IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v124y2023ics0140988323002633.html
   My bibliography  Save this article

The viability of energy efficiency in facilitating Saudi Arabia's journey toward net-zero emissions

Author

Listed:
  • Belaïd, Fateh
  • Massié, Camille

Abstract

Assessing the contribution of energy efficiency to climate change mitigation is crucial as global warming continues to persist. This study assesses the impact of energy efficiency on carbon intensity in Saudi Arabia and evaluates its potential contributions to the country's net-zero emissions target. We analyze time-series data for Saudi Arabia from 1971 to 2020 and estimate a quantile regression model. The results show that energy efficiency is critical for mitigating carbon dioxide emissions, indicating the importance of activating this lever to accelerate the decarbonization process. The model is robust to changes in the proxy for energy efficiency, confirming energy efficiency's ability to mitigate environmental degradation in Saudi Arabia. Moreover, energy efficiency has a greater impact on the twenty-fifth to seventy-fifth quantiles of carbon intensity than on the tenth and ninetieth quantiles. These heterogeneous effects across carbon intensity quantiles should be considered when the Saudi government discusses and sets its decarbonization objectives. We further extend the analysis by forecasting carbon intensity through 2060 under a scenario in which energy efficiency improves. The results show that energy efficiency improvements may account for up to one-fifth of Saudi Arabia's decarbonization by 2060. This finding underscores energy efficiency's importance for achieving climate stability and building a better future.

Suggested Citation

  • Belaïd, Fateh & Massié, Camille, 2023. "The viability of energy efficiency in facilitating Saudi Arabia's journey toward net-zero emissions," Energy Economics, Elsevier, vol. 124(C).
  • Handle: RePEc:eee:eneeco:v:124:y:2023:i:c:s0140988323002633
    DOI: 10.1016/j.eneco.2023.106765
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988323002633
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2023.106765?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. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, January.
    2. 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.
    3. Belaïd, Fateh & Flambard, Véronique, 2023. "Impacts of income poverty and high housing costs on fuel poverty in Egypt: An empirical modeling approach," Energy Policy, Elsevier, vol. 175(C).
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    5. Shokoohi, Zeinab & Dehbidi, Navid Kargar & Tarazkar, Mohammad Hassan, 2022. "Energy intensity, economic growth and environmental quality in populous Middle East countries," Energy, Elsevier, vol. 239(PC).
    6. Tiba, Sofien & Belaid, Fateh, 2020. "The pollution concern in the era of globalization: Do the contribution of foreign direct investment and trade openness matter?," Energy Economics, Elsevier, vol. 92(C).
    7. Krarti, Moncef & Dubey, Kankana & Howarth, Nicholas, 2017. "Evaluation of building energy efficiency investment options for the Kingdom of Saudi Arabia," Energy, Elsevier, vol. 134(C), pages 595-610.
    8. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    9. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    10. Belaïd, Fateh & Al-Sarihi, Aisha & Al-Mestneer, Raed, 2023. "Balancing climate mitigation and energy security goals amid converging global energy crises: The role of green investments," Renewable Energy, Elsevier, vol. 205(C), pages 534-542.
    11. Kolcava, Dennis & Nguyen, Quynh & Bernauer, Thomas, 2019. "Does trade liberalization lead to environmental burden shifting in the global economy?," Ecological Economics, Elsevier, vol. 163(C), pages 98-112.
    12. Gasim, Anwar A. & Agnolucci, Paolo & Ekins, Paul & De Lipsis, Vincenzo, 2023. "Modeling final energy demand and the impacts of energy price reform in Saudi Arabia," Energy Economics, Elsevier, vol. 120(C).
    13. Krarti, Moncef & Aldubyan, Mohammad & Williams, Eric, 2020. "Residential building stock model for evaluating energy retrofit programs in Saudi Arabia," Energy, Elsevier, vol. 195(C).
    14. Belaïd, Fateh & Ranjbar, Zeinab & Massié, Camille, 2021. "Exploring the cost-effectiveness of energy efficiency implementation measures in the residential sector," Energy Policy, Elsevier, vol. 150(C).
    15. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    16. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    17. Nepal, Rabindra & Musibau, Hammed Oluwaseyi & Jamasb, Tooraj, 2021. "Energy consumption as an indicator of energy efficiency and emissions in the European Union: A GMM based quantile regression approach," Energy Policy, Elsevier, vol. 158(C).
    18. Thomakos, Dimitrios D. & Alexopoulos, Thomas A., 2016. "Carbon intensity as a proxy for environmental performance and the informational content of the EPI," Energy Policy, Elsevier, vol. 94(C), pages 179-190.
    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. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    2. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    3. Narula, Subhash C. & Wellington, John F. & Lewis, Stephen A., 2012. "Valuating residential real estate using parametric programming," European Journal of Operational Research, Elsevier, vol. 217(1), pages 120-128.
    4. Jean-Marc Fournier & Isabell Koske, 2012. "The determinants of earnings inequality: evidence from quantile regressions," OECD Journal: Economic Studies, OECD Publishing, vol. 2012(1), pages 7-36.
    5. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    6. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    7. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    8. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 34(2), pages 461-467.
    9. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Working Papers hal-01548710, HAL.
    10. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    11. Marrocu, Emanuela & Paci, Raffaele & Zara, Andrea, 2015. "Micro-economic determinants of tourist expenditure: A quantile regression approach," Tourism Management, Elsevier, vol. 50(C), pages 13-30.
    12. Joachim Wagner, 2014. "Exports, foreign direct investments and productivity: are services firms different?," The Service Industries Journal, Taylor & Francis Journals, vol. 34(1), pages 24-37, January.
    13. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    14. Wagner Joachim & Schank Thorsten & Schnabel Claus & Addison John T., 2006. "Works Councils, Labor Productivity and Plant Heterogeneity: First Evidence from Quantile Regressions," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(5), pages 505-518, October.
    15. George Anastassopoulos & Fragkiskos Filippaios & Paul Phillips, 2007. "An ‘eclectic’ investigation of tourism multinationals’ activities: Evidence from the Hotels and Hospitality Sector in Greece," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 08, Hellenic Observatory, LSE.
    16. Luisa Alamá & Emili Tortosa-Ausina, 2012. "Bank Branch Geographic Location Patterns in S pain: Some Implications for Financial Exclusion," Growth and Change, Wiley Blackwell, vol. 43(3), pages 505-543, September.
    17. Micheline Goedhuys & Leo Sleuwaegen, 2010. "High-growth entrepreneurial firms in Africa: a quantile regression approach," Small Business Economics, Springer, vol. 34(1), pages 31-51, January.
    18. Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.
    19. Jawadi, Fredj & Sousa, Ricardo M., 2013. "Money demand in the euro area, the US and the UK: Assessing the role of nonlinearity," Economic Modelling, Elsevier, vol. 32(C), pages 507-515.
    20. Chao, Shih-Kang & Härdle, Wolfgang K. & Yuan, Ming, 2021. "Factorisable Multitask Quantile Regression," Econometric Theory, Cambridge University Press, vol. 37(4), pages 794-816, August.

    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:124:y:2023:i:c:s0140988323002633. 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.