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 461–480 of 1487 papers
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…
Lu Wang, Chunyan Sun, Xiaoyu Wang
This review surveys AI-enabled low-carbon technologies in the oil and gas industry, covering core needs, applications across key processes, comparisons of AI methods, challenges, and future trends. It serves as a reference for researchers a…
Jiamin Zhang, Yang Qian, Christina W.Y. Wong +1
This study examines how configurations of institutional pressures affect firms' low-carbon behaviors (hard and soft) and the moderating role of AI capability. Using panel data from Chinese A-share listed firms (2007-2022) with cluster analy…
Sheikh Ali Tanzil, Kishor Chandra Meher
This qualitative study examines how Fijian listed companies navigate global ESG mandates and local institutional constraints, using managerial sensemaking. It reveals a 'digital decoupling' where firms use digital infrastructures as a symbo…
Thi Loan Bui, Oanh Thi Tran, Nguyen Thi Kim Oanh +2
This paper proposes an NLP-based method to automatically classify ESG-related information from Vietnamese corporate annual reports, aiming to streamline disclosure analysis and automate ESG assessment. It sits at the intersection of AI and …
İlker Mert, Hüseyin Yağlı, J. Costa +1
This study combines NLP and stochastic techno-economic modeling to analyze Türkiye's national hydrogen strategy. Baseline LCOH is 4.89 €/kg with electricity price as dominant driver. Proactive policy intervention raises the probability of r…
Shaofei Wang, Ruyi Wei, Yuqi Li +4
This study proposes ReDP-VT, a novel deep learning model for forecasting greenhouse gas concentrations using TCCON observations. It leverages frequency-domain inter-gas dependencies to guide Transformer self-attention, capturing dynamic rel…
Shoichiro Hosomi, Soichiro Yamamoto
This study examines the association between CDP scores (climate change, water security, forests) and firm value (Tobin's Q) and profitability (ROA, ROE) using panel data of Japanese manufacturing firms from 2018 to 2023. Results show that t…
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