Conference Agenda

This article performs a descriptive analysis of the changes in investors’ high-carbon equity exposure between 2015 (Paris Agreement) and 2020 (Covid crisis).

With a rich dataset on the ownership structure of around 3,000 companies reporting

emissions, the paper starts by depicting sectoral trends in the evolution of exposures

to quantiles of emitters over the period by differentiating across countries, investor

categories and protfolio sizes. It then focuses on the portfolio weights of around

30,000 investors and examines how exposures to the 100 most greenhouse gas (GHG)

intensive companies have changed. Investor types, countries and portfolio sizes are

associated with the probability of increasing exposure (measured as portfolio weights)

to investor characteristics through a logit model. While, on average, investors are

relatively split on their portfolio allocation, some agent categories exhibit positive

biases in favour of the Top 100. This pattern characterises mostly emerging economies

in Asia and Africa and Pension Funds in Global North financial systems. Finally,

investors who withdrew from the Top 100 did so with a lag with respect to the Paris

Agreement and were replaced by a population of investors seemingly insensitive to

climate-related matters. This pattern implies the emergence of pocket of agents with

higher exposure on average than in the past, suggesting an increase in exposure

concentration. This polarisation of the financial system calls for careful regulatory

monitoring of concerned entities and a closer scrutiny of investors’ motivations to

increase exposure to the Top 100.

This paper develops a tractable dynamic partial equilibrium model to analyze the integration of overshoot flexibilities in an emissions trading scheme. Overshoot describes a temporary violation of a cumulative emission target, which is then offset by a phase of net-negative carbon emissions. This flexibility is used in many high-ambition climate scenarios. We characterize the optimal extent and timing of overshoot in closed form solutions, which turn out to depend crucially on the autonomous growth rate of marginal abatement and removal costs. We then discuss how the optimal overshoot can be implemented through the creation of additional, temporary emission permits (“overshoot permits”) in an existing emissions trading scheme. We calibrate our model to the EU ETS. When we abstract from overshoot damages, we find substantial overshoot up to a multiple of the carbon budget; carbon prices might halve and mitigation costs be reduced by 20 percent. Revenues from carbon pricing increase only for moderate amounts of overshoot but decrease for larger amounts. We discuss key institutional challenges of overshoot governance related to time inconsistency and liability problems. Finally, we discuss how accounting for overshoot damages creates incentives to limit the amount of overshoot.

Carbon offsets must have integrity to truly reduce emissions, but ascertaining their additionality can be a cumbersome and costly process. This paper redefines additionality as a continuous, rather than a binary, measure and proposes a novel additionality regime based on (i) country-sector-specific positive lists that account for context heterogeneity and (ii) issuance discounts that reflect non-additionality risks. We conduct simulation analysis to compare the performance of this proposed regime to traditional additionality tests. Projects are drawn randomly from a synthetic pool of candidate offset projects, which is calibrated on Clean Development Mechanism (CDM) data and whose properties we know. We calculate how the two regimes vary in terms of offset trade volumes, emissions reductions, and efficiency metrics. Under most scenarios, the proposed regime reduces emissions more effectively with less wasted subsidy and exhibits greater resilience to systematic over-crediting. These findings support a standardized assessment approach over the project-specific quality control, and they call for more disaggregated analysis across heterogeneous contexts to understand non-additionality risks and enhance climate mitigation effectiveness.