Have a personal or library account? Click to login
Decarbonisation Pathways of Industry in TIMES Model Cover

References

  1. [1] European Comission. A Clean Planet for all. A European strategic long-term vision. 2018. [Online]. [Accessed: 08.03.2021]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52018DC0773&from
  2. [2] Fleiter T. et al. A methodology for bottom-up modelling of energy transitions in the industry sector: The FORECAST model. Energy Strategy Reviews 2018:22:237–254. <a href="https://doi.org/10.1016/j.esr.2018.09.00510.1016/j.esr.2018.09.005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.esr.2018.09.00510.1016/j.esr.2018.09.005</a>
  3. [3] Dolge K., Kubule A., Blumberga D. Composite index for energy efficiency evaluation of industrial sector: sub-sectoral comparison. Environmental and Sustainability Indicators 2020:8:100062. <a href="https://doi.org/10.1016/j.indic.2020.10006210.1016/j.indic.2020.100062" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.indic.2020.10006210.1016/j.indic.2020.100062</a>
  4. [4] Marques A. C., Fuinhas J. A., Tomás C. Energy efficiency and sustainable growth in industrial sectors in European Union countries: A nonlinear ARDL approach. Journal of Cleaner Production 2019:239:118045. <a href="https://doi.org/10.1016/j.jclepro.2019.11804510.1016/j.jclepro.2019.118045" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jclepro.2019.11804510.1016/j.jclepro.2019.118045</a>
  5. [5] Loulou R., Goldstein G., Kanudia A., Lettila A., Remme U., Noble K. Documentation for the TIMES Model PART I. Concepts and Theory. 2016, [Online]. [Accessed: 15.03.2021]. Available: https://iea-etsap.org/index.php/etsap-tools/model-generators/times
  6. [6] Wang H., Chen W. Modelling deep decarbonization of industrial energy consumption under 2-degree target: Comparing China, India and Western Europe. Applied Energy 2019:238:1563–1572. <a href="https://doi.org/10.1016/j.apenergy.2019.01.13110.1016/j.apenergy.2019.01.131" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.apenergy.2019.01.13110.1016/j.apenergy.2019.01.131</a>
  7. [7] Obrist M. D., Kannan R., Schmidt T. J., Kober T. Decarbonization pathways of the Swiss cement industry towards net zero emissions. Journal of Cleaner Production 2021:288:125413. <a href="https://doi.org/10.1016/j.jclepro.2020.12541310.1016/j.jclepro.2020.125413" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jclepro.2020.12541310.1016/j.jclepro.2020.125413</a>
  8. [8] Calvo V. L. V., Giner-Santonja G., Alonso-Fariñas B., Aguado J. M. The effect of the European Industrial Emissions Directive on the air emission limit values set by competent authorities in the permitting procedure: The case of the Spanish cement industry. Science of the Total Environment 2021:773:145491. <a href="https://doi.org/10.1016/j.scitotenv.2021.14549110.1016/j.scitotenv.2021.14549133940728" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.scitotenv.2021.14549110.1016/j.scitotenv.2021.14549133940728</a>
  9. [9] Hache E., Simoën M., Seck G. S., Bonnet C., Jabberi A., Carcanague S. The impact of future power generation on cement demand: An international and regional assessment based on climate scenarios. International Economics 2019:163:114–133. <a href="https://doi.org/10.1016/j.inteco.2020.05.00210.1016/j.inteco.2020.05.002" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.inteco.2020.05.00210.1016/j.inteco.2020.05.002</a>
  10. [10] Dhar S., Pathak M., Shukla P. R. Transformation of India’s steel and cement industry in a sustainable 1.5 °C world, Energy Policy 2020:137:111104. <a href="https://doi.org/10.1016/j.enpol.2019.11110410.1016/j.enpol.2019.111104" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.enpol.2019.11110410.1016/j.enpol.2019.111104</a>
