Search results

In the context of modern urbanization, optimizing resources such as energy, materials, water and labour is no longer solely an environmental concern but a strategic economic necessity. This chapter underscores the vital connection between smart cities and resource efficiency, highlighting sustainable practices as crucial amidst the ever-expanding urban landscape. This chapter commences by demystifying key terms like ‘smart city,’ ‘data analytics,’ ‘artificial intelligence’ and ‘resource efficiency.’ It illuminates how these concepts intertwine and emphasizes their pivotal roles in shaping urban sustainability. Furthermore, this chapter unravels the multifaceted components of smart cities, showcasing their real-world use cases and the techniques of data analytics and artificial intelligence (AI) driving transformative changes. It draws from an extensive body of research, exemplifying how various data analytics techniques have been leveraged in the realm of smart cities. Towards its conclusion, this chapter provides a comprehensive overview of these techniques and their applications, shedding light on their potential to revolutionize resource management in urban environments. In essence, this chapter serves as a valuable compendium of knowledge, offering insights into the critical synergy between smart cities, data analytics, AI and resource efficiency. It underscores the imperative for cities to harness data-driven insights and technological advancements to achieve sustainable and prosperous urban futures.

This study aims to explore factors that influence tourists’ visiting behaviors towards smart tourism destinations (STDs) by extending the theory of planned behavior.

Partial least squares structural equation modeling (PLS-SEM) technique is used to test the proposed hypotheses by analyzing 413 usable responses that are collected through personal interviews. Moreover, data are collected using structured interviews and analyzed by SmartPLS, 3.3.3.

This study results reveal that STD visit intentions significantly influence STD visiting behaviors among international and domestic tourists in Bangladesh. Moreover, the results show that e-attitude, resource efficiency norms and perceived environmental sustainability have significant impacts on STD visit intentions.

The study findings indicate that destinations’ electronic flowcharts of places, usage of low impact and biodegradable materials and history and culture presented in the forms of games and stories increase travelers’ motivation to visit STDs.

The study provides empirical evidence to support the importance of factors enhancing travelers’ STD visiting behaviors by integrating e-attitude, resource efficiency norms, perceived environmental sustainability and STD visit intentions.

研究人員藉著把計劃行為理論伸延開去,去探索是哪些因素影響著到訪智慧旅遊目的地的旅遊行為。

研究人員以偏最小平方法的結構方程模型 (PLS-SEM) 技術去測試其提出之假設,方法是透過分析取自個人訪談413個可用的回應。這些數據均以結構化訪談方式收集,繼而以 SmartPLS 3.3.3 來進行分析。

研究結果顯示,就於孟加拉的國際和國內旅客而言,到訪智慧旅遊目的地的目的,顯著地影響著智慧旅遊目的地的旅遊行為。研究結果亦顯示,到訪智慧旅遊目的地的目的和意願,會顯著地受電子態度、資源效率規範和感知環境可持續性所影響。

研究結果表明了目的地的處所電子流程圖、低影響和能進行生物降解材料的使用,以及以遊戲和故事形式展示的歷史和文化,均會提昇旅客到訪智慧旅遊目的地的動機。

研究人員透過結合電子態度、資源效率規範、感知環境可持續性和到訪智慧旅遊目的地的目的,給我們提供了經驗性證據,以確認增強及完善影響著旅客到訪智慧旅遊目的地的旅遊行為的因素至為重要。

Today’s businesses are looking for a circular bioeconomy (CBE) to develop a sustainable manufacturing process as industrial operations result in significant amounts of waste materials and the depletion of natural sources. The industry commonly applies techniques such as lean manufacturing (LM), digital innovations (DI) and green practices (GP) for operational and quality improvement. However, publications explaining how these technologies enable the CBE transition are scarce. This study examines CBE components, common practices of each technology facilitating the CBE transition, problems of solitary technology deployment as well as coupling technologies for the CBE transition.

A scoping review was conducted to analyse previous studies in this new field. The data collection is in a quantitative manner, but the data synthesis process follows a similar method of synthesising data in the grounded theory method, which includes familiarisation with the data, open-coding and finalisation of the themes.

Critical components of CBE were identified as biobased goods, industry symbiosis, material resource efficiency, renewable energy, product lifecycle and sharing economy. GP is the most prominent in moderating the CBE transition. We identify each technology has coupled relationships (Lean-4.0, Green-Lean and Green-4.0) technologies facilitated by the circularity concept, which form the core pillars of enablers and advance the CBE paradigm.

