Catarina Midões

Many countries combine carbon pricing with renewable-energy policies. Microeconomic theory predicts this combination yields no additional emissions reductions under cap-and-trade and only potentially under carbon taxation, but at a social welfare loss. We undertook a systematic literature review of emissions-reduction synergy, identifying many ex-ante but only one ex-post study – explained by the lack of policy counterfactuals. In 72% of ex-ante 127 estimates from 80 studies, combining instruments reduces emissions more than either instrument alone. Additional reductions are more common under carbon taxation, as theory predicts, but also occur under cap-and-trade. Across 85 exact synergy estimates, the policy combination yields about 22% greater emissions reductions on average. Regarding welfare effects, 74 of 119 ex-ante estimates from 79 papers indicate some benefit from adding a second instrument. Deviations from theory reflect model assumptions, such as incomplete emissions coverage, implementation constraints, social welfare metrics limited to consumer utility, and market or government failures including fossil fuel subsidies. While ex-post estimates are less likely to detect non-zero synergy, the single ex-post study broadly supports our findings.

Climate shelters contribute to heat resilience directly, as cool spaces, and indirectly, as community venues for information provision. In a 2025 pilot project in Venice, Italy, we analyse, through surveys and interviews, four possible bottlenecks to shelter use: heat risk perception, awareness of shelters, accessibility, and motivation. We test, through a treatment-control experiment, the impact of information provision in shelters. We find that awareness of shelters is the easiest bottleneck to address. Accessibility hinges on funding and governance constraints. Motivation is context and place driven: green, outdoor, spaces are the most popular, but less well prepared. Risk perception is a crucial policy target, since it drives use and adoption of protective behaviours. Yet, it is sticky: individuals, particularly the elderly, underestimate risk. Information provision did not change risk perception. It, however, increased awareness of shelters and adaptation knowledge. Peer effects and seamless integration of events within community centers promote effectiveness.

Climate change interacts with other environmental stressors and vulnerability factors. Some places and, owing to socioeconomic conditions, some people, are far more at risk. The data behind current assessments of the environment-wellbeing nexus is coarse and regionally aggregated, when considering multiple regions/groups; or, when granular, comes from ad hoc samples with few variables. To assess the impacts of climate change, we require data that are granular and comprehensive, both in the variables and population studied. We build a publicly accessible data set, the SHARE-ENV data set, which fulfills these criteria. We expand on EU representative, individual-level, longitudinal data (the SHARE survey), with environmental exposure information about temperature, radiation, precipitation, pollution, and flood events. We illustrate through four simplified multilevel linear regressions, cross-sectional and longitudinal, how full-fledged studies can use SHARE-ENV to contribute to the literature. Such studies would help assess climate impacts and estimate the effectiveness and fairness of several climate adaptation policies. Other surveys can be expanded with environmental information to unlock different research avenues.

Inference on inequality indices remains challenging, even in large samples. Heavy right tails in income and wealth distributions hinder the quality and threaten the validity of asymptotic approximations to finite sample distributions. Attempts to improve on asymptotic approximations by bootstrap techniques or permutation tests are only partial successes. We evaluate a different approach to robust inference, relying on Student t statistics obtained from split samples. This relatively simple ‘t-based’ approach requires no consistent variance estimators, no random sampling of populations, and only mild distributional assumptions. We compare its performance with that of refined bootstrap and permutation techniques. We find that the more complex bootstrap methods still have the edge in one-sample tests, where the t-approach suffers from a negative skew. In two-sample comparisons though, the t-approach offers advantages: it is undersized while bootstrap tests and permutation tests are often oversized. In certain circumstances it is less powerful than permutation tests and bootstrap tests, but for large samples, this difference dissipates. It is also more generally applicable than permutation tests and easily generates confidence intervals. These differences are illustrated with an empirical application using two different sources of household data from the Russian Federation.

The COVID-19 crisis has led to substantial reductions in earnings. We propose a new measure of financial vulnerability, computable through survey data, to determine whether households can withstand a certain income shock for a defined period of time. Using data from the ECB Household Finance and Consumption Survey (HFCS) we analyse financial vulnerability in seven EU countries. We find that, out of the 243 million individuals considered, 47 million are vulnerable to a three month long income shock (the average length of the first wave COVID-19 lockdown), i.e., they cannot afford food and housing expenses for three months without privately earned income. Differences across countries are stark. Individuals born outside the EU are especially likely to be vulnerable. Being younger, a single parent, and a woman are also statistically significant risk factors. Through a tax-benefit microsimulation exercise, we look into the COVID-19 employment protection benefits, the largest income support measure in the countries considered. Considering as our sample individuals in households where someone receives a salary, we derive household net income when employees are laid-off and awarded the COVID-19 employment protection benefits enacted. Our findings suggest that the employment protection schemes are extremely effective in reducing the number of vulnerable individuals. The relative importance of rent and mortgage suspensions, (likewise, widespread COVID-19 policies), in alleviating vulnerability, is highly country dependent.