• Energy Research
• 9. Industry and infrastructure close
• US close
• IT close
• RU close
• DE close
• EU close

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Urban infrastructure investment needs in the developing world are immense, particularly when the additional costs associated with lower carbon, more climate-resilient options are considered. These cannot possibly be financed from fiscal sources and ODA flows alone; private financing will need to be accessed. Focusing on the ability of city governments and subnational urban utilities to mobilize private finance, this paper makes two core arguments. First, private investment in municipal infrastructure requires robust institutional, fiscal and regulatory systems that are often absent in developing countries. Establishing such systems often requires policy and institutional reform, much of which lies beyond the competence and control of city governments themselves. Second, while the marginal investment needs related to climate mitigation and adaptation complicate and aggravate the picture, they do not alter the fundamental requirements of private investors. Put simply, municipalities and utilities will need to satisfy the requirements of regular private finance before they can attract green private finance. This paper looks across the main avenues for city governments to mobilize private finance – municipal borrowing, public–private partnerships, and land value capture instruments – and identifies four broad factors that determine the potential size and scope of city leveraging activity. It then offers a new framework to understand where the most pressing constraints to private investment readiness lie and proposes priority measures that local and national governments, together with development partners and other stakeholders, can take to address them.

The construction industry is considered as one of the least productive, highest energy consuming, and least digitized industries. The Lean Management (LM) philosophy became a significant way for eliminating non-value-added activities and wastes during a building’s lifecycle. However, studies have shown that philosophies are not efficient by themselves to solve the issues of the construction industry. They need to be supported with the appropriate technologies and tools. Therefore, the integrated use of Building Information Modelling (BIM) with LM or Value Engineering (VE) were proposed in the literature. Nonetheless, it was also seen that BIM can provide more insights and improvements when BIM is integrated with data analysis tools to analyze BIM data. In the literature, the synergies between these concepts are generally addressed pairwise, and there is no comprehensive framework which identifies their relationships. Therefore, this study aims to develop a maturity framework that facilitates the adoption of LM, VE, BIM, and Big Data Analytic (BDA) concepts to address long-standing productivity and digitalization issues in the Architecture, Engineering, and Construction (AEC) industry. Design Science Research (DSR) methodology and its three-cycle view (relevance, rigor, and design cycle) were applied to build the proposed maturity framework. Two interviews were performed to identify and observe research problem in relevance cycle. In the rigor cycle, a comprehensive literature review was performed to create a base for the development of the maturity framework. In addition to the developed base of the framework, lean processes were added to this cycle. In the design cycle, the developed framework was evaluated and validated by five experts through face-to-face interviews. The importance of employer’s requirements to adopt the proposed methodologies, the negative impact of change orders, the importance of pre-construction phases to facilitate value creation and waste elimination, and the usage of common data environment with BIM were identified as the prominent application and adaptation issues.

A smart city is a city that uses information and communication technologies (ICT), smart sensors, actuators and video cameras to better support and optimize the delivery of urban services and to address challenges within city communities. A smart city collects and analyzes data from smart IoT sensors (IoT devices/nodes) and video cameras. The data collected by the sensor nodes can be used to develop applications and systems that benefit the city and the community. Smart city industry is projected to be a $400 billion market by 2021, with 600 cities around the globe expected to generate 60% of the world's GDP by 2025, according to McKinsey research. Many cities have adopted at least one smart city project. However, the majority of cities around the globe are not yet smart cities. The key hurdle is securing the huge fund needed to start a smart city project, and ensuring that there are sufficient resources to sustain the project over time. Because implementing new smart city infrastructure is a large and complex financial undertaking that poses a financial hurdle, specifically, for most of the under-developed countries, a viable and cost-effective strategy is needed to tackle this problem and conquer the digital divide. Building upon and leveraging existing infrastructure is the key to address this problem. This minimizes the cost of technology upgrades and allows for an easier integration process. In addition, leveraging existing infrastructure can give cities a better and quicker return on investment. The main objective of this work is to devise an innovative research and development initiative, which builds upon and leverages ongoing global deployment of smart city and outdoor lighting infrastructures trend, to explore the potential and assess the feasibility of gradually transitioning existing Egypt’s cities infrastructure into truly IoT-enabled smart cities. Specifically, we propose and develop an innovative cost-effective, future-proof, scalable and modular end-to-end device-to-cloud connected outdoor Lighting network infrastructure solution that is based on P2P 4G LTE cellular technology to provide the direct connectivity between lighting poles (IoT devices) and the cloud. The proposed intelligent Lighting solution includes three key building blocks:1) light pole-mounted smart control nodes; 2) cloud-based smart city software solution (NetServ) to remotely monitor, manage, and control the entire lighting infrastructure; and 3) P2P 4G LTE cellular network. In the proposed architecture, smart streetlights serve as the framework for a high- bandwidth, low-latency wireless sensor network, capable of transporting large amounts of data in real-time, while concurrently supporting deployment of a wide range of smart city services.

