Abstract
Policymakers combine many different policy tools to achieve emission reductions. However, there remains substantial uncertainty around which mixes of policies are effective. This uncertainty stems from the predominant focus of ex post policy evaluation on isolating effects of single, known policies. Here we introduce an approach to identify effective policy interventions in the EU road transport sector by detecting treatment effects as structural breaks in CO2 emissions that can potentially occur in any country at any point in time from any number of a priori unknown policies. This search for ‘causes of effects’ within a statistical framework allows us to draw systematic inference on the effectiveness of policy mixes. We detect ten successful policy interventions that reduced emissions between 8% and 26%. The most successful policy mixes combine carbon or fuel taxes with green vehicle incentives and highlight that emissions reductions on a magnitude that matches the EU zero emission targets are possible.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout




Data availability
All publicly available data analysed in this study are available from the corresponding author upon request and are also available from online repository Zenodo (https://doi.org/10.5281/zenodo.6768563).
Code availability
The code required to replicate our study is available from the corresponding author upon request and is also available from online repository Zenodo (https://doi.org/10.5281/zenodo.6768563).
References
Net Zero by 2050—A Roadmap for the Global Energy Sector (IEA, 2021).
Axsen, J., Plötz, P. & Wolinetz, M. Crafting strong, integrated policy mixes for deep CO2 mitigation in road transport. Nat. Clim.Change 10, 809–818 (2020).
Planning for Net Zero: Assessing the Draft National Energy and Climate Plans (Ecologic & Climact, 2019).
Trends and Projections in Europe 2021 (European Environment Agency, 2021).
Graf, A., Graichen, J., Matthes, F. C., Gores, S. & Fallasch, F. How to Raise Europe’s Climate Ambitions for 2030 (Agora Energiewende and Öko-Institut e.V., 2019).
Transport and Energy Why Increasing Ambition under the ESR is Unavoidable (2021).
Grant, D., Bergstrand, K. & Running, K. Effectiveness of US state policies in reducing CO2 emissions from power plants. Nat. Clim. Change 4, 977–982 (2014).
Martin, G. & Saikawa, E. Effectiveness of state climate and energy policies in reducing power-sector CO2 emissions. Nat. Clim. Change 7, 912–919 (2017).
Andersson, J. J. Carbon taxes and CO2 emissions: Sweden as a case study. Am. Econ. J. 11, 1–30 (2019).
Bayer, P. & Aklin, M. The European Union emissions trading system reduced CO2 emissions despite low prices. Proc. Natl Acad. Sci. USA 117, 8804–8812 (2020).
Gelman, A. & Imbens, G. Why Ask Why? Forward Causal Inference and Reverse Causal Questions (National Bureau of Economic Research, 2013).
Eskander, S. M. & Fankhauser, S. Reduction in greenhouse gas emissions from national climate legislation. Nat. Clim. Change 10, 750–756 (2020).
Lin, B. & Li, X. The effect of carbon tax on per capita CO2 emissions. Energy Policy 39, 5137–5146 (2011).
Klemetsen, M., Rosendahl, K. E. & Jakobsen, A. L. The impacts of the EU ETS on Norwegian plant's environmental and economic performance. Clim. Change Econ 11, 2050006 (2020).
Colmer, J., Martin, R., Muûls, M. & Wagner, U. J. Does Pricing Carbon Mitigate Climate Change? Firm-Level Evidence from the European Union Emissions Trading Scheme, discussion paper 1728 (Center for Economic Performance, 2020).
Rafaty, R., Dolphin, G. & Pretis, F. Carbon Pricing and the Elasticity of CO2 Emissions, working Paper No. 140 (Institute for New Economic Thinking, 2020).
Pretis, F. & Schwarz, M. Discovering what mattered: Answering reverse causal questions by detecting unknown treatment assignment and timing as breaks in panel models. Preprint at SSRN https://doi.org/10.2139/ssrn.4022745 (2022).
Schwarz, M. & Pretis, F. getspanel. GitHub repository (2021), https://github.com/moritzpschwarz/getspanel/
Estrada, F., Perron, P. & Martínez-López, B. Statistically derived contributions of diverse human influences to twentieth-century temperature changes. Nat. Geosci. 6, 1050–1055 (2013).
Hendry, D. F. et al. First In, First Out: Econometric Modelling of UK Annual CO2 Emissions, 1860–2017 (Economics Group, Nuffield College, Univ. of Oxford, 2020).
Piehl, A. M., Cooper, S. J., Braga, A. A. & Kennedy, D. M. Testing for structural breaks in the evaluation of programs. Rev. Econ. Stat. 85, 550–558 (2003).
