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Digitalization and Environmental Aims in Municipalities

Author

Listed:
  • Tina Ringenson

    (Strategic Sustainability Studies, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

  • Mattias Höjer

    (Strategic Sustainability Studies, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

  • Anna Kramers

    (Strategic Sustainability Studies, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

  • Anna Viggedal

    (Ericsson, Stockholm 115 41, Sweden)

Abstract

Many municipalities express a wish to use digital technologies to achieve environmental aims. However, there is still a need for a better understanding of how this should practically be done, both among municipalities and among ICT developers. We have used workshops and literature studies to formulate technological abilities of digitalization. We use two EU directives that are relevant for municipal environmental goals and combine the activities that these directives indicate with technological abilities of digitalization, in order to formulate practical implementations of digital technology to help these activities and reach the directives’ goals. We suggest that this method can be used for any municipal goal, as follows: (1) Identify the objective (in our case set by the EU-directives); (2) Identify what activities these points will require or generate; (3a) From a municipal viewpoint: Based on the results of 1 and 2, formulate and structure ideas of how digitalization can support the objectives and how those ideas can be implemented; (3b) From a provider’s viewpoint: Investigate what digital solutions supporting 1 and 2 exist, or how existing services can be tweaked to support the objectives and explore how new digital solutions supporting 1 and 2 can be developed.

Suggested Citation

  • Tina Ringenson & Mattias Höjer & Anna Kramers & Anna Viggedal, 2018. "Digitalization and Environmental Aims in Municipalities," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1278-:d:142404
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    References listed on IDEAS

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    1. Atiq Uz Zaman & Steffen Lehmann, 2011. "Challenges and Opportunities in Transforming a City into a “Zero Waste City”," Challenges, MDPI, vol. 2(4), pages 1-21, November.
    2. Mohammad Ahmadi Achachlouei & Åsa Moberg, 2015. "Life Cycle Assessment of a Magazine, Part II: A Comparison of Print and Tablet Editions," Journal of Industrial Ecology, Yale University, vol. 19(4), pages 590-606, August.
    3. Fremstad, Anders, 2017. "Does Craigslist Reduce Waste? Evidence from California and Florida," Ecological Economics, Elsevier, vol. 132(C), pages 135-143.
    4. Anuschka Hilke & Lisa Ryan, 2012. "Mobilising Investment in Energy Efficiency: Economic Instruments for Low‐energy Buildings," IEA Energy Papers 2012/10, OECD Publishing.
    5. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
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    Cited by:

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    2. Anna Visvizi & Miltiadis D. Lytras, 2018. "It’s Not a Fad: Smart Cities and Smart Villages Research in European and Global Contexts," Sustainability, MDPI, vol. 10(8), pages 1-10, August.
    3. Christina Pakusch & Gunnar Stevens & Alexander Boden & Paul Bossauer, 2018. "Unintended Effects of Autonomous Driving: A Study on Mobility Preferences in the Future," Sustainability, MDPI, vol. 10(7), pages 1-22, July.
    4. Magdalena Zioło & Piotr Niedzielski & Ewa Kuzionko-Ochrymiuk & Jacek Marcinkiewicz & Katarzyna Łobacz & Krzysztof Dyl & Renata Szanter, 2022. "E-Government Development in European Countries: Socio-Economic and Environmental Aspects," Energies, MDPI, vol. 15(23), pages 1-17, November.
    5. Aleksey ANISIMOV & Anatoliy RYZHENKOV, 2021. "Current legal issues of digitalization of environmental protection: a view from Russia," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 12, pages 105-122, December.
    6. Hong Nham, Nguyen Thi & Ha, Le Thanh, 2022. "Making the circular economy digital or the digital economy circular? Empirical evidence from the European region," Technology in Society, Elsevier, vol. 70(C).
    7. Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    8. Duygan, Mert & Fischer, Manuel & Ingold, Karin, 2023. "Assessing the readiness of municipalities for digital process innovation," Technology in Society, Elsevier, vol. 72(C).

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