Building-Level Demolition and Material Output Forecasting Using 4d-GIS: A Case Study of Kitakyushu City, Japan

Building-Level Demolition and Material Output Forecasting Using 4d-GIS: A Case Study of Kitakyushu City, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Building-Level Demolition and Material Output Forecasting Using 4d-GIS: A Case Study of Kitakyushu City, Japan Masatoshi HASEGAWA, Hiroaki SHIRAKAWA, Marianne Faith MARTINICO-PEREZ, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7968259/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study proposes a disaggregated modeling approach to estimate building-level demolition probabilities and project future material output. While existing Material Stock and Flow Analysis (MSFA) studies are typically conducted at national or municipal levels, they cannot often reflect differences in local geographic characteristics within cities. To address this issue, a Weibull Accelerated Failure Time (AFT) model was applied to building-level data in Kitakyushu City, Japan, incorporating structural, locational, and demographic variables. Demolition status was identified using geospatial overlays of building data from 2010 and 2018 and linked with spatial attributes such as land use zones, slope angle, and aging rates. The model was used to estimate demolition probabilities and simulate material output through 2040. Results indicate that buildings in aging and less accessible areas are more likely to remain despite the population decline, suggesting a growing risk of vacancy. The framework provides flexible spatial aggregation beyond administrative boundaries and can be introduced incrementally, making it suitable for areas with limited data availability. The study also highlights the importance of including year-of-construction data in building inventories. It will support sustainable urban development and enhance the framework, allowing for more strategic planning in the context of urban shrinkage and circular resource management. 4d-GIS life span waste management industrial ecology spatial analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION Since the period of rapid economic growth, Japan has achieved economically and materially affluent living standards through the massive input, accumulation, and disposal of resources. However, the increased resource input associated with the extraction and processing of natural resources has been closely linked to serious environmental issues, including climate change and biodiversity loss. On the same vein, as some cities around the world are also faced with the challenges of demographic decline and environmental sustainability, the management of existing building stocks has become a growing concern. The urban shrinkage in Japan and other developed nations requires data-driven strategies to balance development, maintenance of infrastructure, and material circularity (Haase et al., 2014 ). In response, the transition toward a circular society, which aims to minimize the ecological burden arising from natural resource use by promoting resource circulation and suppressing waste generation, has become an urgent policy challenge. In Japan, the Fourth Basic Environment Plan was formulated to promote the proper management of material flows, establishing three key indicators: “inputs,” “circulation” (on both the input and output sides), and “outputs.” These indicators have shown substantial improvement since the 1990s; however, in recent years, the recycling rate and the volume of final disposal have exhibited signs of stagnation (Ministry of the Environment,2025). To further improve material flow indicators, it is essential not only to focus on suppressing and streamlining flows but also to pay attention to the vast amount of “stock” already accumulated within society and to utilize it effectively (Pauliuk & Müller, 2014 ). In other words, a transition toward a “stock-type society,” in which high-quality goods are valued and used over the long term, is indispensable for building a sustainable society in the future (Okamoto,2010). Against this backdrop, Material Flow Analysis (MFA) and Material Stock and Flow Analysis (MSFA) have attracted increasing attention as effective methodologies for understanding the patterns of resource input, accumulation, and disposal in urban contexts, and for revealing the structure of material flows and stocks (Fischer-Kowalski & Hüttler, 1999). These approaches contribute to the quantitative assessment of current conditions and the evaluation of policy impacts in pursuit of a circular society, and they have played a significant role in material management at both national and regional levels. However, existing studies also exhibit several limitations. Most existing studies on MFA and MSFA have been conducted at administrative levels such as national or prefectural scales (Hashimoto et al., 2007 ; Heinz et al., 2008; Fishman et al., 2014 ; Wiedenhofer et al., 2015 ; Wang et al., 2024 ), and in some cases at the municipal level (Inada et al., 2022; Guo et al., 2019 ). However, these studies generally lack consideration of intra-municipal variations, such as differences among land use zones or the impacts of urban development within municipalities. Tanikawa et al. (2010) employed a 4d-GIS-based MSFA approach to estimate the lifespan of buildings by land use zone, highlighting the possibility that differences in zoning have a significant impact on building longevity. Incorporating detailed information at the individual building level could improve analytical accuracy and potentially provide valuable insights for small-scale urban interventions, such as community-led town planning initiatives and private-sector urban regeneration projects. However, analyses utilizing 4d-GIS often entail considerable effort and cost in data construction (Marcellus-Zamora et al., 2015 ; Chen et al., 2016 ; Kleemann et al., 2016 ; Bradshaw et al., 2020 ), which tends to limit the scope of study areas and makes it difficult to secure enough samples. Given these challenges, the development of a methodology capable of producing stable parameter estimates even with small sample sizes is a critical research issue. In estimating future material stocks and flows, the survival rate of buildings serves a critical role as it directly influences the timing and magnitude of material outflows from the built environment. Accurate estimation of building survival rates allows for better projections of demolition timing, recovery of resources, and future demand for construction materials. Generally, methods for setting building survival rates can be classified into three main categories. First, survival rates can be adopted from values reported in existing literature such as the work of Komatsu et al. ( 1992 ), providing standard survival rate assumptions based on historical building data. Second, an alternative method involves assigning the inverse of the legally defined service life of buildings as the annual demolition rate. This approach offers a straightforward estimation based on policy or regulatory lifespans, though it may not fully capture real-world variability in building longevity. Third, more detailed survival rates can be derived using empirical data by aggregating demolition and survival records from 4d-GIS (three-dimensional spatial combined temporal information). This data can be analyzed by structure type or geographic region and used to fit statistical approximation curves that describe the probability of survival over time (Tanikawa et al., 2010; Miatto et al., 2019 ) However, each of these approaches has its own limitations. Methods (1) and (2) rely solely on the structural classifications of buildings and therefore fail to account for geographical characteristics such as land use zoning or urban redevelopment pressures, that can significantly influence building lifespans. Method (3), while more flexible and capable of incorporating arbitrary conditions such as geographic area, zoning category, or building use, it faces the challenge of data scarcity. As more detailed criteria are applied, the number of samples in each group tends to decrease, which can compromise the statistical reliability and accuracy of the estimated survival parameters. Furthermore, a common limitation across all three methods is the lack of explanatory variables beyond building age in modeling survival rates. As a result, the differential impacts of spatial characteristics cannot be evaluated. This trade-off between specificity and data sufficiency remains a key methodological consideration in advancing more spatially nuanced modeling of building survival and material flow dynamics. This allows considerable room for improvement in MSFA methodologies to better account for spatial characteristics. To address the limitations of existing methods, this study aims to develop a disaggregate modeling approach for estimating building demolition probabilities that utilizes building-level data. In contrast to aggregated models, disaggregate approach treats demolition and survival outcomes as discrete data, allowing for a more precise analysis of individual building lifespans. By analyzing observations at the building level, this study seeks to construct mathematical models that incorporate a wide range of explanatory variables. By incorporating these detailed variables, the model aims to produce more accurate, spatially explicit estimates of building lifespans, thereby enhancing the reliability of projections related to future material stocks and flows. 2. METHODOLOGY 2.1 Study Area The study area, Kitakyushu City, is a government-designated city located at the northernmost tip of Kyushu Region, Japan. Historically, it developed as a typical industrial city centered on heavy industry, and to this day, various industrial zones based on manufacturing remain distributed throughout the city. The city is served by multiple railway lines, with Kokura Station functioning as the primary hub for both transportation and commercial activity. The area surrounding Kokura Station hosts a concentration of administrative offices, medical institutions, and large commercial facilities, indicating a high degree of urban functionality. In contrast, peripheral areas of the city face challenge such as the emergence of vacant houses due to an aging population and unfavorable geographic conditions, including steep terrain. Against this backdrop, Kitakyushu City is required to shift toward a more compact urban structure in response to rapid population decline and demographic aging, to ensure the sustainable delivery of public services. A detailed map of the study area is provided in Supporting Information (Figure S1 ). To address these challenges, the city formulated the Kitakyushu City Location Optimization Plan in September 2016. It aims to promote a "Compact and Networked Urban Structure," and positions the inducement of residential population into Residential Induction Zones, designated areas surrounding key urban hubs, as a central pillar of urban policy (Kitakyushu City, Location Optimization Plan). 