IDEAS home Printed from https://ideas.repec.org/a/gam/jresou/v8y2019i1p43-d209075.html
   My bibliography  Save this article

Bioenergy Intensity and Its Determinants in European Continental Countries: Evidence Using GMM Estimation

Author

Listed:
  • Mohd Alsaleh

    (Faculty of Economics and Management, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia)

  • A. S. Abdul-Rahim

    (Faculty of Economics and Management, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia)

Abstract

This study contributes to the existing literature by examining bioenergy intensity and its related factors in European continental countries (ECC). Through its focus on European continental (EC), this study extends the existing literature, which mainly covers nationwide studies. The current paper aims to investigate the variables of bioenergy intensity in the ECC during the term 2005–2013, construct its economic variables, and evaluate the volume and significance level of the impact of each variable on bioenergy intensity. To successfully achieve this analysis, a generalised method of moments estimator (GMM) was designed for ECC. The estimated models show that available bioenergy for final consumption has a positive impact on bioenergy intensity in ECC. The largest influence on bioenergy intensity was evaluated for the annual growth of Gross Domestic Product (GDP), followed by the investment and referral that the scale and construction of this economic variable should be taken into consideration and applied as a precious bioenergy regulation and policy instruments for developing bioenergy intensity and efficiency.

Suggested Citation

  • Mohd Alsaleh & A. S. Abdul-Rahim, 2019. "Bioenergy Intensity and Its Determinants in European Continental Countries: Evidence Using GMM Estimation," Resources, MDPI, vol. 8(1), pages 1-14, February.
    • Handle: RePEc:gam:jresou:v:8:y:2019:i:1:p:43-:d:209075
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2079-9276/8/1/43/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2079-9276/8/1/43/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adom, Philip Kofi, 2015. "Determinants of energy intensity in South Africa: Testing for structural effects in parameters," Energy, Elsevier, vol. 89(C), pages 334-346.
    2. Löschel, Andreas & Pothen, Frank & Schymura, Michael, 2015. "Peeling the onion: Analyzing aggregate, national and sectoral energy intensity in the European Union," Energy Economics, Elsevier, vol. 52(S1), pages 63-75.
    3. Filipović, Sanja & Verbič, Miroslav & Radovanović, Mirjana, 2015. "Determinants of energy intensity in the European Union: A panel data analysis," Energy, Elsevier, vol. 92(P3), pages 547-555.
    4. Adom, Philip Kofi, 2015. "Business cycle and economic-wide energy intensity: The implications for energy conservation policy in Algeria," Energy, Elsevier, vol. 88(C), pages 334-350.
    5. Khishtandar, Soheila & Zandieh, Mostafa & Dorri, Behrouz, 2017. "A multi criteria decision making framework for sustainability assessment of bioenergy production technologies with hesitant fuzzy linguistic term sets: The case of Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1130-1145.
    6. Verbič, Miroslav & Filipović, Sanja & Radovanović, Mirjana, 2017. "Electricity prices and energy intensity in Europe," Utilities Policy, Elsevier, vol. 47(C), pages 58-68.
    7. Adom, Philip Kofi, 2015. "Asymmetric impacts of the determinants of energy intensity in Nigeria," Energy Economics, Elsevier, vol. 49(C), pages 570-580.
    8. van Dam, J. & Faaij, A.P.C. & Hilbert, J. & Petruzzi, H. & Turkenburg, W.C., 2009. "Large-scale bioenergy production from soybeans and switchgrass in Argentina: Part A: Potential and economic feasibility for national and international markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1710-1733, October.
    9. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    10. Wu, Yanrui, 2012. "Energy intensity and its determinants in China's regional economies," Energy Policy, Elsevier, vol. 41(C), pages 703-711.
    11. Zheng, Yingmei & Qi, Jianhong & Chen, Xiaoliang, 2011. "The effect of increasing exports on industrial energy intensity in China," Energy Policy, Elsevier, vol. 39(5), pages 2688-2698, May.
    12. Jiang, Lei & Folmer, Henk & Ji, Minhe, 2014. "The drivers of energy intensity in China: A spatial panel data approach," China Economic Review, Elsevier, vol. 31(C), pages 351-360.
    13. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    14. Yu, Huayi, 2012. "The influential factors of China's regional energy intensity and its spatial linkages: 1988–2007," Energy Policy, Elsevier, vol. 45(C), pages 583-593.
    15. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    16. Wang, Chunhua, 2013. "Changing energy intensity of economies in the world and its decomposition," Energy Economics, Elsevier, vol. 40(C), pages 637-644.
    17. Fan, Ruguo & Luo, Ming & Zhang, Pengfei, 2016. "A study on evolution of energy intensity in China with heterogeneity and rebound effect," Energy, Elsevier, vol. 99(C), pages 159-169.
    18. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    19. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    20. Li, Ke & Lin, Boqiang, 2015. "The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model," Energy, Elsevier, vol. 84(C), pages 589-599.
    21. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
    22. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    23. Samuelson, Ralph D., 2014. "The unexpected challenges of using energy intensity as a policy objective: Examining the debate over the APEC energy intensity goal," Energy Policy, Elsevier, vol. 64(C), pages 373-381.
    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. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    2. Hui Sun & Yanan Dou & Shubo Hu & Zhengnan Gao & Zhonghui Wang & Peng Yuan, 2023. "Day-Ahead Bidding Strategy of a Virtual Power Plant with Multi-Level Electric Energy Interaction in China," Energies, MDPI, vol. 16(19), pages 1-27, September.
    3. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.

