This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing 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 101–120 of 1673 papers
V.Bakyalakshmi, S. M., Saranya P +3
This paper proposes HCARO, a Human-Centric Adaptive Reasoning Optimizer that integrates ML/DL-based carbon emission forecasting, human expert rules, policy constraint evaluation, adaptive feedback learning, and multi-objective optimization …
Ana D. Anufrijev
This review synthesizes 2024–2025 literature on AI in ESG reporting, covering benefits, challenges, and risks. AI improves accuracy, coverage, and timeliness of ESG data, lowers compliance costs, and aids regulatory alignment, but introduce…
(著者不明)
A systematic review synthesizing 49 empirical studies (from 231 screened) on how digital innovation relates to corporate sustainability. It finds digital transformation, AI, digital accounting, and supply-chain digitalization generally impr…
Yueh-Cheng Huang
Releases a dataset and code for measuring whether photos in sustainability reports actually evidence the adjacent text claims. Covers 443 Chinese-language reports from 150 listed Taiwanese firms (52,568 pages, 17,450 photos), classifying ea…
Sanoh Yusuf
This paper applies text-mining methods to analyze climate transition risk under uncertainty using Japanese equity market data. It appears to extract transition-risk signals from corporate disclosures and empirically link them to market valu…
Pothin, Florian, Rondeau, Fabien, Razafindrabe, Tovonony +1
This replication package accompanies a study that corrects corporate Scope 3 disclosures category by category across the 15 GHG Protocol categories. Each firm report is classified as activity exclusion, boundary incompleteness, or sound dis…
Bastida Vialcanet, Ramon, Palau-Pinyana, Erola, Marti-Ochoa, Julia
This deposit presents a 65-item Excel coding instrument derived from ESRS E1 (Climate Change) plus item-specific LLM prompts for analyzing corporate annual and sustainability reports. Covering seven dimensions—Governance, Transition Plan, I…
Mohamad Faidullah, Abu Bakar
A critical review of agentic AI governance in sustainability assurance, asking who bears accountability when AI agents perform audit work. It examines accountability gaps and governance safeguards needed to preserve trust in assurance as au…
Kannat Na Bangchang
Using 2022–2024 firm-level data on Thai-listed companies, this study examines financial and environmental drivers of the decision to buy carbon credit insurance. Combining logistic regression with a random forest model, it finds environment…
Di Sha, Arne Johannssen, Yong Liu +2
This paper proposes a hybrid framework combining LLMs, Transformers, and Kolmogorov-Arnold Networks (KAN) for carbon price forecasting and trading decision support. It aims to capture carbon market price dynamics more accurately to aid ETS …
Shuai Lu, Qiangqiang Tao, Li Wang +4
Proposes a human-auditable AI approach for sustainability assessment of production and consumption systems. Using World Bank project portfolios as evidence, it examines circularity and decarbonization outcomes. The emphasis on transparency …
Dami Moon, Sumiaki Kusahata, Yoshihiro Kawahara
This study proposes and validates an automated method using a Vision-Language Model to extract product-specific attributes (e.g., fabric composition) from unstructured user-generated content on C2C platforms and calculate product-specific G…
Khurram Ashfaq, Kainat, Nasir Abbas* +1
This study examines how sustainability reporting compliance and AI intellectual capability affect firm performance, mediated by CSR performance and moderated by AI-integrated leadership. Using PLS-SEM on 356 observations, it finds that both…
Chenyue Xia, Yinliang Xu, Hongbin Sun
This work combines large language models with multi-agent reinforcement learning to schedule data-center workloads for lower carbon emissions while respecting distribution-network constraints. It aims to exploit temporal and spatial load fl…
Dingyao Yu, Huali Cai, 李洪涛 +6
Using 1,161 passenger complaints from China's civil aviation regulator, this study applies Word2Vec and Structural Topic Modeling to identify barriers to low-carbon green-flight participation. Key failures include restricted service choice,…
Kuldeep Singh Kaswan, Avadhesh Kumar, Praveena Nair Rajendra +1
Proposes a lifecycle-intelligence framework to optimize the carbon, energy, and water footprint of AI systems themselves. It targets the environmental burden of AI computing through evaluation and optimization methods. Details are limited a…
Jan Saro, Miroslav Pavelka, Jiří Pilný
FarmCarbon AI is an AI-assisted framework combining IPCC Tier-2 GHG calculations, mitigation-scenario screening, economic screening, and grant-draft generation for Central European dairy farms. Tested on 100 synthetic farm profiles across e…
Pragyan Kumar, Mannan Jain, C. M. Sharma
EcoLife is a cross-platform mobile app integrating student lifestyle tracking (sleep, diet, exercise, study, screen time) with a carbon calculator for transport, food and energy. It combines supervised ML with rule-based recommendations for…
Sanjit Kumar Dash, Subhankar Poddar, Mohammed Altaf Ahmed +1
This paper proposes CarbonBalance, a framework combining a LightGBM/Prophet carbon-intensity forecasting pipeline with an optimization scheduler that shifts non-urgent AI workloads to cleaner times and locations. Evaluated scenarios achieve…
San Arca
This study augments the Fama-French five-factor model with a Brown-minus-Green (BMG) carbon intensity factor to examine carbon risk and US stock returns. Beyond time-series regression, GRS and Fama-MacBeth tests, it applies XGBoost and SHAP…
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