Understanding Carbon Trade Dynamics: A European Union Emissions Trading System Perspective

Avirup Chakraborty

arxiv: 2510.22341 · v2 · submitted 2025-10-25 · 📊 stat.AP · q-fin.TR

Understanding Carbon Trade Dynamics: A European Union Emissions Trading System Perspective

Avirup Chakraborty This is my paper

Pith reviewed 2026-05-18 05:15 UTC · model grok-4.3

classification 📊 stat.AP q-fin.TR

keywords EU ETScarbon marketprice predictabilitynetwork analysisprice-volume elasticitymarket efficiencyemissions tradingAR-GARCH

0 comments

The pith

The EU carbon market shows partial price predictability, concentrated trading power, and anomalous volume responses to price changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper analyzes the European Union Emissions Trading System from 2010 to 2020 to assess its market efficiency. An AR-GARCH model detects short-term return predictability with roughly 60 percent directional accuracy. A weighted network of inter-country transactions reveals a few dominant registries controlling high-value flows. Country-level log-log regressions then uncover heterogeneous elasticities, including cases where traded volumes rise with prices instead of falling. These patterns together indicate that the cap-and-trade system has supported decarbonization while leaving trading mechanisms imperfect.

Core claim

AR-GARCH modeling identifies significant price clustering and short-term predictability in EU carbon prices. Weighted network analysis of transaction data exposes a concentrated market structure in which a small number of registries dominate high-value flows. Country-specific log-log regressions of price on quantity produce elasticities that are heterogeneous and sometimes positive and greater than one, showing volumes increasing with price in several jurisdictions.

What carries the argument

AR-GARCH model combined with weighted inter-country transaction network and country-level log-log price-quantity regressions

If this is right

The system continues to drive emission reductions but its pricing and trading rules contain measurable imperfections.
A small set of dominant registries can exert disproportionate influence on high-value carbon flows.
Positive price-volume elasticities in some countries point to speculation or strategic behavior rather than standard downward-sloping demand.
Policy adjustments could target predictability and concentration to improve overall market performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar patterns of concentration and predictability may appear in other regional cap-and-trade systems if they adopt comparable registry structures.
Reforms that increase market transparency or diversify participation could reduce the documented elasticities and network imbalances.
Extending the analysis to daily or intraday data might reveal whether the short-term predictability holds at finer time scales.

Load-bearing premise

The AR-GARCH specification, network weighting choices, and country-level log-log regressions isolate genuine market inefficiencies rather than sample-period artifacts or unmodeled policy shocks.

What would settle it

Checking whether the reported predictability, network concentration, and positive price-volume elasticities persist or disappear when the same methods are applied to post-2020 transaction and price data would test the central claim.

Figures

Figures reproduced from arXiv: 2510.22341 by Avirup Chakraborty.

read the original abstract

The European Union Emissions Trading System (EU ETS), the world's first and largest cap-and-trade carbon market, is a cornerstone of EU climate policy. This study provides a comprehensive empirical analysis of the EU carbon market's efficiency, price dynamics, and structural network from 2010 to 2020. First, we identify significant price clustering and short-term return predictability using an AR-GARCH model, achieving around 60 percent directional accuracy and a 80 percent hit rate within forecasted confidence intervals. These observed patterns motivate a deeper exploration of market structure. Second, leveraging this insight, a weighted network analysis of inter-country transactions uncovers a concentrated market where a few registries dominate high-value flows and exert disproportionate influence. Finally, building upon the network findings, country-specific log-log regressions of price on traded quantity reveal heterogeneous and sometimes counter-intuitive elasticities; in several cases, positive elasticities exceed unity, indicating that trading volumes rise with prices, a deviation from conventional demand behavior that highlights potential inefficiencies driven by speculation, strategic behavior, or policy distortions. Collectively, these results point to persistent inefficiencies within the EU ETS, including partial predictability, asymmetric market power, and anomalous price-volume relationships, implying that while the system has driven decarbonization, its trading and pricing mechanisms remain imperfect.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper applies standard tools to EU ETS transaction data and reports predictability plus odd elasticities, but these patterns likely reflect policy shocks in the sample rather than structural flaws.

