Abstract
The European Union Emissions Trading System (EU ETS) is the largest cap-and-trade system in the world. Price instability and allowance oversupply are two characteristics that affect the objectives and the efficiency of this policy. In this work, we investigate the impact of storing allowances (“banking”) on the allowance price and the power of the financial sector in the trading network of the ETS. To that end, we use data from the EU Transaction Log (EUTL) along with data on the most important allowance price determinants. We apply a multiple regression analysis that considers many important price determinants that are both endogenous and exogenous to the ETS, and quantify how the allowance price depends on the total volume of stored allowances. Our analysis indicates that banking is a notable—though not the dominant—price determinant, and quantifies its significance. Moreover, we study the role of financial nodes in the ETS trading network. Analyzing the betweenness centrality of financial, regulated, and governmental entities in the trading network of ETS over a period of more than 10 years, we provide strong evidence of the significant power of financial entities in the ETS trading network, which arises due to their role as intermediaries in allowance trading. Our work could provide the basis for a compact and relatively simple tool to evaluate and estimate the performance of one of the most prominent environmental policies, the EU ETS.
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Notes
Aviation has been regulated since mid-2012.
Inspecting the EUTL dataset, we found that the actual date on which regulated firms received their free allowances varied to some extent.
It is possible for a regulated firm to surrender an appropriate amount of allowances after the end of April. Late surrenders are, however, subject to a fine of €100 per allowance.
Carbon leakage refers to the situation where a corporation transfers its production or activity to other countries with weaker emission constraints.
The main difference between futures and forward contracts is that a futures contract is standardized whereas a forward contract is tailor-made.
Prior to 2013, offsets enlarged the existing surplus of allowances.
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Acknowledgements
This research was carried out/funded in the context of the project “Applications of Reverse Greedy Mechanisms to Social Choice Problems” (MIS 5004766) under the call for proposals “Supporting Researchers with Emphasis on New Researchers” (EDULLL 34). The project was cofinanced by Greece and the European Union (European Social Fund, ESF) via the operational program “Human Resources Development, Education and Lifelong Learning 2014-2020.”
We would like to thank the anonymous reviewers for their insightful questions and fruitful suggestions.
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Communicated by Dimitra Vagiona, Lead Guest Editor.
This research was carried out/funded in the context of the project “Applications of Reverse Greedy Mechanisms to Social Choice problems” (MIS 5004766) under the call for proposals “Supporting Researchers with Emphasis on New Researchers” (EDULLL 34). The project was cofinanced by Greece and the European Union (European Social Fund, ESF) through the operational program “Human Resources Development, Education and Lifelong Learning 2014–2020.”
Appendices
Descriptive statistics
See Table 10.
Linear regression models—residual diagnostics
Unit root tests
See Table 14.
Multicollinearity tests
See Tables 15, 16, 17, 18, 19, 20, 21 and 22.
Inverse regression–linear regression on the financial wallet
EUA trading networks: graphical representation sample
Graphical representation of the EUA trading network for the third quarter of 2015. The nodes shown in red, green, and blue indicate governmental, financial, and regulated participants, respectively
The networks studied in the present work generally contain too many nodes to allow clear graphical representations of the networks. However, in order to give readers an idea of how they are structured, a relatively small network is depicted in Fig. 11. This figure shows a graphical representation of the trading network for the third quarter of 2015. The network appears to have a rather strongly connected core and a more loosely connected periphery, in accord with the observations made in previous studies such as Karpf et al. (2018), Borghesi and Flori (2016), and Betz and Schmidt (2016). The central nodes (which have very large numbers of connections) are governmental ones (shown in red). These governmental entities are rare and were overlooked in previous studies. The financial entities (shown in green) are generally highly connected, while the regulated entities (depicted in blue) are quite isolated.
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Dimos, S., Evangelatou, E., Fotakis, D. et al. On the impacts of allowance banking and the financial sector on the EU Emissions Trading System. Euro-Mediterr J Environ Integr 5, 34 (2020). https://doi.org/10.1007/s41207-020-00167-x
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DOI: https://doi.org/10.1007/s41207-020-00167-x