2.2 Flow of this study The overall research framework adopted in this study is illustrated in Fig. 1 . This framework was designed to systematically estimate building-level demolition probabilities and to quantify the associated material output and building lifespan. This approach combines spatial analysis, statistical modeling, and geospatial data integration to address variations in building dynamics. To identify which buildings were demolished within the study period, a building-level Geographic Information System (GIS) datasets from 2010 and 2018 were overlaid. By comparing the attributes across these two time points, the demolition status of each building in 2018 was identified. The demolition judgment followed the approach proposed by Ota et al. ( 2023 ). In the next steps, a range of spatial attributes and demographic attributes was integrated into the building-level dataset to serve as explanatory variables. These attributes included urban planning zones (such as commercial, industrial, residential), Residential Induction Zones (RIZs), population distribution by age group (e.g., proportion of elderly), and average slope angle derived from digital elevation models. Using the assembled dataset, a statistical model was developed to estimate the probability of building demolition. The Accelerated Failure Time (AFT) Weibull model was employed to estimate the probability of building demolition based on structural attributes and spatial conditions. Finally, the developed model was utilized to estimate the future demolition probabilities for the building stock, demolition, and the associated material output volumes. This application extends the analysis beyond the observed data period, enabling projection of urban material flows and potential waste generation. 2.3 Preparation of Input Data and Data Sources The dataset includes attributes for each building—such as structure type (e.g., wooden, steel-reinforced concrete), building usage (e.g., residential, commercial), year of construction, and location. Additionally, each building record includes a binary indicator reflecting whether the building was demolished during observation, determined through a comparison of building status between 2010 and 2018. These input variables serve as essential explanatory factors for modeling demolition probability, as they capture both physical characteristics and contextual information relevant to building lifespan and urban transformation. A detailed summary of these input data is provided in Supporting Information (Table S2). 2.4 Demolition Probability Model In this study, a Weibull Accelerated Failure Time (AFT) model was employed to develop a probabilistic model of building demolition. The Weibull AFT model is a parametric approach within the filed of survival analysis, used to model the time until the occurrence of an event – such as mechanical failure, disease progression, or death – based on a set of explanatory covariates (Carroll, K. J., 2003 ). Among the available parametric distributions, the Weibull distribution is flexible, as it can accommodate increasing, decreasing, or constant hazard rates over time (Klein & Moescberger, 2003). This flexibility makes it especially suitable for modeling the variable life spans of buildings, which may be influenced by diverse structural, spatial, and socio-demographic factors. This study adopts a survival analysis framework to building stock dynamics by modeling the time until demolition as a function of multiple explanatory variables. The duration until demolition serves as the dependent variable, modeled as a function of multiple covariates. These covariates include structural attributes (e.g. building type, year built, and usage), spatial variables (e.g. slope angle and urban planning zones), and demographic indicators at the regional level (e.g., aging rate, working-age population), which capture broader socio-economic conditions that affect decisions regarding building retention or replacement. These factors were quantitatively evaluated to assess their influence on the timing of building demolition, providing insights into the drivers of urban transformation and material turnover. The cumulative distribution function (CDF) of the Weibull AFT model is formulated as follows: $$\:F\left(t\right)=1-{e}^{-\lambda\:\:{t}^{\alpha\:}}$$ The parameters are defined as: \(\:\alpha\:=\frac{1}{\sigma\:}\) , \(\:\lambda\:={e}^{-\left({\beta\:}_{1}{x}_{1}+{\beta\:}_{2}{x}_{2}+\cdots\:+{\beta\:}_{p}{x}_{p}+\mu\:\right)/\sigma\:}\) where F(t) denotes the cumulative demolition rate, t is time, λ is the scale parameter related to failure time, α is the shape parameter, σ represents the scale of the error term, and βx denotes the linear predictor composed of covariates x and their corresponding coefficients β . By using the Weibull AFT model, the timing of building demolition can be explicitly modeled along the time axis. Compared to conventional models such as logistic regression, this approach has the advantage of more accurately reflecting time-dependent behavior. 2.5 Material Intensity Both material outputs from demolition and material inputs during construction were estimated using a consistent methodology based on material intensity values. The material output was calculated by multiplying the total demolished floor area—estimated using the Demolition Probability Model—by the corresponding material intensity. Similarly, the material input was derived by applying the same material intensity to the newly constructed floor area. The material intensity values were calculated using unpublished microdata from the FY2018 Survey on the Actual Conditions of Construction Materials and Labor , provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) through a special data-sharing agreement for academic use. These values were obtained by dividing the amount of each construction material by the associated building floor area. A summary of the material intensity values is provided in Supporting Information (Table S3). 