  11. [11] Brunke J. C., Blesl M. Energy conservation measures for the German cement industry and their ability to compensate for rising energy-related production costs. Journal of Cleaner Production 2014:82:94–111. <a href="https://doi.org/10.1016/j.jclepro.2014.06.07410.1016/j.jclepro.2014.06.074" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jclepro.2014.06.07410.1016/j.jclepro.2014.06.074</a>
  12. [12] Kermeli K. et al. The scope for better industry representation in long-term energy models: Modeling the cement industry Applied Energy 2019:240:964–985. <a href="https://doi.org/10.1016/j.apenergy.2019.01.25210.1016/j.apenergy.2019.01.252" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.apenergy.2019.01.25210.1016/j.apenergy.2019.01.252</a>
  13. [13] Yao Y., E. Masanet. Life-cycle modeling framework for generating energy and greenhouse gas emissions inventory of emerging technologies in the chemical industry. Journal of Cleaner Production 2018:172:768–777. <a href="https://doi.org/10.1016/j.jclepro.2017.10.12510.1016/j.jclepro.2017.10.125" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jclepro.2017.10.12510.1016/j.jclepro.2017.10.125</a>
  14. [14] Geng Z., Zhang Y., Li C., Han Y., Cui Y., Yu B. Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature. Energy 2020:194:116851. <a href="https://doi.org/10.1016/j.energy.2019.11685110.1016/j.energy.2019.116851" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.energy.2019.11685110.1016/j.energy.2019.116851</a>
  15. [15] Han Y., Long C., Geng Z., Zhu Q., Zhong Y. A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management 2019:183:349–359. <a href="https://doi.org/10.1016/j.enconman.2018.12.12010.1016/j.enconman.2018.12.120" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.enconman.2018.12.12010.1016/j.enconman.2018.12.120</a>
  16. [16] Geng Z., Bai J., Jiang D., Han Y. Energy structure analysis and energy saving of complex chemical industries: A novel fuzzy interpretative structural model. Appl. Therm. Eng., 2018:142:433–443.<a href="https://doi.org/10.1016/j.applthermaleng.2018.07.030" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.applthermaleng.2018.07.030</a>
  17. [17] Griffin P. W., Hammond G. P. Industrial energy use and carbon emissions reduction in the iron and steel sector: A UK perspective. Applied Energy 2019:249:109–125. <a href="https://doi.org/10.1016/j.apenergy.2019.04.14810.1016/j.apenergy.2019.04.148" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.apenergy.2019.04.14810.1016/j.apenergy.2019.04.148</a>
  18. [18] Griffin P. W., Hammond G. P. Analysis of the potential for energy demand and carbon emissions reduction in the iron and steel sector. Energy Procedia 2019:158:3915–3922. <a href="https://doi.org/10.1016/j.egypro.2019.01.85210.1016/j.egypro.2019.01.852" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.egypro.2019.01.85210.1016/j.egypro.2019.01.852</a>
  19. [19] Ahlström J. M., Zetterholm J., Pettersson K., Harvey S., Wetterlund E. Economic potential for substitution of fossil fuels with liquefied biomethane in Swedish iron and steel industry – Synergy and competition with other sectors, Energy Conversion and Management 2020:209:112641. <a href="https://doi.org/10.1016/j.enconman.2020.11264110.1016/j.enconman.2020.112641" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.enconman.2020.11264110.1016/j.enconman.2020.112641</a>
  20. [20] Vögele S., Grajewski M., Govorukha K., Rübbelke D. Challenges for the European steel industry: Analysis, possible consequences and impacts on sustainable development. Applied Energy 2020:264:114633. <a href="https://doi.org/10.1016/j.apenergy.2020.11463310.1016/j.apenergy.2020.114633" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.apenergy.2020.11463310.1016/j.apenergy.2020.114633</a>
  21. [21] Lerede D., Bustreo C., Gracceva F., Saccone M., Savoldi L. Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling. Renewable and Sustainable Energy Reviews 2021:140:110742. <a href="https://doi.org/10.1016/j.rser.2021.11074210.1016/j.rser.2021.110742" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.rser.2021.11074210.1016/j.rser.2021.110742</a>
