IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v138y2024ics0140988324005279.html
   My bibliography  Save this article

Heterogeneity in carbon intensity patterns: A subsampling approach

Author

Listed:
  • Hounyo, Ulrich
  • Kakeu, Johnson
  • Lu, Li

Abstract

Carbon intensity, defined as carbon dioxide (CO2) emissions per unit of gross domestic product (GDP), is a critical metric for assessing the effectiveness of climate policy across nations. This paper presents an analysis of the persistence and stationarity of carbon intensity data across 176 countries and 44 regions from 1990 to 2014, employing subsampling confidence intervals. Subsampling is a robust statistical technique that performs well with finite samples and requires minimal assumptions about the data. Our findings categorize countries into three distinct groups based on their carbon intensity patterns: convergent, persistent, and divergent. We observe that climate mitigation policies in countries with a convergent pattern tend to have only temporary effectiveness, whereas in countries with a divergent pattern, such policies can lead to permanent changes. Additionally, using unsupervised learning methods, we delve into the underlying factors influencing these classifications. This study is particularly significant for understanding the long-term impacts of climate policies, offering valuable insights for policymakers and international bodies. By identifying and analyzing these distinct patterns, our research contributes to the strategic planning and implementation of more effective and sustainable climate policies globally, aligning with the goals of international agreements like the Paris Accord.

Suggested Citation

  • Hounyo, Ulrich & Kakeu, Johnson & Lu, Li, 2024. "Heterogeneity in carbon intensity patterns: A subsampling approach," Energy Economics, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:eneeco:v:138:y:2024:i:c:s0140988324005279
    DOI: 10.1016/j.eneco.2024.107819
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324005279
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107819?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. Juvenal Luciana & Taylor Mark P., 2008. "Threshold Adjustment of Deviations from the Law of One Price," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-46, September.
    2. Fallahi, Firouz & Voia, Marcel-Cristian, 2015. "Convergence and persistence in per capita energy use among OECD countries: Revisited using confidence intervals," Energy Economics, Elsevier, vol. 52(PA), pages 246-253.
    3. Marschinski, Robert & Edenhofer, Ottmar, 2010. "Revisiting the case for intensity targets: Better incentives and less uncertainty for developing countries," Energy Policy, Elsevier, vol. 38(9), pages 5048-5058, September.
    4. Romano, Joseph P & Wolf, Michael, 2001. "Subsampling Intervals in Autoregressive Models with Linear Time Trend," Econometrica, Econometric Society, vol. 69(5), pages 1283-1314, September.
    5. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    6. Hounyo, Ulrich, 2023. "A Wild Bootstrap For Dependent Data," Econometric Theory, Cambridge University Press, vol. 39(2), pages 264-289, April.
    7. Andrews, Donald W K & Chen, Hong-Yuan, 1994. "Approximately Median-Unbiased Estimation of Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 187-204, April.
    8. Bruce E. Hansen, 1999. "The Grid Bootstrap And The Autoregressive Model," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 594-607, November.
    9. Gospodinov, Nikolay, 2002. "Median unbiased forecasts for highly persistent autoregressive processes," Journal of Econometrics, Elsevier, vol. 111(1), pages 85-101, November.
    10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    11. Christidou, Maria & Panagiotidis, Theodore & Sharma, Abhijit, 2013. "On the stationarity of per capita carbon dioxide emissions over a century," Economic Modelling, Elsevier, vol. 33(C), pages 918-925.
    12. Fallahi, Firouz & Karimi, Mohammad & Voia, Marcel-Cristian, 2016. "Persistence in world energy consumption: Evidence from subsampling confidence intervals," Energy Economics, Elsevier, vol. 57(C), pages 175-183.
    13. Andrews, Donald W K, 1993. "Exactly Median-Unbiased Estimation of First Order Autoregressive/Unit Root Models," Econometrica, Econometric Society, vol. 61(1), pages 139-165, January.
    14. Katharine Ricke & Laurent Drouet & Ken Caldeira & Massimo Tavoni, 2018. "Country-level social cost of carbon," Nature Climate Change, Nature, vol. 8(10), pages 895-900, October.
    15. Anna Mikusheva, 2007. "Uniform Inference in Autoregressive Models," Econometrica, Econometric Society, vol. 75(5), pages 1411-1452, September.
    Full references (including those not matched with items on IDEAS)

    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. Simionescu, Mihaela, 2022. "Stochastic convergence in per capita energy use in the EU-15 countries. The role of economic growth," Applied Energy, Elsevier, vol. 322(C).
    2. Fallahi, Firouz, 2017. "Stochastic convergence in per capita energy use in world," Energy Economics, Elsevier, vol. 65(C), pages 228-239.
    3. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    4. Fallahi, Firouz & Voia, Marcel-Cristian, 2015. "Convergence and persistence in per capita energy use among OECD countries: Revisited using confidence intervals," Energy Economics, Elsevier, vol. 52(PA), pages 246-253.
    5. Firouz Fallahi, 2019. "Persistence and stationarity of sectoral energy consumption in the US: A confidence interval approach," Energy & Environment, , vol. 30(5), pages 882-897, August.
    6. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    7. Carlos A. Medel & Pablo M. Pincheira, 2016. "The out-of-sample performance of an exact median-unbiased estimator for the near-unity AR(1) model," Applied Economics Letters, Taylor & Francis Journals, vol. 23(2), pages 126-131, February.
    8. Fallahi, Firouz & Karimi, Mohammad & Voia, Marcel-Cristian, 2016. "Persistence in world energy consumption: Evidence from subsampling confidence intervals," Energy Economics, Elsevier, vol. 57(C), pages 175-183.
    9. Kim, Hyeongwoo & Durmaz, Nazif, 2012. "Bias correction and out-of-sample forecast accuracy," International Journal of Forecasting, Elsevier, vol. 28(3), pages 575-586.
    10. Andrews, Donald W.K. & Guggenberger, Patrik, 2012. "Asymptotics for LS, GLS, and feasible GLS statistics in an AR(1) model with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 169(2), pages 196-210.
    11. Christopher J. Neely & David E. Rapach, 2008. "Real interest rate persistence: evidence and implications," Review, Federal Reserve Bank of St. Louis, vol. 90(Nov), pages 609-642.
    12. Elena Pesavento & Barbara Rossi, 2006. "Small‐sample confidence intervals for multivariate impulse response functions at long horizons," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1135-1155, December.
    13. Stanislav Anatolyev & Nikolay Gospodinov, 2012. "Asymptotics of near unit roots (in Russian)," Quantile, Quantile, issue 10, pages 57-71, December.
    14. Fallahi, Firouz, 2012. "The stationarity of consumption–income ratios: Evidence from bootstrapping confidence intervals," Economics Letters, Elsevier, vol. 115(1), pages 137-140.
    15. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    16. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    17. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    18. Claude Lopez & Christian J. Murray & David H. Papell, 2013. "Median-unbiased estimation in DF-GLS regressions and the PPP puzzle," Applied Economics, Taylor & Francis Journals, vol. 45(4), pages 455-464, February.
    19. Müller, Ulrich K. & Wang, Yulong, 2019. "Nearly weighted risk minimal unbiased estimation," Journal of Econometrics, Elsevier, vol. 209(1), pages 18-34.
    20. Marcet, Albert & Jarociński, Marek, 2010. "Autoregressions in small samples, priors about observables and initial conditions," Working Paper Series 1263, European Central Bank.

    More about this item

    Keywords

    Carbon intensity; Confidence interval; Stationarity; Persistence; Subsampling; Unsupervised learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    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:138:y:2024:i:c:s0140988324005279. 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.