Estimating spatiotemporal heterogeneous effects of haze pollution on housing rents in China

Severe haze pollution significantly affects urban life quality and employment location choices, which in turn impact rental housing demand and housing rents. While numerous studies have delved into the effects of haze pollution on the real estate market, with a particular focus on housing prices, there is a notable scarcity of research on its impact within China’s rental housing market. This study employs spatial panel data encompassing 289 Chinese cities from 2015 to 2021 and applies a Geographically and temporally weighted regression (GTWR) model to investigate the spatiotemporal heterogeneous effects of haze pollution on housing rents in urban China. Our results indicate that the GTWR model’s goodness-of-fit surpasses that of the OLS, GWR, and TWR models. The results of the GTWR model reveal that haze pollution has a negative effect on housing rents in China, with the eastern region experiencing a notably stronger negative impact than the western region. Moreover, this negative impact becomes increasingly stronger over time. In addition, population, economic, and social factors significantly impact housing rents in Chinese cities. These findings offer valuable insights into the relationship between haze pollution and housing rents, assisting policymakers in assessing the economic value of air pollution control in urban China

Evaluation of the low-cost depth cameras for non-destructive testing

The primary aim of this paper is to assess the effectiveness of a low-cost stereo (depth) camera as a non-destructive tool for the detection and measurement of cracks in concrete surfaces. The experiment was carried out on four concrete beams with cracks, created with different concrete mixes. The mixes of the four beams were made up of lightweight aggregates with 12% of normal weight aggregates. One beam was used as a reference without fibers, while 3D steel fiber reinforcement, 5D steel fibers reinforcement, and a hybrid fibers mix of 5D steel fiber and synthetic were used for the other three beams. The cracks in the beams were measured manually followed by taking their stereo images with a ZED camera. The ZED images were processed to produce 3D models of the concrete surfaces, which are useful for crack measurement in a three-dimensional framework. The project results are particularly significant in the measurement of all three dimensions (length, width and depth), with less than a 10% error between the actual and the experimental procedure. Relatively, multiple differential approaches gave a less accurate result of a 15% error mainly due to syntax errors. Results suggest that the ZED depth camera is an effective tool for robust detection and measurement of cracks in concrete surfaces

Sustainable investments: assessment of risks

Sustainable investments become a more and more relevant topic in all fields of economics. It is essential to measure both the benefits of sustainable products and risks. This article examines the risks associated with sustainable investments, mainly focusing on green bonds. It highlights financial institutions’ increasing interest in sustainable asset management, including central banks. The study addresses the complexity of integrating climate risk into existing risk management frameworks and the lack of tools for estimating and managing these effects. This research aims to measure the volatility of different fixed-income financial instruments, trying to identify which GARCH model is the best. Our research utilizes Bloomberg data from eight sustainable corporate fixed-income indices. The study’s sample comprises sustainable investment indices within the fixed-income market, selected based on data availability and the representativeness of the asset class. The dataset includes daily closing prices and daily returns of these indices, covering a unified sample period from July 25, 2019, to September 28, 2022. The models used for the research are ARCH, GARCH, TGARCH, EGARCH, and PARCH. The results show that sustainable investments are not risk-free, emphasizing the need for comprehensive risk assessment and management. From the applied models, the results show that the PARCH model is the best for fixed-income indices volatility modeling

Bibliometric analysis of digital financial reporting: a comprehensive review of research trends and emerging topics

Digital Financial Reporting (DFR) has gained significant research attention amid the digital transformation. This study comprehensively reviews DFR research, identifies trends, and highlights emerging topics. Key trends include advancements in sustainability reporting and improved financial reporting quality while emerging topics like XBRL and International Financial Reporting Standards (IFRS) reflect evolving research interests. Utilizing bibliometric methods, the study quantitatively analyzes DFR literature from Scopus, Emerald, Google Scholar, OpenAlex, Crossref, and SAGE. The research involved data sourcing, screening, eligibility selection, and bibliometric analysis. Findings show a dynamic increase in annual publications in DFR, with noticeable peaks and shifts in research focus over time. A notable rise post-2016 culminated in a peak in 2023, indicating sustained scholarly interest and field evolution. This study contributed into how digitalization enhances financial reporting quality, addressing gaps from previous bibliometric analyses. It emphasizes systematic trend analysis, identifying research gaps, and exploring factors driving the digital transformation of financial reporting. These insights guide researchers in developing new variables and strategies to advance DFR solutions, enhancing the accuracy, transparency, and accessibility of financial information through digital innovation

Forecasting pandemic-induced changes in real estate market values through machine learning approaches

In this study, a new temporal segmentation method is used to forecasting the real estate market based on the structural and spatial attributes of 676 houses in Niğde, Türkiye, from the years 2019 to 2022. Artificial Neural Networks (ANN), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN) were employed for model development and comparative performance analysis. According to the results, the ANN model that used temporal variables showed the most successful performance by achieving the highest R2 for 2019 (1. period: 0.979, 2. period: 0.990, 3. period: 0.914, 4. period: 0.831) and 2022 (1. period: 0.971, 2. period: 0.975, 3. period: 0.586, 4. period: 0.896) scores. Additionally, the COD values (5%–10%) and PRD values (0.98 to 1.03) remained within the acceptable range, further validating the model’s reliability. RF model showed more effective performance than other models by achieving the highest R2: 0.510 for 2019 and R2: 0.509 for 2022 when temporal variables were excluded. These findings highlight the importance of integrating time-sensitive parameters into valuation models to improve forecast accuracy and robustness. The study offers a replicable, flexible methodology for crisis-responsive valuation, providing valuable insights for policymakers, investors, and urban planners aiming to mitigate risks and enhance resilience in real estate market decision-making

