Skip to main content

Advertisement

SpringerLink
Log in

Measuring carbon emission sensitivity to economic shocks: a panel structural vector autoregression 1870–2016

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

The study of the link between production, measured in gross domestic product and CO2 emissions, is a topic under intense research. Carbon emissions are moving together with economic shocks (high synchronicity), particularly at troughs and peaks of a business cycle. This research investigates the influence of economic shocks to carbon emissions. Previous studies do not provide a direct empirical evidence on the impact of economic shocks to carbon emissions that are available. We employ structural vector autoregression to explore the impact of economic shocks on carbon emissions in 20 advanced economies from 1870 to 2016. Our empirical results prove a strong, statistically significant connection between emissions and output with a coefficient of elasticity > 1. We identify a strong empirical link using panel structural vector autoregression between carbon emissions and real GDP growth per capita. Up to 40% of the fluctuations in CO2 emissions is explained by combined economic factors (output, population, oil prices, stock exchange). The findings further indicate that carbon emission is determined by energy policy (energy intensity, carbon intensity, relative costs of renewable energy). Our findings contribute to energy policy management, energy, and business cycle research to inspire novel research on energy cycles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Figure 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alam M, Murad MW, Noman A, Ozturk I, 2016. Relationships among carbon emissions, economic growth, energy consumption and population growth: testing environmental Kuznets curve hypothesis for Brazil, China, India and Indonesia. Environ Anthropol eJ.

  • Antonakakis N, Chatziantoniou I, Filis G (2017) Renew Sust Energ Rev 68:808–824

    Article  Google Scholar 

  • Bakhtyar B, 2017. Universal Financial Crisis and Association of Southeast Asian Nations CO2 Emission. Int J Energy Econ Policy

  • Biljanovska N, Grigoli F, Hengge M, (2017). IMF Working Pap 17, 1. https://doi.org/10.5089/9781484325032.001

  • Blanchard Olivier J, Quah D, (1989). Am Econ Rev 19, 655–73.

  • Davaajargal L, Enkhtur M, Dulguun L, Enkhsuren B, 2020. Non-performing loan recovery: the case of Mongolia. J Appl Finance Bank 81–97. https://doi.org/10.47260/jafb/1116

  • Dong K, Hochman G, Zhang Y, Sun R, Li H, Liao H (2018) CO2 emissions, economic and population growth, and renewable energy: empirical evidence across regions. Energy Econ 75:180–192

    Article  Google Scholar 

  • Dudley B (2018) BP statistical review of world energy. BP Statistical Review, London accessed Aug 6, 2018

    Google Scholar 

  • Góes C (2016). Testing Piketty’s hypothesis on the drivers of income inequality: evidence from panel VARs with heterogeneous dynamics. Int Monet Fund.

  • Jordà Ò, Schularick M, Taylor AM (2017) Macrofinancial history and the new business cycle facts. NBER Macroecon Ann 31:213–263

    Article  Google Scholar 

  • Keating JW (1996) Structural information in recursive VAR orderings. J Econ Dyn Control 20:1557–1580. https://doi.org/10.1016/0165-1889(96)00914-1

    Article  Google Scholar 

  • Klein Goldewijk, K, Beusen A, Doelman J, Stehfest E (2016) Anthropogenic land use estimates for the Holocene; HYDE 3.2.1. https://dataportaal.pbl.nl/downloads/HYDE/HYDE3.2/,3.2.1

  • Liddle B, 2013. Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Popul Environ

  • Luvsannyam D, 2018. Panel Structural VARs and the PSVAR Eviews add-in.

  • Mardani A, Streimikiene D, Cavallaro F, Loganathan N, Khoshnoudi M (2018) Carbon dioxide (CO2) emissions and economic growth: a systematic review of two decades of research from 1995 to 2017. Sci Total Environ 649:31–49. https://doi.org/10.1016/j.scitotenv.2018.08.229

    Article  CAS  Google Scholar 

  • Narayan PK, Smyth R (2007) Are shocks to energy consumption permanent or temporary? Evidence from 182 countries. Energy Policy 35:333–341

    Article  Google Scholar 

  • Nordhaus WD (1977) Economic growth and climate: the carbon dioxide problem. Am Econ Rev 67:341–346

    Google Scholar 

  • Ozcan B (2013) Are shocks to energy consumption permanent or temporary? The case of 17 middle east countries. Energy Explor Exploit 31:589–605

    Article  Google Scholar 

  • Pedroni P (2013) Structural Panel VARs. Econometrics 1:180–206

    Article  Google Scholar 

  • Perrier Q, Guivarch C, Boucher O (2019) Diversity of greenhouse gas emission drivers across European countries since the 2008 crisis. Clim Pol 19:1067–1087. https://doi.org/10.1080/14693062.2019.1625744

    Article  Google Scholar 

  • Quandl (2021) A Nasdaq Platform. Energy Production and Consumption. https://www.quandl.com/data/BP-Energy-Production-and-Consumption

  • Ritchie H, Roser M, 2017. CO2 and greenhouse gas emissions. Our world in data.

