Search Results (2,639)

Agricultural land in China represents a major source of nitrous oxide (N₂O) emissions, and as population growth and technological advancements drive agricultural intensification, these emissions are projected to increase. A thorough understanding of historical trends and future dynamics of these emissions is critical for formulating effective mitigation strategies and advancing progress toward the Sustainable Development Goals. This study quantifies N₂O emissions across 31 provinces in China from 2000 to 2021, employing the IPCC coefficient method alongside China’s provincial greenhouse gas inventory guidelines. The spatiotemporal evolution of emission intensities was examined, with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model employed to assess the influence of population, technological development, economic growth, and energy structure. The findings confirm that agricultural land remains the primary source of N₂O emissions, with significantly higher levels observed in eastern coastal regions compared to western inland areas. Implementing targeted mitigation strategies, such as enhanced agricultural- and manure-management practices and region-specific interventions, is imperative to effectively curb the rising emission trends. Full article

Sustainability is one of the emerging areas in building construction, and it is widely investigated in terms of bringing sustainable technology into construction. However, one of the biggest challenges in the construction industry is implementing sustainability. Building contractors and construction practitioners mostly struggle with the implementation of sustainability due to the lack of certain parameters that define sustainability. Therefore, this study investigates the factors for effective sustainability implementation to provide industry practitioners with the benefit of the extensive list of parameters when designing their sustainability program. To research the parameters, a questionnaire was designed and administered to construction professionals with wide experience in sustainability implementation. The respondents were asked to evaluate a set of sustainability factors in the economic, social, and environmental domains. This way, the study provides reflections from construction practitioners working on large projects. The findings indicated that Ethical and Relational Factors in economic sustainability, Historical and Social Relations Factors in social sustainability, and Material Usage Factors in environmental sustainability are the most important for effective sustainability implementation. The findings are expected to help construction practitioners understand metrics of sustainability, effectively manage their projects, and successfully implement sustainability with the consideration of proper metrics. Full article

The increasing use of tractors and implements is replacing manual labor, but adds financial burdens on small-scale farmers due to rising costs. Many farmers have turned to leasing and renting machinery to mitigate these expenses, while repair and maintenance costs remain significant. Government interventions aim to alleviate these burdens, but income disparities between urban and rural areas persist, and the impact of machinery use on climate change and the environment poses further challenges. Strategies like omitting some operation steps and adopting versatile machinery are proposed to cut costs and promote economic sustainability for small-scale farmers. Therefore, this study assessed the economic benefits of using versatile machinery in farming, especially for small-scale rural farmers. Farming processes were divided into field preparation and crop season activities. Field preparation included rotary tillage, ridge formation, and mulching, whereas crop season activities included harvesting and transportation. Annual usage and production cost analyses per hectare, including labor, fuel, and interest, alongside purchasing cost surveys, were conducted. Versatile machinery reduced annual usage costs for field preparation and crop season activities by 63.54% and 71.71%, respectively. This effect was more pronounced for farms under 2 ha, especially those employing manual harvest and transportation. Small-scale farmers, such as those cultivating hot pepper farms, are strongly encouraged to adopt versatile machinery to mitigate expenses and labor costs. The significance of adopting studied methodology will be amplified with the rising cost of labor. Consequently, utilization of versatile machinery in field farming for small-scale farms is projected to increase incomes not through enhanced production, but by significantly reducing the annual usage costs associated with agricultural machinery. This approach not only alleviates financial burdens but also enhances the sustainability of farm management, ensuring long-term viability and environmental stewardship. Full article

