Conferences

Addressing the major challenges of the 21st century, such as climate change, will require complex and ambitious policies that promote social justice. To do so, it is necessary to design efficient policies that do not exacerbate existing inequalities, such as gender or income inequality. In this sense, it is essential to carry out impact analyses of policies from a holistic perspective that evaluates the economy, energy, land, and water systems in an integrated manner before implementing them. While Integrated Assessment Models (IAMs) have been a fundamental tool in the past, micro-simulation models for distributional analysis have the advantage of providing more heterogeneous results that help to more robustly identify the socio-economic impacts of the policies to be implemented. These analyses make it possible to identify the people who will be most affected by policies and to implement compensatory measures to make the policy fairer. Thus, the combination of both models (IAMs and microsimulation models) can provide valuable results for decision making.  MEDUSA is an R package that allows the development of distributional analyses in isolation or in connection with other models such as GCAM. Its extensive database allows for highly disaggregated results, taking into account numerous socio-economic and demographic characteristics of households, such as income level, place of residence, type of family or the degree of feminisation of the household. At the moment, the prototype works for Spain, but the idea is to extend it to all EU countries in the short term. However, the package could be extended to all countries that are able to provide the raw data of the model.

Climate change is often seen as an equity problem, as it is caused primarily by richer countries and households, while its impacts are generally expected to affect poorer countries and households significantly stronger. Climate policy aiming at mitigating these impacts, however, can also have a regressive impact on societies, unless it is designed such that the costs of mitigation are shared progressively depending on wealth differences. At the same time, historical energy transitions have often been driven by wealthy consumers demanding higher quality goods and services, which consequently grew from niche to mainstream technologies. Particularly the transportation sector is a sector difficult to decarbonise, while there are significant differences in contribution between poorer and wealthier users. This study uses a global integrated assessment model (GCAM) with 10 different income groups for each of the 32 regions to compare several decarbonisation scenarios for passenger transportation. On the one hand, implementing a general cap-and-trade policy for transport emissions, while traditionally seen as the economically optimal policy, affects poorer individuals significantly more in terms of access to transport services in a decarbonised world. On the other hand, implementing fixed caps for each country and income group, which cannot be traded with consumers at lower other income groups or countries, and are globally equal for each individual, leads to significantly higher costs for higher income individuals, but does not affect the access to transport services of poorer individuals as strongly. Also, this last alternative leads to a significantly faster take-up of modern clean technologies in transport.

Decarbonisation of the energy sector is a critical task in the efforts to mitigate climate change. As sectoral emissions cuts in modelled pathways aligned with the Paris Agreement are projected to come from at-scale diffusion of emerging or new technologies as well as further development of existing solutions, energy-sector decarbonisation entails major investments in low-carbon technologies. At the same time, a significant chunk of these investments must be made in emerging and developing economies, which currently receive just one-fifth of global energy investments. This underinvestment is, at least partly, due to the large disparities in financing conditions and higher-risk profiles in said countries. Models used to assess decarbonisation pathways typically assume a uniform cost of capital; such assumption, however, does not do justice to real-world conditions and may therefore lead to inaccurate policy recommendations. Moreover, there is considerable uncertainty over how these costs may evolve in the future. In this study, we apply an empirical dataset of estimated cost of capital differentiated by technology and country and explore stakeholder-driven pathways of (de-)risking investments in clean energy vs. fossil-fuel technologies, using an ensemble of two global integrated assessment models and one electricity-system model. Furthermore, we attempt to incorporate a corrective justice dimension in our narratives by assessing the impacts of risk underwriting for low-carbon investments through taxing corporate windfall profits for 2022 and distributing the revenue as subsidies towards high-risk regions.

The success of the targets established in the European Green Deal depends on the correct design of ambitious policies that utilize all available instruments, including energy and environmental taxation. In the “Fit for 55” package, the EC proposed a deep reform of the Energy Taxation Directive (New ETD) to update the current taxation and align it with current environmental goals. However, due to the war in Ukraine, the energy crisis, and the risk of regressive effects the current proposal of the EC is stalled. Therefore, this analysis seeks to provide new evidence from a microsimulation model developed to assess the direct, overnight distributional impacts of the proposed new ETD reform on households. Our aim is to explore whether the proposed EU-level polluter pays instruments can be designed to achieve progressive distributional impacts, to identify policy options that ensure they strengthen social justice without undermining it, and thereby remove social barriers. Moreover, we explore a dimension often underrepresented in distributional analyses, namely gender. Our results indicate that, with the correct design from the outset, environmental tax reforms can be progressive and not increase current inequalities between and within Member States of the EU, including those related to gender.

We attribute variations in key energy sector indicators across global climate mitigation scenarios to climate ambition, assumptions in background socioeconomic scenarios, differences between models and an unattributed portion that depends on the interaction between these. The scenarios assessed have been generated by Integrated Assessment Models (IAMs) as part of a model intercomparison project exploring the Shared Socio-economic Pathways (SSPs) used by the climate science community. Climate ambition plays the most significant role in explaining many energy-related indicators, particularly those relevant to overall energy supply, the use of fossil fuels, final energy carriers and emissions. The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley-Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.