“Quo Vadis urban areas?”: (Re)thinking the future of urban areas using interpretive structural modeling

The world’s population continues to grow at an unprecedented rate, with urban areas experiencing a more rapid rise in population density than rural regions. This demographic shift compels decision-makers to address pressing urban challenges and rethink future structures of cities. However, a vision for global sustainable urban growth remains elusive, as planners often lack comprehensive, credible and dynamic models to guide decision-making. The main purpose of this study is to propose a process-oriented methodology that integrates cognitive mapping, interpretive structural modeling (ISM) and a matrice d’impacts croisés multiplication appliquée à un classement (MICMAC) analysis to evaluate and prioritize key determinants of urban development. Group work sessions involving decision-makers from diverse fields were conducted to identify critical variables influencing urban development. Unlike traditional models, the proposed approach emphasizes participatory decision-making. By combining cognitive mapping and ISM-MICMAC, this study enables the identification of causal relationships among variables and allows decision-makers to anticipate trends and prioritize challenges effectively. The findings were further validated by an external expert to ensure neutrality and reliability. Overall, this study provides a theoretical contribution to decision-making methodologies while offering a practical framework for urban planners to influence cities toward a sustainable future

Integrating LINE BOT and Building Information Model to develop construction information management system

In the lifecycle of construction projects, the participation of various specialized members often leads to challenges in recording or retrieving information in real time. This can result in missing data or inaccurate project control decisions, due to the inability to access essential information swiftly and accurately. Recognizing the ubiquitous use of instant messaging platforms on mobile phones and the widespread adoption of Building Information Modeling (BIM) for information storage and management, this research proposes an innovative integration of chatbots with BIM to establish a robust construction information management system. Utilizing “LINE”, a popular communication software, as the foundation, this study develops four specialized chatbots (LINE BOTs) tailored for different phases of construction projects, namely design, construction, and maintenance. These chatbots employ a rule-based and conversational approach to aid construction personnel in real-time recording and retrieval of project information. During the maintenance phase, users can also access relevant equipment objects through the BIM model using mobile smart devices, further improving maintenance efficiency. Moreover, the chatbots are equipped with a string-matching mechanism to enhance the precision of data recording and retrieval processes. The effectiveness of this system is demonstrated through a case study on a public construction project

Cluster analysis of factors influencing the valuation of real estate objects

The current real estate market analysis reveals challenges in valuation methods, procedure adequacy, and evolving technological approaches. Uncertainty arises from using localised methods for valuing individual real estate objects. A significant concern is the reliability and completeness of valuation data. Researchers emphasise market-driven aspects as trends in real estate valuation. Features for valuation are identified through quantitative characteristics, uncovering components and their nature. The research analyses foreign and domestic practices for real estate object valuation. Challenges include understanding methodological and informational support through mathematical methods and addressing factors affecting real estate object valuation. The need for cluster analysis to identify factors affecting real estate object valuation is recognised. To implement cluster analysis of factors affecting real estate valuation, a method is proposed involving the development of classification features, optimal typological grouping, and clustering implementation technology. Six groups of factors were chosen: spatial formation, urban planning provision, environmental impact, investment indicators, infrastructure provision, and limiting characteristics. An agglomerative process calculated the distance matrix between clusters of factors. The MacQueen k-means clustering method determined final clusters, confirming the validity of the proposed factor groups. The clustering of factors affecting real estate valuation was based on obtained distance data. The result identifies a high level of factors influencing real estate object valuation. Nine coincidences justify this in their clustering with four units of factors influencing real estate object valuation

Study on the polymeric treatment with rice husk silica on sisal fiber in cementicious composites

This research evaluates how treating sisal fibers with expanded polystyrene (EPS) and rice husk silica (RHS) affects their absorption capacity, tensile strength, and adhesion when used in Portland cement matrices. The study on sisal fibers treated with EPS and RHS polymers found that the treatment significantly reduced water absorption by 70%, from 84.67% for untreated fibers to 15.18% for treated ones, due to the hydrophobic nature of EPS. Optical microscopy revealed an irregular polymer layer on the fibers, which, while improving dimensional stability, could impair fiber-matrix interaction. Despite these improvements, the treatment did not notably enhance the mechanical properties of the fibers, as the breaking strength remained similar to untreated fibers, and the rupture displacement slightly decreased

Engagement and flow in the job satisfaction of volunteers at a non-governmental organization (NGO) in Peru

The objective was to analyze how engagement and the state of flow influence the job satisfaction of volunteers. The design was non-experimental, cross-sectional, and the sample consisted of 1023 volunteers, selected through non-probabilistic convenience sampling. Validated questionnaires were used: a) the Utrecht Work Engagement Scale (Schaufeli et al., 2003); b) the Flow Experience Scale (Bakker, 2008); and c) the Job Satisfaction Scale SL-20/23 (Meliá & Peiró, 1998). Reliability was assessed using Cronbach’s alpha. Exploratory and confirmatory factor analyses were conducted. Data were collected and processed using SPSS, AMOS, and SEM. Both flow (0.703) and engagement (0.557) have a significant influence on the job satisfaction of volunteers, with both factors being relevant for improving job satisfaction. The state of flow is a stronger predictor than engagement. It is concluded that there is a significant and positive influence of engagement and flow on the job satisfaction of volunteers. The study of these topics confirms the importance of fostering engagement and flow among volunteers to enhance their job satisfaction