Cost of capital evolution effects on regional mitigation pathways

Background

Participatory approaches have been argued to bring a more diverse range of views into the ΙΑΜ process, by building a better understanding of the social context and supporting more inclusive decision-making. In IAM COMPACT, we have designed a Policy Response Mechanism (PRM), a co-creation process that facilitates collaboration among modelling teams and with stakeholders. The PRM dynamically responds to changing policy priorities, aiming for policy relevance, knowledge exchange, and enhanced trust, by providing different layers of inputs and making modelling socially/politically realistic. Stakeholder engagement in IAM COMPACT is organised into themes (for stakeholders within the EU) and regions (for stakeholders outside the EU). The themes and regions were collaboratively determined within the project, with Bruegel coordinating the process. For the themes, the aim was to have a broad enough coverage to capture a range of issues, but also sufficiently selective to lead a clear research agenda later in the project. 

Overall, 23 questions emerged from high-level exchanges with policy stakeholders (forming the Policy Steering Groups), which we grouped into proposals for seven modelling studies. These proposals were in turn grouped into four overreaching themes and discussed with a wider policy audience (forming the Core Working Groups) selected on geographical and sectoral criteria. The consultative process helped us evaluate the relevance of policy issues, refine the scope of research questions, and establish a balanced group of stakeholders to participate. We conducted one workshop under each theme alongside the corresponding core working group to consult stakeholders on our scenario design and co-define desired outputs as well as a key set of joint input assumptions.

This study is part of the theme Global Effects, which considers trade and macroeconomic issues and their effects on European decarbonisation. During the workshop conducted under this theme, we sought feedback on the scenario design, inputs, and projections of two proposed modelling studies. Following an in-depth discussion on uncertainty factors and how these may play out, the 20 participating stakeholders—among others from DG Trade, DG ECFIN, the European Central Bank, and the National Bank of Belgium—were asked to also fill out a survey on how perceived costs may evolve for different technologies and countries around the world. The study presented here looked at how regionally and technologically differentiated cost of capital projections could affect decarbonisation pathways in Europe and around the world.

Cost of capital evolution 

We drew experts’ insights on how much investment risks may change for a set of technologies by 2050 in high-, middle-, and low-income countries, considering finance and policy derisking or divestment mechanisms that may play out, to determine the final risk percentage change. Figure 1 shows a representation of baseline and future aggregated WACC values by region and technology for each scenario.

Figure 1: Baseline and projections of WACC values per scenario and windfall profits. WACC values aggregated by aggregated region and technology (fossil fuels, renewables, nuclear, CCS, and green hydrogen), (a) in the baseline year; (b) in 2050 based on the survey results (Sv, Sv-F, Sv-NF scenarios); (c) and in 2050 for the Frag scenario. Scenarios are explained in Table A1.

Scenario design

We designed four alternative futures, in terms of cost-of-capital projections. The first explicitly reflects the results of the survey (scenario Sv in the figures below). Seeing how our experts’ responses showed two tendences in terms of cost-of-capital projections across technologies, namely increased risk of fossil-fuel investments and derisking of renewable energy technologies, we disaggregated these trends and formulated two more futures. The following table describes the cost of capital trajectories and assumptions for each modelled scenario. 

# 

Name 

Scenario assumptions 

Current polices till 2030 

Climate Target 

NDC_baselineWACC 

We use baseline empirical WACC values, keeping these values fixed for the entire time horizon. The scenario is based on stated 2030 emission targets pledged in NDCs submitted or announced by June 2023, capturing all mitigation ambition updates during and after the COP26 in Glasgow, implemented on top of current policies. In model regions where current policies exceed the mitigation targets of NDCs, no additional emission constraints are applied. Emissions reductions in NDC scenarios are therefore never less ambitious than current policies imply. For regions that expressed an LTT, such as net-zero commitments or other targets for 2050 or later, emission constraints linearly decline from 2030 emissions from the NDC target towards the LTT. For regions without LTTs, post-2030 the applied policy targets are maintained as minimum levels beyond 2030 to avoid backtracking of achieved policies. This scenario provides the carbon price for the rest of scenarios.  

Current Policies 

NDC (on top of current policies), LTT 

2a 

IAMCOM_RES_Low_Risk 

We use baseline empirical WACCs (2018), while future WACCs decrease for RES & green H2 (see ANNEX F) according to IAM COMPACT survey results. Values between 2018 & 2050 change linearly. Carbon prices are taken from Scenario 1.  

Current Policies 

NDC (on top of current policies), LTT 

2b 

IAMCOM_FF_High_Risk 

We use baseline empirical WACCs (2018), while future WACCs increase for fossil fuels and nuclear (and decreasing for CCS, see ANNEX F) according to IAM COMPACT survey results. Values between 2018 & 2050 change linearly. Carbon prices are taken from Scenario 1. 

Current Policies 

NDC (on top of current policies), LTT 

IAMCOM_Survey 

We use baseline empirical WACC values, while future WACCs change for all technologies according to IAM COMPACT survey results. Values between 2018 & 2050 change linearly (see ANNEX F). Carbon prices are taken from Scenario 1. 

Current Policies 

NDC (on top of current policies), LTT 

WACC_Fragmented 

We use baseline empirical WACC values, while for future WACCs we assume that differences between high-income & low-income countries intensify towards 2050. For high-income countries (incl. China), WACC values converge to the 25th percentile of the average baseline empirical WACC per technology. For middle- and low-income countries, WACC values converge to the 75th percentile of the average baseline empirical WACC per technology. Values between 2018 & 2050 change linearly (see ANNEX G). Carbon prices are taken from Scenario 1. 

Current Policies 

NDC (on top of current policies), LTT 

Windfall_allocation 

We use baseline empirical WACC values, keeping these values fixed for the entire time horizon. We calculated the share of each country’s GDP per capita to the total available subsidy (997.46 billion USD); countries with higher share would get a larger proportion of the subsidy. To account for countries with high GDP but low GDP per capita (e.g., India), we also compute the share of each country’s GDP (PPP) to total GDP. Countries with higher share will also get a higher share of the subsidy. We then apply a penalty function to WACC values, excluding countries with average WACC for RES & green H2 below 6% (as they already have a relatively low WACC and therefore will not get a share of the subsidy). Countries with higher WACC get a higher share of subsidy. Finally, we combine these shares linearly (GDP per capita, GDP_PPP, WACC) and normalise them to find the final subsidy allocation by country (see ANNEX H). Carbon prices are taken from Scenario 1.  

Current Policies 

NDC (on top of current policies), LTT