read the letter

Colleague, the main thing to know is that this paper pulls EU ETS transaction data from 2010-2020 and uses AR-GARCH, weighted networks, and country-level log-log regressions to claim partial price predictability, concentrated trading power, and positive price-volume elasticities above one in some places. Those numbers suggest inefficiencies, yet the patterns probably trace back to the policy interventions that hit during the window instead of revealing deeper market problems. What the work actually does is combine three standard methods on the same transaction dataset. The network section shows a handful of registries handling most high-value flows, which gives a clear picture of concentration. The regressions turn up heterogeneous elasticities, including the counterintuitive cases where volume rises with price. Using actual inter-country flows rather than just aggregate prices is a step that lets the analysis move from time-series patterns to structural observations. The soft spots sit in the sample and the missing checks. The period covers the 2013 move to auctioning, 2014 back-loading, and the 2019 Market Stability Reserve, each of which shifted supply and incentives. Without regime dummies, structural-break tests, or policy fixed effects in the AR-GARCH or regressions, the reported 60 percent directional accuracy and the elasticities could be mechanical responses to those changes. The abstract gives accuracy figures and elasticity signs but no equations, error bars, or robustness details, so it is hard to judge whether the models isolate the claimed inefficiencies. This is the kind of paper that might interest researchers who track carbon-market design or empirical work on environmental trading systems. Someone wanting descriptive facts on EU ETS flows and price responses could find the network maps and country breakdowns useful as background. For stronger claims about persistent flaws that should guide policy fixes, the current version needs more work. I would send it to peer review. Referees can ask for the policy-robustness tests and full specifications, which would either shore up the results or show where they need qualification.

Referee Report

3 major / 2 minor

Summary. The paper conducts an empirical analysis of the EU ETS carbon market from 2010 to 2020. It applies an AR-GARCH model to document price clustering and short-term return predictability (approximately 60% directional accuracy), performs weighted network analysis on inter-country transactions to reveal a concentrated structure with dominant registries, and estimates country-level log-log regressions of price on traded volume that yield heterogeneous elasticities, some positive and exceeding unity. The authors interpret these patterns as evidence of persistent inefficiencies, including partial predictability, asymmetric market power, and anomalous price-volume relationships, while noting that the system has nonetheless supported decarbonization.

Significance. If the reported patterns survive controls for policy-driven regime shifts, the multi-method approach (time-series forecasting, network centrality, and elasticity estimation) offers a useful descriptive portrait of trading frictions in the world's largest cap-and-trade system. The work could inform debates on market-design refinements such as allowance banking rules or registry oversight. However, the absence of explicit robustness checks against the major supply shocks that occurred inside the sample period limits the strength of the inefficiency interpretation.

major comments (3)

[AR-GARCH analysis] AR-GARCH section: the specification does not include regime dummies, structural-break tests, or interactions for the 2013 shift to auctioning, the 2014 back-loading decision, or the 2019 Market Stability Reserve. Without these controls, the reported 60% directional accuracy and confidence-interval hit rate may simply reflect the mechanical effects of known policy interventions rather than intrinsic predictability or speculation.
[Network analysis] Network analysis section: edge weights and centrality thresholds are constructed from raw transaction volumes without adjustment for policy-induced registry migrations or changes in allowance allocation rules. This leaves open the possibility that the observed concentration and influence of a few registries are artifacts of the 2013–2019 institutional changes rather than evidence of asymmetric market power.
[Regression analysis] Country-level log-log regressions: the models omit time fixed effects, policy-period dummies, or interactions with the timing of the MSR and back-loading events. Consequently, the finding that several elasticities are positive and greater than one cannot yet be distinguished from a mechanical response to supply shocks that simultaneously raised prices and altered trading volumes.

minor comments (2)

[Abstract and methods] The abstract states specific accuracy figures but the main text should supply the exact AR-GARCH lag orders, variance parameters, and out-of-sample window used to obtain the 60% directional accuracy.
[Data section] Data-source details (registry identifiers, transaction granularity, handling of zero-volume observations) are insufficiently described for replication.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need to account for major policy regime shifts in the EU ETS during 2010-2020. We agree that explicit robustness checks for the 2013 auctioning transition, 2014 back-loading decision, and 2019 Market Stability Reserve will strengthen the interpretation of our results. We will revise the manuscript to incorporate these controls and report updated findings. Point-by-point responses are provided below.

read point-by-point responses

Referee: AR-GARCH section: the specification does not include regime dummies, structural-break tests, or interactions for the 2013 shift to auctioning, the 2014 back-loading decision, or the 2019 Market Stability Reserve. Without these controls, the reported 60% directional accuracy and confidence-interval hit rate may simply reflect the mechanical effects of known policy interventions rather than intrinsic predictability or speculation.