Schubert, K. Carbon taxation: The French experience, 2014–2019. Coalition of Finance Ministers for Climate Action Workshop on Carbon Taxation, 2019.
Report of the Working Group on Energy Taxation Reform: A Proposal for Implementing the Intentions and Goals of the Government Programme and for Further Development of Energy Taxation (Finnish Ministry of Finance, 2021).
Oster, E. Unobservable selection and coefficient stability: theory and evidence. J. Bus. Econ. Stat. 37, 187–204 (2019).
Wooldridge, J. Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. Preprint at SSRN https://doi.org/10.2139/ssrn.3906345 (2021).
Gillingham, K. T., Houde, S. & van Benthem, A. A. Consumer myopia in vehicle purchases: evidence from a natural experiment. Am. Econ. J. 13, 207–38 (2021).
Gillingham, K., Kotchen, M. J., Rapson, D. S. & Wagner, G. The rebound effect is overplayed. Nature 493, 475–476 (2013).
Ravigné, E., Ghersi, F. & Nadaud, F. Is a fair energy transition possible? Evidence from the French low-carbon strategy. Ecol. Econ. 196, 107397 (2022).
Landis, F., Rausch, S., Kosch, M. & Böhringer, C. Efficient and equitable policy design: taxing energy use or promoting energy savings?. Energy J. 40, 73–104 (2019).
Vivanco, D. F., Kemp, R. & van der Voet, E. How to deal with the rebound effect? A policy-oriented approach. Energy Policy 94, 114–125 (2016).
Freire-González, J. & Ho, M. S. Policy strategies to tackle rebound effects: a comparative analysis. Ecol. Econ. 193, 107332 (2022).
Crippa, M. et al. Population, Total Fossil CO2 and GHG Emissions of All World Countries—2019 Report (Publications Office of the European Union, 2019).
GDP (constant 2010 US$). World Bank Open Data (2020), https://data.worldbank.org/indicator/NY.GDP.MKTP.KD?view=chart
Population, total. World Bank Open Data, https://data.worldbank.org/indicator/SP.POP.TOTL?view=chart (2020).
Pretis, F. Does a carbon tax reduce CO2 emissions? Evidence from British Columbia. Environmental and Resource Economics (2022), https://doi.org/10.1007/s10640-022-00679-w
Castle, J. L., Doornik, J. A., Hendry, D. F. & Pretis, F. Detecting location shifts during model selection by step-indicator saturation. Econometrics 3, 240–264 (2015).
Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econometrics 225, 254–277 (2021).
Pretis, F., Reade, J. & Sucarrat, G. Automated general-to-specific (gets) regression modeling and indicator saturation methods for the detection of outliers and structural breaks. J. Stat. Softw. 86(3), 1–44 (2018).
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).
Zou, H. The adaptive lasso and its oracle properties. J. Am. Stat. Assoc. 101, 1418–1429 (2006).
Okui, R. & Wang, W. Heterogeneous structural breaks in panel data models. J. Econometrics 220, 447–473 (2021).
Nielsen, B. & Qian, M. Asymptotic properties of the gauge of step-indicator saturation. Working Paper, University of Oxford (2018).
ACEA Tax Guide (ACEA) (2022).
Rumscheidt, S. Road user charging in the European Union. CESifo DICE Rep. 12, 54–57 (2014).
Carbon Pricing Dashboard (World Bank, 2022); https://carbonpricingdashboard.worldbank.org/
Acknowledgements
We thank O. Edenhofer, A.B. Martinez, R. Tol and the participants at the EC2 Conference 2021, the Federal Reserve Virtual Seminar on Climate Economics and the Climate Econometrics Seminar for valuable feedback and suggestions. F.P. and M.S. gratefully acknowledge funding from the Clarendon Fund and the Robertson Foundation. F.P. is also grateful to funding from Social Sciences and Humanities Research Council of Canada (SSHRC). The views expressed here are those of the authors and not necessarily those of the Ministry of Finance or the Austrian government.
Author information
Authors and Affiliations
Contributions
F.P., L.N., M.S. and N.K. designed the analysis. F.P. and M.S. wrote the core programme code. L.N. collected the data. L.N. and N.K. conducted most of the analyses. All authors interpreted results and designed figures. N.R. and N.K. wrote the manuscript with contributions from all authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Energy thanks Patrick Bayer, Edgar Hertwich and Md. Saniul Alam for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Sections A–C and Tables 1–12.
Rights and permissions
About this article
Cite this article
Koch, N., Naumann, L., Pretis, F. et al. Attributing agnostically detected large reductions in road CO2 emissions to policy mixes. Nat Energy 7, 844–853 (2022). https://doi.org/10.1038/s41560-022-01095-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41560-022-01095-6