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 81–100 of 394 papers
Xiaobin Wang, Yuning Jiang, Zhihua Zhang +4
This paper proposes a contextual information-based distributionally robust optimization method for decarbonizing oil and gas field power systems. It integrates renewable energy while managing uncertainty, aiming to reduce operational costs …
Fan Zhang, Bo Wen, Ditao Niu +4
This study integrates life-cycle carbon accounting, statistical analysis, Monte Carlo simulation, and machine learning to assess embodied carbon in RC buildings. Using data from 124 buildings in Shaanxi Province, it finds material productio…
Murali Krishna Pasupuleti
This monograph presents an integrated research architecture for planetary resilience, combining Earth-system AI, ClimateTech modeling, and sustainable energy intelligence. It covers uncertainty-aware design, physics-guided ML, MLOps, and se…
Ömer Algorabi, Ersin Namlı
This study compares deep learning (GRU) and boosting models for forecasting electricity generation in Türkiye and Germany, then evaluates 2030 BAU, ST, and EC scenarios. GRU performs best for Türkiye, boosting for Germany. Scenario analysis…
Yang Lu, Joston Gary
This study develops an environmental systems engineering framework for carbon-management decision support in prefabricated construction, integrating production and transport decisions under dynamic energy prices and carbon signals. A tri-ob…
Junhao Ren, Honglin Gao, Sijie Wang +5
This paper proposes a multi-agent reinforcement learning approach to jointly design a low-carbon peer-to-peer (P2P) energy market and bidding strategies in microgrids. It optimizes economic and environmental outcomes, promoting renewable in…
Will Alpine, Nathan B. Geldner, Holly Alpine +1
This study models AI as a bidirectional productivity amplifier in a global CGE model, finding that AI-driven productivity gains in fossil fuel sectors lead to net CO2 emission increases of 0.47-1.8 Gt annually, outweighing reductions from r…
Joseph Sanderson
Climate change intensifies food safety risks; digital technologies (AI/ML, IoT, blockchain) strengthen risk analysis via monitoring, prediction, and traceability. Three case studies (AI sorting, blockchain supply chain, IoT cold chain) show…
Shi Yang, Polina Ellina, Kyriakos Christofi +2
This study develops an ESG disclosure quality evaluation framework tailored to the agricultural chemicals industry, based on GRI 2021 Standards and Chinese regulatory requirements. Using qualitative content analysis and AHP, it constructs a…
Lu Gong
This study analyzes how digital monitoring and ESG disclosure affect the assetization efficiency of agricultural carbon sinks. Using data from typical agricultural regions, it demonstrates that high-quality monitoring and ESG disclosure sig…
Ammar Ali Gull, Irfan Haider Shakri, Faizul Haque +1
This study examines the impact of external sustainability assurance and Big-4 providers on GHG emissions using a global sample of 7,968 firm-year observations from 34 countries. It finds a negative association, stronger in stakeholder-orien…
Yu Qiao, Xinwen Deng, Xiaoying Huang +7
This study proposes a UAV-UGV collaborative delivery framework for urban last-mile logistics, developing a multi-objective optimization model considering efficiency, safety, and low-carbon performance. A visual simulation platform using HTM…
K. Zhou, Y. H. Cao, H. Yang
Using data from Shanghai and Shenzhen A-share listed companies (2010-2023), this study employs a two-way fixed-effects model to analyze the impact of venture capital (VC) on corporate carbon performance. VC improves carbon performance by al…
Qiusheng Gao, Meichen Yao, Liang Duan
This paper proposes a new paradigm for low-carbon retrofitting of existing wastewater treatment plants by coupling AI fine-tuned operation with dynamic in-situ retrofitting. AI optimizes operations to reduce energy consumption and greenhous…
Fahrettin Pala, Emine Kaya, Esra Nur Akpınar +3
Using panel data from 50 countries (2002-2019), this study examines how AI, renewable energy technology innovation (RETI), and regulatory quality (RQ) affect low-carbon energy transition. AI promotes transition across all income groups, whi…
Xiaojie Lin, Jian Li, Rui Jing +4
This paper reviews the role of AI in transforming energy systems for carbon neutrality. It highlights how data-driven prediction, physics-informed learning, reinforcement learning, digital twins, and generative models enhance renewable fore…
Jiachen Shen, Jian Shi, Hui Zhong +3
This paper applies mechanism-guided graph learning to evaluate carbon border policies (e.g., CBAM) in interconnected European electricity markets, integrating AI methods with climate policy assessment to quantify cross-border policy spillov…
Kollia M
This Perspective proposes the ZERO framework to shift AI data-centre sustainability evaluation upstream, before resource consumption. It combines hard admissibility constraints, workload characterisation, local resource-state assessment, fe…
Zhang Q, Sheng S, Liu T
A two-stage lexicographic framework for geo-distributed AI inference that preserves service while optimizing carbon and water. Trace-driven analysis of 26,392 requests reveals a 21-point carbon-water frontier, quantifying trade-offs. Provid…
Yao, Huaying
This replication package audits corporate climate-target claims against mandatory verified emissions from the EU Transaction Log and US EPA GHGRP. Using only public data, it parses 1,779 claims into six adjudication tiers and documents sens…
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