    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. Amuakwa-Mensah, Franklin & Klege, Rebecca A. & Adom, Philip K. & Amoah, Anthony & Hagan, Edmond, 2018. "Unveiling the energy saving role of banking performance in Sub-Sahara Africa," Energy Economics, Elsevier, vol. 74(C), pages 828-842.
    2. Adom, Philip Kofi & Adams, Samuel, 2018. "Energy savings in Nigeria. Is there a way of escape from energy inefficiency?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2421-2430.
    3. Antonietti, Roberto & Fontini, Fulvio, 2019. "Does energy price affect energy efficiency? Cross-country panel evidence," Energy Policy, Elsevier, vol. 129(C), pages 896-906.
    4. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    5. Wu, Shu & Ding, Song, 2021. "Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector," Energy Economics, Elsevier, vol. 99(C).
    6. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Jiao, Zhiming & Han, Cuicui, 2019. "How do industrialization and trade openness influence energy intensity? Evidence from a path model in case of Bangladesh," Energy Policy, Elsevier, vol. 133(C).
    7. Jin, Taeyoung, 2022. "Impact of heat and electricity consumption on energy intensity: A panel data analysis," Energy, Elsevier, vol. 239(PA).
    8. 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.
    9. Trinh, Hai Hong & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Vo, Diem Thi Hong, 2022. "Examining the heterogeneity of financial development in the energy-environment nexus in the era of climate change: Novel evidence around the world," Energy Economics, Elsevier, vol. 116(C).
    10. Huang, Junbing & Hao, Yu & Lei, Hongyan, 2018. "Indigenous versus foreign innovation and energy intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1721-1729.
    11. Huang, Junbing & Du, Dan & Tao, Qizhi, 2017. "An analysis of technological factors and energy intensity in China," Energy Policy, Elsevier, vol. 109(C), pages 1-9.
    12. Huang, Junbing & Lai, Yali & Hu, Hanlei, 2020. "The effect of technological factors and structural change on China's energy intensity: Evidence from dynamic panel models," China Economic Review, Elsevier, vol. 64(C).
    13. Dargahi, Hassan & Khameneh, Kazem Biabany, 2019. "Energy intensity determinants in an energy-exporting developing economy: Case of Iran," Energy, Elsevier, vol. 168(C), pages 1031-1044.
    14. Tafadzwa Ruzive & Thando Mkhombo & Simbarashe Mhaka & Nomahlubi Mavikela & Andrew Phiri, 2019. "Electricity Intensity and Unemployment in South Africa: A Quantile Regression Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 31-40.
    15. An, Hui & Xu, Jianjun & Ma, Xuejiao, 2020. "Does technological progress and industrial structure reduce electricity consumption? Evidence from spatial and heterogeneity analysis," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 206-220.
    16. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    17. Jiang, Xuemei & Duan, Yuwan & Green, Christopher, 2017. "Regional disparity in energy intensity of China and the role of industrial and export structure," Resources, Conservation & Recycling, Elsevier, vol. 120(C), pages 209-218.
    18. Kitazawa, Yoshitsugu, 2001. "Exponential regression of dynamic panel data models," Economics Letters, Elsevier, vol. 73(1), pages 7-13, October.
    19. Han, Chirok & Kim, Hyoungjong, 2014. "The role of constant instruments in dynamic panel estimation," Economics Letters, Elsevier, vol. 124(3), pages 500-503.
    20. Eschenhof, Sabine, 2009. "Standard Taylor rules revisited: A cross country study for European countries," Darmstadt Discussion Papers in Economics 196, Darmstadt University of Technology, Department of Law and Economics.

    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:jresou:v:8:y:2019:i:1:p:43-:d:209075. 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.