3. RESULTS 3.1 Material Input and Output of Construction Materials in Kitakyushu City Figure 2 shows the spatial distribution of material input and output related to construction activities in Kitakyushu City, Japan between 2010 and 2018. The data reveal that both material inflows (associated with new construction and building renovations) and outflows (primarily from building demolitions) were predominantly concentrated in the central urban area, particularly in the vicinity south of Kokura Station and its surrounding neighborhoods. This pattern suggest that these central districts experienced significant construction turnover during the study period, likely due to urban redevelopment, population density, and land use demand. Additionally, a distinct concentration of material input was observed in the coastal industrial zones, indicating ongoing development or expansion activities in these areas. These zones, typically characterized by large-scale infrastructure and industrial facilities, may have required substantial quantities of construction materials for upgrades, maintenance, or new installations. The spatial patterns shown in the figure highlight the importance of both functional land use and urban form in the material flow dynamics within the city. © OpenStreetMap contributors, © CARTO Figure 2 : Spatial distribution of Material Flow between 2010 and 2018: a) Material INPUT and b) Material OUTPUT Figure 3 presents the aggregated material input and output of construction materials by land use zone and building use type. In residential zones, material input and output were predominantly associated with detached houses, indicating that most construction and demolition activities in these areas involved low-rise, single-family dwellings. In industrial zones, both material input and output were heavily concentrated around factory buildings, reflecting large-scale industrial development and potential site renewal or expansion. In commercial zones, a wide variety of building uses were observed, indicating a more complex and heterogeneous building landscape. This diversity is likely due to the commercial zones’ transitional geographical position between the coastal industrial areas and the mountainous residential zones. As a result, commercial zones exhibit characteristics of both adjacent land use types, supporting a blend of residential, industrial, and service-oriented structures. a) Material Input and b) Material Output 3.2 Parameter Estimation and Validation In this study, a Weibull Accelerated Failure Time (AFT) model was developed to examine how building structure, usage, locational attributes, and regional demographic characteristics influence building lifespan (Table 1 ). The main estimation results and their interpretations are summarized below. One of the most notable findings is the effect of demographic aging. The aging rate showed a significant positive effect on building longevity, with an estimated coefficient of − 1.890 and an Expected Time Ratio (ETR) of 1.99. This indicates that a 100% increase in the aging rate nearly doubles the expected building lifespan. This result is highly statistically significant (p < 0.001), suggesting that demographic aging may lead to delayed building replacement and prolonged use of existing structures, but at the same time increase in vacant houses. Regarding building use, apartment buildings demonstrated an ETR of 1.30, implying a lifespan approximately 30% longer than that of the reference category. In contrast, commercial and public facilities had slightly shorter expected lifespans, with ETRs around 0.96. For structural characteristics, wooden buildings showed a modestly extended lifespan (ETR = 1.07), while steel structures were associated with shorter durability, with an ETR of 0.78. In terms of urban planning zones, buildings located in commercial and industrial zones exhibited longer lifespans, both with ETRs of 1.22, indicating lifespans over 20% longer than those in the reference zone. Meanwhile, buildings in the unclassified zone had a slightly increased lifespan (ETR = 1.06). However, since unclassified zones often include hazard-prone or under-serviced areas, the extended durability of buildings in such zones may contribute to the increased number of vacant houses. Table 1 Estimated Parameters of the Weibull AFT Model Estimate Value z value p value ETR Intercept -1.384 3.800 392.49 < 2e-16 building area -0.0001 0.000 6.07 1.3e-09 1.00 Wooden structure -0.186 0.068 12.96 < 2e-16 1.07 Steel structure 0.690 -0.251 -45.75 < 2e-16 0.78 Residential use 0.141 -0.051 -6.80 1.1e-11 0.95 Apartment use -0.728 0.264 25.90 < 2e-16 1.30 Commercial use 0.119 -0.043 -3.97 7.3e-05 0.96 Public use 0.103 -0.037 -3.72 0.0002 0.96 Industrial use -0.168 0.061 6.60 4.0e-11 1.06 Special use -0.180 0.066 2.13 0.0330 1.07 Urban area: Commercial zone -0.551 0.200 58.64 < 2e-16 1.22 Urban area: Industrial zone -0.555 0.202 35.25 < 2e-16 1.22 Urban area: Unclassified zone -0.159 0.058 12.15 < 2e-16 1.06 Mean slope angle -0.011 0.004 13.03 < 2e-16 1.00 Residential Induction Zones -0.013 0.005 1.81 0.0711 1.00 population aging rate -1.890 0.686 54.37 < 2e-16 1.99 Working-age population -0.0001 0.000 11.83 < 2e-16 1.00 σ 0.364 -474.68 < 2e-16 Log-likelihood = − 577,219.1; Likelihood ratio test = 18,592.8 on 16 df, p < 0.001. Model fit was significantly improved compared to the null model (intercept only). 3.3 Estimation of Future Demolition Probability The estimated building-level demolition probabilities for the year 2040 are presented below. Figure 4 depicts the central urban area surrounding Kokura Station, the primary transportation and commercial hub of Kitakyushu City. In the area surrounding Kokura Station, most buildings exhibit relatively low demolition probabilities; however, a concentrated cluster of buildings with probabilities exceeding 80% is observed near the station core. These high-probability buildings tend to be spatially grouped at the block level, suggesting that while surrounding areas have undergone planned redevelopment, this particular zone may have been excluded or left behind. By projecting demolition probabilities at the individual building level, this analysis allows for the visualization of potential future patterns of building removal across different urban contexts. Such spatially detailed forecasts provide a foundation for more informed and rational planning related to infrastructure renewal, land-use reconfiguration and urban development. Moreover, overlaying these