  • Roinioti AJ, Koroneos C (2017) The decomposition of CO2 emissions from energy use in Greece before and during the economic crisis and their decoupling from economic growth. Renew Sustain Energy Rev 76:448–459

    Article  Google Scholar 

  • Sadorsky PA, 2020. Energy related CO2 emissions before and after the Financial Crisis. Sustainability.

  • Saidi K, Hammami S (2015) The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Rep 1:62–70

    Article  Google Scholar 

  • Schoellhamer DH (2001) Singular spectrum analysis for time series with missing data. Geophys Res Lett 28:3187–3190

    Article  Google Scholar 

  • Shahbaz M, Khraief N, Hammoudeh S (2018) How do carbon emissions respond to economic shocks? Evidence from low-, middle-, and high-income countries. J Energy Dev 44:161–196

    Google Scholar 

  • Shiller RJ (1989) Market volatility. MIT press, Cambridge

  • Shiller RJ (2015) Revised and Expanded, REV - Revised, 3rd edn. Princeton University Press, Princeton. https://doi.org/10.2307/j.ctt1287kz5

  • Shiller RJ (2015) Irrational exuberance: Revised and expanded third edition. Princeton University Press, Princeton

  • Sobrino N, Monzon A (2014) The impact of the economic crisis and policy actions on GHG emissions from road transport in Spain. Energy Policy 74:486–498. https://doi.org/10.1016/j.enpol.2014.07.020

    Article  Google Scholar 

  • United Nations, Department of Economic and Social Affairs, Population Division (2019) World Population Prospects 2019

  • Vautard R, Yiou P, Ghil M (1992) Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena 58:95–126

    Article  Google Scholar 

  • Vincentas G, Girdenas S, Rovas A (2010). Impact of the 1930s economic crisis and their relationship to global warming, BEH, April, 2010. Peer.

  • Wang K-M (2013) The relationship between carbon dioxide emissions and economic growth: quantile panel-type analysis. Qual Quant 47:1337–1366

    Article  Google Scholar 

  • Wang S, Fang C, Wang Y, Huang Y, Ma H (2015) Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis. Ecol Indic 49:121–131

    Article  CAS  Google Scholar 

  • Zhu Q, Peng X, 2012. The impacts of population change on carbon emissions in China during 19782008. Environ Impact Assess Rev

Download references

Acknowledgements

We are grateful to Prof. Ilhan Ozturk to his valuable suggestions for improving the paper. Comments from the editor and four anonymous reviewers are gratefully acknowledged.

Availability of data and materials

Data available at https://www.icloud.com/iclouddrive/0hdLCYNcvmgD-XKdI1uRLk0KQ#data

Funding

This paper was funded under the project line ZIP UNIRI of the University of Rijeka, for the project ZIP-U N lRl-130-5-20.

Author information

Authors and Affiliations

  1. Juraj Dobrila University, Pula, Croatia

    Marinko Skare & Damian Skare

  2. Lithuanian Energy Institute, Kaunas, Lithuania

    Dalia Streimikiene

Authors
  1. Marinko Skare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dalia Streimikiene
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Damian Skare
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Marinko Skare; methodology: Marinko Skare; formal analysis and investigation: Marinko Skare; writing-original draft preparation: Marinko Skare, Damian Skare; writing-review and editing: Damian Skare; funding acquisition: Dalia Stremikiene; resources: Dalia Stremikiene; supervision: Dalia Stremikiene.

Corresponding author

Correspondence to Marinko Skare.

Ethics declarations

Ethics approval and consent to participate

Not involving human participants, human data.

Consent for publication

No third party details, images, or videos

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Ilhan Ozturk

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Skare, M., Streimikiene, D. & Skare, D. Measuring carbon emission sensitivity to economic shocks: a panel structural vector autoregression 1870–2016. Environ Sci Pollut Res 28, 44505–44521 (2021). https://doi.org/10.1007/s11356-021-13636-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-13636-9

Keywords

JEL

Search

Navigation