Citizen science projects globally have increasingly been implemented in collecting and analysing environmental data. At the same time, these initiatives are often project-based and, therefore, of short duration. This presents a challenge as data from such activities may not be used in research and political decision-making. This research aims to explore the barriers and solutions for establishing a sustainable long-term citizen-based plastic monitoring strategy in fresh waters based on a case study in Accra, Ghana. This case study is particularly relevant due to the significant issue of plastic pollution in the region, limited official monitoring data to effectively address the problem, and the potential role of citizen science in addressing this data gap in the long term. Data on barriers and solutions were collected based on eight expert interviews, a survey amongst 17 stakeholders, and subsequent roundtable discussions with 24 experts and stakeholders from academia, the private sector, the public sector, and civil society. From this, we identified 30 types of barriers and 21 types of solutions to implement citizen science in the long term. These barriers and solutions relate to five fields of action (social, economic, environmental, technical, and management and governance) and four stakeholder groups (public sector, private sector, civil society, and academia) to implement solution pathways for long-term citizen science projects. Based on our findings, we make suggestions as to how citizen science can be implemented more sustainably in the future in different fields of action and from various institutional perspectives. With this, we hope to advance the potential use of citizen science and citizen science data in research and policy design related to plastics in fresh waters. Full article

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing. Full article

This study proposes incorporating generative artificial intelligence large language models (LLMs) into the Master of Science (M.Sc.) curriculum on digitization in construction. The aim was to help students generate computer code to solve, automate, and streamline practical challenges in advanced construction engineering and management (CEM). To this end, a host of problem-based learning (PBL) individual assignments and collaborative team projects were developed, alongside a combination of flipped classroom models and blended learning lessons, in order to teach effective interactions with LLMs and mitigate concerns, such as bias and hallucination. The effective interaction with LLMs not only facilitated code generation, which would otherwise be complex without additional formal training, but also provided a platform for strengthening basic project management skills, such as departmentalization, work breakdown structuring, modularization, activity delegation, and defining key performance indicators. The effectiveness of this approach was quantitatively and qualitatively evaluated within two new modules, Digital Engineering and Construction and Digital Technologies in Field Information Modeling. These modules were offered over three semesters each as part of a new M.Sc. program in Technology and Management in Construction at the Karlsruhe Institute of Technology. It was observed that 86.4% of students fully completed the PBL projects, while the remaining 13.6% achieved over 50% completion across all six semesters. Furthermore, anonymous student surveys indicated a teaching quality index of 100% in five semesters and 96.4% in one semester. These preliminary results suggest that the proposed strategy can be used to effectively integrate LLMs to support students in code generation for open-ended projects in CEM. Further research was, however, found to be necessary to ensure the sustainable revision and redesign of the problems as LLM capabilities evolve. Full article

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile. Full article

This study explores integrating Building Information Modeling (BIM) technology into risk management practices for construction projects, aiming to enhance project performance through improved risk identification, assessment, and mitigation. The research employs the Analytical Hierarchy Process (AHP) to prioritize BIM-based strategies across multiple risk management dimensions, including technical, financial, sustainability, and time management. The findings demonstrate that BIM-based financial strategies rank highest among BIM-driven risk management, followed by sustainability and time. In contrast, technical, operation, and maintenance capabilities have the lowest rank. Given the high priority of BIM financial strategies, they have been applied to conduct sensitivity analysis; the sensitivity analysis results demonstrate the dynamic nature of a BIM sub-criteria strategy in response to changes in the weight of financial considerations. As financial concerns diminish, the shift towards sustainability, health, safety, and time efficiency underscores the importance of a more balanced approach in BIM strategy prioritization. BIM-based risk management improves project outcomes by enabling real-time data-driven decision-making, enhancing stakeholder collaboration and optimizing resource use, cost control, and sustainability. This research contributes to theoretical and practical advancements in construction risk management, suggesting that BIM can be a transformative tool for optimizing project performance while addressing the complexities and uncertainties inherent in the construction industry. Full article

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate. Full article

This paper aims to provide a framework for guiding organizations toward Agile practices for project management. Through a case study analysis conducted in an information technology company, the paper aims to explore the interplay between current organization structure and project management approaches, the set of competencies, and the cultural change required for a transition toward Agile transformation. The paper provides a framework comprising four main blocks necessary for guiding organizations toward Agile practices for successful project management. The framework proposed demonstrates that for organizations to be nimble and to improve teamwork performance, it is necessary to undertake a set of changes in culture, competencies, structure, and approaches. The findings highlight the importance of a structured approach to Agile transformation, with particular emphasis on top management support and continuous learning. By formalizing Agile initiatives through structured approaches and continuous engagement, organizations can foster a shared sense of purpose among team members, ensuring sustained commitment and facilitating smoother transitions. Full article

