A Novel Evaluation Approach for Emissions Mitigation Budgets and Planning towards 1.5 °C and Alternative Scenarios

Achieving ambitious climate targets, such as the 1.5 degrees C goal, demands significant financial commitment. While technical feasibility exists, the economic implications of delayed action and differing scenarios remain unclear. This study addresses this gap by analyzing the investment attractiveness and economic risks/benefits of different climate scenarios through a novel emissions cost budgeting model. A simplified model is developed using five global scenarios: announced policies (type 1 and 2), 2.0 degrees C, and 1.5 degrees C. A unit marginal abatement cost estimated the monetary value of avoided and unavoided emissions costs for each scenario. Net present value (NPV) and cost-benefit index (BI) were then calculated to compare the scenario attractiveness of the global emission budgets. The model was further applied to emissions budgets for China, the USA, India, and the European Union (EU). Increasing discount rates and gross domestic product (GDP) led to emission increases across all scenarios. The 1.5 degrees C scenario achieved the lowest emissions, while the baseline scenario showed the highest potential emissions growth (between 139.48% and 146.5%). Therefore, emphasis on the need for further financial commitment becomes important as the emissions' abatement cost used as best case was estimated at USD 2.4 trillion per unit of 1 Gtons CO2 equivalent (eq.). Policy delays significantly impacted NPV and BI values, showcasing the time value of investment decisions. The model's behavior aligns with real-world observations, including GDP growth influencing inflation and project costs. The simplified model could be coupled to existing integrated assessment frameworks or models (IAMs) as none offer cost-benefit analysis of climate scenarios to the best of our knowledge. Also, the model may be used to examine the economic attractiveness of carbon reduction programs in various nations, cities, and organizations. Thus, the model and analytical approach presented in this work indicate promising applications.