Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Hassani, Hossein; Huang, Xu; MacFeely, Steve; Entezarian, Mohammad Reza

doi:10.3390/bdcc5030028

Cited by 66 publications

(42 citation statements)

References 232 publications

(241 reference statements)

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“…Global real GDP collapsed in 2020, and in many countries economic growth is not expected to return to pre-pandemic levels until 2022 or 2023. Big data can contribute to achieving the goal of economic growth (target 8.1) through data analytics and the monitoring of indicators [168]-for example, through network analyses of logistical networks that contain information about economic growth [169]. AI can promote job creation, entrepreneurship, and innovation (target 8.3)-for instance, in the bio-based economy, which is likely to be one of the main beneficiaries of the AI-driven acceleration of data mining and forecasting and the resulting creation of new companies and jobs in the microbial biotechnology sector [170].…”

Section: Sdg 8: Decent Work and Economic Growthmentioning

confidence: 99%

“…Advanced cloud computing and the use of big data processing platforms could help establish global partnerships that work on the same SDGs and realize the potential of big data in the context of sustainable development [230]. Concerns about fair data sharing, data ownership, and data privacy hinder partnership collaboration and full use of existing data but could be addressed-alongside mutually consenting policies and agreements-through multi-party computation (MPC) protocols and blockchain technology [168,231]. MPC protocols are an important technical tool for implementing the mechanisms that guarantee the increased utility or accuracy of data [231].…”

Section: Sdg 15: Life On Landmentioning

confidence: 99%

“…The use of blockchain technology can facilitate the secure exchange and processing of big data-as it was invented to support and record transactions of value without the approval of a central authority (cf. Sharing Economy)-and thus support the building of global partnerships [168,232].…”

Section: Sdg 15: Life On Landmentioning

confidence: 99%

“…(11) Another concern is ensuring model security, i.e., that malware does not manipulate data and lead to incorrect decisions of ML models [104,171]. (12) Lastly, ethical concerns, as well as privacy, ownership, and security issues, are prevalent [104,164,168].…”

Section: Contribution and Recommendationsmentioning

confidence: 99%

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The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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Section: Sdg 8: Decent Work and Economic Growthmentioning

confidence: 99%

Section: Sdg 15: Life On Landmentioning

confidence: 99%

Section: Sdg 15: Life On Landmentioning

confidence: 99%

Section: Contribution and Recommendationsmentioning

confidence: 99%

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The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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“…We also use the published materials on the topic of sustainable development and implementation of the SDGs in emerging economies of such researchers as Alam et al (2021, Hassani et al (2021), Sebestyén and Abonyi (2021), Tabares (2021), Tang et al (2021), and Ullah et al (2021).…”

Section: Theoretical Basis Literature Review and Gap Analysismentioning

confidence: 99%

Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies

Popkova

Sergi

2021

Risks

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The relevance of this study lies in the fact that financial risk is a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. The purpose of this article is to clarify specific aspects of financing factors and financial risk related to social entrepreneurship in developing countries (in comparison with the standard financial risk related to commercial entrepreneurship) in order to analyze the influence of the financing factors of social entrepreneurship on sustainable development, as well as to determine the potential for the development of social entrepreneurship through financial risk management. To achieve this goal, this article uses the methodology of econometrics—dataset modelling of financial risk management in social entrepreneurship to achieve sustainable development in emerging economies. On the basis of the results of this study, firstly, it is substantiated that the financial risks entailed by social entrepreneurship differ from the standard financial risk present in commercial entrepreneurship. Specific factors of the financing of sustainable development in emerging economies are determined and, on the basis of this, financial risks specific to social entrepreneurship in emerging economies are identified as follows: (1) reduced stimulus to use financial resources in long-term investments, which disrupts the stability and decreases inclusion; (2) joint public–private investments and decreased investment in R&D; and (3) expanded investment in the skills required for jobs and “markets of tomorrow”. Secondly, a contradictory influence of financing factors on sustainable development is demonstrated. Thirdly, a large potential for the development of social entrepreneurship by means of financial risk management (maximum reduction) was identified. With the minimization of financial risk, social entrepreneurship would demonstrate substantial progress, with an increase of 99.61% (more than 50%) from 45.18 points to 90.18 points. A novel contribution of this paper to the extant literature consists of the specification of the essence and specifics of social entrepreneurship in emerging economies through the identification of financial risks and the provision of recommendations for their management.

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Survey for Big Data Platforms and Resources Management for Smart Cities

Alves

Chaves

Rodrigues

et al. 2022

Lecture Notes in Computer Science

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Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Cited by 66 publications

References 232 publications

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies

Survey for Big Data Platforms and Resources Management for Smart Cities

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