Search Results (55)

The continuous expansion of China’s cities has led to a divergence in economics, population, and public service levels among different districts within the city. This has led to different housing prices, due to the resulting impact on housing supply and demand. Previous studies, although taking into account the possible differences in housing prices among different districts, have not focused on the extent to which districts affect housing prices. This study analyzes the housing price boundary effects among different districts in Hangzhou, China, using spatial discontinuity regression methods and data on newly built housing transactions from 2010 to 2021. This study also examines the impact of the integration policy, which acts to integrate suburban counties with the main urban area of Hangzhou, and whether that policy decreases the district boundary effect. The results show that the administrative boundary effect of housing prices in Hangzhou is significant, with most districts experiencing a house price boundary effect exceeding 10%. Encouraging regional integration policies effectively reduces the housing price gap that results from internal administrative divisions within the city. Full article

In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and in-house protection—before setting prices. We demonstrate that cybersecurity efforts and pricing are interdependent: investment choices significantly alter market outcomes by influencing consumer trust and competitive dynamics. Our analysis reveals that a bug bounty program is preferable when consumer sensitivity to security and the probability of ethical vulnerability disclosures are high, while in-house protection becomes optimal when firms must rebuild credibility from a weaker competitive position. Furthermore, initial service quality gaps between firms critically shape both investment intensity and pricing behavior. By jointly endogenizing security efforts and prices, this study offers new insights into strategic cybersecurity management and provides practical guidance for software firms seeking to integrate security initiatives with competitive pricing strategies. Full article

Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGBoost further quantifies predictive disparities: subjective factors like property service satisfaction (R² = 0.64, MAPE = 3.72) outperform objective metrics (e.g., dining facilities, R² = 0.36), yet objective housing prices demonstrate greater stability (MAPE = 3.11 vs. subjective MAPE = 6.89). (3) Nonlinear thresholds are identified for household income and green space coverage (>15%, saturation effects). These findings expose critical mismatches—residents prioritize localized services over citywide economic metrics, while objective amenities like healthcare accessibility (threshold = 1 km) require spatial recalibration. By bridging spatial nonstationarity (MGWR) and nonlinearity (XGBoost), this study advances a dual-path framework for adaptive urban governance, the community-level prioritization of high-impact subjective factors (e.g., service quality), and data-driven spatial planning informed by nonlinear thresholds (e.g., facility density). The results offer actionable pathways to align smart urban development with socio-spatial equity, emphasizing the need for hyperlocal, perception-sensitive regeneration strategies. Full article

With global climate challenges intensifying, the cement industry, as a major CO₂ emitter, has attracted significant attention regarding its emission reduction potential and strategies. Advanced economies like the European Union use carbon pricing to spur innovation, while emerging countries focus on incremental solutions, such as fuel substitution. Combining LMDI decomposition and the LEAP model, this study examines Jiangsu Province as a test bed for China’s decarbonization strategy, a highly efficient region with carbon intensity 8% lower than the national average. Historical analysis identifies carbon intensity, energy mix, energy intensity, output scale, and economic effects as key drivers of emission changes. Specifically, the reduction in cement production, real estate contraction, lower housing construction, and reduced production capacity are the main factors curbing emissions. Under an integrated technology strategy—including energy efficiency, fuel and clinker substitution, and CCS—CO₂ emissions from Jiangsu’s cement sector are projected to decrease to 17.28 million tons and 10.9 million tons by 2060 under high- and low-demand scenarios, respectively. Clinker substitution is the most significant CO₂ reduction technology, contributing about 60%, while energy efficiency gains contribute only 3.4%. Despite the full deployment of existing reduction methods, Jiangsu’s cement industry is expected to face an emissions gap of approximately 10 million tons to achieve carbon neutrality by 2060, highlighting the need for innovative emission reduction technologies or carbon trading to meet carbon neutrality goals. Full article