projections with Residential Induction Zones (RIZs) can enhance the strategic alignment of urban development strategies. This approach offers a valuable decision-support tool for policymakers and serve as a valuable tool for supporting policy formulation. As a contrasting case, results for the area surrounding Edamitsu Station—representative of a peripheral district with different urban dynamics—are provided in Supporting Information (Figure S4). Based on the Orthoimagery from GSI Maps, provided by the Geospatial Information Authority of Japan (GSI). Figure 4 : Estimated Building Demolition Rate by 2040 in the Kokura Station Area 3.3 Future Projection of Material Output Figure 5 presents the projected spatial distribution of material output resulting from building demolitions based on the estimated demolition probabilities. The results reveal notable spatial variation in material output, largely influenced by building use type and locational characteristics. Distinct distributional patterns emerge for each building category, highlighting the spatial heterogeneity of future material output within the Kitakyushu City, Japan. Material output from detached houses and apartment buildings is projected to be widely distributed, with concentration in the mountainous southern areas extending from the railway corridors. This pattern reflects the historical expansion of low- to medium-density residential neighborhoods in less central, topographically complex area of the city. Commercial buildings, on the other hand, display a pronounced clustering of output around major transportation nodes, particularly in the vicinity of Kokura Station, the city's primary commercial and transit center. Public buildings exhibit spatial tendencies similar pattern to residential buildings, with demolition-driven material output concentrated along key railway corridors and in within southern parts of the city. In contrast, industrial buildings show a distinct spatial pattern with material output heavily concentrated in coastal zones, consistent with the Kitakyushu City’s industrial zoning and land use designations. Moreover, it was confirmed that a certain level of material output is expected even in areas with relatively low accessibility, particularly those situated farther from railway stations. The average cumulative amount of material output per mesh from 2022 to 2040 was 55.2 thousand tons per 22 years within 500 meters of railway stations, while it was 22.3 thousand tons per 22 years in areas outside the 500-meter radius. These peripheral zones may face challenges in redevelopment and reintegration into the urban core. Accordingly, policy measures that promote residential relocation concurrent with demolition events could be strategically leveraged to support population consolidation in more accessible, transit-oriented areas. Such relocation strategies not only enhance land-use efficiency but also contribute to long-term urban sustainability by aligning material flow management with broader spatial planning and demographic objectives. In areas with low transportation accessibility, it is important to consider strategies for post-demolition land use and population redistribution. In particular, promoting residential relocation at the time of building demolition may serve as a policy opportunity to encourage population consolidation in more accessible, station-adjacent areas. © OpenStreetMap contributors, © CARTO Figure 5 : Material Output between 2018 and 2040 by building use-type by Spatial Mesh: a) Residential and Apartment buildings, b) Commercial buildings and Special Purpose, c) public building and d) Industrial and Others The projected results for the future material output from building demolitions are summarized in Figs. 6 and 7 , which respectively illustrate trends within and outside the Residential Induction Zones (RIZs). The comparative analysis highlights distinct differences in building composition and temporal dynamic of material output across these spatial categories. Within RIZs, material output is predominantly associated with apartment buildings and commercial buildings, reflecting the denser and more mixed-use character of these zones. In contrast, areas outside RIZs are characterized by a higher proportion of detached houses and industrial buildings, indicative of lower-density residential development and industrial zoning patterns. In terms of total material output over time, the RIZ areas are projected to reach their peak between 2041 and 2045, with a maximum estimated annual output of 1.143 million tons. Conversely, areas outside RIZs are projected to peak earlier, between 2036 and 2040, with an annual maximum output of around 749 thousand tons. Further disaggregation of building use types outside RIZs reveals that material output from detached houses is anticipated to peak between 2026 and 2030, reaching an estimated 233 thousand tons per year. In comparison, apartment buildings in the same area are projected to peak much later, between 2051 and 2055, with an annual output of 130 thousand tons per year. This temporal lag indicates that detached houses are likely to reach the end of their service life earlier than apartment buildings, reflecting differences in construction period, structural durability, and redevelopment cycles. Apartment buildings tend to occur later than that for detached houses. a) In Residential Induction Zone, b) Outside the Residential Induction Zone 3.4 Distribution of Building Lifespans Figure 7 illustrates the distribution of building lifespans, measured as Mean Lifetime (MLT), categorized by building use type and location relative to the Residential Induction Zone (RIZ). In this study, MLT is defined as the number of years at which the cumulative demolition probability reaches 50%, representing the expected median service life of buildings under prevailing conditions. The results reveal substantial variation