For many companies, carbon footprint numbers feel reliable—often presented in dashboards or reports with decimal-level accuracy. But behind that precision may lie a different truth: significant errors, sometimes exceeding 30%. Why? Because the quality of carbon data depends not on presentation but on how it’s gathered, calculated, and updated.
In many cases, footprint data is based on outdated emission factors or industry-wide averages that don't reflect your company’s real operations. For example, two suppliers making the same product can have completely different energy sources, production technologies, and waste rates. Yet if both are assigned the same emission value, the result can be misleading.

Many businesses struggle to collect primary data from suppliers, especially those deep in the value chain. When this happens, companies often fill the gaps using estimates or default values. While this approach is understandable, it creates a chain of assumptions that amplifies inaccuracy.
For example, if your logistics partner provides only generic fuel data instead of actual route-level details, the calculated footprint might miss key variations—like longer distances, empty returns, or inefficient transport modes. In complex supply chains, these small assumptions compound into large-scale discrepancies.

Another common issue is using static emission factors from public databases. These values, while accessible, often reflect average conditions from several years ago. In addition, they don’t account for recent changes in energy mix, material sourcing, or process improvements.
Dynamic data, on the other hand, adjusts to real-time inputs from your own operations and your supply partners. Without this adaptability, companies may overestimate—or underestimate—their actual environmental impact.

Accurate carbon data starts with better visibility and smarter tools. In addition to reducing manual work, digital carbon tracking platforms connect directly with operational systems and suppliers. They help capture up-to-date data, minimize reliance on assumptions, and ensure that each part of your supply chain reflects its real emissions.
Moreover, modern tools often provide built-in checks for anomalies, making it easier to detect outliers and correct errors before they scale. By improving accuracy, you don’t just meet reporting needs—you make better decisions for materials, design, and logistics.