Modelling a Market Stability Reserve in Carbon Markets

Schopp, Anne; Acworth, William; Huppmann, Daniel; Neuhoff, Karsten

doi:10.2139/ssrn.2616333

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…Likewise, we assume that with the increased LRF the factor of 2.2% will be used for all future trading periods. (European Parliament and the Council of the European Union, 2018) 10 This assumption is similar to Perino and Willner (2016) and Schopp et al (2015) who use constant counterfactual emissions of 1900 million tonnes CO 2 e and 2200 million tonnes CO 2 e, respectively. The sensitivity of this assumption is calculated and further discussed in section 3.2.…”

Section: Results Under Current Regulationmentioning

confidence: 97%

The reformed EU ETS - Intertemporal emission trading with restricted banking

et al. 2019

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With the increase of the linear reduction factor (LRF), the implementation of the market stability reserve (MSR) and the introduction of the cancellation mechanism (CM), the EU ETS changed fundamentally. We develop a discrete time model of the intertemporal allowance market that accurately depicts these reforms assuming that prices develop with the Hotelling rule as long as the TNAC is non-empty. A sensitivity analysis ensures the robustness of the model results regarding its input parameters. The accurate modelling of the EU ETS allows for a decomposition of the effects of the individual amendments and the evaluation of the dynamic efficiency. The MSR shifts emissions to the future but is allowance preserving. The CM reduces the overall emission cap, increasing allowance prices in the long run, but does not significantly impact the emission and price path in the short run. The increased LRF leads with 9 billion cancelled allowances to a stronger reduction than the CM and is therefore the main price driver of the reform.

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Section: Results Under Current Regulationmentioning

confidence: 97%

The reformed EU ETS - Intertemporal emission trading with restricted banking

et al. 2019

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“…Second, a small number of recent papers examines the idea of introducing a quantity-based adjustment mechanism to the EU ETS (Fell, 2015;Schopp et al, 2015;Kollenberg & Taschini, 2015;Ellerman et al, 2015;Perino & Willner, 2015). The so-called "Market Stability Reserve (MSR)"-to be introduced in Phase 4 of the EU ETSaims at rectifying the structural problem of allowances surplus by creating a mechanism according to which annual auction volumes are adjusted in situations where the total number of allowances in circulation is outside a certain predefined range (EC, 2014).…”

Section: Introductionmentioning

confidence: 99%

Combining Price and Quantity Controls Under Partitioned Environmental Regulation

Abrell

2016

SSRN Journal

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Change combines cutting-edge scientific research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being data-driven, the Joint Program uses extensive Earth system and economic data and models to produce quantitative analysis and predictions of the risks of climate change and the challenges of limiting human influence on the environmentessential knowledge for the international dialogue toward a global response to climate change.To this end, the Joint Program brings together an interdisciplinary group from two established MIT research centers: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers-along with collaborators from the Marine Biology Laboratory (MBL) at Woods Hole and short-and long-term visitors-provide the united vision needed to solve global challenges.At the heart of much of the program's work lies MIT's Integrated Global System Model. Through this integrated model, the program seeks to discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex connections among the many forces that will shape our future; and improve methods to model, monitor and verify greenhouse gas emissions and climatic impacts.This reprint is intended to communicate research results and improve public understanding of global environment and energy challenges, thereby contributing to informed debate about climate change and the economic and social implications of policy alternatives. Abstract: This paper analyzes hybrid emissions trading systems (ETS) under partitioned environmental regulation when firms' abatement costs and future emissions are uncertain. We show that hybrid policies that introduce bounds on the price or the quantity of abatement provide a way to hedge against differences in marginal abatement costs across partitions. Price bounds are more efficient than abatement bounds as they also use information on firms' abatement technologies while abatement bounds can only address emissions uncertainty. Using a numerical stochastic optimization model with equilibrium constraints for the European carbon market, we find that introducing hybrid policies in EU ETS reduces expected excess abatement costs of achieving targeted emissions reductions under EU climate policy by up to 89 percent. We also find that under partitioned regulation there is a high likelihood for hybrid policies to yield sizeable ex-post cost reductions. -Ronald G. Prinn and John M. Reilly, Joint Program Co-Directors MIT Joint Program on the Science and Policy of Global Change

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“…However, they are focussed on individual sectors, thus ignoring feedback loops to the overall economy, for example via income effects of households. Due to their limited scope they can incorporate greater technological detail, and more heterogeneous behavioural assumptions, for example limited banking capabilities of actors in the EU ETS [11] or stochastic foresight of agents. As with CGE models, they will compute an optimal equilibrium (point or pathway, depending on whether the model is static or dynamic).…”

Section: 3mentioning

confidence: 99%