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

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  • Avirup Chakraborty

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.

Suggested Citation

  • Avirup Chakraborty, 2025. "Understanding Carbon Trade Dynamics: A European Union Emissions Trading System Perspective," Papers 2510.22341, arXiv.org, revised Apr 2026.
  • Handle: RePEc:arx:papers:2510.22341
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    References listed on IDEAS

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    1. Zhao, Yuan & Gong, Xue & Zhang, Weiguo & Xu, Weijun, 2024. "Forecasting carbon futures returns using feature selection and Markov chain with sample distribution," Energy Economics, Elsevier, vol. 140(C).
    2. Jihun Kim & Kwangwoo Park, 2021. "Improving liquidity in emission trading schemes," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1397-1411, September.
    3. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
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