• Target Input:

• Allow users to set clear, quantifiable decarbonization goals (e.g., “80% emissions reduction by 2040”).

• These targets then serve as anchor points that drive all subsequent scenario modeling and recommendations.

• Goal Anchoring:

• The tool should use these inputs to work backward, identifying the necessary interventions and timelines required to reach the specified target.

• This “backward planning” ensures that the recommended solutions are directly tied to the organization’s strategic objectives.

• Customizable Sliders or Input Fields:

• Integrate adjustable controls that let users set weightings between competing priorities—such as cost efficiency versus speed of decarbonization.

• For instance, a slider could allow decision-makers to shift the focus toward the quickest route to decarbonization or the most cost-efficient pathway, depending on their current strategic focus.

• Scenario Differentiation:

• Generate multiple output scenarios that reflect different strategic emphases: one scenario could be optimized for rapid decarbonization, while another minimizes costs over the long term.

• Offer a balanced scenario as a default that takes a middle-ground approach.

• Multi-Factor Analysis:

• Incorporate key factors such as CAPEX, OPEX, technology maturity, and implementation complexity into the model.

• This allows the tool to evaluate how changes in each variable impact both the cost and the timeline to achieve the decarbonization target.

• Sensitivity Analysis:

• Allow users to see how sensitive the recommendations are to changes in assumptions. For example, if technology costs drop faster than expected, the model could show how that accelerates the pathway to the decarbonization target.

• Time-Phased Projections:

• Develop dynamic projections that illustrate how metrics (like emissions intensity or cost curves) evolve over time under each strategic scenario.