in MLT across different building use types. Apartment buildings (APT) showed the longest lifespans, with median values ranging from 65.6 to 65.9 years. Detached houses (RES) had moderate lifespans, with median values between 50.7 and 52.9 years. In contrast, commercial (COM), industrial (IND), and other (OTH) buildings categories exhibited relatively shorter lifespans. According to the survey conducted by Hagishima et al. ( 2002 ) in Kitakyushu City, the average lifespan of wooden buildings was 55.7 years, while that of non-wooden buildings was 59.15 years. Since the average lifespan obtained in this study is close to these values, the results can be considered generally valid. The influence of RIZ designation on MLT appears to be varied use-type dependent. For APT, the median lifespan remained consistent at approximately 66 years, regardless of whether the building was located inside or outside a RIZ, suggesting that zoning designation has a limited direct impact on the longevity of multi-family residential structures. In the case of RES, a small difference of approximately two years was observed between RIZ and non-RIZ areas. However, this variation falls within the range of standard deviation (SD ≈ 7–8 years), suggesting that the observed difference is not statistically significant. These findings suggest that zoning designation alone does not strongly determine building lifespan. Instead, RES longevity is more likely influenced by factors such as distribution of construction years, regional redevelopment patterns, land market dynamics, and sociodemographic characteristics. While the direct effects of RIZ designation on building lifespan appear limited, there maybe important indirect impacts that influence long-term urban form and material efficiency. In particular, RIZ designation may contribute to increased population density and redevelopment activity, especially through the replacement of detached houses with higher-density apartment buildings in central urban areas. These shifts not only extend the average service life of buildings, given the typically longer lifespan of apartments, but also enhance material efficiency by consolidating infrastructure and optimizing land use. In contrast, areas located outside RIZs face a different set of challenges. Declining population in these peripheral zones can lead to decreased housing demand, which in turn elevates the risk of underutilized or abandoned properties. This issue is especially acute for apartment buildings situated outside RIZs, where the rate of demolition may lag behind the pace of population decline. This vacancy concern is particularly pronounced for APT located outside RIZs, where demolition may not keep pace with population decline, potentially exacerbating the vacancy problem. Kitakyushu City's Location Optimization Plan provides a policy framework aimed at addressing these concerns. The city has set a target to set to relocate approximately 7% of its population from areas outside RIZs to within RIZs over a five-year period. In line with this objective, the population outside RIZs is projected to decrease from around 250,000 in 2010 to approximately 180,000 by 2040. However, if building demolition proceeds only at its natural pace without targeted intervention, this demographic transition may result in a significant mismatch between population decline and building stock reduction. Such a disparity would likely lead to a substantial increase in vacant housing, further straining municipal resources and undermining the goals of compact, sustainable urban development. Therefore, future urban planning must incorporate proactive and coordinated strategies that synchronize demolition timing with demographic and spatial trends. This includes aligning housing stock reduction with patterns of population decline, prioritizing high-vacancy areas for strategic deconstruction, and integrating land-use reconfiguration into broader urban shrinkage policies. Only through such intentional planning can cities effectively manage the dual challenges of population decline and excess building stock, while promoting more resilient, efficient, and sustainable urban environments. 4. DISCUSSION 4.1 Relevance to Local Urban Planning The method developed in this study presents a flexible and data-efficient framework for estimating building lifespans and projecting material output, thereby offering a valuable contribution to contemporary municipal planning. In contrast to conventional approaches, which often require extensive datasets and are limited in spatial resolution, the proposed model allows for reliable estimation even with a relatively small sample size. This makes it particularly well-suited for regions with limited data availability or for applications at smaller spatial scales. A key advantage of this approach lies in its disaggregate structure, which estimates demolition probabilities at the individual building level. This enables highly flexible spatial aggregation, facilitating analysis across a range of spatial units, from traditional administrative boundaries and urban planning zones to more localized geographies such as elementary school districts, street blocks, or areas delineated by railway station catchments (e.g., east versus west sides). Such spatial granularity significantly enhances the model’s applicability to a diverse urban context and planning requirements. Moreover, the method extends beyond large-scale, city-wide master planning applications. Its adaptability makes it equally applicable to smaller-scale urban initiatives, including community-based planning initiatives and private-sector-led urban regeneration efforts. The model’s capacity to be implemented incrementally, starting from localized areas makes, aligns well with “small-start” planning approaches. This scalability enhances its practical utility and positions are method as a robust tool for flexible, data-informed urban formulation and land-use decision-making in a wide range of governance and planning settings. 