Enterprise AI Analysis
Mapping the Research Trends and Hotspots in “Artificial Intelligence and Environmental Sustainability” ——A Comprehensive Bibliometric Analysis
Authors: Xiaoyu Li, Haohan Meng, Jinhua Sun, Lei Gu
Affiliation: College of International Studies, National University of Defense Technology, Nanjing, Jiangsu, China
This bibliometric study analyzes 344 papers from 2014-2025 on 'Artificial Intelligence and Environmental Sustainability' from the Web of Science. It reveals a significant surge in interest post-2019, with China, UK, US, and India leading research. While collaboration networks are emerging, they remain somewhat loose. Key hotspots include AI applications in climate change mitigation, smart city development, and renewable energy.
Unlocking the Future: AI for a Sustainable World
Our comprehensive bibliometric analysis reveals the rapidly escalating importance of Artificial Intelligence in driving environmental sustainability. From research trends to global collaborations, discover the pivotal metrics shaping this critical interdisciplinary field.
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Documents Analyzed (2014-2025)
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Peak Publications in 2024
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Top Journal Share (Sustainability)
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Leading Country (China)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The field of AI and Environmental Sustainability has witnessed a remarkable growth, particularly after 2019. The earliest paper dates back to 2014, and after a period of low activity, publications surged from 2020 to 2023, with 2024 marking an unprecedented peak of 190 papers. This growth correlates strongly with increased citation counts, indicating rising academic influence and recognition. The journal Sustainability leads the field with 14.33% of papers, focusing on smart cities, climate change, and resource management. Journal of Cleaner Production is also highly active, emphasizing pollution control and resource efficiency.
Analysis reveals an emerging, though still weak, global collaboration network. Among 1499 authors, only 58 meet the minimum threshold, forming distinct but limited networks. Simon Elias Bibri from the Swiss Federal Institute of Technology is the most prolific author, with 4 papers and 170 citations. Institutionally, 819 entities contributed, with the University of British Columbia leading with 6 papers and 446 citations. While some regional clusters exist (e.g., West Asian universities), many institutions remain isolated. Nationally, China leads with 67 papers, followed by the UK (49), US (45), and India (44). The UK and India show the most extensive international cooperation networks, signaling a trend towards closer global collaboration.
The core research hotspots revolve around 'artificial intelligence' (128 occurrences), 'environmental sustainability' (54), and 'sustainability' (46). Key technologies include 'machine learning,' 'deep learning,' 'big data,' and 'blockchain.' Applications are diverse, spanning climate change mitigation, smart city development, and renewable energy. Keyword burst analysis highlights 'big data,' 'climate change,' 'supply chain,' 'economic growth,' and 'energy consumption' as emerging trends, indicating a growing focus on AI's practical benefits for economic development and ecological protection. The field is actively exploring AI's positive impacts while acknowledging its potential dual-edge nature without sustainable design.
Enterprise Process Flow
Research Questions
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Research Methods
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Data Collection
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Data Analysis & Visualization
The study followed a systematic bibliometric research framework, beginning with clear research questions, defining methodologies, gathering data from Web of Science, and concluding with visual analysis.
14.33%
Top Journal Share ('Sustainability')
The journal 'Sustainability' published the highest proportion of papers in the field, demonstrating its significant focus on interdisciplinary sustainable development research, including smart cities, climate change, and resource management.
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Papers in 2024 (Record High)
The year 2024 saw an unprecedented surge in publications, with 190 papers, almost tripling the previous year's volume, highlighting the rapid advancement and adoption of AI technologies in environmental sustainability research.
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This study leveraged bibliometrics for a comprehensive and objective overview of the field's landscape and trends, an approach offering distinct advantages over traditional methods. |
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Pioneering Research: Simon Elias Bibri's Influence
Simon Elias Bibri from the Swiss Federal Institute of Technology stands out as a highly influential author in the field of AI and Environmental Sustainability. With 4 papers and 170 citations, his work primarily focuses on the critical intersection of artificial intelligence, smart cities, and sustainable development. His contributions have garnered significant academic recognition, shaping the discourse around how AI can drive sustainable urban transformation.
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Your AI Sustainability Implementation Roadmap
Implementing AI for environmental sustainability requires a strategic approach. Here’s a typical phased roadmap to guide your enterprise transformation.
Phase 1: Discovery & Strategy
Assess current environmental impact, identify key sustainability goals, and evaluate AI's potential. Develop a clear AI strategy aligned with enterprise objectives and regulatory compliance.
Phase 2: Data Foundation & Pilot
Establish robust data collection for environmental metrics. Implement a pilot AI project in a focused area (e.g., energy management, waste optimization) to demonstrate early value and refine models.
Phase 3: Integration & Scaling
Integrate successful AI solutions across relevant operations. Scale up initiatives to broader areas like supply chain optimization, climate forecasting, and smart infrastructure.
Phase 4: Monitoring & Evolution
Continuously monitor AI system performance and environmental impact. Adapt and evolve AI models with new data and technologies to ensure long-term sustainability and maximize ROI.
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