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 3121–3140 of 33989 papers
Mariusz Baranowski, Piotr Cichocki, Piotr Cichocki
This study applies BERTopic topic modeling to Polish press coverage (2015-2023) to map how energy transition narratives are constructed and contested. It identifies which actors shape the discourse and how framing shifts over time. The work…
Bai B, Li GQ, Hou J
This study develops a process-parameter-driven system dynamics model that predicts monthly carbon emissions from open-pit metal mines across drilling, blasting, haulage, beneficiation and tailings handling. Validated on 2024 data (relative …
(著者不明)
This work applies AI (machine learning and data analytics) to measuring and mitigating carbon footprints. It likely centers on automating Scope 1/2/3 accounting and optimizing emission-reduction measures. With no abstract available, methods…
Gulmuxamedova, Dilbar
This paper models Uzbekistan's sustainable energy transition using input–output and CGE (computable general equilibrium) analysis. It examines how policy instruments and investment mechanisms shape transition pathways. It offers quantitativ…
Ruttanawijit, Kornpaphop
This note maps Thailand's welding-standard landscape for nearshore floating PV and hydrokinetic structures across four regimes: national industrial standards, Ministry of Labor welder certification, public-works inspection rules, and classi…
Giulio Pascucci
This dataset supports Pascucci et al. (2026) on tracking CO2 and seismicity during offshore CCS using fiber-optic DAS/DTS sensors. It provides processed DAS waveforms, RMS amplitudes, CO2-brine interface depth picks, and PSD estimates. It a…
Mukhammadiyaminova, Shakhzoda Sherzodovna
Using an Uzbek textile component maker as a case, this article analyses how the EU's CSRD value-chain cap (Directive 2026/470) and the GHG Protocol's 2026 Scope 3 revision reshape carbon-data requests to non-EU second-tier suppliers. The ca…
Alfaro, Carmen, Viejo Monge, Ismael, Sagües, Javier +10
The METAWAVE project explores microwave-based heating as a low-carbon alternative to fossil-fuel furnaces in energy-intensive industries. Applications include ceramic tile firing, asphalt production/recycling, and aluminum anode baking. Sim…
(著者不明)
This study estimates facility-level greenhouse gas emissions for U.S. hospitals by using machine learning to impute incomplete self-reported data. It addresses the widespread missingness in healthcare emissions reporting, enabling more comp…
Clarissa Bergman‐Fonte, Diego R. Amaral, Pedro R. R. Rochedo +2
This study integrates the ORION refining-sector optimization model with the BLOEM bioenergy model to assess how soybean oil co-processing in Brazilian refineries affects the sector under decarbonization scenarios. It finds shifts in investm…
Shwe Sin Han, Thushara Addanki, Anurag Chidire +3
Using an enhanced linear optimization framework, this study shows carbon capture can enable high solar PV penetration in ASEAN by providing dispatchable low-carbon power that cuts curtailment and storage needs. CCS deployment more than halv…
Woody M.
This study examines how charging strategies for electrified delivery vehicles affect life-cycle greenhouse gas emissions, accounting for the time-varying carbon intensity of the electricity grid. It shows that when and how fleets charge mat…
Verma M.
This paper examines the time-varying interactions between artificial intelligence and sustainable finance in the post-pandemic era. It aims to capture how AI development and ESG/green finance market dynamics influence each other over time. …
Chen-Fan Lee, Yu-Ching Wang, Yu-Hsun Lin
This paper is the first to address long-term carbon price forecasting, a gap in the LTSF literature. Inspired by volatility clustering in carbon markets, the authors design a simple yet effective multiscale feature set that boosts forecasti…
Haiyan Jiang, Caihong Chen
This study examines how energy transition metals (e.g. copper, lithium, nickel) affect environmental sustainability, incorporating oil price volatility as a mediating factor. Without an abstract, methods remain unclear, but it likely highli…
Xuewei Guo, Yirong Wang, Jie Zhang +5
This study reveals that the environmental footprint of biomass power plants in China varies by up to a hundredfold. It suggests that feedstock sourcing and site conditions drive large differences in environmental burden, challenging uniform…
Pengyu Chen, Zhongzhu Chu, Sarula Bai +1
Using cross-national panel data for 51 countries (1996–2023), this study applies a two-way fixed effects model to test whether AI governs physical climate risks. Effects operate through risk sensing, seizing, and action integration, with AI…
Yinhao Shen, Yanlan Zhao, Jingqin Ji +7
This review traces fluorine's journey from mining to lithium-ion batteries through resource, chemistry, and industrial-transformation lenses. It examines fluorine-based materials (e.g., electrolytes) as enablers of EV and energy-storage dec…
Xuguang Zuo, Boyu Wu, Chuanwang Sun
This paper empirically examines how carbon pricing instruments (carbon taxes and emissions trading) affect the sustainable energy transition, focusing on energy concentration and the energy industry. Authored by researchers across Chinese a…
Yilun Luo, Esmaeil Ahmadi, Benjamin Craig McLellan +1
This study examines how carbon pricing policy interacts with capacity mechanisms, asking whether the two conflict or reinforce each other. It addresses the policy-design tension between decarbonization and power-supply adequacy. Detailed me…
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