4.2 Vacant and Retained Structures In this study, demolition probabilities were estimated at the individual building level, allowing for the projection of both future remaining building stock and anticipated demolition volumes. However, the remaining stock, as defined in the current analysis, includes both buildings and those that are not—such as vacant or abandoned structures. At present, the model does not differentiate between these two categories. The current discussion is primarily limited to referencing the relationship between building lifespans and the objectives outline in the Location Optimization Plan, as well considering the potential emergence and spatial distribution of vacant buildings. However, to more accurately evaluate the future composition and functional utility of the building stock, it is essential to distinguish between buildings in active use and those that are not. This differentiation has critical implications not only for estimating material flows and circularity, but also assessing broader environmental outcomes, such as energy consumption and greenhouse gas emissions associated with the built environment. As a prospective research direction, the integration of a vacancy rate estimation model as a sub-component of the existing framework could offer a more nuanced and comprehensive understanding of building stock dynamics, By incorporating qualitative attributes, specifically, the operational status of buildings, such as model extension would enhance the analytical precision and policy relevance of material stock analysis. This would, in turn, support more informed decision-making in the context of urban sustainability, infrastructure planning, and resource-efficient development. 4.3 Importance of Determining the Year of Construction In this study, access to year-of-construction data for structures within Kitakyushu City, Japan, enabled age-based analysis of the building stock. However, in many regions of Japan, commonly used building datasets—such as Z-map—often lack construction year information, which presents a key limitation when applying the same methodology to other regions. Building age is recognized as a crucial variable in numerous analytical contexts, including future projection and causal modeling. This study, therefore, emphasizes the significance of incorporating year-of-construction information as part of essential building attribute data. Although much of the relevant building data is managed by local government agencies, access and sharing remain limited due to privacy protection concerns. In addition, the relatively low level of precedent in applying such data—both within government and in collaboration with external stakeholders—represents a barrier to broader and more effective utilization. The results of this study may be positioned as a pilot example of how municipal big data can be effectively leveraged. It is hoped that this case will encourage the advancement of data-sharing practices and the strategic use of government-held datasets in the future. 5. CONCLUSION This study presented a building-level modeling framework to estimate future demolition probabilities and material output in the context of urban shrinkage, using Kitakyushu City as a case study. By applying a Weibull Accelerated Failure Time model to disaggregated geospatial and structural data, the analysis enabled the identification of spatially uneven demolition trends across the city. While centrally located buildings with high accessibility showed moderate demolition probability—often associated with ongoing redevelopment—peripheral areas exhibited higher retention rates despite declining population levels, raising concerns over future vacancy and underutilization. One of the key contributions of this study lies in its methodological flexibility. The proposed approach requires a relatively small number of input data inputs, yet it delivers spatially detailed, building-specific outputs that can be aggregated across a variety of planning units. This capacity makes the model well-suited for both top-down and bottom-up planning contexts, including neighborhood-scale initiatives and broader city-level strategies. Furthermore, its scalability and adaptability align well with incremental “small-start” urban planning approaches, facilitating localized and strategic land-use interventions. The study also underscored the importance of incorporating building age data, which serves as a key explanatory variable in modeling building life cycles and material stock dynamics. However, access to year-of-construction information remains limited in many Japanese municipalities due to privacy and institutional constraints. This work offers a pilot example of how municipal big data, when properly utilized, can generate valuable insights for urban analysis, potentially encouraging broader data-sharing practices and strategic use of government-held datasets. Finally, the integration of vacancy modeling was identified as a crucial next step. The current model does not distinguish between retained buildings that are occupied and those that are vacant or obsolete. Differentiating between these categories would significantly improve the model’s utility for evaluating future building stock composition, estimating material flows, and assessing environmental impacts such as energy consumption and emissions. Expanding the model to account for building operational status would enhance its relevance for sustainable urban planning, infrastructure strategy, and circular resource management. Declarations SUPPORTING INFORMATION Supporting information is linked to this article on the JIE website: Supporting Information S1: This supporting information provides Thematic Map of Kitakyushu City, Japan, Showing Key Urban Center and Sloped