Current industrial production is driven by increasing globalization, which has led to a steady increase in production volumes and complexity of products aimed at the pursuit of meeting the needs of customers. In this context, one of the main tools in the management of customer value is Lean Manufacturing or Production, though it is considered primarily as a set of tools to reduce the total cost of the resources needed to achieve such needs. This philosophy has recently been enriched in the literature with case studies that link Lean Management (LM) with the improvement of environmental sustainability. The consequence is an expansion of the Computer Integrated Manufacturing (CIM); indeed, CIM, currently, combining and integrating the key business functions (e.g., business, engineering, manufacturing, and information management) with a view of the life cycle, does not highlight the strategic role of the environmental aspects. In order to deal with the increasingly rapid environmental degradation that is reflected in society, in terms of both economy and quality of life, Industrial Ecology (IE) introduced a new paradigm of principles and instruments of analysis and decision support (e.g., Life Cycle Assessment—LCA, Social Life Cycle Assessment -SLCA, Material Flow Account—MFA, etc.) that can be considered as the main basis for integrating the environmental aspects in each strategy, design, production, final product, and end of life management, through the re-engineering of processes and activities towards the development of an eco-industrial system. This paper presents the preliminary observations based on a analysis of both theories (LM-IE) and provides a possible assessment of the key factors relevant to their integration in a “lean environmental management”, highlighting both positives (lights) and possible barriers (shadows).

To contribute to small and medium enterprises (SMEs) sustainable transition into the circular economy, the study proposes the activation of organizational learning (OL) processes – denoted here as multi-level knowledge creation, transfer, and retention processes – as a key phase in introducing circular business models (CBMs) at SME and supply chain (SC) level. The research employs a mixed-method approach, using the focus group methodology to identify contextual elements impacting on CBM-related OL processes, and a survey-based evaluation to single out the most frequently used OL processes inside Italian construction SMEs. As main result, a CBM-oriented OL multi-level model offers a fine-grained understanding of contextual elements acting mutually as barriers and drivers for OL processes, as possible OL dynamics among them. The multi-level culture construct – composed of external stakeholders’, SC stakeholders’, and organizational culture – identify the key element to activate CBM-oriented OL processes. Main implications are related to the identification of cultural, structural, regulatory, and process contextual elements across the external, SC, and organizational levels, and their interrelation with applicable intraorganizational and interorganizational learning processes. The proposed model would contribute to an improved implementation of transitioning into the circular economy utilizing sustainable business models in the construction SMEs.

At the present stage of the transition to sustainable development, when the share of a green economy in technologically advanced countries reaches one third of GDP, overcoming the structural crisis of the Russia is inseparable from reducing the technological gap and structural imbalances in the economy. These imbalances alienate Russia from ecologically and technologically advanced countries. Therefore, it is highly relevant to develop a methodology for assessing the structural convergence of the economy in the context of the transition to sustainable development, accompanied in developed countries by a new type of structural shift caused by the expansion of convergent technologies, the emergence of new principles of sectoral genesis and the formation of a green economy. The deepening of the divergent nature of Russian economy development was the result of a series of negative structural shifts that consolidated the dominance of extractive and resource-intensive industries, which threaten the final loss of technological identity, consolidation of the recessive trend and a lag in the transition to sustainable development. Therefore, today it is extremely important to formulate a methodology for assessing the structural convergence of Russian economy with technological and ecologically advanced countries, taking into account both quantitative and qualitative aspects.