The implementation of photovoltaic and home storage systems in multi-family houses (MFHs) in Germany lags significantly behind their development in single-family houses. The Landlord-to-Tenant (L2T) power supply model is meant to reduce this gap, yet few projects have been implemented to date. In this model, the landlord must fulfill the tenants’ power demand through a combination of photovoltaic generation and storage and electricity from the grid, for which the landlord pays an auxiliary electricity price that greatly influences the financial viability of a project. Our contribution focuses on the impact of electricity price variations and recent policy changes on the financial viability of small-scale L2T concepts. We considered component investment costs, building sizes, photovoltaic yields, and future developments. Recent policy changes have improved the financial viability of L2T projects, increasing the maximal auxiliary electricity price for which an investment is viable by 13 ct/kWh for a four-party MFH. Minimal auxiliary electricity prices justifying the installation of home storage systems (HSSs) decreased by 9 ct/kWh from 2020 to 2023. Autarky rates are substantially different across the considered scenarios, with the autarky rate being defined as the percentage of consumption of self-generated energy relative to the total energy consumption. For a 22-party MFH the autarky rate decreases by 17% compared to a 4-party MFH. HSSs have the potential to increase autarky rates while maintaining the financial viability of L2T projects. Full article

The real estate market plays a pivotal role in most nations’ economy, showcasing continuous growth. Particularly noteworthy is the rapid expansion of the digital real estate sector, marked by innovations like 3D visualization and streamlined online contractual processes, a momentum further accelerated by the aftermath of the Coronavirus Disease 2019 (COVID-19) pandemic. Amidst this transformative landscape, artificial intelligence emerges as a vital force, addressing consumer needs by harnessing data analytics for predicting and monitoring rental prices. While studies have demonstrated the efficacy of machine learning (ML) algorithms such as decision trees and neural networks in predicting house prices, there is a lack of research specifically focused on rental property prices, a significant sector in Brazil due to the prohibitive costs associated with property acquisition. This study fills this crucial gap by delving into the intricacies of rental pricing, using data from the city of São Carlos-SP, Brazil. The research aims to analyze, model, and predict rental prices, employing an approach that incorporates diverse ML models. Through this analysis, our work showcases the potential of ML algorithms in accurately predicting rental house prices. Moreover, it envisions the practical application of this research with the development of a user-friendly website. This platform could revolutionize the renting experience, empowering both tenants and real estate agencies with the ability to estimate rental values based on specific property attributes and have access to its statistics. Full article

The changing demographics of the global population represent a significant challenge for humanity. Such changes have an impact on the functioning of the economy, including the housing market, and necessitate constant monitoring. This study evaluated the spatial diversity of all the large and medium-sized cities in the West Pomeranian Voivodeship, situated in the north-west of Poland, in terms of three key factors: demographic potential, housing stock and their price levels. Furthermore, the interactions between the cities’ positions in the rankings, which were created on the basis of the aforementioned phenomena, were identified. In order to achieve the objectives of the study, the linear object ordering method, the Hellwig pattern method and Kendall’s tau rank correlation coefficient were employed. The research was conducted using data from the years 2018 to 2022, sourced from the databases of the Polish Statistical Office and the Analysis and Monitoring System of the Real Estate Market. The study observed a relatively strong positive correlation between the positions of cities in the ranking created for demographic potential and the level of residential property prices for the year 2020. The correlation between the positions of cities in the rankings for demographic potential and housing real estate stock was found to be very weak. The case of Koszalin was identified as an optimal location for residence due to the existing residential property stock and its prices. This was corroborated by the city’s residents, who also enabled the city to be ranked at the top of a ranking created for this phenomenon through the diagnostic variables for demographic potential. This article addresses a research gap, as, to the best of our knowledge, the indicated relationships have not yet been analysed in the contexts presented in the article. Full article

