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Long-term energy efficiency analysis requires solid energy statistics: The case of the German basic chemical industry


  • Saygin, D.
  • Worrell, E.
  • Tam, C.
  • Trudeau, N.
  • Gielen, D.J.
  • Weiss, M.
  • Patel, M.K.


Analyzing the chemical industry’s energy use is challenging because of the sector’s complexity and the prevailing uncertainty in energy use and production data. We develop an advanced bottom-up model (PIE-Plus) which encompasses the energy use of the 139 most important chemical processes. We apply this model in a case study to analyze the German basic chemical industry’s energy use and energy efficiency improvements in the period between 1995 and 2008. We compare our results with data from the German Energy Balances and with data published by the International Energy Agency (IEA). We find that our model covers 88% of the basic chemical industry’s total final energy use (including non-energy use) as reported in the German Energy Balances. The observed energy efficiency improvements range between 2.2 and 3.5% per year, i.e., they are on the higher side of the values typically reported in literature. Our results point to uncertainties in the basic chemical industry’s final energy use as reported in the energy statistics and the specific energy consumption values. More efforts are required to improve the quality of the national and international energy statistics to make them useable for reliable monitoring of energy efficiency improvements of the chemical industry.

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  • Saygin, D. & Worrell, E. & Tam, C. & Trudeau, N. & Gielen, D.J. & Weiss, M. & Patel, M.K., 2012. "Long-term energy efficiency analysis requires solid energy statistics: The case of the German basic chemical industry," Energy, Elsevier, vol. 44(1), pages 1094-1106.
  • Handle: RePEc:eee:energy:v:44:y:2012:i:1:p:1094-1106
    DOI: 10.1016/

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    References listed on IDEAS

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    3. Bühler, Fabian & Guminski, Andrej & Gruber, Anna & Nguyen, Tuong-Van & von Roon, Serafin & Elmegaard, Brian, 2018. "Evaluation of energy saving potentials, costs and uncertainties in the chemical industry in Germany," Applied Energy, Elsevier, vol. 228(C), pages 2037-2049.
    4. Jinchao Li & Yuwei Xiang & Huanyu Jia & Lin Chen, 2018. "Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
    5. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    6. Alexander Kramer & Fernando Morgado‐Dias, 2020. "Artificial intelligence in process control applications and energy saving: a review and outlook," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(6), pages 1133-1150, December.
    7. Changsheng Li & Lei Zhu & Tobias Fleiter, 2014. "Energy Efficiency Potentials in the Chlor-Alkali Sector — A Case Study of Shandong Province in China," Energy & Environment, , vol. 25(3-4), pages 661-686, April.
    8. Sergej Vojtovic & Alina Stundziene & Rima Kontautiene, 2018. "The Impact of Socio-Economic Indicators on Sustainable Consumption of Domestic Electricity in Lithuania," Sustainability, MDPI, vol. 10(2), pages 1-21, January.
    9. Song, Chenxi & Li, Mingjia & Wen, Zhexi & He, Ya-Ling & Tao, Wen-Quan & Li, Yangzhe & Wei, Xiangyang & Yin, Xiaolan & Huang, Xing, 2014. "Research on energy efficiency evaluation based on indicators for industry sectors in China," Applied Energy, Elsevier, vol. 134(C), pages 550-562.
    10. Ke, Jing & Price, Lynn & McNeil, Michael & Khanna, Nina Zheng & Zhou, Nan, 2013. "Analysis and practices of energy benchmarking for industry from the perspective of systems engineering," Energy, Elsevier, vol. 54(C), pages 32-44.

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