For complex systems with a large number of inputs and expansive problem space, scenario analysis can be used to draw conclusions for certain sets of inputs with specific values (i.e., scenarios, Figure ). Each scenario should be based on a coherent and internally consistent set of assumptions about key driving forces (e.g., demographics and socio-economic development) and the relationships among these key driving forces. Scenario analysis is often placed in a future setting, and scenarios based on different sets of assumptions are often compared to inform decision-making. For example, to mitigate GHG emissions and curb global warming within 1.5°C above the pre-industrial level, different scenarios, each consisting of varying technologies, have been evaluated for decarbonization. Similar applications of scenario analysis can be found in other topics (e.g., water and wastewater treatment, renewable energy, waste management 232 ), or for a certain technique (e.g., using different scenarios of electricity mix in LCA to assess the environmental sustainability of electric vehicles ). In essence, scenario analysis divides the entire problem space into discrete, representative sub-spaces. It reduces the dimensionality of the system (fewer or less uncertain inputs) and simulation needs, while providing a detailed understanding of distinct alternatives that may be of particular interest for policymaking or implementation in specific contexts.