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 81–100 of 1232 papers
Deepmala Suryavanshi
This review critically examines AI applications for environmental and climate challenges, including climate forecasting, air/water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon e…
Yu Z.
This study builds a LightGBM model that predicts corporate carbon emission risk from listed firms' ESG disclosure data. SHAP values are used to interpret which disclosure features drive predictions, adding transparency to the ML pipeline. I…
Shivangi Agrawal, Neha Sarin
This study integrates AI into green finance investment strategies, covering renewable energy investment, ESG assessment, portfolio optimization, and risk management. Using SVM, Random Forest, regression, sentiment analysis, and Monte Carlo …
Zefree Lazarus Mayaluri
A reproducibility package for a study on financial-scale dependence in weak-label classification of ESG disclosure-quality flags using Indian BRSR data. It bundles executable Python scripts and a Colab notebook, model benchmarking, feature …
Sumitra Nuanmeesri
A trip-level carbon prediction framework for community-based tourism using only 34 pre-travel proxy features to avoid target leakage. Trained on 13,000 synthetic profiles from 650 respondents, CatBoost achieved MAE 1.28 kgCO2e and R²=0.948,…
Seonghun Min
Analyzing 641 REIT-years across the US, UK, Australia, Singapore and Japan (2020–2025), the study shows sustainability reporting rose from 43.1% to 63.9%, with new adoption far outpacing discontinuation. Using TF-IDF, Structural Topic Model…
Eric T. Sletten, Lenz Fiedler, Pooja Dwivedi +3
This paper argues that automation and machine learning can close the sustainability data gap in the chemical industry. It proposes integrating ML with automated experimentation for closed-loop optimization, embedding sustainability into pro…
Nguyen Tan Dat, Nguyen Ngọc Quang, Nguyen Thi Phuong Dung +1
Using annual reports of 199 listed Vietnamese manufacturers (2016–2024), the study builds a dictionary-based ESG disclosure intensity measure (matched occurrences per 1000 n-grams) and tests its association with ROA, ROE, and Tobin's Q. Onl…
Meiwen Bu, Siyi Zhang, Wanlu Wang
A case study of Zhongtian Technology examining how digital intelligence technology affects the immunity (resistance, adaptability, recovery) of China's SRDI enterprises. It finds digital intelligence raises firm immunity, mediated by improv…
Rasmi M
A review explaining why AI energy estimates diverge, separating what is counted (training vs inference, operational vs embodied, carbon vs water) from how it is counted (meters, trackers, calculators, LCA). It contrasts a famous early estim…
Leevesh Kumar
This chapter reviews machine learning approaches for predicting carbon emissions across the construction lifecycle—material manufacturing, transport, on-site activities, and operations. It examines regression, ANN, SVM, and ensemble models …
Ahmed K. Elnagar, E. Álvarez, Abdelkader Mohamed Sghaier Derbali
A systematic literature review (2018–2025) on how cloud infrastructure, AI analytics, and ESG integration interact. It finds AI/ML improves ESG scoring, risk assessment, and sustainable investment decisions, while cloud enables scalability …
Bama Raja Segaran
Supplementary package for a paper on carbon-credit consistency screening and auditability. It includes Sentinel-2 processing scripts, aggregate NDVI summaries, statistical screening and Random Forest configurations, correlation code, and lo…
and Dr. Akula Srinivas Dr. Gunda Srinivas
This study examines how AI and ESG factors may reshape the future of financial reporting. It considers how expanding ESG disclosure and AI adoption could transform reporting practice. No abstract is available, so methodology and findings ca…
Ratnakar Radheyshyam Pandey
This paper examines how cloud platforms can overcome fragmented, non-scalable traditional ESG data collection and reporting. It argues that cloud capabilities integrate structured and unstructured ESG data from supply chains, financial syst…
Karmendra Pandey
This white paper defines a per-agent-task carbon ledger that extends beyond single model calls to full agentic workflows—planner, retrieval, tool calls, retries, and verifier passes—computing per-step energy × PUE × grid carbon intensity pl…
Mori H.
Proposes an artificial neural network (ANN) method for assessing carbon pricing risk. It appears to apply machine learning to quantify how carbon price movements create risk for firms and investors. Empirical results are unverified as no ab…
Galina Bădicu, Svetlana Mihaila, V. Grosu
This study examines how AI can improve sustainability-report assurance efficiency while preserving ethical integrity and standards compliance. Using a practitioner survey plus LLM/NLP analysis of ESG reports, it finds ethical safeguards (bi…
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…
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