Sustainability Engineer | CDP | GHG Accounting and Inventory Development | Life Cycle Analysis | GSI Environmental
Sustainability Engineer at GSI Environmental, supporting clients with climate and sustainability initiatives that translate technical requirements into practical, implementable solutions. Her work focuses on data-driven sustainability systems, regulatory readiness, and operationalizing emerging ESG expectations.
It’s hard to concisely summarize 2025 as a Sustainability Engineer. Every few weeks seemed to bring another regulatory update – an amendment passed, a draft was out, a comment period had opened. But despite the shifting regulatory priorities, the workload that hit my desk remained the same. Companies continued to calculate GHG emissions, products were still selected for life-cycle analyses, and inventories still underwent verification. What changed was how the work was done.
2025 marked a change in accessibility and capability for ESG and reporting software. Improved data integration and automation made these tools more viable for companies navigating evolving regulations, and in turn reshaped day-to-day workflows. For the first time, if felt as though software were beginning to encroach on the tasks that made up a majority of my workload.
The increase in AI-embedded modules reduced processing time, knocking off hours from tasks like data transformations and emission factor assignment. Having spent much of my early career working through GHG calculations by hand, I was skeptical of having parts of my workload automated – it felt like losing part of my identity.
Learning to work alongside these tools, instead of pitting myself against them, was key. Relinquishing the hours spent processing data manually and meant I could spend more time approaching data analytically, reevaluating data management, investigating emissions hot spots, and developing insights and decarbonization solutions.
Using software as a calculation engine certainly enabled me to work on projects orders of magnitude larger and more complicated. However, with the scaling of data comes the scaling of critical review needed to ensure data integrity. Larger datasets introduce higher likelihood of edge case scenario appearance. Manually processing data built foundational knowledge that prepared me to intuitively identify emissions anomalies and erroneous energy consumption – QA/QC problems that software hadn’t caught up to yet.
As software continues to build features and develop new capabilities, that balance between scale and scrutiny will become increasingly central to my role. I can’t reasonably try to predict what 2026 will bring, but I look forward to looking at GHG data from a broader lens.
Return to 2025: Working Through the Mess and the Shift that Defined the Year