Authors: We acknowledge that the current AR-GARCH model does not explicitly control for these policy events. While the specification was intended to capture overall short-term dynamics, we agree that regime shifts could influence the results. In the revision, we will add period dummies for pre/post-2013, around back-loading, and MSR implementation, along with Bai-Perron structural break tests. We will report whether the directional accuracy remains near 60% after these adjustments. revision: yes
Referee: Network analysis section: edge weights and centrality thresholds are constructed from raw transaction volumes without adjustment for policy-induced registry migrations or changes in allowance allocation rules. This leaves open the possibility that the observed concentration and influence of a few registries are artifacts of the 2013–2019 institutional changes rather than evidence of asymmetric market power.

Authors: We concur that raw volumes may capture institutional changes. The revised network analysis will normalize edge weights by total allocated allowances and include subsample robustness checks excluding major transition periods. This will help assess whether the concentrated structure and dominant registries persist beyond policy-driven effects. revision: yes
Referee: Country-level log-log regressions: the models omit time fixed effects, policy-period dummies, or interactions with the timing of the MSR and back-loading events. Consequently, the finding that several elasticities are positive and greater than one cannot yet be distinguished from a mechanical response to supply shocks that simultaneously raised prices and altered trading volumes.

Authors: We agree that the absence of time fixed effects and policy dummies leaves the elasticities open to confounding by supply shocks. In the revision, we will add year fixed effects, policy-period dummies, and event-timing interactions. We will also estimate phase-specific models to evaluate the robustness of the positive elasticities exceeding unity. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical outputs from external data

full rationale

The paper reports results from standard statistical procedures (AR-GARCH fits for predictability, weighted network metrics on transaction flows, and country-level log-log regressions) applied directly to external EU ETS registry data. No derivation step equates a claimed prediction or result to a quantity defined by the paper's own fitted parameters, normalizations, or self-citations; all quantities are computed from observed prices, volumes, and registry links rather than being forced by construction. The analysis is therefore self-contained against the external dataset and does not reduce to tautological re-expression of its inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard time-series and network assumptions plus the interpretation that observed elasticities signal inefficiency; no new entities are postulated.

free parameters (2)

AR-GARCH lag orders and variance parameters
Fitted to price series to produce the reported directional accuracy and hit-rate figures.
Network edge weights and centrality thresholds
Chosen to identify dominant registries in the transaction graph.

axioms (2)

domain assumption Transaction records from 2010-2020 accurately represent all EU ETS trades and are free of material reporting gaps.
Invoked for both network construction and regression inputs.
domain assumption Positive price-volume elasticities greater than one indicate speculation or policy distortion rather than normal market clearing.
Used to label the regression results as anomalous and inefficient.

pith-pipeline@v0.9.0 · 5748 in / 1556 out tokens · 63778 ms · 2026-05-18T05:15:24.213964+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

we identify significant price clustering and short-term return predictability using an AR-GARCH model... weighted network analysis... country-specific log-log regressions of price on traded quantity
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

AR(3) model... GARCH(1,1)... Eigenvector Centrality... price elasticity of demand

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

[1]

(2024).Database for the European Union Transaction Log

Abrell, J. (2024).Database for the European Union Transaction Log. Available athttps://www.euets.info

work page 2024
[2]

(2024).Inefficiencies of Carbon Trading Markets.https: //arxiv.org/abs/2408.06497

Borri, N., Liu, Y., Tsyvinski, A., & Wu, X. (2024).Inefficiencies of Carbon Trading Markets.https: //arxiv.org/abs/2408.06497

work page arXiv 2024
[3]

(2016).Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature

Hintermann, B., Peterson, S., & Rickels, W. (2016).Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature. Review of Environmental Economics and Policy, 10(1), 108–128

work page 2016
[4]

G., & Rhodes, M

Ibikunle, G., Gregoriou, A., Hoepner, A. G., & Rhodes, M. (2016).Liquidity and Market Efficiency in the World’s Largest Carbon Market. British Accounting Review, 48(4), 431–447

work page 2016
[5]

(2024).The Predictability of Carbon Futures Volatility: New Evidence from the Spillovers of Fossil Energy Futures Returns

