Data-driven Sustainability

EnviroByte applies data-driven methods with environmental science, software engineering, and machine learning to increase efficiency and effectiveness of sustainability reporting, planning, monitoring and management, including Greenhouse Gas emissions, Criteria Air Contaminants, etc.

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Automation Software EngineeringData Analytics(Quantitative Methods)MachineLearningDomain KnowledgeEnvironmental Science(Emission Reporting & Regulation)Existing GHGReporting SoftwareExcel-BasedCalculator
A Scalable and Customizable Emissions Reporting Tool

EmissionX™

We build the products that we want to use ourselves. This is why we design EmissionX with easy verification and assurance in mind by integrating data science tools (e.g. Jupyter Lab) with CI/CD processes, which offers the most flexibility, transparency, and customization.

  • Alberta TIER, BC GHG, SK OBPS, ECCC OBPS
  • GHG Protocol
  • US EPA GHGRP
  • ECCC NPRI, ECCC GHGRP
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500+

GHG Project

10+

Life Cycle Assessment

20+

Climate Change Adaptation

120+

Big Data

Advanced Data Analytics

In this project, we extracted actual operating data from a public database- Petrinex (Canada’s Petroleum Information Network), which contains more than 35 million records for 18 in situ oil sands extraction schemes. We analyze fuel gas use, steam injection, solvent co-injection, oil production, flare, and vent volumes.

  • Data mining for in situ oil extraction
  • Fuel consumption benchmarking
  • Carbon/emission intensity benchmarking