Influence of The Carbon Border Adjustment Mechanism on The Imports of Passenger Vehicles to Germany
DOI:
https://doi.org/10.62051/fgwgez02Keywords:
Carbon Border Adjustment Mechanism, Automotive Industry, Imports, Gravity Model.Abstract
Climate policy is increasingly influencing trade patterns through carbon-cost differentials. This paper investigates how the EU Carbon Border Adjustment Mechanism (CBAM) may reconfigure Germany's passenger-vehicle imports once carbon pricing at the border becomes binding. Using a simulation-based gravity framework with a balanced panel of 47 exporters (2008–2024), the author constructed two CBAM exposure measures: (i) a Structure Effect capturing ETS-aligned carbon-price asymmetries scaled by technological capability, and (ii) an Effective Cost interaction that amplifies penalties for carbon-intensive producers. Results show a two-channel mechanism: the Structure Effect is positively correlated with German imports, consistent with sourcing shifts toward technologically advanced, low-carbon suppliers, while the Effective Cost significantly curtails imports from high-emission exporters—particularly non-EU countries fully exposed to border carbon pricing. Overall, CBAM is predicted to generate a moderate change in import levels but a meaningful reallocation in Germany's supplier composition, with implications for supply-chain resilience and the distribution of adjustment pressure across exporting countries.
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[1] G. Erbach, J. Liselotte, towards climate neutrality – Fit for 55 package, European Parliamentary Research Service, Luxembourg, 2024.
[2] G. Erbach, CO₂ emission standards for new cars and vans – "Fit for 55 package", European Parliamentary Research Service, Luxembourg, 2021.
[3] European Commission, Carbon Border Adjustment Mechanism (CBAM) – Information on aluminium imports, Luxembourg, 2023.
[4] German Environment Agency, Introduction of a Carbon Border Adjustment Mechanism (CBAM) in the European Union, Dessau-Roßlau, 2023.
[5] B. Bye, K.R. Kaushal, H.B. Storrøsten, EU’s carbon border adjustment mechanism (CBAM): Industrial effects, Clim. Policy, 2025, pp. 1–13. DOI: https://doi.org/10.1080/14693062.2025.2557236
[6] T. Puls, The automotive industry in 2024, German Economic Institute, Cologne, 2024.
[7] A. Buylova, N. Nasiritousi, CBAM: Bending the carbon curve or breaking international trade, Swedish Institute for European Policy Studies (SIEPS), Stockholm, 2024.
[8] H. Müller, L. Schmidt, Compliance costs and market competitiveness under CBAM, Eur. Econ. Rev., 2022.
[9] Sustainability Performances, Evidence and Scenarios (SPES), Understanding and assessing CBAM: Vulnerability and impacts in the European Union, SPES Research Consortium, 2025.
[10] Holovko, A. Marian, M. Apergi, the role of the EU CBAM in raising climate policy ambition in trade partners, Institute for Advanced Sustainability Studies (IASS), Potsdam, 2021.
[11] I.D. Smith, I. Overland, K. Szulecki, The EU’s CBAM and its “significant others”: Three perspectives on the political fallout from Europe’s unilateral climate policy initiative, J. Common Mark. Stud. 62, 2024, pp. 603–618. DOI: https://doi.org/10.1111/jcms.13512
[12] J.D. Martinez, T.Z. Bahlinger, M. Beckmann, C. Bode, S. Bort, R. Brühl, et al., Impact of the European Carbon Border Adjustment Mechanism (CBAM) on German industry, Junior Manag. Sci. 9, 2024, pp. 1964–1993.
[13] F. Del Pero, M. Delogu, M. Pierini, Life cycle assessment in the automotive sector: A comparative case of internal combustion engine and electric vehicles, Procedia Struct. Integr. 12, 2018, pp. 521–537. DOI: https://doi.org/10.1016/j.prostr.2018.11.066
[14] S. Wang, X. Wang, S. Chen, Global value chains and carbon emission reduction in developing countries: Does industrial upgrading matter? Environ. Impact Assess. Rev. 97, 2022, Article 106895. DOI: https://doi.org/10.1016/j.eiar.2022.106895
[15] Q.A. Malik, S. Hussain, N. Ullah, A. Waheed, M. Naeem, M. Mansoor, Simultaneous equations and endogeneity in corporate finance, J. Asian Financ. Econ. Bus. 8, 2020, pp. 72–83.
[16] Y. Hwang, J. Kim, Analysis of the effects of rural convergence industry policy on regional agricultural economy, J. Agric. Life Environ. Sci. 37, 2025, pp. 1–15.
[17] J. Bai, S.H. Choi, Y. Liao, Feasible generalized least squares for panel data with cross-sectional and serial correlations, Empir. Econ. 60, 2021, pp. 309–326. DOI: https://doi.org/10.1007/s00181-020-01977-2
[18] Abadie, S. Athey, G.W. Imbens, J. Wooldridge, when should you adjust standard errors for clustering, Q. J. Econ. 138, 2023, pp. 1–35. DOI: https://doi.org/10.1093/qje/qjac038
[19] J.C. Driscoll, A.C. Kraay, Consistent covariance matrix estimation with spatially dependent panel data, Rev. Econ. Stat. 80, 1998, pp. 549–560. DOI: https://doi.org/10.1162/003465398557825
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