Search Results (60)

In today’s urban environment, disasters are not isolated events but part of continuous, complex processes that threaten both sustainable urban development and effective emergency management. Traditional emergency management practices are hindered by departmental silos and fragmented information exchanges, which often lead to conflicting interests, unclear responsibilities, ineffective tools, and imprecise task divisions. In response, our study repositions emergency management within the broader context of sustainable urban development by emphasizing resource optimization, strengthened inter-agency coordination, and strategic decision support to achieve UN Sustainable Development Goal 11. Based on observations from 31 departments in Dongtai City, we identified key contradictions within the current activity system. Guided by activity theory, we designed the Cross-Organizational Emergency Intelligence System (COEIS), which synchronizes real-time data across agencies via a novel information exchange mechanism. Implementation in a real-world setting and evaluation using grounded theory demonstrated that the COEIS enhances collaborative efficiency and decision support capabilities, thereby improving inter-organizational resilience. This study makes both theoretical and practical contributions by integrating the DSRM, activity theory, and grounded theory, offering a replicable pathway for transforming fragmented crisis management infrastructures into sustainable and resilient networks aligned with urban development strategies. Full article

Management 5.0 represents a new paradigm in business strategy and leadership that integrates sustainability, advanced digital technologies, and human-centered decision-making. The article explores the application of machine learning technologies for forecasting demand for eco-friendly vehicles as a key tool for enhancing manufacturers’ competitiveness. This research supports key UN Sustainable Development Goals (SDGs), including SDG 7 (Clean Energy), SDG 9 (Innovation and Infrastructure), SDG 11 (Sustainable Cities), and SDG 12 (Responsible Consumption). Based on an analysis of the European market from 2019 to 2023 and forecasting through 2027, a comprehensive approach was developed using ARIMA, Prophet, and Random Forest models. Empirical findings indicate that implementing predictive analytics can reduce inventory costs by 18–25% and optimize working capital by 15–20%. Model performance varied by market type: Random Forest excelled in smaller markets, while Prophet delivered strong results in trend-stable environments. The results confirm that accurate demand forecasting, supported by machine learning technologies, creates significant competitive advantages in the era of management 5.0 through production process optimization and improved market positioning. Full article

Cities’ role as major hubs of human activity and economic development is essential in attaining sustainable development, fostering a balance between economic, social, and environmental development, especially in light of the growing concern over Anthropocene-induced environmental issues like global warming and climate change. The United Nations Sustainable Development Goals (SDGs) represent a historic call for coordinated international action in this area, with SDG 11 specifically identifying “Sustainable Cities and Communities” as a primary objective. Therefore, it is clear that a paradigm shift in our approach to these challenges in terms of our thinking, sensibility, behavior, and responses is necessary. Implicitly, in view of their pivotal role in environmental sustainability, development of “smart” cities as healthy, citizen-friendly, economically viable, and sustainable cities for our future generations in today’s globally integrated world, as predominant centers of human settlement and activity with multinational enterprises driving economic growth, gains the immediate attention of researchers. In this light, this study aims to identify and thereafter prioritize key indicators of a smart city using the structured and consistency-focused best–worst multi-criteria decision-making (BWM) method, suitable for expert-driven decision-making with limited comparisons. While the UN’s SDG 11 promotes safe and resilient cities, our findings suggest a disparity in how local officials prioritize certain dimensions such as safety or recreation. This disconnect warrants closer examination of localized policy drivers. The findings of this study indicate that according to experts, among others, the priority themes are, in order, water and sanitation, wastewater, health, the environment, and the economy. Thus, these represent a key take-away for multinational enterprises for identifying and assessing significant thrust domains and areas of opportunity for intervention and contribution to the UN SDGs. It also enables a replicable framework for synergy between the public and private sectors towards contrastive intervention in other cities across the globe. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

