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Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal

Author

Listed:
  • Angélica Pigola

    (Master and Doctoral Pos-Graduate Program in Administration, University Nove de Julho (UNINOVE), São Paulo 01504-000, Brazil)

  • Priscila Rezende da Costa

    (Master and Doctoral Pos-Graduate Program in Administration, University Nove de Julho (UNINOVE), São Paulo 01504-000, Brazil)

  • Luísa Cagica Carvalho

    (Department of Economics and Management, School of Business and Administration, Institute Polytechnic of Setúbal, 2910-761 Setúbal, Portugal)

  • Luciano Ferreira da Silva

    (Master and Doctoral Pos-Graduate Program in Administration, University Nove de Julho (UNINOVE), São Paulo 01504-000, Brazil)

  • Cláudia Terezinha Kniess

    (Civil Engineering Program, Department of Research and Graduate Studies Stricto Sensu, University São Judas Tadeu (USJT), São Paulo 05503-001, Brazil)

  • Emerson Antonio Maccari

    (Master and Doctoral Pos-Graduate Program in Administration, University Nove de Julho (UNINOVE), São Paulo 01504-000, Brazil)

Abstract

Innovativeness is a characteristic of digital technologies (DT), and they have been assuming an important role in economic, social, and environmental dimensions. Therefore, DT are relevant contributors for sustainable development goal (SDG) achievements. This study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal. An independent sample t -test analysis and Levene test are performed to identify potential artificial intelligence-driven digital technologies (AI-Driven DT) as favorable facilitators for SDG achievements in Brazil and Portugal. Based on the findings, a broader analysis is provided, to (i) indicate potential favorable SDGs, (ii) discuss differences between the countries in AI-Driven DT preferences in each SDG, and (iii) make recommendations for potential technologies that could receive more attention and investments in both regions to make emergent digital technologies succeed, with a particular emphasis on cleaner production. The analysis is organized into three dimensions: economic, social, and environment. At the end, a closing discussion is provided about the key guidelines and prospects that could be adopted to keep a strong and positive shift of AI-Driven DT developments and applications towards fully supporting the attainment of the SDG of United Nations Organization (ONU) Agenda 2030.

Suggested Citation

  • Angélica Pigola & Priscila Rezende da Costa & Luísa Cagica Carvalho & Luciano Ferreira da Silva & Cláudia Terezinha Kniess & Emerson Antonio Maccari, 2021. "Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13669-:d:699559
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    1. Eleonora Santos & Milena Carvalho & Susana Martins, 2023. "Sustainable Water Management: Understanding the Socioeconomic and Cultural Dimensions," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    2. Geraldo Cardoso de Oliveira Neto & Roberto Rodrigues Leite & Wagner Cezar Lucato & Rosangela Maria Vanalle & Marlene Amorim & João Carlos Oliveira Matias & Vikas Kumar, 2022. "Overcoming Barriers to the Implementation of Cleaner Production in Small Enterprises in the Mechanics Industry: Exploring Economic Gains and Contributions for Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    3. Walter Cardoso Satyro & Jose Celso Contador & Sonia Francisca de Paula Monken & Anderson Ferreira de Lima & Gilberto Gomes Soares Junior & Jansen Anderson Gomes & João Victor Silva Neves & José Robert, 2023. "Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    4. Guo Li & Jinfeng Wang & Xin Wang, 2023. "Construction and Path of Urban Public Safety Governance and Crisis Management Optimization Model Integrating Artificial Intelligence Technology," Sustainability, MDPI, vol. 15(9), pages 1-19, May.

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