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 271 papers
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…
Jan-Erik Schirmer
This chapter examines the dual role of AI in climate change: while extensive AI use poses climate risks due to its carbon footprint, selective AI application can enhance corporate climate governance, particularly in reporting and planning u…
Thong Hoang, Mykhailo V. Klymenko, Xiwei Xu +6
This paper introduces an agentic ESG lifecycle framework that integrates multiple AI agents to automate ESG reporting tasks such as information extraction, verification, and report updating. It transforms static ESG reporting into a dynamic…
Muhammad Usman, Yongming Huang, Muhammad Sohail Amjad Makhdum
This study analyzes the impact of AI, green finance, environmental technologies, and renewable energy on GHG emissions in BRICS-T countries. Using second-generation econometric methods, it finds that AI, green finance, and renewable energy …
M. Wheeler, Brandi Everett, Victor R. Prybutok
This paper proposes an Environmental Asset-Cost Framework to systematically evaluate the trade-off between AI's environmental benefits and resource consumption. Through a review of 73 empirical studies, it reveals that AI's environmental im…
R. Santos, María Isabel Casal Reyes
This systematic review examines how digital technologies (AI, analytics, platforms) shape green economic outcomes and how environmental constraints and governance reshape digital diffusion. Four domains are analyzed: labor market restructur…
Md Arafat Rahman, Sazzad Hossain Shaon
This study empirically investigates the role of AI in enhancing ESG disclosures in Bangladesh, using a survey of 434 respondents. It finds that AI integration significantly improves ESG reporting, fostering sustainability culture and climat…
Vibhavari Prasad Mane
This systematic review explores how AI techniques (machine learning, NLP, predictive analytics) enhance ESG measurement, portfolio management, risk analysis, and sustainability disclosure. It also highlights challenges like data quality, al…
Filippo di Pietro, Pilar Giráldez‐Puig, P. Palos-Sanchez +1
This study uses a systematic literature review and bibliometric analysis to examine machine learning applications in climate-related financial challenges. It finds growing AI adoption in emissions forecasting, green investment, and sustaina…
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