Zhang, Z., Li, S., & Zhang, W. (2024).The Predictability of Carbon Futures Volatility: New Evidence from the Spillovers of Fossil Energy Futures Returns. Journal of Futures Markets, 44(3), 512–530

work page 2024
[6]

(2024).Forecasting Carbon Futures Returns Using Feature Selection and Markov Chain with Sample Distribution

Zhao, Y., Gong, X., Zhang, W., & Xu, W. (2024).Forecasting Carbon Futures Returns Using Feature Selection and Markov Chain with Sample Distribution. Energy Economics, 140, 108079

work page 2024
[7]

(2022).Carbon Emission Trading and Equity Markets in China: How Liquidity is Impacting Carbon Returns?Economic Research-Ekonomska Istraˇ zivanja, 35(1), 402–420

Zhang, J., & Han, W. (2022).Carbon Emission Trading and Equity Markets in China: How Liquidity is Impacting Carbon Returns?Economic Research-Ekonomska Istraˇ zivanja, 35(1), 402–420

work page 2022
[8]

(2021).Improving Liquidity in Emission Trading Schemes

Kim, J., & Park, K. (2021).Improving Liquidity in Emission Trading Schemes. Journal of Futures Markets, 41(9), 1397–1411

work page 2021
[9]

EUETS.INFOhttps://www.euets.info/download

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(2025).Surplus of Allowances

European Commission. (2025).Surplus of Allowances. Retrieved fromhttps://climate.ec.europa.eu/eu- action/carbon-markets/eu-emissions-trading-system-eu-ets/market-stability-reserve en#: ~:text=In%202013%2C%20the,381%20million%20allowances

work page 2025
[12]

(2025).Market Stability Reserve

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work page 2025
[13]

(2005).Efficiency and Return Predictability: What can we learn from stock market data?C.U

D¨ ome, B. (2005).Efficiency and Return Predictability: What can we learn from stock market data?C.U. Budapest. May 2, 2005

work page 2005
[14]

Gov UK.UK ETShttps://www.gov.uk/government/publications/uk-emissions-trading-scheme- uk-ets-policy-overview/uk-emissions-trading-scheme-uk-ets-a-policy-overview?utm source= chatgpt.com#what-is-the-uk-ets: ~:text=The%20UK%2C%20Scottish,1%20January%202021

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(2011).Weighted network analysis: Applications in genomics and systems biology.Springer

Horvath, S. (2011).Weighted network analysis: Applications in genomics and systems biology.Springer

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[16]

(1994).Social network analysis: Methods and applications.Cambridge Univer- sity Press.https://doi.org/10.1017/CBO9780511815478

Wasserman, S., & Faust, K. (1994).Social network analysis: Methods and applications.Cambridge Univer- sity Press.https://doi.org/10.1017/CBO9780511815478

work page doi:10.1017/cbo9780511815478 1994
[17]

O., & Tebaldi, P

Bloch, F., Jackson, M. O., & Tebaldi, P. (2021).Centrality measures in networks.https://arxiv.org/ pdf/1608.05845

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Varian, H. R. (2014).Intermediate Microeconomics: A Modern Approach (9th ed.). W.W. Norton & Com- pany. Link to the codes used in the study: Github 14 Appendix Table 3:OLS and LAD Regression Results: Log Quantity on Log Price by Country Pair and Period From To Period OLS LAD N ˆβ0 p-val ˆβ1 p-val ˆβ0 p-val ˆβ1 p-val France France 2010–2012 10.70 0.000 1.1...

work page 2014

[1] [1]

(2024).Database for the European Union Transaction Log

Abrell, J. (2024).Database for the European Union Transaction Log. Available athttps://www.euets.info

work page 2024

[2] [2]

(2024).Inefficiencies of Carbon Trading Markets.https: //arxiv.org/abs/2408.06497

Borri, N., Liu, Y., Tsyvinski, A., & Wu, X. (2024).Inefficiencies of Carbon Trading Markets.https: //arxiv.org/abs/2408.06497

work page arXiv 2024

[3] [3]

(2016).Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature

Hintermann, B., Peterson, S., & Rickels, W. (2016).Price and Market Behavior in Phase II of the EU ETS: A Review of the Literature. Review of Environmental Economics and Policy, 10(1), 108–128

work page 2016

[4] [4]

