Shivika Mittal

Energy-sector decarbonisation requires large-scale investment in low-carbon technologies, yet only a limited share flows to low- and middle-income countries, partly due to higher financing costs and perceived risks. Most modelling exercises do not fully account for how the cost of capital may vary across regions and technologies, potentially influencing policy insights. We examine how plausible, expert-informed long-term trends in de-risking clean energy and increasing risks for fossil fuels could shape decarbonisation pathways, using an empirical dataset differentiated by country and technology. We also evaluate a “corrective justice” policy that taxes corporate windfall profits and redistributes revenues to support low-carbon investments in higher-risk regions. Results suggest that incorporating differentiated cost-of-capital trajectories may improve mitigation outcomes and help narrow the gap between current commitments and long-term climate targets, while indicating potential underestimation of risks associated with bioenergy-based negative emissions technologies in mitigation scenarios for high-income nations.

Artificial Intelligence (AI) may impact greenhouse gas emissions in both positive and negative ways, yet these impacts are not represented in recent assessments of climate change mitigation. Uncertainty surrounding future AI capability and adoption, its impacts on energy systems, and limited data availability complicates the inclusion of AI impacts. We conduct an expert-led process, ascertaining plausible impacts, and quantify ranges in their magnitude to 2040. We present a new scenario framework for AI and climate change that considers interactions between AI growth, climate policy, and AI policy. We translate elicited insights into illustrative scenarios and assess their implications using an integrated assessment model. Cumulative global CO2 emissions by 2040 range from +11% above to 1.4% below a baseline without AI impacts, depending on the scale of AI growth and policy intervention. Our results highlight emissions risks and opportunities from AI and the need for explicit representation in mitigation scenarios.

The European Union’s (EU) climate strategy, anchored in the European Green Deal, the Fit-for-55 package, and updated National Energy and Climate Plans (NECPs), requires a rapid transformation of the energy system to meet the legally binding target of net-zero greenhouse gas emissions by 2050 and a 55% reduction by 2030 relative to 1990 levels. Yet, how national plans align with EU-wide ambition, alongside the implications for investment, infrastructure, and power-system operation, remain insufficiently assessed. We address this gap by linking an EU-specific implementation of a prominent integrated assessment model with Member State-level disaggregation (GCAM-Europe) with a higher-resolution European electricity system model (EXPANSE). This modelling framework captures national heterogeneity, sectoral detail, and spatiotemporal variability in electricity demand, renewable supply, and storage, enabling the assessment of grid investments and infrastructure. We analyse four scenarios representing EU-wide (Fit-for-55) or national (NECP) targets, implemented through explicit policies (POLICY) or cost-optimal carbon caps (COST_OPTIMAL). Results show all scenarios achieve the −55% fossil CO2 reduction by 2030, with the electricity sector driving the largest chunk. Renewable energy nearly doubles, with POLICY scenarios accelerating electrification and heat pump deployment, while COST_OPTIMAL scenarios leaning more on biofuels. Efficiency targets are partially met, with POLICY scenarios distributing savings more evenly across Member States compared to concentrated reductions (COST_OPTIMAL). By 2035, power system transformation diverges strongly: COST_OPTIMAL scenarios expand about 1,250 GW of new capacity, concentrated in few resource-rich regions, while POLICY scenarios reach around 1,750GW with broader spatial distribution, requiring higher investments in renewables and grids. Average wholesale electricity prices are higher and more heterogeneous under POLICY scenarios, reflecting carbon costs, transmission bottlenecks, and reliance on fossil backup. Results highlight trade-offs between economic efficiency and equitable burden-sharing, underscoring the importance of coordinated EU governance, infrastructure planning, and complementary policies to balance cost-effectiveness with political feasibility and social acceptance.

The European Union’s (EU) climate strategy—anchored in the European Green Deal, the Fit-for-55 package, and updated National Energy and Climate Plans (NECPs)—requires a rapid transformation of the energy system to meet the legally binding target of net-zero greenhouse gas emissions by 2050 and a 55% reduction by 2030 relative to 1990 levels. Yet, how national plans align with EU-wide ambition, alongside the implications for investment, infrastructure, and power-system operation, remain insufficiently assessed. We address this gap by linking an EU-specific implementation of a prominent integrated assessment model with Member State-level disaggregation (GCAM-Europe) with a higher-resolution European electricity system model (EXPANSE). This modelling framework captures national heterogeneity, sectoral detail, and spatiotemporal variability in electricity demand, renewable supply, storage, and grid constraints. We analyse four scenarios representing EU-wide (Fit-for-55) or national (NECP) targets, implemented through either explicit policies (POLICY) or cost-optimal carbon caps (FREE). Results show that all scenarios achieve the − 55% fossil CO2 reduction by 2030, with the electricity sector driving the largest chunk. Renewable energy nearly doubles, with POLICY scenarios accelerating electrification and heat pump deployment, while FREE scenarios lean more on biofuels. Efficiency targets are only partially met, with POLICY scenarios distributing savings more evenly across Member States compared to concentrated reductions (FREE). By 2035, power system transformation diverges strongly: FREE scenarios expand about 1,160 GW of new capacity, concentrated in a few resource-rich regions, while POLICY scenarios reach around 1,680 GW with broader spatial distribution, requiring higher overall investment in renewables and grids. Average wholesale electricity prices are higher and more heterogeneous under POLICY scenarios, reflecting carbon costs, transmission bottlenecks, and reliance on fossil backup. These results highlight trade-offs between economic efficiency and equitable burden-sharing, underscoring the importance of coordinated EU governance, infrastructure planning, and complementary policies to balance cost-effectiveness with political feasibility and social acceptance.