The proliferation of electronic goods manufacturing and the subsequent rise in electronic waste (e-waste) generation necessitate the establishment of efficient Waste of Electrical and Electronic Equipment (WEEE) reverse logistics systems, fostering collaborative efforts among manufacturers, retailers, and government agencies. Given its importance, this theme has received considerable attention in recent literature. This study focused on investigating the relationships between socio-spatial characteristics and the distribution of WEEE collection points in the city of São Paulo, Brazil. To this end, data mining (DM) techniques were applied to generate rules representing knowledge that explains the relationship among the considered variables. The results achieved (accuracy 81.25% and Kappa statistic 74.71%), indicating consistent patterns, demonstrate the potential of the proposed approach to aid WEEE reverse chain management. From a practical point of view, the knowledge produced is an important support for decision-making on the installation of new collection points, considering the socio-spatial characteristics of the target locations. In addition, this research contributes to the responsible management of solid waste recommended by the Brazilian National Solid Waste Policy (NSWP), as well as to the advancement of the United Nations’ Sustainable Development Goals (UN SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), by fostering sustainable practices in waste management and resource utilization within urban contexts. Full article

This research paper explores the visual potential of the multi-angled façade system, allowing office employees to achieve optimal exposure to the external environment through the room façade. This contributes to sustainability objectives by enhancing indoor climate quality, promoting health and well-being, and aligning with the UN Sustainable Development Goals 3, 9, and 11. This façade concept provides a solution to the issue of shading devices being fully closed for long periods due to intense solar radiation on the room’s window. The concept of a multi-angled window involves incorporating two differently oriented window sections within each façade along a vertical axis (right and left), rather than tilting them upward or downward. The larger section is oriented more toward the north to maximize daylight access and external views, while the smaller section faces south to enhance passive solar heating. The visual potential is assessed based on the periods when the solar shading devices are not fully closed—meaning one section of the multi-angled façade may remain open while the other is shaded. To evaluate this, along with the resulting energy consumption and indoor climate, the software program IDA ICE version 4.8 is utilized. Simulation results indicate that the duration of complete shading closure is significantly lower for a multi-angled façade compared to a flat façade, in some instances nearly half, thereby improving visual comfort, daylight availability, and heat gain while simultaneously reducing spatial energy consumption. Full article

Urbanization in Jaipur, India, has led to a 42% decline in green cover over the past two decades, exacerbating urban heat, air pollution, groundwater depletion, and reduced livability. Green Infrastructure (GI) offers a sustainable solution, but effective implementation requires robust, data-driven strategies. This study employs geospatial technologies—GIS, remote sensing, and Multi-Criteria Decision Analysis (MCDA)—to develop a suitability model for Urban Green Infrastructure (UGI) planning. Using an entropy-based weighting approach, the model integrates environmental factors, including the Normalized Difference Vegetation Index (NDVI), which fell by 18% between 2000 and 2020; Land Surface Temperature (LST), which increased by 1.8 °C; soil moisture; precipitation; slope; and land use/land cover (LULC). Proximity to water bodies was found to be a critical determinant of suitability, whereas land surface temperature and soil moisture played significant roles in determining UGI feasibility. The results were validated using NDVI trends and comparative analysis with prior studies so as to ensure accuracy and robustness. The suitability analysis reveals that 35% of Jaipur’s urban area, particularly peri-urban regions and river corridors, is highly suitable for UGI interventions, thereby presenting significant opportunities for urban cooling, flood mitigation, and enhanced ecosystem services. These findings align with India’s National Urban Policy Framework (2018) and the UN Sustainable Development Goal 11, supporting climate resilience and sustainable urban development. This geospatial approach provides a scalable methodology for integrating green spaces into urban planning frameworks across rapidly urbanizing cities. Full article

