The following section describes Climate Action Accelerator’s solution-based approach to financial impact assessment. We use here for the purpose of the demonstration a hypothetical example of energy consumption and emission reduction targets for 2026 and 2030.

Gather information needed for financial modelling

• Collect existing activity and financial information. For example, for energy solutions: yearly consumption from the grid and from generators in kWh, yearly costs of energy from the grid and from generators in currency (EUR for example).
• Identify proxies when activity data is not available, using Climate Action Accelerator’s expertise and benchmarks. For example, there is rarely an accurate measure of energy production from generators. Using an estimation of the number of kWh produced by litre of diesel purchased provides an estimation of the energy consumption from generators.
• Establish essential baseline costing indicators from the baseline data. Examples include the cost of energy from the grid and generators, expressed in EUR/kWh.

Calculate yearly costs projections, regardless of the implementation of climate solutions

• Apply activity (excluding inflation) growth to baseline activity indicators such as energy consumption in kWh.
• Estimate yearly cost for activity indicators by adding yearly inflation, using specific assumptions when required (using the World Bank projections for energy costs for example).
• Calculate the projected yearly costs by multiplying activity indicators and cost indicators.

Estimate savings generated by the implementation of climate solutions, if any

• Calculate the yearly impact of climate solutions identified in the roadmap on activity indicators, using reduction assumptions from the trajectory. For instance, if considering a decrease in energy consumption by 10% by 2026 (phase 1), and 20% by 2030 (phase 2), the model uses “implementation rates”¹²⁹ for both phases as summarised below to get the yearly decrease:

• These targets determine the yearly decrease of key activity indicators, such as kWh consumed for examples. This decrease is translated into savings, using the estimated cost per kWh including inflation. The table below shows an example of yearly decrease in kWh for a flat consumption over the seven years of the roadmap.

Estimate the running costs and investments linked to the implementation of climate solutions

• Similarly to savings, running costs and investments require a combination of activity indicators, unit costs including inflation, and impact of solutions to determine their financial impact. Examples of unit costs include: the cost of a solar equipment maintenance training, or the cost of minor waste management equipment.
• The table below summarises the hypothetical running costs associated with the installation of solar panels for an organisation requiring the training of 25 new employees every year; this considers the pace of implementation of solar energy and a turnover rate of employees.

• The table below summarises the investments associated with the installation of solar panels and batteries, as part of the renewable energy solution.

Additional elements of the model

• Uncertainty: depending on the accuracy of data sources, an uncertainty factor of 5% to 20% is applied to each solution. Savings are diminished by this uncertainty factor, while running costs and investments are increased. This approach provides a level of conservatism in the model.
• Savings are used to support the roadmap, which means they are:
  • kept within the organisation rather than by the donors. This means that reduced costs for travel will not result in lower funding levels or funders taking back these operational savings.
  • re-allocated to climate and environmental solutions of the roadmaps. This means these savings are assumed to be reallocated internally to support investments and increased costs inherent to the roadmap, even if supported by a different internal cost centre.

Considerations for successful implementation

• The financial model is underpinned by multiple operational assumptions, linked to the overall GHG reduction objectives: energy consumption, air travel, fleet consumption, etc. Organisations must ensure they have the right data collection systems in place to track the progress of their roadmap.
• Financial assumptions underpinning the model (cost of kWh, savings from reduced travel) are also essential as they underpin the financial viability of the model. These assumptions must be checked, any variation understood, and the model must be updated as the implementation progresses and provides more accurate information.