Sustainable development has traditionally been a focus of macroeconomic modeling, providing valuable insights at a broad scale. However, the growing importance of sustainability at the microeconomic level—particularly within individual companies—necessitates aligning traditional business performance metrics with sustainability goals, such as reducing carbon footprints and other environmental impacts.

One key challenge in environmental sustainability modeling is the reliance on indicators that are not directly measurable, such as greenhouse gas (GHG) emissions. These indicators are often estimations rather than exact measurements because it is nearly impossible to measure emissions directly from most sources. Instead, GHG emissions are typically estimated using sophisticated methodologies and reliable data from scientific research.

Given the theoretical nature of emissions data, applying simulation modeling to this field is a natural choice. Simulation allows companies to create detailed models of their supply chains, enabling them to estimate environmental impacts more accurately. Moreover, simulation provides the ability to validate these estimations by comparing them with more traditional performance indicators. For example, if a simulation model can accurately replicate a supply chain’s operational or financial historical performance, it can be considered a reliable representation of material and resource flows.

By converting these flows using scientifically validated environmental data—such as GHG emission factors and global warming potential (GWP) factors—businesses can accurately assess the carbon footprint of their supply chains. This is achieved by simulating the behavior of the supply chain, considering factors like stock levels, production policies, transportation logistics, and material usage.