Life Cycle Assessment & Carbon Footprint of IIoT Devices and Smart Products

To equip participants with practical skills to measure, analyse, and improve the lifecycle environmental performance of IIoT devices and smart products.

• Understand ISO-based LCA fundamentals.
• Assess hardware and embodied carbon impacts.
• Model device energy, connectivity, and cloud emissions.
• Evaluate battery, recycling, and end-of-life scenarios.
• Apply Dynamic LCA and sensitivity analysis.
• Develop data-driven sustainability models and dashboards.

🗓️ Module 1: IIoT LCA Fundamentals & Hardware Footprint

• Objective: Understand Life Cycle Assessment principles and quantify the embodied environmental footprint of Industrial IoT hardware and components.
• ISO 14040/14044 standards and fundamental Life Cycle Assessment concepts.
• Cradle-to-Gate vs. Cradle-to-Grave system boundaries.
• Environmental impacts of PCBs, integrated circuits, sensors, and microcontrollers.
• Critical raw materials and lifecycle impacts of batteries.
• Industrial enclosures, component manufacturing, and production footprints.
• Using open LCA datasets and life cycle inventory databases.

🛠️ Hands-on Lab (Google Colab)

• Task: Build an IIoT BOM Embodied Carbon Calculator to estimate the carbon footprint of sensors, microcontrollers, PCBs, batteries, and other hardware components.
• Tools Covered: Python, Brightway2/Brightway25, ActivityBrowser, openLCA, USEEIO, Agribalyse, and Google Colab.

🗓️ Module 2: Use-Phase, Edge Computing & Cloud Footprint

• Objective: Quantify operational emissions from IIoT devices, communication networks, edge computing, and cloud infrastructure across the use phase.
• IIoT device power consumption and duty-cycle modeling.
• Energy impacts of LoRaWAN, NB-IoT, 5G, Wi-Fi, and Zigbee communication technologies.
• Edge vs. cloud computing environmental trade-offs.
• Data center, cloud storage, data transmission, and network-related emissions.
• Dynamic electricity-grid carbon factors and regional emission variability.
• Battery replacement cycles and their influence on lifecycle impacts.

🛠️ Hands-on Lab (Google Colab)

• Task: Model IIoT operational and digital infrastructure emissions by combining device energy consumption, communication activity, edge workloads, and cloud-computing footprints.
• Tools Covered: Python, Cumulator, CodeCarbon, Eco2AI, and Google Colab.

🗓️ Module 3: End-of-Life, Dynamic LCA & Digital Product Passports

• Objective: Evaluate end-of-life scenarios, integrate real-time operational data into Dynamic LCA, and develop decision-ready sustainability outputs for IIoT systems.
• E-waste management, recycling, material recovery, and disposal pathways.
• Recycling credits, substitution methods, and avoided-burden approaches.
• Device obsolescence, repair, reuse, refurbishment, and lifecycle extension strategies.
• Real-time telemetry-driven Dynamic Life Cycle Assessment.
• Digital Product Passports (DPP) for lifecycle traceability and sustainability reporting.
• Sensitivity analysis, scenario comparison, uncertainty interpretation, and ISO-compliant reporting.

🛠️ Hands-on Lab (Google Colab)

• Task: Build a Real-Time Dynamic LCA Dashboard & End-of-Life Scenario Model to compare recycling, reuse, replacement, and disposal pathways with publication-ready sustainability outputs.
• Tools Covered: Python, Brightway2/Brightway25, openLCA, ActivityBrowser, CodeCarbon, Eco2AI, and Google Colab.

• Academicians and Faculty
• Ph.D. Scholars and Researchers
• R&D Engineers
• IoT and Embedded Systems Professionals
• LCA and Sustainability Professionals
• Product Developers and Design Engineers
• ESG and Environmental Consultants
• Industry Professionals working in smart manufacturing, electronics, automation, and connected products

• Build an IIoT BOM carbon calculator.
• Estimate operational and digital infrastructure emissions.
• Compare lifecycle and end-of-life scenarios.
• Identify environmental hotspots.
• Apply dynamic data to LCA models.
• Generate research- and industry-ready LCA outputs.