Community-based care services offered by senior centers are vital for supporting older adults’ independent living. The number of senior centers has escalated in China in recent years. Despite scholarly interest in the potential accessibility of senior centers, research on older adults’ realized access remains scarce. Using the geo-referenced check-in data of 2382 users of senior centers in Nanjing, China, this study aims to fill this gap by examining the disparities between older adults’ potential and realized access to senior centers and the influence of multilevel spatial and non-spatial factors. This study indicates that potential access is often significantly overestimated compared with the actual accessibility of senior centers, with older adults’ distances of realized access (mean = 1319 m) being considerably greater than potential access (mean = 325 m). Spatial and regression analyses confirm that older adults living in newly built, lower-priced houses in the inner city are more likely to travel longer distances to reach senior centers. Spatial proximity is less effective in predicting realized access for those living further from senior centers. Instead, the location and service quality of senior centers play a more prominent role. These findings enrich our understanding of older adults’ access to community-based care, informing planning and policy interventions for the development of age-friendly communities. Full article

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture. Full article

The ability of the cross-city commuting labor force to obtain housing has a profound impact on the development of the housing market and the enhancement of social welfare, but whether cross-city commuting has facilitated housing purchases remains to be verified However, the research on whether cross-city commuting behavior promotes labor force housing purchase in metropolitan areas is still lacking, especially in China, where the culture of buying houses is relatively special. This article used field survey data from the 2023 China Metropolitan Area Occupation and Housing Status Sampling Survey to empirically analyze whether cross-city commuting has facilitated housing purchases within metropolitan areas. The analysis was conducted by constructing a baseline model, a mediation effect model, and a subsample regression model. The results show that the cross-city commuting facilitated housing purchase within metropolitan areas, and the location preference is to purchase a house with a distance of 20–40 km from the workplace, but the contribution of the cross-city commuting to multi-suite purchases is relatively low. Mechanism analysis shows that compared to the workers who work and live in peripheral areas or the workers who work and live in cores, intercity commuters are promoted to purchase housing by relatively higher income and inducement of the housing price gap. The above conclusions still hold after controlling potential endogeneity issues and in robustness tests. The research of this paper can provide a new perspective for alleviating the housing inequality in the metropolitan area. Full article

The rapid expansion of the sharing economy has ignited diverse perspectives regarding its sustainability implications. Nevertheless, a comprehensive study examining the influence of host–guest interactions on sustainable consumption behaviour is yet to be conducted. To fill the abovementioned gap, this research crawls online data and corresponding consumer reviews of 46,360 properties listed on Muniao Short Rent. Employing latent Dirichlet allocation (LDA) to model sustainable consumption reviews and conducting subsequent regression analysis using SPSS, this research empirically demonstrates that the host–guest interaction frequencies and positive emotions during interaction positively influence guests’ sustainable consumption behaviours within the sharing-accommodation context. This research proposes the significance of the host–guest relationship for green consumers and argues that factors such as price and house type negatively moderate the host–guest interactions and guests’ sustainable consumption initiatives. Full article

The policy objectives of affordable housing programs in China are two-fold: on the one hand, they are designed to assist low- and moderate-income families and reduce inequality; on the other hand, they are intended to lower commodity housing prices. However, the effects of affordable housing land on housing prices, particularly the between-city variation and the mechanisms behind the market effects, have not been sufficiently examined, making it difficult to evaluate the housing policy and improve it accordingly. In this study, we address these gaps by using a prefecture-level panel dataset covering 2009–2020, obtained from national land and housing transaction information platforms. We use a threshold model to investigate the threshold effect of population size and a mediating model to uncover the channels through which the supply of affordable housing land affects housing prices. The results confirm that the affordable housing land supply can have a beneficial influence in terms of slowing down the increase in housing prices. The population size plays a significant role in explaining the between-city market effect variations. In cities with a population greater than 10.78 million, increasing the supply of affordable housing land would cause the housing prices to increase. Meanwhile, in cities with smaller populations, increasing the supply of affordable housing land could lower the housing prices. The underlying mechanisms of the market effects vary across cities with different population sizes. Although affordable housing land crowds out commodity housing land in all cities, housing demand diversion only exists in cities with a smaller population. At present, China is experimenting with city-specific housing policies; our findings imply that decision makers should explore additional policy options, besides building on incremental construction land, in order to make housing more affordable in supercities in China. Full article