G., & Rhodes, M

Ibikunle, G., Gregoriou, A., Hoepner, A. G., & Rhodes, M. (2016).Liquidity and Market Efficiency in the World’s Largest Carbon Market. British Accounting Review, 48(4), 431–447

work page 2016

[5] [5]

(2024).The Predictability of Carbon Futures Volatility: New Evidence from the Spillovers of Fossil Energy Futures Returns

Zhang, Z., Li, S., & Zhang, W. (2024).The Predictability of Carbon Futures Volatility: New Evidence from the Spillovers of Fossil Energy Futures Returns. Journal of Futures Markets, 44(3), 512–530

work page 2024

[6] [6]

(2024).Forecasting Carbon Futures Returns Using Feature Selection and Markov Chain with Sample Distribution

Zhao, Y., Gong, X., Zhang, W., & Xu, W. (2024).Forecasting Carbon Futures Returns Using Feature Selection and Markov Chain with Sample Distribution. Energy Economics, 140, 108079

work page 2024

[7] [7]

(2022).Carbon Emission Trading and Equity Markets in China: How Liquidity is Impacting Carbon Returns?Economic Research-Ekonomska Istraˇ zivanja, 35(1), 402–420

Zhang, J., & Han, W. (2022).Carbon Emission Trading and Equity Markets in China: How Liquidity is Impacting Carbon Returns?Economic Research-Ekonomska Istraˇ zivanja, 35(1), 402–420

work page 2022

[8] [8]

(2021).Improving Liquidity in Emission Trading Schemes

Kim, J., & Park, K. (2021).Improving Liquidity in Emission Trading Schemes. Journal of Futures Markets, 41(9), 1397–1411

work page 2021

[9] [9]

EUETS.INFOhttps://www.euets.info/download

work page

[10] [10]

ICAP.https://icapcarbonaction.com/en/ets-prices

work page

[11] [11]

(2025).Surplus of Allowances

European Commission. (2025).Surplus of Allowances. Retrieved fromhttps://climate.ec.europa.eu/eu- action/carbon-markets/eu-emissions-trading-system-eu-ets/market-stability-reserve en#: ~:text=In%202013%2C%20the,381%20million%20allowances

work page 2025

[12] [12]

(2025).Market Stability Reserve

European Commission. (2025).Market Stability Reserve. Retrieved fromhttps://climate.ec.europa.eu/ eu-action/carbon-markets/eu-emissions-trading-system-eu-ets/market-stability- reserve en#:~:text=The%20Market%20Stability%20Reserve%20(MSR)%20was,2018%20and%20began% 20operating%20in%202019

work page 2025

[13] [13]

(2005).Efficiency and Return Predictability: What can we learn from stock market data?C.U

D¨ ome, B. (2005).Efficiency and Return Predictability: What can we learn from stock market data?C.U. Budapest. May 2, 2005

work page 2005

[14] [14]

Gov UK.UK ETShttps://www.gov.uk/government/publications/uk-emissions-trading-scheme- uk-ets-policy-overview/uk-emissions-trading-scheme-uk-ets-a-policy-overview?utm source= chatgpt.com#what-is-the-uk-ets: ~:text=The%20UK%2C%20Scottish,1%20January%202021

work page

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(2011).Weighted network analysis: Applications in genomics and systems biology.Springer

Horvath, S. (2011).Weighted network analysis: Applications in genomics and systems biology.Springer

work page 2011

[16] [16]

(1994).Social network analysis: Methods and applications.Cambridge Univer- sity Press.https://doi.org/10.1017/CBO9780511815478

Wasserman, S., & Faust, K. (1994).Social network analysis: Methods and applications.Cambridge Univer- sity Press.https://doi.org/10.1017/CBO9780511815478

work page doi:10.1017/cbo9780511815478 1994

[17] [17]

O., & Tebaldi, P

Bloch, F., Jackson, M. O., & Tebaldi, P. (2021).Centrality measures in networks.https://arxiv.org/ pdf/1608.05845

work page arXiv 2021

[18] [18]

Varian, H. R. (2014).Intermediate Microeconomics: A Modern Approach (9th ed.). W.W. Norton & Com- pany. Link to the codes used in the study: Github 14 Appendix Table 3:OLS and LAD Regression Results: Log Quantity on Log Price by Country Pair and Period From To Period OLS LAD N ˆβ0 p-val ˆβ1 p-val ˆβ0 p-val ˆβ1 p-val France France 2010–2012 10.70 0.000 1.1...

work page 2014