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 61–80 of 740 papers
Joan van Eeckhout, Miona Dimic, Cecilio Ángulo
Agrixel is a new interoperable geographic standard for agricultural data, leveraging AI and machine learning to integrate externalities like carbon footprint, energy use, and water management. It supports FAIR data principles and aims to en…
Duncan J. Reynolds
This paper reveals that AI-related carbon emissions are largely omitted from NHS mandatory Green Plan reporting. It identifies three gaps: lack of granularity obscuring AI workloads' energy intensity, exclusion of hardware lifecycle emissio…
Duncan J. Reynolds
This paper reveals that AI-related carbon emissions are largely omitted from mandatory NHS Green Plan reporting. Current Scope 1-3 accounting misses emissions from AI workloads' energy intensity, hardware life-cycle (e.g., GPUs), and unproc…
Girish K A, Chiranthana Shreya, Nischitha H E +2
This paper presents an autonomous robot using AI and image processing to detect plants and spray fertilizers precisely. It reduces chemical waste and farmer exposure, enhancing sustainability. Experiments demonstrate improved spraying accur…
Yinfeng Chen, Ying Hu
Using panel data from China's carbon-intensive industries (2007-2023) and double debiased machine learning, this study shows that GVC participation and domestic content ratio promote low-carbon innovation. Mediation paths include upgrading …
Wen‐Chi Yang, Wen‐Min Lu, Irene Wei Kiong Ting +1
This study analyzes 37 firms in TSMC's value chain using inverse DEA and chance-constrained DEA to examine ESG initiatives' impact on eco-efficiency. Environmental dimension negatively affects eco-efficiency, while social and governance dim…
Çağdaş Civelek
This study compares Türkiye's agricultural GHG emissions with EU-27 countries and forecasts 2025-2030 emissions using machine learning. Agricultural emissions account for ~12% of total, ranking second in total but ninth lowest in intensity.…
Elena Claudia BADEA (FLOREA), A. Olteanu (Burca), M. Bunea +1
This study proposes an integrated competency framework for auditors in the AI era, expanding roles from financial audit to ESG assurance and AI tool use. Based on a structured literature review of 22 articles (2019-2025), it identifies six …
Hwang S.
This paper proposes an LLM-agent framework to automatically assess IFRS S2-aligned climate disclosures in Korean corporate ESG reports. It aims to efficiently check completeness and consistency, identifying disclosure gaps. Contributes to t…
Naila Rana Andira, Philipp Sander, A. Pehlken
Industry accounts for three-quarters of global GHG emissions, and procurement changes affect product emissions. This paper integrates an event-based architecture with ERP systems to dynamically calculate and visualize material GHG emissions…
Hamidah Nurhayati, Muslimin Muslimin, Fajrin Satria Dwi Kesumah
This study analyzes panel data from 30 firms listed on the IDX ESG Leaders Index (2021-2025) to examine the impact of AI and ESG disclosure on firm value. Using fixed-effects regression, it finds no significant standalone effects, but firm …
Abdulazeez Y.H. Saif-Alyousfi
This study proposes a hybrid framework integrating TVP-VAR connectedness with LSTM deep learning to analyze and forecast volatility spillovers in green financial markets, including green bonds, renewable energy stocks, and carbon markets. U…
Yuxiang Xiao, Xiong Zheng
This study develops a reproducible EnergyPlus-based framework combining parametric simulation, interpretable sensitivity analysis, and machine learning surrogates to analyze operational energy and carbon coupling for Beijing hotels. From 20…
Francisco Climent, Pedro Carmona, Alexandre Momparler
This dataset comprises European global equity retail mutual funds with five-year performance records (2020-2024) from Refinitiv Eikon. It includes variables such as fund return, ESG scores (environmental, social, governance), expense ratio,…
Aiting Xu, Senhao Fang, Jiapeng Chen +1
This study proposes a carbon emission forecasting framework integrating STIRPAT economic theory with an adaptive neural architecture (GSGDN). It captures regional dependencies via geographically weighted regression and reduces operational c…
Hadia Arshad
This review comprehensively analyzes AI's environmental footprint (energy, water, carbon, e-waste, rare earths) and its climate mitigation potential. Data center electricity reached 415-470 TWh in 2024-25, with AI workloads projected to rea…
Tony Newton
This paper addresses the paradox that AI systems providing ecosystem services also consume water through their infrastructure. It proposes the AI–Nature Reciprocity Gate, a local, seasonal, full-scope standard to verify whether nature-based…
Nisha Shankar, K. Sagar
Proposes a framework integrating AI and blockchain for carbon credit trading tailored to Indian tourism firms. Introduces the Emission Reduction Progress Factor, uses Entropy Optimal Transport for P2P trading, and particle swarm optimizatio…
Xie, Tengyue
An intelligent emergency decision-support system for transnational renewable energy projects is proposed. It fuses structured and textual evidence via conflict-aware Dempster-Shafer and uses cost-sensitive case-based retrieval to diagnose r…
Michael Addai
This study investigates AI adoption as a driver of sustainability reporting among Ghana Stock Exchange-listed firms. It explores how AI transforms reporting practices, highlighting challenges and opportunities for disclosure in an emerging …
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