IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v83y2019icp415-429.html
   My bibliography  Save this article

CO2 emissions and economic activity: A short-to-medium run perspective

Author

Listed:
  • Fosten, Jack

Abstract

This paper looks at the short-to-medium run impact of economic activity on CO2 emissions in the United States, shifting the existing focus away from the long-run Environmental Kuznets Curve (EKC). Our novel methodological approach combines discrete wavelet transforms with dynamic factor models. This allows us to (i) estimate economic and emissions cycles at different frequencies, and (ii) let economic activity be estimated from many different economic variables, rather than focussing on a small number as in existing studies. From our results, one might at first conclude that emissions are not linked to economic activity in the short-run. However, when looking at the cycles uncovered at timescales of length one to three years, we see that there are indeed strong linkages. Policymakers therefore cannot be exclusively long-termist when evaluating the impact of economic policy on the environment.

Suggested Citation

  • Fosten, Jack, 2019. "CO2 emissions and economic activity: A short-to-medium run perspective," Energy Economics, Elsevier, vol. 83(C), pages 415-429.
  • Handle: RePEc:eee:eneeco:v:83:y:2019:i:c:p:415-429
    DOI: 10.1016/j.eneco.2019.07.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988319302361
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2019.07.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Halicioglu, Ferda, 2009. "An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey," Energy Policy, Elsevier, vol. 37(3), pages 1156-1164, March.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    4. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
    5. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    6. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    7. Holtz-Eakin, Douglas & Selden, Thomas M., 1995. "Stoking the fires? CO2 emissions and economic growth," Journal of Public Economics, Elsevier, vol. 57(1), pages 85-101, May.
    8. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 353-377.
    9. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    10. Ludvigson, Sydney C. & Ng, Serena, 2007. "The empirical risk-return relation: A factor analysis approach," Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
    11. Fulvio Ortu & Andrea Tamoni & Claudio Tebaldi, 2013. "Long-Run Risk and the Persistence of Consumption Shocks," Review of Financial Studies, Society for Financial Studies, vol. 26(11), pages 2876-2915.
    12. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    13. António Rua, 2011. "A wavelet approach for factor‐augmented forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 666-678, November.
    14. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
    15. Burnett, J. Wesley & Bergstrom, John C. & Wetzstein, Michael E., 2013. "Carbon dioxide emissions and economic growth in the U.S," Journal of Policy Modeling, Elsevier, vol. 35(6), pages 1014-1028.
    16. Al-Mulali, Usama & Saboori, Behnaz & Ozturk, Ilhan, 2015. "Investigating the environmental Kuznets curve hypothesis in Vietnam," Energy Policy, Elsevier, vol. 76(C), pages 123-131.
    17. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    18. Jack Fosten & Daniel Gutknecht, 2020. "Testing Nowcast Monotonicity with Estimated Factors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 107-123, January.
    19. Lau, Lin-Sea & Choong, Chee-Keong & Eng, Yoke-Kee, 2014. "Investigation of the environmental Kuznets curve for carbon emissions in Malaysia: Do foreign direct investment and trade matter?," Energy Policy, Elsevier, vol. 68(C), pages 490-497.
    20. Pablo-Romero, M.P. & Cruz, L. & Barata, E., 2017. "Testing the transport energy-environmental Kuznets curve hypothesis in the EU27 countries," Energy Economics, Elsevier, vol. 62(C), pages 257-269.
    21. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    22. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    23. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    24. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    25. Xyngis, Georgios, 2017. "Business-cycle variation in macroeconomic uncertainty and the cross-section of expected returns: Evidence for scale-dependent risks," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 43-65.
    26. Selden Thomas M. & Song Daqing, 1994. "Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?," Journal of Environmental Economics and Management, Elsevier, vol. 27(2), pages 147-162, September.
    27. Kang, Byoung Uk & In, Francis & Kim, Tong Suk, 2017. "Timescale betas and the cross section of equity returns: Framework, application, and implications for interpreting the Fama–French factors," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 15-39.
    28. Maximilian Auffhammer & Ralf Steinhauser, 2012. "Forecasting The Path of U.S. CO_2 Emissions Using State-Level Information," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 172-185, February.
    29. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    30. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, July.
    31. Soytas, Ugur & Sari, Ramazan, 2009. "Energy consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member," Ecological Economics, Elsevier, vol. 68(6), pages 1667-1675, April.
    32. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    33. Soytas, Ugur & Sari, Ramazan & Ewing, Bradley T., 2007. "Energy consumption, income, and carbon emissions in the United States," Ecological Economics, Elsevier, vol. 62(3-4), pages 482-489, May.
    34. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexandra-Anca Purcel, 2020. "New insights into the environmental Kuznets curve hypothesis in developing and transition economies: a literature survey," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(4), pages 585-631, October.
    2. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    3. Jebabli, Ikram & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Quantile connectedness between CO2 emissions and economic growth in G7 countries," Resources Policy, Elsevier, vol. 81(C).
    4. Juan, Aranzazu de & Poncela, Maria Pilar & Ruiz Ortega, Esther, 2023. "Economic activity and C02 emissions in Spain," DES - Working Papers. Statistics and Econometrics. WS 37975, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2022. "The relationship between carbon-intensive fuel and renewable energy stock prices under the emissions trading system," Energy Economics, Elsevier, vol. 114(C).
    6. Nestor Shpak & Solomiya Ohinok & Ihor Kulyniak & W³odzimierz Sroka & Armenia Androniceanu, 2022. "Macroeconomic Indicators and CO2 Emissions in the EU Region," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 24(61), pages 817-817, August.
    7. Marco Quatrosi, 2020. "Analysis of monthly CO2 emission trends for major EU Countries: a time series approach," SEEDS Working Papers 1520, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    8. Erdost Torun & Afife Duygu Ayhan Akdeniz & Erhan Demireli & Simon Grima, 2022. "Long-Term US Economic Growth and the Carbon Dioxide Emissions Nexus: A Wavelet-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    9. Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022. "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN 2022003, Université catholique de Louvain, Louvain Finance (LFIN).
    10. Lin, Boqiang & Xu, Bin, 2020. "Effective ways to reduce CO2 emissions from China's heavy industry? Evidence from semiparametric regression models," Energy Economics, Elsevier, vol. 92(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    2. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    3. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    4. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    5. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    6. Muhammad, Anees & Ishfaq, Ahmed, 2011. "Industrial development, agricultural growth, urbanization and environmental Kuznets curve in Pakistan," MPRA Paper 33469, University Library of Munich, Germany.
    7. Tiba, Sofien & Omri, Anis, 2017. "Literature survey on the relationships between energy, environment and economic growth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1129-1146.
    8. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    9. Xyngis, Georgios, 2017. "Business-cycle variation in macroeconomic uncertainty and the cross-section of expected returns: Evidence for scale-dependent risks," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 43-65.
    10. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    11. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
    12. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    13. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    14. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    15. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    16. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    17. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    18. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    19. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    20. repec:dau:papers:123456789/11663 is not listed on IDEAS
    21. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.

    More about this item

    Keywords

    CO2 emissions; Environmental degradation; Factor model; Wavelets; Cycles; Short-to-medium run;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:83:y:2019:i:c:p:415-429. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.