Frontiers in Emerging Engineering & Technologies

  1. Home
  2. Archives
  3. Vol. 1 No. 01 (2024): Volume 01 Issue 01 2024
  4. Articles
Frontiers in Emerging Engineering & Technologies

Article Details Page

"Green Steel" Adoption Playbook for DOTs

Authors

  • Vinod Kumar Enugala Department of Civil Engineering, University of New Haven, CT, USA

Keywords:

Green Steel Adoption Playbook, Environmental Product Declarations (EPDs), Embodied Carbon Benchmarking, Multi-Criteria Decision Analysis (MCDA), Correlation-Based Feature Selection (CFS)

Abstract

Greenhouse gas emissions caused by the embodied carbon of steel are one of the most significant ways in which steel is utilized in transportation infrastructure projects because the industry has one of the highest embodied carbon rates. This research constructs an end-to-end Green Steel Adoption Playbook for U.S. state Departments of Transportation (DOTs) to define, size, and confirm low-carbon steel procurement with no safety, cost, or time risks to the organization. A mixed-methods strategy is adopted in the context of life-cycle assessment benchmarking on plant-specific Environmental Product Declarations (EPDs), exploratory data analysis, and visual analytics as filters of the data quality, principal component analysis diagnostics, and correlation-based feature selection, to determine transparent procurement requirements. A multi-criteria decision analysis (MCDA) model combines cost, carbon performance, and disclosure risk, and the simulations rank winner patterns, greenhouse gas reductions, cost premiums, and supply coverage at 10% to 30% weighting to carbon. Practical feasibility and administrative effects are bolstered by semi-structured DOT officials, fabricators, and mills. Findings demonstrate that dramatic short-term CO2 emissions cuts (for up to 40%) could be obtained at less than 5% cost premiums, CFS-selected features delivered resilient and explanatory scoring, and PCA could be employed to validate levels of robustness. Traceability of more than 90 percent was realized through a verification protocol that involved post-award audits and cross-checking mill-test reports. The playbook provides DOTs with practical recommendations, such as defining functional units, procurement scoring functions, verification procedures, and implementation plans, to trigger market indicators on green steel, adhere to Buy America/Buy Clean strategies, as well as infrastructural decarbonization. The framework is scalable, and it allows constant optimization.

References

ASTM International. (2017). Standard Specification for Carbon Structural Steel (A36/A36M-14). ASTM.

Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. Knowledge and Information Systems, 34(3), 483–519.

Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices

Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Helina. http://doi.org/10.47363/JEAST/2022(4)E168

Dufresne, M. L., & Jorgensen, D. (2019). Analyzing material flows in transportation infrastructure: A life cycle assessment approach. Journal of Environmental Management, 241, 85–98. https://doi.org/10.1016/j.jenvman.2019.04.026

EN 15804. (2012). Sustainability of construction works: Environmental product declarations. European Committee for Standardization.

Fischedick, M., Marzinkowski, J., Winzer, P., & Weigel, M. (2014). Techno-economic evaluation of innovative steel production technologies. Journal of Cleaner Production, 84, 563-580. https://doi.org/10.1016/j.jclepro.2014.05.063

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

Gupta, S., & Sharma, R. (2020). Carbon footprint of steel manufacturing: Challenges and opportunities in sustainable production practices. Resources, Conservation, and Recycling, 156, 104711. https://doi.org/10.1016/j.resconrec.2020.104711

ISO. (2006a). ISO 14040: Environmental management – Life cycle assessment – Principles and framework. International Organization for Standardization.

ISO. (2006b). ISO 14025: Environmental labels and declarations – Type III environmental declarations – Principles and procedures. International Organization for Standardization.

Jackson, J. E. (2011). A user’s guide to principal components. John Wiley & Sons.

Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. https://www.ashwinanokha.com/ijeb-v22-4-2023.php

Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. Retrieved from https://www.ashwinanokha.com/ijeb-v22-4-2023.php

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Kumar, P., & Malik, N. (2020). Emissions mitigation strategies in steelmaking: Opportunities for public procurement. Energy Reports, 6, 306–317. https://doi.org/10.1016/j.egyr.2020.01.021

Laurent, A., Olsen, S. I., & Hauschild, M. Z. (2012). Limitations of carbon footprint as indicator of environmental sustainability. Environmental Science & Technology, 46(16), 9244–9251.

Nyati, S. (2018). Revolutionizing LTL carrier operations: A comprehensive analysis of an algorithm-driven pickup and delivery dispatching solution. International Journal of Science and Research (IJSR), 7(2), 1659-1666. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203183637

Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804–1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230

Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf

Sadeghian, A., Golpira, H., & Rajabi, M. (2020). Green public procurement and sustainable infrastructure development: A systematic review. Environmental Impact Assessment Review, 80, 106311. https://doi.org/10.1016/j.eiar.2019.106311

Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253

Simcoe, T., & Toffel, M. W. (2014). Government green procurement spillovers: Evidence from municipal building policies in California. Journal of Environmental Economics and Management, 68(3), 411-434.

Singh, S., & Sharma, R. (2020). Implementing green supply chain management practices in the construction industry: Benefits and challenges. Journal of Cleaner Production, 276, 124095. https://doi.org/10.1016/j.jclepro.2020.124095

Singh, V. (2021). Generative AI in medical diagnostics: Utilizing generative models to create synthetic medical data for training diagnostic algorithms. International Journal of Computer Engineering and Medical Technologies. https://ijcem.in/wp-content/uploads/GENERATIVE-AI-IN-MEDICAL-DIAGNOSTICS-UTILIZING-GENERATIVE-MODELS-TO-CREATE-SYNTHETIC-MEDICAL-DATA-FOR-TRAINING-DIAGNOSTIC-ALGORITHMS.pdf

Singh, V. (2022). Visual question answering using transformer architectures: Applying transformer models to improve performance in VQA tasks. Journal of Artificial Intelligence and Cognitive Computing, 1(E228). https://doi.org/10.47363/JAICC/2022(1)E228

Smith, L., & Jordan, K. (2018). Implementing low-carbon supply chains in public procurement: A case study in the transportation sector. Journal of Cleaner Production, 172, 1230–1240. https://doi.org/10.1016/j.jclepro.2017.10.113

Sow, O., & Zaid, A. (2019). Decarbonization pathways in steelmaking: The role of green hydrogen and carbon capture. Energy Reports, 5, 1140-1149. https://doi.org/10.1016/j.egyr.2019.07.012

Vigon, B. W., & Jensen, A. A. (2018). Life Cycle Assessment: Data Quality Issues and Guidelines. Journal of Cleaner Production, 177, 146-162.

Vogl, V., Åhman, M., & Nilsson, L. J. (2018). Assessment of hydrogen direct reduction for fossil-free steelmaking. Journal of Cleaner Production, 203, 736-745. https://doi.org/10.1016/j.jclepro.2018.08.279

Wang, Y., Zhang, J., & Yang, Z. (2021). Sustainable procurement in the public sector: A comparative study of practices in the construction industry. Journal of Public Procurement, 21(3), 315-341. https://doi.org/10.1108/JOPP-11-2020-0059

Downloads

Published

2024-04-03

How to Cite

Vinod Kumar Enugala. (2024). "Green Steel" Adoption Playbook for DOTs. Frontiers in Emerging Engineering & Technologies, 1(01), 01–23. Retrieved from https://irjernet.com/index.php/feet/article/view/168