The Smart Carbon Footprint Revolution—Examining Interdisciplinary Progress

Dear Colleagues,

As we are at the crossroads of climate change and digital transformation, humanity faces a dual mandate: to decode the ‘black box’ of carbon emissions and to code a ‘green operating system’ for our sustainable future where algorithms and governance protocols run in harmony.

This Special Issue seeks cutting-edge research that leverages interdisciplinary innovations that decode, optimize, and mitigate carbon footprints across scales—from smart cities to global supply chains—with a focus on emerging technologies such as AI-driven analytics, energy-saving computing, and next-generation communications.

We are pleased to invite researchers to contribute cutting-edge studies that bridge information technology, environmental science, and governance innovation. These works will advance the academic dialog on scalable technology-enabled climate solutions.

This Special Issue aims to decipher carbon footprint dynamics through spatially explicit models enhanced by AI and remote sensing, uncover hidden drivers using computational social science methods, and foster cross-disciplinary synergy between environmental studies and IT, highlighting scalable digital tools for decarbonization.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

1. AI and data science for carbon intelligence:

Dynamic carbon footprint models with green adjustment coefficients (e.g., integrating renewable energy penetration rates);

Transfer learning for cross-regional carbon data imputation in developing economies;

NLP/sentiment analysis of social media to predict emission trends;

Agent-based simulations or graph neural networks (GNNs) to quantify hidden cascading effects in urban and industrial systems.

2. Computational infrastructure for low-carbon systems:

Malmquist index-based benchmarking of data center energy productivity;

Algorithms to minimize AI/cloud computing footprints (e.g., sparse training);

Federated learning for edge-device carbon monitoring in IoT networks;

Quantum-inspired algorithms to optimize national/regional carbon trading markets.

3. Communication technologies for emission mitigation:

Applications of remote sensing and IoT for high-resolution emission mapping and real-time monitoring;

Novel network protocols that prioritize low-latency climate monitoring data;

LEO satellite constellations for real-time global carbon flux measurements;

Digital twin cities with 5G/6G-enabled traffic/energy flow simulations.

4. Policy informatics and human-centric computing:

Causal inference models to quantify ICT-driven vs. policy-driven carbon reductions;

Generative AI for synthesizing cross-disciplinary climate policy scenarios;

VR/AR interfaces to visualize personal carbon footprints via mobile apps;

Blockchain-enabled transparency in carbon markets or dynamic efficiency assessments of regional decarbonization efforts.

We look forward to receiving your contributions.

Dr. Shaohua Liu
Prof. Dr. Zhili Sun
Guest Editors

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• carbon footprint measurement
• dynamics analysis
• digital governance
• AI-driven modeling
• green computing
• next-generation communications
• energy-saving technology
• smart cities