  22. [22] Xu Y., Szmerekovsky J. System dynamic modeling of energy savings in the US food industry. Journal of Cleaner Production 2017:165:13–26. <a href="https://doi.org/10.1016/j.jclepro.2017.07.09310.1016/j.jclepro.2017.07.093" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jclepro.2017.07.09310.1016/j.jclepro.2017.07.093</a>
  23. [23] Seck G. S., Guerassimoff G., Maïzi N. Heat recovery with heat pumps in non-energy intensive industry: A detailed bottom-up model analysis in the French food & drink industry. Applied Energy 2013:111:489–504. <a href="https://doi.org/10.1016/j.apenergy.2013.05.03510.1016/j.apenergy.2013.05.035" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.apenergy.2013.05.03510.1016/j.apenergy.2013.05.035</a>
  24. [24] Jonkman J., Castiglioni A., Akkerman R., van der Padt A. Improving resource efficiency in the food industry by using non-conventional intermediate products, Journal of Food Engineering 2020:287:110198. <a href="https://doi.org/10.1016/j.jfoodeng.2020.11019810.1016/j.jfoodeng.2020.110198" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.jfoodeng.2020.11019810.1016/j.jfoodeng.2020.110198</a>
  25. [25] Central Statistical Bureau of Latvia, RUG031. Volume indices of industrial production. [Online]. [Accessed: 13.03.2021]. Available: http://data1.csb.gov.lv/pxweb/en/rupnbuvn/rupnbuvn__rupn__ikgad/RUG031.px/table/tableViewLayout1/
  26. [26] Central Statistical Bureau of Latvia, ENG020. Energy balance, TJ, thsd toe (NACE Rev.2). [Online]. [Accessed: 13.03.2021]. Available: http://data1.csb.gov.lv/pxweb/en/vide/vide__energetika__ikgad/ENG020.px/
  27. [27] Central Statistical Bureau of Latvia, ENG030. ETS balance, TJ (NACE Rev.2). [Online]. [Accessed: 13.03.2021]. Available: http://data1.csb.gov.lv/pxweb/en/vide/vide__energetika__ikgad/ENG030.px/
  28. [28] Central Statistical Bureau of Latvia, ENG040. Non-ETS balance, TJ (NACE Rev.2), [Online]. [Accessed: 13.03.2021]. Available: http://data1.csb.gov.lv/pxweb/en/vide/vide__energetika__ikgad/ENG040.px/
  29. [29] Kubule A., Locmelis K., Blumberga D. Analysis of the results of national energy audit program in Latvia. Energy 2020:202:117679. <a href="https://doi.org/10.1016/j.energy.2020.11767910.1016/j.energy.2020.117679" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.energy.2020.11767910.1016/j.energy.2020.117679</a>
  30. [30] Capros P. et al. EU Reference Scenario 2016. 2016. [Online]. [Accessed: 13.03.2021]. Available: https://ec.europa.eu/energy/sites/ener/files/documents/ref2016_report_final-web.pdf
  31. [31] Central Statistical Bureau of Latvia, ENG190. Average prices of energy resources for final consumers (excluding VAT), [Online]. [Accessed: 13.03.2021]. Available: http://data1.csb.gov.lv/pxweb/en/vide/vide__energetika__ikgad/ENG190.px/
  32. [32] The Parliament of the Republic of Latvia, Law On Excise Duties. Latvijas Vestnesis 161, 2003. [Online]. [Accessed: 02.02.2021]. Available: http://likumi.lv/ta/en/en/id/81066-on-excise-duties
  33. [33] The Parliament of the Republic of Latvia, Natural Resources Tax Law. Latvijas Vestnesis 209, 2005. [Online]. [Accessed: 02.02.2021]. Available: http://likumi.lv/ta/en/en/id/124707-natural-resources-tax-law
  34. [34] The Danish Energy Agency, Catalogue of technology data for energy technologies. [Online]. [Accessed: 02.02.2021]. Available: https://ens.dk/en/our-services/projections-and-models/technology-data
  35. [35] De Vita A. et al. E3Mlab PRIMES model Technology pathways in decarbonisation scenarios, 2018.
DOI: https://doi.org/10.2478/rtuect-2021-0023 | Journal eISSN: 2255-8837 | Journal ISSN: 1691-5208
Language: English
Page range: 318 - 330
Published on: Jun 28, 2021
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2021 Signe Allena-Ozolina, Dzintars Jaunzems, Ieva Pakere, Andra Blumberga, Gatis Bazbauers, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.