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 321–340 of 1055 papers
Yilin Wang, Xujing Dai, Xueyan Li
This study examines whether China's Green Finance Reform and Innovation Pilot Zones (GFRIZ) policy improved urban supply chain resilience, using panel data for 285 cities from 2012-2024 and a partially linear Double Machine Learning approac…
Nehad Gaši, Kemal Spahić, Bekir Fulan
This paper applies AI to optimize smart intersections powered by renewable energy, using machine learning on traffic, energy, and weather data for dynamic signal control. It reduces congestion, improves safety, and cuts CO₂ emissions, contr…
Jian Xu, Jie Song, Yifan Yan +3
This study constructs an energy production revolution index (EIT) for the Yellow River Basin and uses stacking ensemble and Lasso-STIRPAT models to project carbon emissions and EIT from 2025-2045. Scenario analysis reveals optimal pathways …
Xiao Zhang, Zhaoqiao Ding
This paper proposes a smart cold chain system integrating IoT sensors and machine learning to enable dynamic monitoring and evaluation of full-chain carbon footprint. Real-time data collection and predictive models optimize energy consumpti…
Shaohuai Feng
This repository provides supplementary data for the manuscript on the SCOPE framework, a reproducibility-driven variable screening method for carbon price forecasting. It includes raw and processed data, additional analyses, supplementary f…
H. T. Dinh, Hà Thị Thu Bùi, H. Nguyen +3
Using survey data from 853 hotel employees in Vietnam, this study employs PLS-SEM to examine how AI and CSR influence employee green behavior (EGB), green finance (GF), and green performance (GP). Findings show AI positively impacts GF and …
Mohamed Vajeebu Mohamed Hussain Pareeth
This review proposes an AI-driven optimization framework for green hydrogen production networks in Saudi industrial cities (NEOM, Yanbu, Jubail, etc.). It integrates machine learning, digital twins, stochastic optimization, and explainable …
Sanjana Kewalramani, Sharvari Nimje, Tanishq Rajpuria +2
This review systematically organizes research applying AI/ML to Kubernetes scheduling for improving energy efficiency and reducing carbon emissions. It shows that ML-based workload placement optimization can effectively reduce data center e…
T. Sindhu, Dr. K. Sivakumar, Dr. D. Jayanthi +2
This review examines geospatial technologies (GIS, remote sensing, UAV, LiDAR) combined with machine learning for carbon stock estimation in mango orchards. Integration of satellite imagery and ML models (Random Forest, SVM, ANN) improves p…
Winda Wulandari, Sri Widyastuti, Harnovinsah Harnovinsah +1
This systematic literature review reconceptualizes financial reporting integrity as a socio-technical governance phenomenon shaped by governance accountability, sustainability legitimacy, and technological transparency. Analyzing 28 Scopus …
Yaacob Ibrahim, Khaled Hussainey, Taghred Mokhtar Sayed Moawad
This study examines whether carbon exposure affects corporate borrowing costs in the MENA region using a panel of 771 firm-year observations from 2016 to 2023. Combining fixed effects, IV, GMM, quantile regression, and machine learning, it …
Ke W
This paper frames environmental monitoring as collaborative governance infrastructure under ESG and net-zero policies, and explores how 'Quantum Intelligence (QI)'—combining quantum optimization and quantum machine learning—can reshape the …
(著者不明)
This paper demonstrates that integrating IoT and BI platforms improves Scope 3 emissions reporting accuracy by 40% in logistics. It emphasizes data unification from a governance perspective, offering new possibilities for carbon tracking an…
Seyed Ali Hosseini, Francesco Grimaccia, A. Niccolai
This paper introduces fuzzy logic into reward design for deep reinforcement learning agents in carbon emission trading (ETS), aiming to optimize trading strategies. Fuzzy rewards improve decision-making under uncertainty, balancing emission…
Md Qamruzzaman
Using panel regressions and a deep learning (MLP) model on 62 listed Bangladeshi financial institutions (2005-2023, 1,178 firm-years), this study finds that intellectual, human, and natural capital disclosures each positively and significan…
Sina Rostami, Muhammad Bilal, Mehrdad Taghipour
This scoping review systematically maps technologies and strategies to reduce energy use and carbon footprints in operating rooms, evaluating methods from HVAC optimization to AI-driven management. It identifies a gap in kinetic energy harv…
Venkata Krishna Reddy, Aadam Quraishi, G. C. Prashant +1
This paper proposes a deep learning-based decision support system (DSS) for renewable energy (RE) investment and deployment. It uses CNNs to extract features from weather, energy, and economic data, RNNs to forecast energy demand and supply…
Lila Simon, Abino Simon, Aalbin Simon
This chapter explores the integration of AI and renewable energy for a sustainable energy ecosystem. It reviews the renewable landscape in India and globally, detailing AI applications in solar, wind, hydro, biomass, and geothermal, includi…
Michelangelo Misuraca, Luca D’Aniello, Maria Spano
This study analyzes renewable energy social representations in Reddit climate discourse (2018-2026, 91,817 comments) using a computational adaptation of the Hierarchical Evocation Method. Combining theory-informed lexical anchoring with dat…
Bingler J.A.
This paper uses ClimateBert, a specialized language model, to analyze corporate climate risk disclosures, focusing on 'cheap talk' (substantively empty claims) and 'cherry-picking' (selective disclosure of favorable information). It offers …
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