This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open 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 541–560 of 716 papers
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…
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,…
(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 …
Huajian Jiang
ESG-DocQA is a 300-sample benchmark for evidence-grounded question answering over corporate ESG reports. Constructed with three-reviewer validation, it achieves substantial inter-reviewer reliability (Fleiss' kappa = 0.644). It includes ver…
Mareta Putri, I. Mutia, S. F. Kartasari
This paper conducts a systematic literature review on the role of AI in sustainability reporting, analyzing 30 relevant studies from 1,087 initial articles. It finds benefits in efficiency, accuracy, transparency, and big data management, w…
Namratha Bm
This paper examines how AI can enhance sustainable investment decisions. AI-enabled platforms analyze ESG data and use natural language processing to support investors, potentially accelerating the transition to a sustainable economy. Howev…
Sharif Mohd., Mohammad Fakhrul Islam, R. Ramachandran +1
This paper uses bibliometric analysis of SCOPUS data (2015-2024) to map AI/ML research in sustainable development. It finds growing applications in energy, emissions, environmental monitoring, climate mitigation, agriculture, and water mana…
Furong Cai, E. Bolisani, M. Nakash +1
This systematic review of 80 articles (2004-2025) explores how AI enhances knowledge management (KM) for sustainable business transformation in the context of Industry 5.0. It identifies KM, AI, and sustainability as core motor themes, with…
(著者不明)
This paper proposes an AI-enabled competency framework and development pathway for cultivating green marketing talent in vocational colleges, aiming to support the net-zero transition. It addresses the gap between education and practice by …
Ma W.
This paper explores the synergistic spillover effects of AI, cloud computing, and blockchain on green finance. It suggests that these technologies can enhance efficiency and transparency in environmental investments, but lacks specific empi…
Browse by Topic