GX & Decarbonization Research

J Zhang, Yan Gong, Hairuo Wang +4

This study integrates emergy analysis, carbon footprint accounting, and LSTM neural network modeling to investigate how river–coast connectivity affects coastal ecosystem services and blue carbon in the Yellow River Delta. High-connectivity…

Ni Li

This study quantifies the carbon footprint of Yunnan's cut rose supply chain using LCA, and proposes a low-carbon transformation path combining AI precision agriculture, blockchain traceability, and IoT-enabled collaborative governance. It …

Yan Hao, Yanmei Yuan, Hui Liu

This study proposes a data-driven approach for fertilization behavior recognition and low-carbon decision optimization using multi-source agricultural time-series data. It combines LSTM with attention mechanism for behavior recognition and …

Ning Hu, Zhiyuan Tang, Shuaijia He +3

This paper proposes a multi-agent reinforcement learning based bidding strategy for virtual power plants (VPPs) in the spot market under carbon trading. It develops an AI method for VPPs to optimize bids considering carbon costs, improving …

Yu-Cian Lin, Ying-Chieh Chan

This study develops a multi-objective optimization framework for urban greening that visualizes trade-offs among cost, carbon sequestration, and shading. Using 3D parametric modeling and evolutionary algorithms with localized data from Taiw…

Sarah Meier, Ben Balmford, Ville Inkinen

This paper evaluates the additionality of protected areas in Bolivia using a Random Survival Forest model to predict deforestation risk. On average, protected areas reduce deforestation by 0.19 percentage points (68%), but effects are highl…

Simona Bigerna, Tulia Gattone, Cosimo Magazzino

This paper examines structural constraints on energy transition in low- and lower-middle-income countries. It finds that fossil fuel dependence, resource rents, and methane-intensive production reinforce carbon lock-in, while renewable depl…

(著者不明)

This paper explores AI-driven carbon accounting methods to enhance transparency in ESG reporting. It discusses how automation of data collection and analysis can improve accuracy in Scope 1/2/3 calculations and disclosure reliability. The w…

Obaid Ullah, BenYan Tan, Ali Zeb +1

This paper applies machine learning to examine how artificial intelligence, carbon pricing, and green finance affect renewable energy affordability in advanced economies. It quantifies the impact of policy interventions and identifies drive…

Haizhou Wang, School of Business, University of Chinese Academy of Social Sciences, Beijing, 102488, China

This paper empirically examines how China's carbon emissions trading pilot promotes corporate digital transformation, using dual machine learning and difference-in-differences on panel data of A-share listed firms from 2010-2021. It identif…

Timileyin Abiodun, Nnamdi Nwulu, Peter Olukanmi

This paper proposes a multi-agent AI framework integrated with blockchain-based audit trails to secure industrial greenhouse gas monitoring infrastructure. It enhances data integrity and transparency, supporting credible carbon accounting a…

C. Ghenai

This study assesses the performance of green hydrogen power systems for AI data centers and develops AI-based short-term energy forecasting models. It models an off-grid solar PV/hydrogen fuel cell system and evaluates technical, financial,…

Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon +5

This paper proposes an emission-aware Reinforcement Learning (SAC-based) strategy for EV charging scheduling, incorporating real-time carbon intensity and renewable energy availability. Tested on EV2Gym with EirGrid data, it achieves up to …

(著者不明)

This scoping review examines the application of artificial intelligence (AI) to forest carbon accounting. It finds that AI methods such as machine learning and remote sensing can improve estimation accuracy and monitoring efficiency, but al…

Habes Ali Khawaldeh, Moath Bani Fayyad, Mohammad Al-Smairan, Wasseem Al Rousan and Omar Alnhoud

This paper designs a fixed-dome biogas plant for a 200-cow dairy farm in Jordan, performing techno-economic and environmental assessment with LSTM-based AI forecasting. Results show a 4-year payback, LCOE ~0.093 USD/kWh, and 28.46 tCO2/year…

Amir Ahmad Dar, Vanshita Arora, Harith Yas

This chapter examines the potential and challenges of AI in addressing the climate crisis. It explores applications in renewable energy, low-carbon cities, and biodiversity monitoring, while also critically discussing AI's energy footprint …

Huong Dinh Thi, Trang Trần Văn, Ngoc Anh Nguyen +2

This study examines how AI-driven CSR and green HRM enhance low-carbon behavior and financial performance in the hospitality sector, offering practical implications.

Huajian Jiang

This paper introduces ESG-DocQA, a benchmark dataset for evidence-grounded question answering over corporate ESG reports. It contains 300 samples with verification, comparison, and inference questions, validated by three reviewers achieving…

Edina K.D.

This paper explores the role of AI and automation in green investment platforms, framing next-generation ESG. Lacking empirical data, it contributes to conceptual discussion on technology-driven sustainability finance.

Lemuel Kenneth David

This paper explores the integration of AI with climate modeling to improve sustainability forecasting and economic decision-making under climate uncertainty. From Xi'an Jiaotong University, it suggests applications in scenario analysis and …