Blockchain for Sustainability: Enhancing Transparency in Green Supply Chains

Authors

  • Edward Said

Abstract

Supply chain sustainability requires trust, traceability, and accountability. This study explores the use of blockchain technology to build transparent, tamper-proof systems that monitor environmental compliance, carbon emissions, and resource sourcing. We present a framework integrating smart contracts and IoT devices for real-time tracking of sustainable practices. The analysis includes pilot deployments in the agriculture and textile sectors, showcasing how decentralized ledgers can strengthen ESG (Environmental, Social, Governance) efforts.

References

Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).

Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).

Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).

Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).

Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19. Retrieved from https://ijsdcs.com/index.php/ijsdcs/article/view/696/260

The Critical Role of Accurate Balance Carry Forward in Preventing Financial Irregularities. (2022). International Journal of Interdisciplinary Finance Insights, 1(1), 1-13. https://injmr.com/index.php/ijifi/article/view/143

Chittineni, S. (2022). Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures. American Journal of AI & Innovation, 4(4).

Chittineni, S. (2023). Enhancing Messaging Systems with AI: Predictive Load Balancing in JMS and IBM MQ. American Journal of AI & Innovation, 5(5).

Chittineni, S. (2023). Shadow Comparator with AI: A Machine Learning Approach for Anomaly Detection in Production Systems. Australian Journal of Cross-Disciplinary Innovation, 5(5).

Mitigating Risks and Ensuring Compliance: The Necessity of Regular Upgrades to SAP Financial Products Subledger (FPSL) (C. S. A. Mohammed , Trans.). (2023). International Journal of Creative Research In Computer Technology and Design, 5(5), 1-11. https://jrctd.in/index.php/IJRCTD/article/view/75

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3), 633-705.

Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53(6), 24-26.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Published

2023-01-16

Issue

Section

Articles