS-SEM. Results show all three d…

A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani

This paper empirically examines the combined impact of AI, big data, and fintech on ESG disclosure quality and corporate transparency using PLS-SEM on a sample of 312 professionals from India, UAE, and UK. Results show all three digital con…

Abdullah Albizri, Ahmad Jumah

This study develops a large language model (LLM) approach to measure firms' conformity with the TCFD framework from unstructured sustainability reports. Analyzing a sample of U.S.-listed firms, it finds that higher TCFD conformity is associ…

Vijay Kumar

This paper examines the dual role of AI in sustainability: as an enabler of resource efficiency and green transition, and as a consumer of energy and resources. It discusses the Green AI movement prioritizing energy-efficient algorithms ove…

Deepak Gupta, Ziyodullayev Sodiq, Matkarimov Mansur +4

This chapter examines the convergence of AI and climate finance, exploring machine learning for climate risk modeling, deep learning for ESG rating accuracy, NLP for climate disclosure extraction, and predictive models for transition risks.…

Pradnyesh Khairnar , Karishma Rathod and Asst. Prof. Dineshwari Bisen

This paper proposes the Green Data Center Framework integrating renewable energy, advanced thermal management, and AI-driven resource optimization. Three pilot sites demonstrate PUE of 1.2, 75% renewable energy coverage, 50% reduction in se…

Hafiane, Doha El, Magri, Abdelmounime El, Myasse, Ilyass El +2

This paper proposes a real-time energy management framework for hybrid low-voltage microgrids using multi-agent systems (MAS). Co-simulated with JADE and MATLAB/Simulink, the approach increases renewable energy utilization by 10% and reduce…

Han-Teng Liao, Karen Ang

This paper proposes a regenerative socio-technical roadmap for AI infrastructure that addresses Scope 3 emissions and e-waste by integrating IEEE IRDS sustainability considerations. It reframes AI as a system-of-systems governed by planetar…

Xueyang Li, Jin Ma

This study examines the impact of green finance on city-level carbon intensity using econometric models and machine learning (SHAP) on data from 285 Chinese cities. Green bonds and green investment show the strongest decarbonization effects…

Rimi Gusliana Mais, Munir Munir

This study proposes an AI-driven carbon accountability framework integrating real-time emissions data, financial flows, and governance mechanisms. Using Indonesia as a case, it demonstrates how AI enhances transparency and efficiency in cli…

Fateh Belaïd

This paper analyzes over 1200 peer-reviewed studies on clean cooking and electricity access using computational topic modeling. It finds that only 57.6% of publications quantify impacts beyond access, and only the health and household air p…

Saqib Munir, Mushab Rashid, Abdul Ghaffar

Using Chinese data from 1999-2023, this study examines the association between environmental accounting indicators, government R&D expenditure, and renewable energy adoption via ARDL and machine learning. Regulatory quality has a positive l…

Samruddhi Pandit, Anuja Mukherjee, Rugved Dani +1

This paper proposes a five-layer AI-enabled smart agriculture framework for climate-resilient and sustainable food systems. It integrates climate inputs, sensing, predictive analytics, decision support, and sustainability outcomes. Case stu…

Shijie Ma, Jingzhi Hou, Haoran Niu +1

Using a debiased machine learning framework and causal forest analysis on Chinese high-emission listed firms (2009–2024), this study reveals a 'threshold-trigger' mechanism: once climate pressures exceed firm endurance, companies shift to s…

Xiaolong Zhang, Congjun Rao, Abdulrahman Almandeel +2

This paper proposes a spatiotemporal graph-based grey model incorporating fractional calculus to improve greenhouse gas emission forecasting, potentially outperforming traditional methods.