Greenhouse gases (GHGs) regulate atmospheric energy flow by absorbing infrared radiation, despite constituting less than 1% of the atmosphere. Their concentrations result from a dynamic balance between sources (emissions) and sinks (removal processes), both of which are influenced by human activities. Understanding greenhouse gas interactions with radiation has advanced significantly, improving the quantification of Earth’s energy imbalance. Radiative forcing serves as the basis for calculating Global Warming Potentials (GWPs), which compare the warming effects of different GHGs relative to CO
2 over specified time horizons [1]. However, conventional GWPs, as defined by the Intergovernmental Panel on Climate Change (IPCC), assume static values, typically expressed as GWP-20/100/500, representing a gas’s heat-trapping effectiveness over a 20, 100, or 500 year time period [2]. This approach, however, lacks accuracy in dynamic systems where (i) emission sources fluctuate hourly, such as changes in an energy grid’s fuel mix, and (ii) short-term carbon reduction targets must be met. Another essential aspect of dynamic emission accounting is the consideration of time-varying emission factors, which arise from fluctuations in energy fuel mixes, such as those seen in state-wide electricity grids. The composition of this fuel mix, consisting of various energy generation technologies, leads to emission factors that shift
hourly or even in mere minutes.
Hence, this study introduces an optimization framework that integrates time-varying emission factors within a scheduling time horizon while incorporating dynamic GWPs at a network scale through a life cycle assessment (LCA) methodology. By incorporating these time-dependent characterization factors and aligning them with operational timelines or carbon neutrality targets (e.g., net-zero by 2050), the framework enables higher-resolution emission quantification and illustrates the problematic effects of greenhouse gases on a realistic, dynamic timescale. However, emission quantification is only one aspect of the framework, as it also optimizes the total cost of operations as a key objective through comprehensive techno-economic analysis (TEA). The framework, illustrated by a detailed natural gas extraction and production case study, demonstrates that incorporating a time-dependent GWP metric (GWP-X) into the optimization framework rather than relying on the conventional GWP-100 approach, more effectively captures the evolving impacts of global warming over time.
