Conference Agenda

This paper decomposes and compares the distributional impact of uniform national carbon taxes across six EU countries. We quantify the contribution of the key determinants of the carbon tax burden to its impact on inequality and regressivity indicators. We identify large cross-country differences in carbon tax burdens, their composition, and the drivers of the within-country distributional impact. A carbon tax is regressive in all countries, but carbon tax burdens and their impact on income inequality are larger in poorer countries of our sample. Cross-country differences in the primary driver of carbon tax regressivity suggest that the most effective policy lever to mitigate carbon tax regressivity differs across countries. Differences in the composition of the consumption basket play an important role in most countries, but not all. Differences in savings rates play the most important role in the wealthier countries of our sample. The carbon intensity of consumption plays a larger role in the poorer countries of our sample. Overall, this article suggests that differences in the structure of carbon tax incidence and the drivers of its distributional impact across countries pose a challenge to cross-country policy learning, and highlights the need for in-depth country-level and comparative analysis.

Sustainable forest management is a complex dynamic problem. We develop a nested dynamic bioeconomic model of forests that generate interdependent products that differ in their growth cycles, rates of growth, and potential harvest frequency. We apply our model to bamboo forest management, which involves making decisions about the timing and quantity of bamboo stem harvests and bamboo shoot harvests. Both bamboo shoots and bamboo stems are valuable products. The harvesting of bamboo stems entails cutting down the bamboo plant, while the harvesting of bamboo shoots -- which grow annually from the bamboo plant -- does not. To solve for the optimal bamboo forest management strategy, our novel nested dynamic bioeconomic model nests an inner finite-horizon within-year daily dynamic programming problem that captures daily bamboo shoot growth within a season, inside an outer finite-horizon between-year annual dynamic programming problem that captures annual bamboo stem growth from year to year. We compare the optimal bamboo stem and shoots harvesting strategy with the actual harvesting decisions made by bamboo farmers in Zhejiang province, China. Our research has important implications for the sustainable management of forests worldwide, particularly when the forests produce products that can be harvested at more frequent intervals than the trees themselves.

This paper examines the optimal timing and use of carbon capture technology for mitigating climate change in the presence of learning-by-doing dynamics and increasing resource extraction costs. The first-best optimum solution and optimal policy instruments are derived. Numerical results show that a large initial decrease in fossil energy production is optimal. Carbon capture technologies are part of the long-term solution only when fossil fuel resource costs are low or non-increasing. If the resource cost increases rapidly, the technology never deploys even when learning-by-doing occurs.

A key identification assumption required for causal claims of instrumental variables (IVs) is the exclusion restriction. This paper assesses the validity of this assumption for topographic variation as a widely used IV in empirical economics. A systematic literature review of leading economics journals identifies 56 different variables that are, according to the reviewed literature, causally influenced by topographic variation. We interpret this as strong indication for a problematic prevalence of confounding variables in most topography-based IV studies that cannot be controlled for without consequential violations of the exclusion restriction. Roads and extension of the electricity grid are an example of the intertwined causal relationship. We assess this case in more depth for Dinkelman (2011), a seminal study that popularized the use of topographic-variation IVs. We apply robustness tests and bias analyses and discuss two common issues in the literature, the conditioning on exclusion-restriction variables and IV weakness in the presence of potential exclusion-restriction violations. Our results strongly hint at violations of the exclusion restriction for the specific case of Dinkelman (2011), and at the difficulties of using topographic variation as a natural experiment in general.

Passenger road transport is a major contributor to greenhouse gas emissions. To achieve decarbonisation targets, the car fleet must be electrified. However, electrification alone will not solve all the negative externalities associated with cars. Reducing the size of the car fleet could also be advantageous. Carsharing might offer a way to reconcile current car usage habits with a reduction in the size of the car fleet, while helping to electrify the fleet more quickly. However, it could also reduce the flexibility potential of BEVs in the electricity system. In this paper, we use an open-source electricity sector model (DIETER) and an open-source vehicle time series tool (emobpy) to study the impact of electric carsharing on the electricity sector in Germany in 2030, assuming a future electricity system highly dominated by renewable energy sources (RES). To overcome the computational challenges associated with including detailed BEV time series in a power system model, we apply sequence clustering techniques to derive representative car usage statistics and identify the uses most likely to switch to carsharing in the medium term. Our results show that switching to electric carsharing moderately increases costs in a flexibility constrained electricity sector. This cost effect is mitigated when considering settings that take into account other sources of flexibility and depends mostly on the type of charging strategy considered. We also show that the BEV fleet can still be operated in line with RES availability and hence contribute to their integration in the power system. We conclude that electric carsharing does not cause much damage to the electricity sector, but could potentially brings many benefits in terms of street space, noise and congestion.

This paper addresses econometric challenges arising in panel data analyses related to IPAT (environmental Impact of Population, Affluence and Technology) models and other applications typically characterized by a large-$N$ and large-$T$ structure. This poses specific econometric complexities due to nonstationarity and cross-sectional error correlation, potentially affecting consistent estimation and valid inference. We provide a concise overview of these complications and how to deal with these with appropriate tests and models. Moreover, we apply these insights to empirical examples based on the IPAT identity, offering insights into the robustness of previous findings. Our results suggest that using standard panel techniques can lead to biased estimates, incorrect inference, and invalid model adequacy tests. This can potentially lead to flawed policy conclusions. We provide practical guidance to practitioners for navigating these econometric issues.

This paper studies the pricing incentives of a monopolist constrained by a revenue cap endogenously determined by her costs in a so-called base year. Such regulation is employed, among others, to govern electricity distribution operators in Germany. We show that the revenue cap may incentivize excessive supply in the base year to reap profits in the non-base years. A connected set of price caps exists so that a hybrid regulation consisting of any element in this set and the cost-based revenue cap unambiguously improves welfare and, under some conditions, even leads to the socially optimal outcome.