This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 221–240 of 271 papers
Yun Zhu, Qinghan Chen, Ma Zhong
This study proposes a framework for evaluating environmental information disclosure quality (EIDQ) of chemical firms, using a generative AI (DeepSeek-V3.2) scoring system. Analyzing 190 ESG reports from 38 Chinese chemical firms (2020-2024)…
Elham Jahanbakhsh
This study develops a deployment-oriented AI and IoT disclosure index (AIDI) from U.S. annual and sustainability reports (2018-2024) and tests its association with EPA GHG emissions intensity and workplace injury rates. Using fixed-effects …
Pratiti Nayak, Bhavya Tondon, Raushan Tara Jaswal
This paper discusses how AI integration into sustainable finance can facilitate incorporating ESG measurements into decision-making and achieve SDGs, while also addressing challenges such as data quality, compliance, and ethical dilemmas.
Santosh Kumar, Rashi Gupta
This paper examines the role of AI and automation in green finance, covering machine learning for ESG scoring, greenwashing detection, and dynamic portfolio optimization toward green bonds and impact funds aligned with SDGs.
(著者不明)
This paper examines the intersection of AI governance, law, policy, and ethics with green finance and ESG. It discusses regulatory frameworks and ethical considerations for AI applications in sustainable finance.
Nur Muhammad Fadilah, Binastya Anggara Sekti
This chapter comprehensively explores the application of AI in ESG analytics, including machine learning, NLP, and automation for predictive modeling, objective scoring, and continuous monitoring. It covers applications in banking, fintech,…
Hardini Ariningrum, Elsa Fitriandini, Choirunnisa Ramadhani +1
This literature review analyzes the integration of AI and ESG in modern accounting systems. Reviewing 20 peer-reviewed articles from 2021-2025, it finds that AI enhances efficiency, accuracy, and transparency of ESG reporting through automa…
Yamin Qiao, Xiaoyu Min, Sijia Ge +2
This paper examines the dual role of artificial intelligence in achieving carbon neutrality, focusing on emissions forecasting, energy consumption optimization, and sustainable strategy development. It suggests AI can be both a tool and a d…
Kun Luo, Jiale Xue, Puspavathy Rassiah +1
This paper explores the use of AI to align ESG disclosure with actual corporate practices. It proposes methods using NLP and machine learning to enhance the reliability of disclosed information.
Muhammad Muhitur Rahman, Abdulaziz I. Almulhim, Bijoy Mitra +4
This paper models GHG emissions from solid waste management using causality-driven AI. Applied to the UAE case study, it demonstrates emission reduction potentials.
Tai Mule
This study proposes a model to calibrate credibility and auditability of generative AI-based sustainability disclosures using 71 sets of ESG data from Chinese A-share listed companies. By integrating ERNIE, DeBERTa, and SLCI models, and int…
Ari Wibowo
This paper examines how AI reshapes corporate governance in terms of accountability, transparency, and ESG compliance from a law and economics perspective. It compares EU ex-ante safeguards, US disclosure-based liability, and ASEAN principl…
Boni Benyamino Lumban Tobing
This systematic review of 35 peer-reviewed Scopus articles examines AI's role in enhancing ESG disclosure quality and transparency. It identifies five themes: AI improves accuracy, detects greenwashing, boosts perceived authenticity, streng…
Jianxian Wu
This study uses quasi-experimental evidence to examine whether artificial intelligence enhances the decarbonization potential of the circular bioeconomy. Findings suggest AI significantly improves resource efficiency and process optimizatio…
Ida Ayu Putu Ayunda Aprilia Sugiantari
This study explores the potential of integrating AI into ESG reporting. AI enables real-time data collection and analysis to enhance transparency, accuracy, and efficiency, but faces challenges such as high costs, data quality, and ethical …
Percy Antonio Vilchez Olivares, Brandelt Jesús Artorga de la Cruz
This systematic literature review examines the use of AI and data analytics in ESG disclosure, analyzing 45 peer-reviewed articles from 2020-2025. It identifies four major themes: NLP for ESG text mining, machine learning for ESG scoring, A…
Przemysław Pluskota, Kamila Słupińska, Agata Wawrzyniak +1
This paper analyzes how banks apply AI in digital/mobile banking to implement and communicate ESG strategies, focusing on environmental dimensions. Using mixed methods including case studies and European regulatory frameworks, it finds AI s…
Boutahir Omar, Lahlou-Kassi Habiba
This systematic review analyzes 40 studies on AI in sustainable finance, identifying five research clusters: ESG integration, FinTech innovation, risk detection, theoretical foundations, and ethical frameworks. It highlights AI's potential …
Chabot M.
This paper applies textual analysis and AI methods to climate disclosures of European energy firms, evaluating patterns and transparency, demonstrating AI's effectiveness in disclosure analysis.
Ene Yalçın S.
This paper proposes a forecasting framework for greenhouse gas emissions using advanced machine learning algorithms. The title suggests a methodological contribution to enhance emission prediction, though specific methods and results are un…
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