The motivation for this research stems from the United Nations Sustainable Development Goals (UN SDGs), specifically SDGs 7, 11, 12, and 13, which focus on the mitigation of climate change and sustainable economic development. This study examined the impact of renewable energy use, institutional quality, and production expansion on consumption-based carbon dioxide (CCO₂) emissions in BRICS countries (Brazil, Russia, India, China, and South Africa) from 1996 to 2020. To achieve this, we applied advanced econometric techniques, including second-generation cointegration and unit root tests, along with the novel panel method of moments quantile regression (MMQR). The Westerlund cointegration test confirmed the presence of a long-run co-movement among renewable energy usage, economic growth, institutional quality, and environmental quality, suggesting a stable equilibrium relationship between these variables. The results from MMQR reveal that GDP has a positive and statistically significant effect on CCO₂ emissions across all quantiles, indicating that economic expansion contributes to environmental degradation. In contrast, renewable energy consumption and institutional quality show negative and significant impacts on CCO₂ emissions, indicating their mitigating effect on environmental deterioration. As a robustness check, the findings from fixed-effect OLS (FE-OLS), generalized method of moments (GMM), and common correlated effects mean group (CCEMG) estimations broadly confirm the results of MMQR. These findings underscore the importance of renewable energy consumption and strong institutional frameworks in promoting environmental sustainability. Full article

Entry into the profession of archaeology generally requires the completion of an archaeological field school, which teaches proper field, laboratory, and curation methodologies. Archaeology as a discipline has been making strides towards integrating cross-disciplinary methods to increase the depth and breadth of the subject and enhancing inclusivity. These efforts have been mirrored in the approaches of some archaeological field schools, but not necessarily in a systematic fashion. This paper presents a cohesive framework for an archaeological field school that integrates cross-disciplinary training and inclusivity by model of the United Nations Sustainable Development Goals (UN SDGs), specifically SDGs 11: Sustainable Cities and Communities, 13: Climate Action, 4: Quality Education, and 11: Reduced Inequalities. Both how the framework could be implemented across a variety of archaeology field schools and how it has been implemented in the Florida Public Archaeology Network (FPAN) field school held in Jupiter, Florida, are discussed. Furthermore, we present preliminary survey data from field school participants to demonstrate how this field school supports SDG 10: Reduced Inequalities. Full article

Cheong Wa Dae, a site of profound historical and cultural significance, holds great potential to be reimagined as a sustainable cultural landmark that meets contemporary social, economic, and environmental needs. This research explores strategies to preserve its historical identity while transforming it into a dynamic and accessible public space. Using a qualitative approach, this study integrates history and architectural reviews and sustainability frameworks, including alignment with the UN’s 17 Sustainable Development Goals (SDGs). Programs were evaluated through the Sustainability Impact Assessment tool to measure their ecological, cultural, and socio-economic impacts. The findings reveal that targeted strategies—such as utilizing the physical environment for global events, promoting biodiversity, and enhancing engagement through cultural and culinary experiences—are essential for sustainable transformation. These initiatives align with 11 of the 17 SDGs, with 7 goals showing a Direct Positive Impact and 4 showing an Indirect Positive Impact. This study concludes that by merging heritage preservation with innovation and sustainability, Cheong Wa Dae can evolve into a vibrant, economically viable public space and a model for cultural place-making, fostering public engagement, economic growth, and long-term ecological benefits. Full article

Residential greening is a critical strategy for mitigating the negative impacts of urbanization on the environment, biodiversity, and human well-being. Proper plant species selection is essential for the success of residential greening projects, as it influences the ecological, aesthetic, and health outcomes. This review provides a comprehensive framework for selecting plant species for residential greening, considering environmental suitability, aesthetic values, maintenance requirements, and potential health effects. The plant’s adaptability to local climatic conditions, soil type, and water availability are key considerations. Aesthetic factors like plant form, texture, color, and seasonal interest should be balanced with maintenance needs, including pruning, fertilization, and pest control. Potential health concerns, like allergenic pollen or toxic properties, must also be evaluated while deploying residential greeneries. The guide emphasizes the importance of selecting native or well-adapted non-invasive species to support local biodiversity and minimize ecological disruption. Employing a systematic approach to plant selection for urban vegetation and residential greening initiatives can enhance the environmental, social, and health benefits. Plant species invasiveness is a critical global concern, with substantial ecological, economic, and social impacts that demand careful consideration in species selection and management. This method maximizes these advantages and promotes long-term sustainability and resilience against the challenges posed by climate change. This present review supports the UN’s Sustainable Development Goal 11: Sustainable Cities and Society. Full article

