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 241–260 of 274 papers
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…
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 …
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…
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…
Hebbar K.S.
This paper explores the potential of AI-powered reporting to enhance regulatory compliance in green investments. AI technologies can improve accuracy and efficiency in environmental disclosures, reducing greenwashing risks. It proposes a fr…
Manu K.S.
This paper explores the impact of AI on sustainable finance, likely covering ESG data analysis, climate risk assessment, and green investment strategies within a changing global economy.
Baplawat A.
This paper discusses the potential role of artificial intelligence in ESG regulation and green finance law. It explores how AI-driven automation can enhance sustainability disclosures and combat greenwashing, though it lacks empirical evide…
Gao Yi, Chen Yuan, Chao Su +4
This paper systematically compares discriminative AI (DAI) and generative AI (GAI) in environmental science, discussing their applications, limitations, and synergies. It highlights the shortcomings of traditional methods in climate change …
Yao Wang, Yitao Li, Zhen Ruan +2
Using data from China's A-share listed companies (2010-2022) and a quasi-natural experiment based on the National Big Data Comprehensive Pilot Zone policy, this study finds that big data advancements foster corporate green development throu…
Akhil Shaikh
This review explores AI applications for climate change mitigation and adaptation, using agricultural greenhouses as a case study. A BPMN framework integrating IoT, satellite data, and AI algorithms is proposed for real-time monitoring, dec…
Yilei Zhao, Wentao Zhang, Lei Xiao +3
This paper introduces ESGAgent, a hierarchical multi-agent system with specialized tools for in-depth ESG analysis. It also presents a three-level benchmark from 310 corporate sustainability reports. Empirical results show 84.15% accuracy o…
Dikarev, Evgeny
This paper explores the use of AI to reduce food waste and improve sustainability in the restaurant industry. It analyzes predictive analytics, smart inventory systems, and computer vision to enhance resource efficiency and meet ESG goals. …
(著者不明)
This study explores how AI, ML, and DLT can support sustainable finance and the transition to a green economy through literature review and expert interviews. It finds that these technologies improve risk management, transparency, and decis…
Shuying Gao, Meifang Zhou
This paper highlights the unclear data requirements for monitoring green transition risks and proposes using AI large models and digital twin technology for active diagnosis, precise identification, and intelligent policy formulation. It ad…
F. A. Samiul Islam
This study proposes an AI-driven Smart Waste-to-Energy framework (AI-CIR-WtE) integrating LSTM, NSGA-II, digital twins, and blockchain for Dhaka, Bangladesh. It forecasts waste generation, optimizes routing and energy efficiency, and quanti…
WenYi Li, Wenyu Liu, Mengya Deng +2
This paper systematically reviews the transformative impact of large language models (LLMs) on accounting practices and explores future application scenarios. It highlights potential to enhance efficiency, transparency, and innovation in fi…
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