Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

Blockchain Technology: A Catalyst For Eco-Friendly Product Validation

K Satya Mounika
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Venkata Jithendra
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Vijay Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Hari Krishna
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Chandra Shekar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India

Published 2025-04-20

Keywords

  • Blockchain,
  • greenness certification,
  • competitive pricing,
  • green product,
  • supply chain

How to Cite

K Satya Mounika, P Venkata Jithendra, K Vijay Kumar, P Hari Krishna, & P Chandra Shekar. (2025). Blockchain Technology: A Catalyst For Eco-Friendly Product Validation. International Journal of Computational Learning & Intelligence, 4(4), 633–644. https://doi.org/10.5281/zenodo.15250396

Abstract

The rise of blockchain technology has reshaped competition between traditional and ecofriendly products by offering a means to verify sustainability claims. This study uses game theory to examine how blockchain integration influences market dynamics between retailers selling eco-friendly and conventional goods. Two pricing models are considered: one for a firm using blockchain certification for its eco-friendly product and another for a manufacturer of traditional products. The study assesses two key factors blockchain adoption costs and product quality choices. Findings indicate that blockchain does not inherently expand the market for eco-friendly products but intensifies competition as eco-conscious consumer numbers grow. While blockchain may alleviate some competitive pressures, its presence alone does not guarantee an edge for eco-friendly products. Companies leveraging blockchain must also secure strong bargaining power to outperform conventional products.

