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 381–400 of 1414 papers
Mega Silvia, Deyan Zhao, Donghuan Bai +11
This study examines whether credible climate transition plans are associated with subsequent carbon performance among Fortune Global 500 non-financial firms. Using panel data from 239 firms (2018-2023), a Climate Transition Plan Credibility…
Abdul Kadar Muhammad Masum, Md. Abul Kalam Azad, Najmus Saadat +3
This study proposes a Deep-Tree Fusion (DTF) framework integrating tree-based logic with deep learning to improve ESG score prediction. Using 11,000 firm-years from 2015-2025, DTF achieves R²=0.97866, outperforming 10 baseline models. It of…
Rejaul Karim, Md. Mustaqim Roshid, Bablu Kumar Dhar +1
This study uses bibliometric methods to analyze 72 Scopus-indexed papers (2019-2024) on green fintech, focusing on climate finance, digital innovation, and environmental governance. It identifies key technological domains such as blockchain…
Inyangala¹ A, Waswa¹ AK, Angeyo² HK
This study develops an explainable stacked ensemble machine learning framework for rare earth element (REE) prospectivity mapping in the Ruri Carbonatite Complex, Kenya. Combining geological, geochemical, and radiometric data, the model ach…
A Joy and Yasmin Shaik F
This paper critically examines the environmental footprint of AI development and deployment, including energy, carbon, water, e-waste, and minerals. Data centre electricity hit 415 TWh in 2024, with AI workloads surging 50% in 2025, project…
Fiona Lippert, Andrew Gerald Barr, Marcos Herreras-Giralda +7
GHG retrievals rely on costly physics-based forward models, limiting real-time processing. This study develops neural network emulators for Sentinel-5, comparing end-to-end and hybrid approaches. The hybrid method achieves high accuracy (<1…
Nicholas Dietrich, David McShannon, Merel Huisman +2
This study quantified the impact of deep learning training policies on CO2 emissions and performance for chest radiograph classification. Prospective early stopping preserved performance while reducing emissions by up to 38% and improving c…
Suhib O. A. Amro, Sepanta Naimi, Changiz Ahbab
This paper proposes an AI-driven generative design framework integrating machine learning surrogates with NSGA-III and PSO to optimize building renovation for net-zero performance. Validated on 12 European case studies, it achieves 84.7% op…
Murali Krishna Pasupuleti
This monograph proposes an integrated architecture for AI-verified carbon credit intelligence, combining uncertainty-aware measurement, causal additionality analysis, ML, geospatial analytics, digital MRV, and market governance. It treats v…
Gyamfi B.A.
This paper uses causal machine learning and Bayesian methods to analyze the impact of green financing on corporate leverage and dividend policy. It estimates the causal effects of green bonds and sustainability-linked loans on firm financia…
Van Tam N.
This paper maps the causal relationships of generative AI adoption for net-zero built environments using expert insights and paradox theory. It reveals complex interactions between opportunities and challenges, offering implications for pol…
Jing Song, Xiaoyu Xu, Qi Li +2
This study proposes an improved AHP-Ridge Regression hybrid model for evaluating consumer trust in cross-border B2C e-commerce, integrating expert knowledge with consumer data. Validated on 387 survey responses, it achieves better predictiv…
F. Kelechi, A. Aribisala
This paper proposes a blockchain-based (Hyperledger Fabric) automated system for tracking Scope 1 and 2 emissions in indigenous Nigerian oil and gas companies. It reduces manual reporting time by 75%, eliminates errors, and ensures IFRS S2-…
Yanan Li, Dan Shi, Xiaojiao Qiao +1
Using data from Chinese A-share listed firms (2009-2023), this study shows that focal firms' digital transformation significantly reduces carbon emission intensity of upstream suppliers and downstream customers. Supply chain concentration a…
Francesco Sotti, Elettra Giulia Beatrice Bandi, Patrizia Tettamanzi
This study empirically analyzes how digitalization in sustainability reporting affects the textual quality (clarity, conciseness, tone) of non-financial disclosures among Italian listed companies. Against the backdrop of regulatory tighteni…
Ahlam El Ferrad, Aya Klaffa, Mohamed Oudgou +1
A systematic bibliometric review following PRISMA guidelines examines the link between digitalization, AI, and ESG disclosure using Scopus data. It finds 56% annual growth, concentration in China, and underrepresentation of Africa. Digitali…
Esther Curtin, Kerry A. Brown, Elizabeth McGill +3
This cross-sectional study quantified greenhouse gas emissions of 205 AI-generated recipes from a health app using LCA data. Plant-based recipes had lowest emissions, while red meat recipes were over fourfold higher than seafood/poultry. Mo…
Okon Udo Frank, Julius U. Akpabio, Aniefiok Livinus
This study develops AI models (ANN and SVR) to predict CO2 trapping efficiency with high accuracy, validated on 260 data points from a gas flare site in the Niger Delta. The ANN model achieved R2=0.9993, and CO2 mass fraction was the most i…
Caixuan Zhan
This paper develops a two-echelon cross-border supply chain model integrating blockchain, vertical spillover of emission reduction, and consumer low-carbon preference under EU CBAM. Using Stackelberg games, it compares centralized vs. decen…
Guorui Wang, Liang Zhong, Yixuan Zeng
This review systematically synthesizes evolutionary game theory (EGT) fused with AI (deep reinforcement learning, federated learning, blockchain) across three scales: enterprise-level industrial symbiosis, system-level smart energy operatio…
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