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Journal ArticleDOI

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

TL;DR: In this article , the authors present a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 Sustainable Development Goals to date, and they show that data-based analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing databased policies, prioritizing actions, and optimizing the allocation of resources.
Abstract: 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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Citations
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Journal ArticleDOI
TL;DR: In this article , the authors present the digitainability assessment framework (DAF), which is a tool for guiding participatory action for the implementation of digital interventions in the context of sustainable development.
Abstract: Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is essential specifically for Sustainable Development Goals (SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact will help us align the visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed digitainability assessment framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. This paper demonstrates how DAF can be instrumental in guiding participatory action for the implementation of digitainability practices. This paper summarizes the insights developed during the Digitainable Spring School 2022 (DSS) on “Sustainability with Digitalization and Artificial Intelligence,” one of whose goals was to operationalize the DAF as a tool in the participatory action process with collaboration and active involvement of diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The participating experts worked together to identify a DI and gather and analyze evidence by operationalizing the DAF. The four DIs identified in the process are as follows: smart home technology (SHT) for energy efficiency, the blockchain for food security, artificial intelligence (AI) for land use and cover change (LUCC), and Big Data for international law. Each of the four expert groups addresses different DIs for digitainability assessment using different techniques to gather and analyze data related to the criteria and indicators. The knowledge presented here could increase understanding of the challenges and opportunities related to digitainability and provide a structure for developing and implementing robust digitainability practices with data-driven insights.

1 citations

Journal ArticleDOI
TL;DR: In this paper , a hybrid multi-criteria decision-making (MCDM) model is used to evaluate potential risks caused by data-driven technologies in sustainable supply chains and reveal the most critical sustainability dimension that is affected from these risks.
Abstract: PurposeWith the rapid change that has taken place with digitalization and data-driven approaches in supply chains, business environment become more competitive and reaching sustainability in supply chains become even more challenging. In order to manage supply chains properly, in terms of considering environmental, social and economic impacts, organizations need to deal with huge amount of data and improve organizations' data management skills. From this view, increased number of stakeholders and dynamic environment reveal the importance of data-driven technologies in sustainable supply chains. This complex structure results in new kind of risks caused by data-driven technologies. Therefore, the aim of the study to analyze potential risks related to data privacy, trust, data availability, information sharing and traceability, i.e. in sustainable supply chains.Design/methodology/approachA hybrid multi-criteria decision-making (MCDM) model, which is the integration of step-wise weight assessment ratio analysis (SWARA) and TOmada de Decisao Interativa Multicriterio (TODIM) methods, is going to be used to prioritize potential risks and reveal the most critical sustainability dimension that is affected from these risks.FindingsResults showed that economic dimension of the sustainable supply chain management (SSCM) is the most critical concept while evaluating risks caused by data-driven technologies. On the other hand, risk of data security, risk of data privacy and weakness of information technology systems and infrastructure are revealed as the most important risks that organizations should consider.Originality/valueThe contribution of the study is expected to guide policymakers and practitioners in terms of defining potential risks causes by data-driven technologies in sustainable supply chains. In future studies, solutions can be suggested based on these risks for achieving sustainability in all stages of the supply chain causes by data-driven technologies.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the authors analyzed the impacts of AI in sustainable development goals and its role in meeting the 17 Sustainable Development Goals (SDGs) and its 169 targets, focusing on the progression of the SDGs (sustainable development goals).
Abstract: Artificial Intelligence is transforming the way we live and work and increasingly replace cognitively human ways of making decisions. The so called “algorithmocracy” or the ecosystem that we all now inhabit, where algorithms govern many aspects of our behavior, has the potential to bias and be deployed at large scales. Because the automation of decisions by algorithms promise efficiency and resource maximization, AI technologies can be used to meet the 17 Sustainable Development Goals and its 169 targets. This article aims to analyze the impacts of AI in SDGs. It draws a few fundamental inductions for ESG (climate, social, governance) amidst fast innovative and social change. This study consolidates the viewpoints of ecological, social and public strategy to dissect the effects of AI on sustainable development with a particular spotlight on the progression of the SDGs (sustainable development goals). It draws a few experiences on administrative learning and authority development for worldwide sustainability.

