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 201–218 of 218 papers
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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 …
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…
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…
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…
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…
Huajian Jiang
We present ESG-DocQA, a benchmark dataset for evidence-grounded question answering over corporate ESG reports, with 300 validated samples annotated by three annotators, enabling document-image grounding.
Nophea Sasaki
AIMER proposes a reproducible AI-assisted protocol to extract structured ESG data from corporate sustainability reports. It details the workflow for document acquisition, preprocessing, AI extraction, coding, validation, and evidence synthe…
Gao Aixiong, Hong He, Quan Zhang
This study examines the causal impact of industrial artificial intelligence (AI) on urban carbon emissions using panel data from 260 Chinese cities (2005-2019). A novel city-level industrial AI index is constructed. Results show that indust…
Roberto Moro-Visconti
This paper shows that AI adoption in corporate governance improves ESG performance by 8-15% through reducing information frictions and expanding the basin of attraction for high-ESG outcomes. Using 450 firms over 2014-2024, the author finds…
Z. Baharom
This critical review examines machine learning (ML) in corporate financial sustainability (CFS), highlighting risks of automated greenwashing and reinforced inequalities in ESG scoring and predictive analytics. It proposes a four-pillar fra…
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…
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…
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 …
Yassine Charabi
This paper presents a system-dynamics model quantifying the temporal mismatch between immediate AI infrastructure emissions and delayed system-level savings. Using IEA scenarios, it calculates cumulative carbon debt and analyzes policy leve…
Emmanuel Ohimai Ojo, Prince Michael Akwabeng, Gloria Opoku Darkoh +1
This systematic review examines AI applications in carbon emission monitoring and green investment decisions, analyzing 55 studies from 2021-2025. It finds that machine learning, deep learning, and hybrid algorithms significantly improve em…
Dr.Rachana Saxena, Hashim Khan, Dr.Mohsina Hayat
This paper critically examines how AI is reshaping corporate sustainability reporting and green accounting. It analyzes AI applications such as automated carbon accounting, environmental footprint analysis, real-time monitoring, and AI-assi…
Tianyi Ren
This paper examines how AI-powered carbon footprint tracking can reshape green investment decision-making by delivering near-real-time, high-resolution estimates of Scope 1-3 emissions, especially Scope 3. It synthesizes methods combining N…
Nadia Mansour
This systematic review of 42 studies (2018–2025) examines AI, IoT, and blockchain in sustainable finance. It identifies three application areas: enhanced MRV of environmental impacts, improved physical and transition risk control via predic…
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