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation. Full article

The rapid pace of urbanisation has heightened the need for sustainable urban regeneration projects on a global scale. These projects primarily aim to address the increasing demand for buildings and to cultivate liveable urban environments. Over the years, a substantial amount of data has been collected and analysed as a result of various urban regeneration efforts. These data indicate that the success of urban transformation is influenced by a wide range of interconnected factors rather than the mere creation of new areas to meet demand. The primary objective of this study is to carry out a comprehensive and systematic literature review that assesses the effectiveness of sustainable urban regeneration (SUR) projects. This review examines a range of existing factors, including policies, management practices, and economic considerations, while also highlighting frequently overlooked elements such as climate change. Moreover, the study investigates how these factors influence the success of SUR initiatives and seeks to identify their overall impact on the effectiveness of such projects. Specifically, it explores the common challenges encountered, the criteria for performance evaluation, and the strategies that lead to successful SUR projects while emphasising their interconnections. An in-depth assessment of urban regeneration projects is provided, focusing on performance indicators across multiple dimensions: social, cultural, economic, environmental, and physical. A novel organisation involving all stakeholders and addressing climate change is also proposed to ensure the success of the SUR projects. Lastly, the findings highlight existing gaps in the current literature, offering valuable insights for future research and identifying potential directions for further exploration. Full article

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk. Full article

According to current studies, the thermal effects of global warming will affect urban areas more intensely. In the face of this situation, strategies for the improvement and management of urban green spaces are becoming increasingly important in sustainable landscape design. These strategies promote social sustainability by positively affecting individuals’ physical and psychological well-being, taking into consideration ecological sustainability. Projections regarding global warming emphasize that the density of hardscapes and green spaces, the selection of plant species, and the distribution of plants considered within the scope of this study should be taken into account. This research was conducted in the Görükle neighborhood of Bursa, focusing on the role of temperature-regulating elements in 14 neighborhood parks. Systematic temperature measurements were carried out in the research area on the 10th, 20th, and 30th of July and August, specifically between 12:00 and 13:00, during peak temperature hours. The presence of parks that are close to each other and relatively far away from each other in the study area was seen as advantageous to filter the effects of plant differences in similar conditions. Furthermore, evaluating these various factors together highlights the multifaceted nature of thermal comfort. Designated temperature measurement points included three points (hard surfaces and hard surfaces surrounded by vegetation and planted green spaces) in each park. An analysis utilizing SPSS and the RayMan program revealed that parks with a softscape to hardscape ratio of approximately two to one experienced temperature reductions of 2.5 to 3 °C. Furthermore, the findings indicate that coniferous trees have a more significant impact on thermal comfort compared to deciduous trees. The significant differences identified in this study underscore essential considerations for urban design processes aimed at achieving sustainability. Full article

This research focuses on how market sentiment affects corporate governance in the Chinese market. The sample covers the years from 2014 to 2023. Market sentiment is estimated using a cross-sectional absolute deviation (CSAD) model, and earnings quality is used as an indicator of the consequences of corporate governance. Both mutual fund shareholding and the number of firm visits by mutual fund analysts are verified as effective corporate governance instruments that work well in a regular market but become ineffective when the market sentiment is high. The reason for this is that managers’ expectations change, and they may believe that disclosing good news during high-sentiment market periods significantly increases the share prices and helps them meet their performance requirements. In a high-sentiment market, an incentive contract encourages managers to take on projects with inappropriate risk or even manipulate earnings. One potential solution is to adopt venture capital firms’ high-water mark and clawback clauses to prevent managers from focusing on short-term goals rather than seeking long-term business sustainability. Full article