IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i12p2860-d1412626.html
   My bibliography  Save this article

Energy Intensity Forecasting Models for Manufacturing Industries of “Catching Up” Economies: Lithuanian Case

Author

Listed:
  • Egidijus Norvaiša

    (Laboratory of Energy Systems Research, Lithuanian Energy Institute, Breslaujos 3, LT-44403 Kaunas, Lithuania)

  • Viktorija Bobinaitė

    (Laboratory of Energy Systems Research, Lithuanian Energy Institute, Breslaujos 3, LT-44403 Kaunas, Lithuania)

  • Inga Konstantinavičiūtė

    (Laboratory of Energy Systems Research, Lithuanian Energy Institute, Breslaujos 3, LT-44403 Kaunas, Lithuania)

  • Vaclovas Miškinis

    (Laboratory of Energy Systems Research, Lithuanian Energy Institute, Breslaujos 3, LT-44403 Kaunas, Lithuania)

Abstract

The objective of this research was to construct energy intensity forecasting models for key manufacturing industries, with a particular focus on “catching up” European economies. Future energy intensity values serve as the foundation for energy demand forecasts, which are essential inputs for the analysis of countries’ decarbonisation scenarios. The Lithuanian case is analysed in the context of its efforts to reach the economic development level of the most advanced European Union (EU) countries. The scientific literature and energy policy analysis, interdependence (correlation and regression), tendency and case analysis, logical economic reasoning, and graphical representation methods have been applied. The energy intensity forecasts until 2050 were based on historical statistical data of value added and final energy consumption of EU countries from 2000 to 2021. The analysis of historical trends revealed a remarkable decrease in industrial energy intensity in most EU countries, including Lithuania. Given the rapid pace of decline in historical energy intensity, the values observed in individual Lithuanian industries have already reached levels comparable to the most economically advanced EU countries. Four econometric trendlines were employed to construct forecasting models for energy intensity. The results for Lithuania demonstrated that the selected trendlines exhibited a high degree of fit with historical energy intensity data from the EU, as evidenced by their R 2 values. Furthermore, the forecasts were shown to be highly accurate, with their MAPEs remaining below 10% in most cases. Nevertheless, the logarithmic trendline was found to be the most accurate for forecasting energy intensity in total manufacturing (MAPE = 4.0%), non-metallic minerals (MAPE = 3.5%), and food, beverages, and tobacco (MAPE = 4.1%) industries, with the exponential trendline in the chemical industry (MAPE = 8.7%) and the moving average in the total manufacturing industry (MAPE = 4.0%), food industries (MAPE = 4.0%), and remaining aggregate industries (MAPE = 14.5%). It is forecasted that energy intensity could decline by 8 to 16% to 1.10–1.20 kWh/EUR in Lithuania’s manufacturing industries by 2050.

Suggested Citation

  • Egidijus Norvaiša & Viktorija Bobinaitė & Inga Konstantinavičiūtė & Vaclovas Miškinis, 2024. "Energy Intensity Forecasting Models for Manufacturing Industries of “Catching Up” Economies: Lithuanian Case," Energies, MDPI, vol. 17(12), pages 1-34, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2860-:d:1412626
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/12/2860/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/12/2860/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Karen Fisher-Vanden & Yong Hu & Gary Jefferson & Michael Rock & Michael Toman, 2016. "Factors Influencing Energy Intensity in Four Chinese Industries," The Energy Journal, , vol. 37(1_suppl), pages 153-178, January.
    2. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    3. Yang, Xue & Su, Bin, 2019. "Impacts of international export on global and regional carbon intensity," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    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. Zhu, Bangzhu & Su, Bin & Li, Yingzhu & Ng, Tsan Sheng, 2020. "Embodied energy and intensity in China’s (normal and processing) exports and their driving forces, 2005-2015," Energy Economics, Elsevier, vol. 91(C).
    2. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    3. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    4. Tang, Le & Jefferson, Gary, 2024. "A DSGE model of energy efficiency with vintage capital in Chinese industry," Economic Modelling, Elsevier, vol. 132(C).
    5. Iris Claus & Les Oxley & Siqi Zheng & Cong Sun & Ye Qi & Matthew E. Kahn, 2014. "The Evolving Geography Of China'S Industrial Production: Implications For Pollution Dynamics And Urban Quality Of Life," Journal of Economic Surveys, Wiley Blackwell, vol. 28(4), pages 709-724, September.
    6. Chen, Zhongfei & Huang, Wanjing & Zheng, Xian, 2019. "The decline in energy intensity: Does financial development matter?," Energy Policy, Elsevier, vol. 134(C).
    7. Lizhan Cao & Zhongying Qi, 2017. "Theoretical Explanations for the Inverted-U Change of Historical Energy Intensity," Sustainability, MDPI, vol. 9(6), pages 1-19, June.
    8. Yao Qian & Lang Sun & Quanyi Qiu & Lina Tang & Xiaoqi Shang & Chengxiu Lu, 2020. "Analysis of CO 2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China," Energies, MDPI, vol. 13(5), pages 1-21, March.
    9. Zhang, Wei & Wang, Nan, 2021. "Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment," Energy, Elsevier, vol. 221(C).
    10. Chen, Yu & Lin, Boqiang, 2021. "Understanding the green total factor energy efficiency gap between regional manufacturing—insight from infrastructure development," Energy, Elsevier, vol. 237(C).
    11. Hongyun Han & Shu Wu, 2018. "Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China," Sustainability, MDPI, vol. 10(12), pages 1-23, December.
    12. Su, Bin & Ang, B.W., 2020. "Demand contributors and driving factors of Singapore’s aggregate carbon intensities," Energy Policy, Elsevier, vol. 146(C).
    13. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    14. Lin, Boqiang & Sai, Rockson, 2021. "A multi factor Malmquist CO2emission performance indices: Evidence from Sub Saharan African public thermal power plants," Energy, Elsevier, vol. 223(C).
    15. Ajayi, V. & Reiner, D., 2018. "European Industrial Energy Intensity: The Role of Innovation 1995-2009," Cambridge Working Papers in Economics 1835, Faculty of Economics, University of Cambridge.
    16. Chen, Jiandong & Xie, Qiaoli & Shahbaz, Muhammad & Song, Malin & Li, Li, 2022. "Impact of bilateral trade on fossil energy consumption in BRICS: An extended decomposition analysis," Economic Modelling, Elsevier, vol. 106(C).
    17. Wu, F. & Zhou, P. & Zhou, D.Q., 2020. "Modeling carbon emission performance under a new joint production technology with energy input," Energy Economics, Elsevier, vol. 92(C).
    18. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    19. Haider, Salman & Danish, Mohd Shadab & Sharma, Ruchi, 2019. "Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis," Energy Economics, Elsevier, vol. 81(C), pages 454-464.
    20. Lin Boqiang & Kui Liu, 2017. "Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry," Sustainability, MDPI, vol. 9(7), pages 1-16, July.

    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:gam:jeners:v:17:y:2024:i:12:p:2860-:d:1412626. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.