Nowadays, we are heading towards global decarbonisation, with each sector involved contributing partial solutions to the problem, without realising that an overall vision is necessary. Photovoltaics emerged as a technology that requires a lot of surface area, which is why it has been integrated into buildings and other human infrastructures (BPVI). The effects of the implementation of AVS on an island have been analysed, observing the territory’s energy use, population, and social and topographical realities, collecting all the peculiarities that could be affected by a massive implementation of this technology. The method to be followed is a SWOT and TOWS analysis, widely employed in all types of scientific studies. The increase in the island’s resilience has been assessed, as has its decreasing its dependence on the outside. In this case, it has been observed that conventional PV is currently being installed on agricultural land to decarbonise electricity production, which mostly relies on oil and does not consider that the island is a territory with a high food dependence on the outside; a high unemployment rate; a high factor of soil desertification, meaning fires are frequent; a high rate of abandonment of agricultural land; and a shortage of flat land. Therefore, we affirm that the island’s carbon footprint will increase by not taking all these factors into account. In addition to punishing the local economy by destroying fertile soil, local food and jobs, the current method of energy production increases the need for subsidies to import food products from abroad. In addition, we claim that the use of AVS reduces the water needs of the crop, which is relevant on an island with great water scarcity. It is concluded that 11 of the 17 UN Sustainable Development Goals would be improved with the use of agrovoltaic technology. Full article

To date, a plethora of research has been published investigating the value of using Digital Twin (DT) technology in the construction industry. However, the contribution of DT technology to promoting social sustainability in the industry has largely been unexplored. Therefore, the current paper aims to address this gap by exploring the untapped potential of DT technology in advancing social sustainability within the construction industry. To this end, a comprehensive systematic literature review was conducted, which identified 298 relevant studies. These studies were subsequently analysed with respect to their use of DT technology in supporting social sustainability. The findings indicated that the studies contributed to 8 of the 17 UN Sustainable Development Goals (SDGs), with a strong focus on SDG11 (77 publications), followed by SDG3 and SDG9, with 58 and 48 studies, respectively, focusing on promoting health and well-being and fostering resilient infrastructure and innovation. Other contributions were identified for SDG13 (30 studies), SDG7 (27 studies), SDG12 (26 studies), SDG4 (21 studies), and SDG6 (11 studies), covering areas such as climate action, responsible consumption, affordable energy, quality education, and clean water and sanitation. This paper also proposes future research directions for advancing DT technology to further enhance social sustainability in the construction industry. These include (i) enhancing inclusivity and diversity, (ii) workforce safety and well-being, (iii) training and skill development, (iv) policy and regulatory support, and (v) cross-disciplinary collaboration. Full article

Warsaw is among European cities with the worst atmospheric air quality, mainly due to very high pollution emitted by the residential sector and road traffic. This results in high concentrations of particulate matter and nitrogen oxides, often exceeding WHO standards. The paper discusses the current and expected effects of actions taken by the Warsaw authorities, to significantly improve air quality in the city. The policy directly addresses one of the UN Sustainable Development Goals (SDG 11, Sustainable Cities and Communities). The analysis presented in the paper consists of two stages. The first, covering the years 2018–2029, deals with the ongoing Clean Air Program, which assumes primarily the reduction, and ultimately the complete elimination, of coal combustion in all heat sources of the residential sector. This sector is widely identified as the main source of urban air quality degradation, especially in Polish cities due to the dominant share of coal in the fuel mix. The second part of the corrective measures, covering the period 2024–2034, primarily concerns the reduction of nitrogen oxide pollution, mainly from traffic. The latter takes into account the expected effects of the introduction of a Low-emission Zone (LEZ) in the city center (launched in July 2024) and implemented in five two-year stages, in which car emission limits will be gradually tightened. According to the analysis results, the implementation of the Clean Air Program can result in about a 20% reduction in annual average PM_2.5 concentrations by 2024, with a small (about 9%) reduction in NO_x. At the same time, a significant reduction in NO_x levels can be achieved by full implementation of the LEZ, especially within the zone boundaries (more than 50%). An important factor here is the size of the zone. The paper compares the effectiveness of two being considered versions, differing in size zones. Full article

To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM₁₀, PM_2.5, NO₂, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau. Full article