IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v50y2012icp154-160.html
   My bibliography  Save this article

Alternative projection of the world energy consumption-in comparison with the 2010 international energy outlook

Author

Listed:
  • Chang, Yusang
  • Lee, Jinsoo
  • Yoon, Hyerim

Abstract

A projection of future energy consumption is a vital input to many analyses of economic, energy, and environmental policies. We provide a benchmark projection which can be used to evaluate any other projection. Specifically, we base our projection of future energy consumption on its historical trend, which can be identified by an experience model. We compare our projection with forecasts by the U.S. Energy Information Administration (EIA) for eight countries—U.S., China, India, Brazil, Japan, South Korea, Canada, and Mexico. We find that the EIA's projections are lower than ours in the case of China, the U.S., India, Japan, and Mexico. This indicates that for these five countries, the EIA uses assumptions which cannot be rationalized by historical data.

Suggested Citation

  • Chang, Yusang & Lee, Jinsoo & Yoon, Hyerim, 2012. "Alternative projection of the world energy consumption-in comparison with the 2010 international energy outlook," Energy Policy, Elsevier, vol. 50(C), pages 154-160.
    • Handle: RePEc:eee:enepol:v:50:y:2012:i:c:p:154-160
    DOI: 10.1016/j.enpol.2012.07.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421512006544
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2012.07.059?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. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    2. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Blok, Kornelis, 2010. "Analyzing price and efficiency dynamics of large appliances with the experience curve approach," Energy Policy, Elsevier, vol. 38(2), pages 770-783, February.
    3. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    4. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    5. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    6. Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2009. "Energy demand models for policy formulation : a comparative study of energy demand models," Policy Research Working Paper Series 4866, The World Bank.
    7. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    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. Yu Sang Chang & Byong-Jin You & Hann Earl Kim, 2020. "Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights," Sustainability, MDPI, vol. 12(7), pages 1-34, April.
    2. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    3. Liao, Hua & Cai, Jia-Wei & Yang, Dong-Wei & Wei, Yi-Ming, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA's projection," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 90-96.
    4. Rashidi, Hamidreza & GhaffarianHoseini, Ali & GhaffarianHoseini, Amirhosein & Nik Sulaiman, Nik Meriam & Tookey, John & Hashim, Nur Awanis, 2015. "Application of wastewater treatment in sustainable design of green built environments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 845-856.
    5. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    6. Rosenberg, Eva & Lind, Arne & Espegren, Kari Aamodt, 2013. "The impact of future energy demand on renewable energy production – Case of Norway," Energy, Elsevier, vol. 61(C), pages 419-431.
    7. Hann-Earl Kim & Yu-Sang Chang & Hee-Jin Kim, 2021. "Dynamic Electricity Intensity Trends in 91 Countries," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    8. Vítor JPD Martinho, 2018. "A transversal perspective on global energy production and consumption: An approach based on convergence theory," Energy & Environment, , vol. 29(4), pages 556-575, June.

    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. Méjean, Aurélie & Hope, Chris, 2013. "Supplying synthetic crude oil from Canadian oil sands: A comparative study of the costs and CO2 emissions of mining and in-situ recovery," Energy Policy, Elsevier, vol. 60(C), pages 27-40.
    2. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    3. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    4. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    5. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    7. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    8. Rout, Ullash K. & Fahl, Ulrich & Remme, Uwe & Blesl, Markus & Voß, Alfred, 2009. "Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model," Energy Policy, Elsevier, vol. 37(7), pages 2814-2830, July.
    9. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    10. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
    11. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    12. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, 2016. "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
    13. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," NBER Working Papers 19921, National Bureau of Economic Research, Inc.
    14. Handayani, Kamia & Krozer, Yoram & Filatova, Tatiana, 2019. "From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning," Energy Policy, Elsevier, vol. 127(C), pages 134-146.
    15. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    16. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," CESifo Working Paper Series 4705, CESifo.
    17. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
    18. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    19. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    20. Zhang, Da & Chai, Qimin & Zhang, Xiliang & He, Jiankun & Yue, Li & Dong, Xiufen & Wu, Shu, 2012. "Economical assessment of large-scale photovoltaic power development in China," Energy, Elsevier, vol. 40(1), pages 370-375.

    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:enepol:v:50:y:2012:i:c:p:154-160. 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/enpol .

    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.