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 481–500 of 1487 papers
Michelle Doorasamy, O. Fapetu, Pelumi Abdulmalik Adewumi
Using panel data from Nigeria and South Africa (2010-2022), this study examines how climate finance (CF) and environmental risk accounting (ERA) affect firm value. Employing AI-powered textual analysis to measure climate financial exposure …
Oskars Bagants, Raita Rollande, Juris Klonovs
The construction sector accounts for ~34% of global energy-related CO₂ emissions, yet carbon accounting is not integrated into cost estimation and procurement. This study proposes the AI-driven 'EEBOQ' framework, combining LLMs, ontology-ba…
Tuğba Yeğin
This study examines how GenAI-based carbon footprint feedback (AI-CFB) influences consumer purchase intentions for renewable energy-powered products (REPPs). Analyzing 841 Turkish participants with PLS-SEM, it finds AI-CFB as an antecedent …
Shawn Wang
Using panel data from 266 Chinese cities (2011-2020), this study finds an inverted U-shaped relationship between AI development and urban carbon emission intensity (CEI): AI initially increases CEI but reduces it after a turning point. By 2…
Chen Song, Zhihao Gao
This paper empirically examines how CEO-chairperson dialect connections affect corporate carbon emissions. Findings show that such connections significantly increase emissions, especially in competitive industries, non-state-owned firms, an…
Wen Xiang, Jianzhong Gao
Using survey data from 10,057 kiwifruit growers in China, this study applies machine learning (random forest, SHAP) to analyze how farmers' digital capabilities affect initial and sustained adoption of low-carbon agricultural technologies. …
Helena M. Ramos, Chetan Rishi, Óscar E. Coronado-Hernández +3
This study proposes an MCDA framework combining machine learning (EL-SVM, decision tree, logistic regression) with AHP to evaluate candidate sites for hybrid renewable energy systems (HRES) integrating solar, wind, hydropower, and storage. …
Zeeshan, Mohammad
Scope3Scout is a 16-agent platform that automates ESG and CSRD supply chain compliance verification using a multi-agent architecture. It automates verification of supplier data and was a TinyFish Accelerator finalist. It demonstrates a nove…
Umesh Ganesh Iyer, Sukhpal Singh Gill
This paper explores how AI-driven cloud resource management can contribute to net-zero carbon emission goals. It proposes methods to optimize energy consumption and reduce carbon emissions through AI, enhancing sustainability of cloud data …
Sunday Idowu
An IoT platform using ESP32 and PZEM-004T sensors monitors household electricity usage and CO2 emissions in real time. Tested with 100W incandescent and LED bulbs, it achieved <3% error and <2s response time. CO2 is calculated using an emis…
I. P. Okwuosa, O. J. Odejobi
This study optimizes biogas steam reforming for low-carbon hydrogen production using ANN integrated with GA. The ANN was trained on Aspen HYSYS simulation data, and GA optimized operating conditions to achieve a hydrogen mole fraction of 0.…
Tao Wu, Xinyu Li, Jie Li +2
This paper proposes a multi-agent system enhanced by large language models (LLMs) to optimize low-carbon energy scheduling under load and renewable uncertainties. It applies AI to energy transition, improving grid operation efficiency and s…
Xin Huang, Yixin Li, Keteng Jiang +3
This paper proposes a low-carbon optimal dispatch strategy that models dynamic carbon emission factors of thermal power units using spatiotemporal attention and absolute position encoding, integrated into a power-carbon coupled scheduling f…
Shankar Subramanian Iyer, Dr Brinitha Raji, Dr. Rajesh Arora +1
This scoping review examines the integration of circular economy (CE) and AI/digital technologies in the energy sector, combining 143 peer-reviewed sources with interviews of 15 industry leaders. It finds that CE offers significant value bu…
LEKBIR, DJAMEL
This study develops an Estimation-to-Compensation framework using double/debiased machine learning and causal forests to estimate installation-level heterogeneous effects of the EU ETS on ~1,900 German installations, validated on Chinese pi…
Mohammadi, Sepehr, Mostafa, Sherif, Jadidi, Zahra
This is a coded dataset and reproducibility package for a PRISMA-informed evidence map of AI and computational methods for carbon decision support in the built environment. It includes a 457-record audited database (335 primary studies, 102…
E. P. Egbe, E. O. Diemuodeke, E. G. Udonkwo +1
This paper develops a data-driven GHG accounting software for Nigeria's oil and gas sector, automating Scope 1 and 2 emissions estimation based on the API Compendium. It addresses manual, fragmented practices by integrating operational and …
Eduardo Andres Pardo-Piñashca, Muhammad Shahbaz, Ioannis Kyriakou +1
This paper examines AI as a general-purpose technology and its impact on the energy trilemma (security, equity, sustainability). It discusses AI's transformative role in optimizing energy systems and informing policy, offering insights for …
Kashaf
This review outlines smart plantation as a nature-based solution integrating strategic vegetation placement with digital technologies (AI, sensors, remote sensing) to reduce air pollutants, store carbon, and improve urban microclimates. It …
Natalia Fabra, Clément Leblanc, Mateus Souza
This study quantifies the distributional impacts of the Iberian solution, a wholesale electricity market intervention in Spain and Portugal during the 2021-2023 European energy crisis, using machine learning and market simulations. The cris…
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