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. Yang, Xue & Su, Bin, 2019. "Impacts of international export on global and regional carbon intensity," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. 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.
    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. Jing Bai & Chuang Tu & Jiming Bai, 2024. "Measuring and decomposing Beijing’s energy performance: an energy- and exergy-based perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17617-17633, July.
    3. Jianguo Zhou & Baoling Jin & Shijuan Du & Ping Zhang, 2018. "Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei," Energies, MDPI, vol. 11(6), pages 1-17, June.
    4. Wang, Qiang & Song, Xiaoxin, 2021. "How UK farewell to coal – Insight from multi-regional input-output and logarithmic mean divisia index analysis," Energy, Elsevier, vol. 229(C).
    5. Singer, Gregor, 2024. "Complementary inputs and industrial development: can lower electricity prices improve energy efficiency?," LSE Research Online Documents on Economics 122365, London School of Economics and Political Science, LSE Library.
    6. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    7. Lizhan Cao & Hui Wang, 2022. "The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
    8. Liu, Hongxun & Du, Kerui & Li, Jianglong, 2019. "An improved approach to estimate direct rebound effect by incorporating energy efficiency: A revisit of China's industrial energy demand," Energy Economics, Elsevier, vol. 80(C), pages 720-730.
    9. 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.
    10. 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.
    11. Lin, Boqiang & Tan, Ruipeng, 2017. "Sustainable development of China's energy intensive industries: From the aspect of carbon dioxide emissions reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 386-394.
    12. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, vol. 9(5), pages 1-13, April.
    13. Tang, Le & Jefferson, Gary, 2024. "A DSGE model of energy efficiency with vintage capital in Chinese industry," Economic Modelling, Elsevier, vol. 132(C).
    14. 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.
    15. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    16. Azam, Muhammad & Younes, Ben Zaied & Hunjra, Ahmed Imran & Hussain, Nazim, 2022. "Integrated Spatial-Temporal decomposition analysis for life cycle assessment of carbon emission intensity change in various regions of China," Resources Policy, Elsevier, vol. 79(C).
    17. Kuosmanen, Natalia & Maczulskij, Terhi, 2022. "The Role of Firm Dynamics in the Green Transition: Carbon Productivity Decomposition in Finnish Manufacturing," ETLA Working Papers 99, The Research Institute of the Finnish Economy.
    18. Chen, Zhongfei & Huang, Wanjing & Zheng, Xian, 2019. "The decline in energy intensity: Does financial development matter?," Energy Policy, Elsevier, vol. 134(C).
    19. 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.
    20. Zhang, Dayong & Li, Jun & Ji, Qiang, 2020. "Does better access to credit help reduce energy intensity in China? Evidence from manufacturing firms," Energy Policy, Elsevier, vol. 145(C).

    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.