1 citations

Journal ArticleDOI
TL;DR: In this article , the current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work and a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy are discussed.
Abstract: Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

1 citations

Journal ArticleDOI
TL;DR: In this article , a Conceptual Waste Framework used by international organizations to evaluate solid waste management (SWM) practices has been updated, identifying opportunities, barriers, and best practices for effective SWM.
Abstract: Growing populations and consumption drive solid waste management (SWM) challenges; globalization of transport, food production, and trade, including waste trading, distributes risks, worldwide. Using Waste Hierarchy (3Rs) and Circular Economy (CE) concepts, we updated a Conceptual Waste Framework used by international organizations to evaluate SWM practices. We identified the key steps, and the important factors, as well as stakeholders, which are essential features for effective SWM. Within this updated conceptual framework, we qualitatively evaluated Global SWM strategies and practices, identifying opportunities, barriers, and best practices. We find that, although a few exceptional countries exhibit zero waste compliance, most, fare poorly, as exhibited by the high waste generation, incineration, and disposal (open dumping, landfilling) volumes. In the Global North, SWM strategies and practices rely heavily on technologies, economic tools, regulatory frameworks, education, and social engagement to raise stakeholder awareness, and enhance inclusion, and participation, while in the Global South, many governments take sole legal responsibility for SWM, seeking to eliminate waste, as a public "nuisance". Separation and recycling in the Global South are mainly implemented by "informal" economies, in which subsistence needs, drive recyclable materials retrieval. Imported, regionally inappropriate tools, economic constraints, weak policies and governance, waste trading, non-inclusive stakeholder participation, data limitations, and limited public awareness continue to pose major waste and environmental management challenges across nations. Within the context of the framework, we conclude that best practices from around the world can be used to guide decision-making, globally. Despite variations in drivers and needs across regions, nations in both the Global North and South need to improve WH and CE compliance, and enhance stakeholders' partnership, awareness, and participation throughout the SWM process. Partnerships between the Global North and South could better manage traded wastes, reduce adverse impacts, and enhance global environmental sustainability and equity, supporting UN Sustainable Development Goals. This article is protected by copyright. All rights reserved. Integr Environ Assess Manag 2023;00:0-0. © 2023 SETAC.
References
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01 Jan 2016
TL;DR: The Scoping meeting on collaboration between Regional Seas Programmes and Regional Fisheries Bodies in the Southwest Indian Ocean is described in this article, where the authors propose a framework for collaboration between regional sea programmes and regional fisheries bodies in the Indian Ocean.
Abstract: Information document of the Scoping meeting on collaboration between Regional Seas Programmes and Regional Fisheries Bodies in the Southwest Indian Ocean

13,752 citations

Posted Content
TL;DR: The extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research is evaluated.
Abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge base. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can be biased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contradictory evidence has become progressively harder. The quality of evidence underpinning decision-making and action has been questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process by synthesizing research in a systematic, transparent, and reproducible manner with the twin aims of enhancing the knowledge base and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.

7,368 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluate the process of systematic review used in the medical sciences to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research and highlight the challenges in developing an appropriate methodology.
Abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge hase. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can he hiased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contrad ictory evidence has hecome progressively harder. The quality of evidence underpinning decision-making and action has heen questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process hy synthesizing research in a systematic, transparent, and reproducihie manner with the twin aims of enhancing the knowledge hase and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.

7,020 citations

Journal ArticleDOI
TL;DR: Haemorrhage and hypertensive disorders are major contributors to maternal deaths in developing countries and these data should inform evidence-based reproductive health-care policies and programmes at regional and national levels.

3,593 citations

Journal ArticleDOI
Eric J. Topol1
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Abstract: The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

2,574 citations