Conference Agenda

To prevent carbon leakage induced by the European Union (EU) Emission Trading System (ETS), the EU has designed a Carbon Border Adjustment Mechanism (CBAM) that taxes imports based on their carbon content at the ETS carbon price. Since estimating the carbon content of imports is very complex, CBAM will be applied only to a few emissions-intensive sectors. As a consequence, CBAM is unlikely to effectively eliminate leakage as it can easily be avoided by offshoring final production, creating distortions across sectors. We propose a simple alternative route towards leakage prevention with significantly lower information requirements and administrative burden which can be applied to all tradable sectors: the Leakage Border Adjustment Mechanism (LBAM). LBAM offsets ETS-induced cost disadvantages of domestic producers relative to foreign competitors and requires knowledge only about product-specific output-to-emissions elasticities and import demand and export supply elasticities but does not depend upon information on the carbon content of imports. We propose a structural trade model that allows us 1) to compute Leakage Border Adjustment Taxes (LBATs) — i.e., product-level tariffs on imports and subsidies on exports – that prevent leakage induced by ETS; 2) to compare LBAMs and CBAMs in terms of welfare and emissions.

The European Union Emissions Trading System (EU ETS) sets a common carbon price for covered emissions, yet EU member states continue to apply overlapping national policies. This paper studies how such regulatory differences shape region- and sector-specific implicit carbon prices and, through trade linkages within the Single Market, reallocates industrial production and CO2 emissions across the EU. Using a quantitative trade-and-environment model with key parameters estimated from German firm-level data, we recover implicit carbon price paths for 2005–2019 and relate them to EU ETS futures and energy prices. Our findings reveal a decline in implicit carbon prices across the EU, with a sharper reduction in Germany than in the rest of the EU. Both this common decline and the German-EU divergence are associated with higher emissions in Germany. In a counterfactual in which the rest of the EU follows Germany’s implicit carbon price path, German industrial CO2 emissions would have been substantially lower, with offsetting increases in the rest of the EU. These intra-EU production and emissions shifts are consistent with Germany becoming the European pollution haven. We discuss whether this pattern reflects allocative efficiency under emissions trading or distortions caused by overlapping national policies.

This paper introduces the Tail Climate Transition Risk Exposure (TCaRE) measure, designed to evaluate the dynamic exposure of industry portfolios to climate transition risk (CTR). TCaRE is intended to capture the time-varying fluctuations in a firm's stock returns amidst changes in CTR due to the high level of uncertainty surrounding climate-related policies. The analysis is structured within a multi-factor pricing model and a dynamic tail-beta estimator, permitting the exploration of returns of 58 US industries and the EU-ETS carbon allowance market from 2009 to 2023. CTR presents a significant challenge for firms making the transition to a low-carbon economy. Our results highlight that both firm-specific characteristics and broader market trends affect CTR. Industry dynamics, climate events, and additional external factors such as market volatility and policy uncertainty contribute to the complexity of TCaRE. We also discover that TCaRE is priced by the market in a complex and non-linear way, with factors like carbon reduction efforts, focus on climate change, and the coronavirus disease 2019 pandemic influencing this price. Our study's findings are crucial for better understanding different industries' exposure to CTR - a key concern for policymakers and investors.

Artificial intelligence (AI) interacts with climate in various ways. Its impact on emissions and the costs and benefits of emission reduction are uncertain. A critical challenge is to align AI investment with global climate policies. We propose a unified framework, integrating AI's impact on emissions, economic production, and climate damages. We extend the DICE model to a dynamic general equilibrium climate-economy model with endogenous AI growth. By boosting both labor productivity and capital deepening, AI investment increases future gross economic output, and so emissions. It also requires more energy- and emission-intensive capital. At the same time, the application of AI technologies lowers the carbon intensity of the broader economy. Moreover, AI-powered adaptation alleviates climate damages. We distinguish between ICT-like and Industrial Revolution (IR)-like prospects for AI development. Calibrated to the best available evidence, we find that AI development is net polluting. Under current low abatement, ICT-like AI raises global warming further by an additional 0.1°C in 2100, while IR-like AI raises it by 0.8°C. The associated climate costs are 1.6% and 23% in permanent consumption loss, offsetting roughly one‑fifth and one‑quarter of AI’s economic gains, respectively. Consequently, the optimal AI investment rates are reduced from its maximum potential---for ICT-like AI, by 0.12 p.p. to 1.96% in 2050 and by 0.47 p.p. to 3.25% in 2100. Notably, proactive climate policy can largely reverse this investment decline, indicating that it is a complement to, rather than a substitute for, AI development. We further show that the trade-off between AI and abatement investment is driven primarily by their relative marginal economic returns, not by AI's emission impact.