This paper examines the macroeconomic and distributional effects of the European Union’s transition to Net Zero emissions through a gradually increasing carbon tax. I develop a New Keynesian Environmental DSGE model with two household types and distinct energy and non-energy sectors. Five alternative uses of carbon tax revenues are considered: equal transfers to households, targeted transfers to Hand-to-Mouth households, subsidies to green energy firms, and reductions in labor and capital income taxes. In the absence of technological progress, the carbon tax policy induces a persistent increase in energy prices and a reduction in GDP, investment, and consumption. Headline inflation falls below zero in the medium run, reflecting weaker aggregate demand. Distributional outcomes vary significantly depending on the implemented revenue recycling scheme: targeted transfers are the most progressive but entail larger macroeconomic costs, while subsidies and tax cuts mitigate output and investment losses but are less effective in narrowing the consumption gap. A limited foresight scenario, in which agents learn about policy targets sequentially, generates more volatile adjustment paths and temporary inflationary spikes around announcements, but long-run outcomes remain close to the baseline.

Climate policy design must balance emissions mitigation with concerns for fairness, particularly as climate change disproportionately affects the poorest households within and across countries. Integrated Assessment Models used for global climate policy evaluation have so far typically not considered inequality effects within countries. To fill this gap, we develop a global Integrated Assessment Model representing national economies and subnational income, mitigation cost, and climate damage distribution and assess a range of climate policy schemes with varying levels of effort sharing across countries and households. The schemes are consistent with limiting temperature increases to 2 °C and account for the possibility to use carbon tax revenues to address distributional effects within and between countries. We find that carbon taxation with redistribution improves global welfare and reduces inequality, with the most substantial gains achieved under uniform taxation paired with global per capita transfers. A Loss and Damage mechanism offers significant welfare improvements in vulnerable countries while requiring only a modest share of global carbon revenues in the medium term. The poorest households within all countries may benefit from the transfer scheme, in particular when some redistribution is made at the country level. Our findings underscore the potential for climate policy to advance both environmental and social goals, provided revenue recycling mechanisms are effectively implemented. In particular, they demonstrate the feasibility of a welfare improving global climate policy involving limited international redistribution.

Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.

This paper examines how firms’ exposure to supply chain disruptions (SCD) affects firm outcomes in the European Union (EU). Exploiting heterogeneous responses to workplace closures imposed by sourcing countries during the pandemic as a shock to SCD, we provide empirical evidence that firms in industries relying more heavily on foreign inputs experience a significant decline in sales compared to other firms. We document that external finance, particularly bank financing, plays a critical role in mitigating the effects of SCD. Furthermore, we highlight the unique importance of bank loans for small and solvent firms. Our findings also indicate that highly diversified firms and those sourcing inputs from less distant partners are less vulnerable to SCD.

Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers’ automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.

In a multi-sector and multi-region framework, this paper employs a dynamic general equilibrium model to analyze climate-resilient economic development (DGE-CRED) in Vietnam. We calibrate sector and region-specific damage functions and quantify climate variable impacts on productivity and capital formation for various shared socioeconomic pathways (SSPs 119, 245, and 585). Our results based on simulations and cost-benefit analyses reveal a projected 5 percent reduction in annual GDP by 2050 in the SSP 245 scenario. Adaptation measures for the dyke system are crucial to mitigate the consumption gap, but they alone cannot sufficiently address it. Climate-induced damages to agriculture and labor productivity are the primary drivers of consumption reductions, underscoring the need for focused adaptation measures in the agricultural sector and strategies to reduce labor intensity as vital policy considerations for Vietnam’s response to climate change.

This study employs bilateral data on external assets to examine the impact of climate policies on the reallocation of international capital. We find that the stringency of climate policy in the destination country is significantly and positively associated with an increase in the allocation of portfolio equity and banking investment to that country. However, it does not show significant effects on the allocation of foreign direct investment and portfolio debt. Our findings are not driven by valuation effects, and we present evidence that suggests diversification, suasion, and uncertainty mitigation as possible underlying mechanisms.

This paper investigates how state ownership affects financial reporting practices in China. Using several measures of state (government) ownership, we show that a one-standard-deviation increase in state ownership decreases financial statement comparability by 36.61%, and the impact is more pronounced when the central authority has majority control of the company. Moreover, lower earnings quality and lower levels of accounting conservatism among state-owned enterprises (SOEs) may explain the lower accounting comparability between SOEs and non-SOEs (NSOEs). Additionally, similar (different) managerial objectives converge (diverge) financial statement comparability between SOEs and NSOEs. Last, the geographical locations of firms also contribute to financial statement comparability. We employ a difference-in-differences design, changes regression and entropy balancing to mitigate potential endogeneity bias.