Intel Sustainability Impact Analysis: Optimizing Device Repurposing Strategy

📌 Project Overview

As part of Intel's commitment to reducing e-waste and CO2 emissions, this project analyzes the lifecycle data of repurposed hardware (laptops, desktops, tablets) from 2024. The goal was to determine which device categories and deployment regions yield the highest environmental benefit versus cost.

Using SQL, I evaluated logistical costs, refurbishment expenses, and energy grid intensity to build a data-driven deployment strategy for the "level up" sustainability program.

🔍 Key Findings & Recommendations

1. Geographic Strategy: Target High-Intensity Grids

• Insight: Deploying devices to Asia yields the highest CO2 return per kWh saved due to the region's higher average grid carbon intensity.
• Recommendation: Prioritize Asian markets for refurbishment deployment. However, logistical shipping costs must be weighed against these savings. If shipping exceeds the carbon benefit, North America remains the secondary optimal target.

2. Device Lifecycle Strategy

• Insight: Devices older than 6 years incur significantly higher refurbishment costs.
• Recommendation: These devices should only be refurbished if they are deployed to regions with the "dirtiest" energy grids (where energy efficiency gains have the most impact). Otherwise, they fail the Cost-Effectiveness Index and should be recycled for raw materials instead.

3. Standardization Efficiency

• Recommendation: The program should prioritize collecting and refurbishing device models that share common components. This economies-of-scale approach reduces repair time and creates a more consistent inventory for bulk deployment.

🛠️ Technical Skills Applied

• SQL (Structured Query Language):
  • Complex Filtering: Used WHERE and AND/OR logic to segment devices by age and type.
  • Aggregations: Utilized SUM() and AVG() to calculate total CO2 savings and average refurbishment costs per region.
  • Case Logic: Implemented CASE WHEN statements to categorize devices into efficiency tiers (High vs. Low Impact).
  • Joins: (If applicable) Combined datasets linking device inventory to regional energy grid data.

📊 Sample Analysis Logic

To determine the "Cost-Effectiveness Index," I analyzed the relationship between refurbishment cost and potential carbon offset:

Logic: (Est. CO2 Savings * Grid Intensity Factor) / Refurbishment Cost

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This project was completed as part of a professional data analytics certification.