References

  1. Yang, J., Ma, C., Li, D., & Liu, J. (2022). Mapping the knowledge on blockchain technology in the field of business and management: A bibliometric analysis. IEEE Access, 10, 60585–60596.
  2. Omar, I. A., Jayaraman, R., Debe, M. S., Hasan, H. R., Salah, K., & Omar, M. (2022). Supply chain inventory sharing using Ethereum blockchain and smart contracts. IEEE Access, 10, 2345–2356.
  3. Li, J., Maiti, A., Springer, M., & Gray, T. (2020). Blockchain for supply chain quality management: Challenges and opportunities in context of open manufacturing and industrial Internet of Things. International Journal of Computer Integrated Manufacturing, 33(12), 1321–1355.
  4. Guo, S., Sun, X., & Lam, H. K. S. (2023). Applications of blockchain technology in sustainable fashion supply chains: Operational transparency and environmental efforts. IEEE Transactions on Engineering Management, 70(4), 1312–1328.
  5. Lin, X., Huang, X., Liu, S., Li, Y., Luo, H., & Yu, S. (2022). Competitive price-quality strategy of platforms under user privacy concerns. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 571–589.
  6. Kang, Y., Shi, X., & Liu, S. (2023). Sales channel competition for supply chain with the blockchain technology. International Transactions in Operational Research. Advance online publication.
  7. Liu, S., Hua, G., Kang, Y., Cheng, T. C. E., & Xu, Y. (2022). What value does blockchain bring to the imported fresh food supply chain? Transportation Research Part E: Logistics and Transportation Review, 165, 102859.
  8. Hu, H., Li, Y., & Li, M. (2022). Decisions and coordination of green supply chain considering big data targeted advertising. Journal of Theoretical and Applied Electronic Commerce Research, 17(3), 1035–1056.
  9. Liu, S. S., Hua, G., Ma, B. J., & Cheng, T. C. E. (2023). Competition between green and non-green products in the blockchain era. International Journal of Production Economics, 264, 108970.
  10. Zhang, N., Wu, J., Li, B., & Fu, D. (2023). Research on green closed-loop supply chain considering manufacturer’s fairness concerns and sales effort. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 333–351.
  11. Choi, T.-M., & Siqin, T. (2022). Blockchain in logistics and production from blockchain 1.0 to blockchain 5.0: An intra-inter-organizational framework. Transportation Research Part E: Logistics and Transportation Review, 160, 102653.
  12. Shahzad, K., Zhang, Q., Zafar, A. U., Ashfaq, M., & Rehman, S. U. (2023). The role of blockchain-enabled traceability, task technology fit, and user self-efficacy in mobile food delivery applications. Journal of Retailing and Consumer Services, 73, 103331.
  13. Chen, T., Li, Y., & Xu, F. (2023). Traceability strategy choice in competing supply chains based on blockchain technology. International Transactions in Operational Research. Advance online publication.
  14. Wan, P. K., Huang, L., & Holtskog, H. (2020). Blockchain-enabled information sharing within a supply chain: A systematic literature review. IEEE Access, 8, 49645–49656.
  15. Ahmed, S. T., Vinoth Kumar, V., Mahesh, T. R., Narasimha Prasad, L. V., Velmurugan, A. K., Muthukumaran, V., & Niveditha, V. R. (2024). FedOPT: federated learning-based heterogeneous resource recommendation and optimization for edge computing. Soft Computing, 1-12.
  16. Ahmed, S. T., Kumar, V. V., Singh, K. K., Singh, A., Muthukumaran, V., & Gupta, D. (2022). 6G enabled federated learning for secure IoMT resource recommendation and propagation analysis. Computers and Electrical Engineering, 102, 108210.
  17. Fathima, A. S., Basha, S. M., Ahmed, S. T., Mathivanan, S. K., Rajendran, S., Mallik, S., & Zhao, Z. (2023). Federated learning based futuristic biomedical big-data analysis and standardization. Plos one, 18(10), e0291631.
  18. Ahmed, S. T., Singh, D. K., Basha, S. M., Abouel Nasr, E., Kamrani, A. K., & Aboudaif, M. K. (2021). Neural network based mental depression identification and sentiments classification technique from speech signals: A COVID-19 Focused Pandemic Study. Frontiers in public health, 9, 781827.
  19. Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755.
  20. Fathima, A. S., Basha, S. M., Ahmed, S. T., Khan, S. B., Asiri, F., Basheer, S., & Shukla, M. (2025). Empowering consumer healthcare through sensor-rich devices using federated learning for secure resource recommendation. IEEE Transactions on Consumer Electronics
  21. Fathima, A. S., Reema, S., & Ahmed, S. T. (2023, December). ANN based fake profile detection and categorization using premetric paradigms on instagram. In 2023 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-6). IEEE
  22. Xu, X., Sethi, S. P., Chung, S.-H., & Choi, T.-M. (2023). Ordering COVID-19 vaccines for social welfare with information updating: Optimal dynamic order policies and vaccine selection in the digital age. IISE Transactions, 1–17.
  23. Saxena, N., & Sarkar, B. (2023). How does the retailing industry decide the best replenishment strategy by utilizing technological support through blockchain? Journal of Retailing and Consumer Services, 71, 103151.
  24. Choi, T.-M. (2019). Blockchain-technology-supported platforms for diamond authentication and certification in luxury supply chains. Transportation Research Part E: Logistics and Transportation Review, 128, 17–29.
  25. Li, G., Fan, Z.-P., & Wu, X.-Y. (2023). The choice strategy of authentication technology for luxury e-commerce platforms in the blockchain era. IEEE Transactions on Engineering Management, 70(3), 1239–1252.
  26. Ma, B. J., Kuo, Y.-H., Jiang, Y., & Huang, G. Q. (2023). RubikCell: Toward robotic cellular warehousing systems for e-commerce logistics. IEEE Transactions on Engineering Management. Advance online publication.
  27. Zhou, Y., Yan, S., Li, G., Xiong, Y., & Lin, Z. (2023). The impact of consumer skepticism on blockchain-enabled sustainability disclosure in a supply chain. Transportation Research Part E: Logistics and Transportation Review, 179, 103323.
  28. Madapuri, R. K., & Senthil Mahesh, P. C. (2017). HBS-CRA: Scaling impact of change request towards fault proneness: Defining a heuristic and biases scale (HBS) of change request artifacts (CRA). Cluster Computing, 22(S5), 11591–11599. https://doi.org/10.1007/s10586-017-1424-0
  29. Dwaram, J. R., & Madapuri, R. K. (2022). Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India. Concurrency and Computation: Practice and Experience, 34(27). https://doi.org/10.1002/cpe.7310
  30. Reddy, B. S. H. (2025). Deep learning-based detection of hair and scalp diseases using CNN and image processing. Milestone Transactions on Medical Technometrics, 3(1), 145–5. https://doi.org/10.5281/zenodo.14965660
  31. Reddy, B. S. H., Venkatramana, R., & Jayasree, L. (2025). Enhancing apple fruit quality detection with augmented YOLOv3 deep learning algorithm. International Journal of Human Computations & Intelligence, 4(1), 386–396. https://doi.org/10.5281/zenodo.14998944