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Related Experiment Video

Updated: Jul 6, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Artificial intelligence-based digital transformation and environmental sustainability.

Di Wu¹, Yuge Zhang², Lihong Zhao²

¹Shandong University of Ecology and Environment, Weifang, Shandong, China.

|March 26, 2026

Summary

Green AI integrates artificial intelligence (AI), blockchain, and the Internet of Things (IoT) to boost environmental sustainability. This framework enhances energy efficiency and reduces carbon emissions for a greener future.

Related Experiment Videos

Last Updated: Jul 6, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Area of Science:

Environmental Science
Computer Science
Sustainable Development

Background:

Artificial intelligence (AI) offers potential for environmental sustainability but faces challenges like scattered applications and complex data integration.
Existing AI applications lack interoperability and defined metrics, hindering their effectiveness in supporting sustainable practices.
A holistic and interoperable solution is needed to leverage AI for environmental sustainability.

Purpose of the Study:

To introduce Green AI, a novel framework combining AI, blockchain, and the Internet of Things (IoT).
To develop a scalable and secure architecture for enhancing energy efficiency, reducing carbon emissions, and improving environmental monitoring.
To provide a unique, reproducible model for cost-effective resource management and sustainable development.

Main Methods:

Utilizing blockchain for secure, transparent management of real-time data from smart grids, environmental sensors, and energy markets via IoT devices.
Employing advanced machine learning algorithms for maximizing renewable energy integration and estimating energy demand.
Designing a unified architecture to improve energy system sustainability and resource management.

Main Results:

The Green AI framework ensures transparency and accountability in energy transactions.
Enhanced energy efficiency and reduced carbon emissions are achieved through optimized energy demand and renewable energy integration.
Improved accuracy in environmental monitoring and cost-effective resource management are demonstrated.

Conclusions:

The Green AI framework offers a scalable, secure, and interoperable solution for environmental sustainability challenges.
It provides a practical model for integrating intelligent technologies to achieve sustainable development goals.
The framework guides stakeholders in utilizing AI, blockchain, and IoT for a greener and more resilient future.