This study explores the impact of accessibility on property pricing and land economies by advanced spatial analysis techniques, focusing on Shanghai as a representative metropolis. Despite the impact of metro systems on residential property values, which has been frequently assessed, a research gap exists in understanding this phenomenon in Asian, particularly Chinese, urban contexts. Addressing this gap is crucial for shaping effective urban land use policy and improving the land economy rationally in China and similar settings facing urban challenges. To assess the impact of metro station accessibility on property prices in Shanghai, with extensive rail transit, and to deeply explore the overall impact of land value varieties driven by metro on urban development, we conducted a comprehensive analysis, with discussion about future aspirations for land planning and management along with landscape and facility design, and measures to improve land economy. The procedures involved creating neighborhood centroids to represent accessibility and using the Euclidean distance analysis to determine the shortest paths to metro stations. Our evaluation incorporated a hedonic pricing model, considering variables like neighborhood characteristics, housing attributes, and socio-economic factors. Advanced spatial analysis encompassing Ordinary Least Squares (OLS) regression and XGBoost analysis were employed to explore spatial effects, and Geographically Weighted Regression (GWR) helped examine spatial patterns and address autocorrelation challenges. Results revealed a negative association between distance to metro station and property prices, indicating a non-linear and spatially clustered relationship and heterogeneous spatial pattern. We dissected the non-linear results in detail, which complemented the conclusion in existing research. This study provides valuable insights into the dynamic interplay between metro accessibility and housing market behaviors in a significant Asian urban context, offering targeted suggestions for urban planners and governors to decide on more reasonable land use planning and management strategies, along with landscape and infrastructure design, to promote not only the healthy growth of the real estate market but also the sustainable urban development in China and similar regions. Full article

The distinct cultural environment of various regions leads to unique consumer preferences for building facades, including the colours and materials that are used for the exteriors of condominium buildings. Understanding these preferences holds significant industry reference value for urban planning authorities and residential development companies. However, the colour and material aesthetic preferences of consumers for building facades have not received much research attention. To fill this gap, this study empirically investigates these preferences within the cultural context of Fuzhou, China. Using house prices as a reference perspective and econometric methods as research tools, this study explores the specific aesthetic preferences of urban consumer groups and compares the preferences of groups with different levels of consumption. The results confirm the existence of specific consumer preferences for building facade colours and materials and a close connection among the variations in these preferences and various combinations of facade colours and materials. Different quantities and types of materials can lead to distinct preferences for the quantities and features of facade colours. Apart from providing precise professional insights for urban planning authorities and residential developers, this study also offers a feasible conceptual reference for future studies to be conducted in other regions. Full article

Green buildings (GBs) enable the efficient use of resources while minimizing environmental impacts. Yet, GBs’ worldwide uptake is still hindered by various barriers, including the perception of being significantly more expensive than conventional ones. In Israel, several studies have investigated the willingness of prospective homebuyers to pay price premium (PP) for GBs and the associated affecting factors. However, these studies focused solely on the Jewish population and no similar study was carried out in the Arab sector. The present study attempts to bridge this knowledge gap by conducting a face-to-face survey among 215 potential Arab homebuyers in two cities in Israel characterized by a high percentage of Arab residents. Study results were compared to those found in a previous study in the Israeli Jewish sector. Findings indicate that despite their lower familiarity with the GB concept and attributes, prospective Arab homebuyers are willing to pay a much higher PP (10.56% compared to 6.58%) for purchasing a green apartment. This unexpected finding may be attributed to the higher motivation that Israeli Arabs have to improve their housing conditions and social status, which can be related to their larger households, higher household crowding, and stronger perception of housing as a long-term investment. Full article