This study demonstrates that combining lean principles with green technology and digital technologies can effectively decrease waste and resource usage in biobased manufacturing processes, therefore endorsing the concept of resource efficiency in circular bioeconomy models.

The results allow entrepreneurs to strategically incorporate different existing technologies to meet CBE fundamental objectives by initiating it with dual technologies and facilitate industry professionals and regulators to support the improvement of environmental sustainability performance in the manufacturing industry. The management will be able to focus on the common practices across the technologies, which have a dual benefit for both operational and environmental performance.

The paper makes the first attempt to present the synergic impact of the three quality management technologies on a new concept of sustainability, CBE.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

As more and more cities adopt resilient, inclusive and sustainable principles, circular economy (CE) emerges as a critical ally in developing smart cities. The technology-driven environment of smart cities and CE's resource-efficient principles are perfectly compatible, overcoming challenges and highlighting benefits like increased resource efficiency. The integration of CE in smart cities is thoroughly analyzed in this chapter using VOSviewer and RStudio, tracing its history and pointing urban innovators towards a circular future. The categorization of obstacles to CE implementation in smart cities, the provision of knowledge about barriers and the facilitation of methodical solutions constitute a critical focus of research. Additionally, by analyzing pertinent literature, this investigation highlights the importance of integrating the concepts of the CE and the smart cities, illuminating the interdisciplinary nature of this field. This synthesis provides policymakers and planners with the knowledge they can use to advance sustainable urbanization effectively.

The circular economy (CE) is a sustainable economic model that has the potential to create new opportunities, reduce environmental impact and enhance social well-being. Ho Chi Minh City (HCMC), the largest city in Vietnam, has experienced rapid economic growth in recent years, but at the cost of the environment and public health. The city could reduce waste, conserve resources and promote sustainable production and consumption by adopting CE principles. Employing qualitative research, including content analysis, we construct a SWOT analysis to assess HCMC's strengths, weaknesses, opportunities and threats in the CE context. The city possesses several strengths, such as a vast potential for a CE and a robust economic foundation. However, it also faces multiple weaknesses, including insufficient infrastructure, inadequate citizen and business awareness and participation, ineffective policy enforcement and a deficiency of standards for recycled products. This chapter will conclude that the CE presents an opportunity for HCMC to reduce its dependence on imported raw materials, increase local value creation and create new jobs in the CE sector.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Facilities management (FM) organizations are pivotal in enhancing the resilience of buildings against climate change impacts. While existing research delves into the adoption of digital technologies by FM organizations, there exists a gap regarding the specific utilization of artificial intelligence (AI) to address climate challenges. This study aims to investigate the drivers and barriers influencing the adoption and utilization of AI by South African FM organizations in mitigating climate change challenges.

This study focuses on South Africa, a developing nation grappling with climate change’s ramifications on its infrastructure. Through a combination of systematic literature review and an online questionnaire survey, data was collected from representatives of 85 professionally registered FM organizations in South Africa. Analysis methods employed include content analysis, Relative Importance Index (RII), and Total Interpretative Structural Modeling (TISM).

The findings reveal that regulatory compliance and a responsible supply chain serve as critical drivers for AI adoption among South African FM organizations. Conversely, policy constraints and South Africa’s energy crisis emerge as major barriers to AI adoption in combating climate change challenges within the FM sector.

This study contributes to existing knowledge by bridging the gap in understanding how AI technologies are utilized by FM organizations to address climate challenges, particularly in the context of a developing nation like South Africa. The research findings aim to inform policymakers on fostering a conducive environment for FM organizations to harness AI in fostering climate resilience in built assets.

The advent of the 21st century marks a pivotal era where rapid urbanization intersects with technological advancements, giving rise to the concept of smart cities. These urban environments harness information and communication technologies to improve service efficiency and enhance the quality of life. Parallel to this development is the emergence of circular economy (CE) models, recognized globally as an essential response to environmental challenges. This chapter delves into the integration of CE principles in smart cities, emphasizing a shift from traditional linear models towards sustainable, resource-efficient urban landscapes. Exploring the evolution of smart cities and CEs, this chapter highlights synergies and potential benefits of this integration while acknowledging significant challenges. These include technological, infrastructural, financial, policy-related and social–cultural barriers. Through a comprehensive analysis of literature, case studies and best practices, effective strategies to overcome these challenges are presented. This chapter emphasizes the roles of technological innovation, policy reform, stakeholder engagement and community involvement in driving this transformation. This chapter identifies future research areas and emerging trends, underscoring the profound impact of integrating CE principles in smart cities. This integration is pivotal for shaping sustainable and resilient urban futures, thereby redefining the paradigm of urban development in the modern era.