Climate mitigation scenarios from Integrated Assessment Models (IAMs) typically assume smooth, predictable transitions, leaving them ill-equipped to assess the impacts of disruptive events. Here we introduce the Disruptive Events-Resilient Pathways (DERP) framework, a methodology that maps mitigation pathways along two dimensions: Mitigation Action Ambition and Resilience to Socioeconomic Impacts. The framework employs narrative-based societal archetypes to systematically explore how different resilience capacities shape mitigation strategies under disruption. Our application to stylised heatwave and technological disruptions reveals that socioeconomic resilience is a precondition for a capital-efficient transition, that a myopic focus on single technologies creates hidden vulnerabilities by delaying structural change, and that a diversified, resilient strategy can hedge against these risks at little to no additional cost. The DERP framework is therefore a valuable diagnostic tool, advancing scenario analysis to a new frontier by considering long-term transition dynamics alongside the resilience required to navigate disruptions.

Despite recent improvements to electricity access in lower-income countries, reliability remains low for many. Local renewable electricity infrastructure supplementing the national grid offers a promising route to improved reliability for rural communities. However, improvements in the reliability of national grids create risks for investors including the possibility of “stranded” renewable assets. We use energy-system modelling to explore ways in which solar photovoltaic (PV)-based mini-grids could be interconnected with national grids. We explore the impact of reduced electricity demand to quantify the investment risks of losing customers. Our results indicate that national grid–connected mini-grids can reduce the unit electricity costs for communities whilst also increasing reliability and reducing the carbon intensity of electricity in line with Sustainable Development Goal (SDG) 7. Reductions in demand have a minimal impact at lower levels but at moderate levels are likely to undermine economic viability. Finally, we discuss policy interventions to facilitate and protect investing in national grid-connected mini-grids.

Energy-sector decarbonisation, which is a prerequisite to tackling climate change, entails major low-carbon technology investments across the globe. However, currently only a fraction of those investments is directed towards emerging and developing economies, partly due to large disparities in financing conditions and perceived investment risks. Modelling exercises underpinning decarbonisation strategies typically do not consider how costs of capital may evolve across regions and technologies, which can have a strong impact on resulting policy insights. We explore the impacts of various plausible, expert-informed, long-term trends of (de-)risking investments in clean energy versus fossil-fuel technologies on decarbonisation pathways on top of an empirical dataset of costs of capital, differentiated by technology and country. We also assess the effects of a ‘corrective justice’ policy of risk underwriting for low-carbon investments, whereby corporate windfall profits are taxed, and the revenues redistributed as subsidies towards higher-risk regions. Results indicate considerable mitigation and investment potential in better informed planning with plausible long-term cost-of-capital projections, which could contribute towards bridging the emissions gap between efforts reflecting the ambition embedded in Nationally Determined Contributions and longer-term climate targets, as well as possible underestimation of the risk entailed in bioenergy-based negative emissions technologies in mitigation scenarios for developed nations.

Ensembles of climate change mitigation scenarios present users with a collection of strategies for limiting global warming. These strategies may differ in their associated feasibility challenges, mitigation co-impacts, and ultimately their relative societal desirability. Understanding these scenario characteristics is therefore crucial when scenarios are used to inform strategic decisions. One approach to enhance this understanding is to establish scenario archetypes and select contrasting illustrative scenarios from a larger ensemble. We present a new multidimensional framework for the systematic comparison of scenarios at the global or regional level. We illustrate the framework with comparisons in seven dimensions: economic feasibility, mineral resource availability, impacts on societal resilience, near-term scenario robustness, environmental sustainability, interregional fairness, and speed of societal transformation. Using cluster analysis, the framework can be used to select a group of illustrative scenarios with contrasting scores across the dimensions. Beyond the selection of scenarios, our exploration and evaluation framework also allows the identification of gaps in the scenario space that may be of interest but are not covered by the literature. We demonstrate these use cases by applying our framework to a set of mitigation scenarios that limit warming to 1.5 °C. Our results show our framework systematically selects contrasting scenarios, with our illustrative pathways having diverging energy mixes and uses of carbon dioxide removal. Further, we highlight considerable regional differences in the distribution of indicator and dimension scores as a key area for further investigation.