Research has identified that there is a paucity of reviews covering green public procurement (GPP) in terms of environmentally responsible behavior and sustainability policy adoption. Using, comprehensively, the most recent (2017–2020) and relevant (Web of Science- and Scopus-indexed) empirical sources, our paper fills the gap in the literature by focusing on the main developing streams of research, that is: How GPP drives the circular economy; GPP of construction and building materials; environmental and supply chain management measures in GPP; the procurement of sustainable innovation; environmental policy objectives of GPP as regards energy, pollution, carbon footprint, and climate change; GPP as an environmental policy mechanism for production and use of sustainable goods and services; and GPP as an integral component of sustainable development and performance. Further investigations can explore hot topics related to the role of GPP in the automated algorithmic decision-making processes taking place in data-driven smart sustainable cities because the harnessing, among other things, of sensing and computing technologies, network connectivity systems, and the Cognitive Internet of Things will fulfill the incessant exigencies of public administration.

In this article, we cumulate previous research findings indicating that organizations advance to superior phases of environmental management development in order to attain corporate sustainability by the use of participative decision-making. We contribute to the literature on corporate sustainability management and performance by showing that the correlation between sustainable development governance, organizational knowledge, sustainable organizational development, and corporate sustainability, which shapes corporate environmental and sustainability management. Throughout June 2020, we conducted a quantitative literature review of ProQuest, Scopus, and the Web of Science databases, with search terms including “corporate sustainability”, “corporate sustainability management”, “corporate sustainability performance”, “sustainability reporting”, “sustainable supply chain management”, “sustainable corporate development”, and “environmental management systems”. As we inspected research published exclusively in the past two years, only 338 articles met the eligibility criteria. By eliminating the findings that were questionable, unsubstantiated by replication, or too general, and due to space limitations, we selected 93, mainly empirical, sources. Future research should investigate whether corporate governance systems, through organizational sustainability practices and performance reporting, can shape operational environmental sustainability and sustainable organizational culture.

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.