Area. Supporting Information S2: This supporting information provides Input Data for Demolition Probability Model. Supporting Information S3: This supporting information provides Material Intensity. Supporting Information S4: This supporting information provides Estimated Building Demolition Rate by 2040 in the Edamitsu Station Area Conflict of Interest Statement: The authors declare no conflict of interest. Data Availability Statement: The data used in this study contain personally identifiable information and are therefore not publicly available due to privacy and ethical restrictions. ACKNOWLEDGMENTS This research was supported by the Environment Research and Technology Development Fund (JPMEERF20231005, JPMEERF20252RB1). This research was also supported by JSPS KAKENHI Grant Numbers JP23H00531,JP24K03140, JP25H01206, MEXT Grant Number JPJ010039,and JST Grant Number JPMJPF2204. References Bradshaw, J., Jit Singh, S., Tan, S.-Y., Fishman, T., & Pott, K. (2020). GIS-Based Material Stock Analysis (MSA) of Climate Vulnerabilities to the Tourism Industry in Antigua and Barbuda. Sustainability , 12 (19). https://doi.org/10.3390/su12198090 Carroll, K. J. (2003). 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Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data. J Ind Ecol , 28 (6), 1409-1421. https://doi.org/10.1111/jiec.13550 Wiedenhofer, D., Steinberger, J. K., Eisenmenger, N., & Haas, W. (2015). Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25. J Ind Ecol , 19 (4), 538-551. https://doi.org/10.1111/jiec.12216 Additional Declarations No competing interests reported. Supplementary Files supportinginformationhasegawa.doc Supporting Information S1: This supporting information provides Thematic Map of Kitakyushu City, Japan, Showing Key Urban Center and Sloped Area. Supporting Information S2: This supporting information provides Input Data for Demolition Probability Model. Supporting Information S3: This supporting information provides Material Intensity. Supporting Information S4: This supporting information provides Estimated Building Demolition Rate by 2040 in the Edamitsu Station Area Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 14 Dec, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 28 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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17:25:07","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119883,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/944f218602720212641b2542.html"},{"id":95671508,"identity":"3ce95704-b9f3-4496-8bd4-fa5279c10978","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":544851,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Framework and Workflow\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/ba06aa2aa328b73818efa263.png"},{"id":95671504,"identity":"c1be12b0-4780-4e42-86f2-83450b1fae59","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1854492,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/a8530b9c27140e7e9d0b4c10.png"},{"id":95671505,"identity":"760b05d9-6ed4-4343-b179-cd083c9e7415","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":258791,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/96bf0f3a70f28fba46209c3a.png"},{"id":95671514,"identity":"fc39f042-37d6-45c0-ae63-10976f366bcf","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3046370,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/480b506f91e2efcb8ceb35cf.png"},{"id":95671510,"identity":"7e00d8d5-996c-4c5e-819b-920ada76a401","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3351702,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/3fe72bb0ea56a701e2816639.png"},{"id":95671533,"identity":"0dd4c095-14ab-4dbf-a189-7fb4249a535e","added_by":"auto","created_at":"2025-11-11 17:25:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":524887,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/b4e1520590cb5130dc4db87d.png"},{"id":95671526,"identity":"f9d33b03-16f1-40e3-850d-4e05ea3dffaf","added_by":"auto","created_at":"2025-11-11 17:25:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":152559,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/e7126d7b39cdf0ee2f5e7410.png"},{"id":96238971,"identity":"948fdf76-39f8-40c8-ab6a-06e885bbfbef","added_by":"auto","created_at":"2025-11-19 06:59:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15579153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/b2d290f6-96fd-425f-88a7-7e3644a96225.pdf"},{"id":95671513,"identity":"79e94ea9-5790-4f70-ba3a-5d6de23df50f","added_by":"auto","created_at":"2025-11-11 17:25:06","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3406848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupporting Information S1: \u003c/strong\u003eThis supporting information provides Thematic Map of Kitakyushu City, Japan, Showing Key Urban Center and Sloped Area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S2: \u003c/strong\u003eThis supporting information provides Input Data for Demolition Probability Model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S3:\u003c/strong\u003e This supporting information provides Material Intensity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S4:\u003c/strong\u003e This supporting information provides Estimated Building Demolition Rate by 2040 in the Edamitsu Station Area\u003c/p\u003e","description":"","filename":"supportinginformationhasegawa.doc","url":"https://assets-eu.researchsquare.com/files/rs-7968259/v1/9a50064c302b8f10ce4ccf35.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Building-Level Demolition and Material Output Forecasting Using 4d-GIS: A Case Study of Kitakyushu City, Japan","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSince the period of rapid economic growth, Japan has achieved economically and materially affluent living standards through the massive input, accumulation, and disposal of resources. However, the increased resource input associated with the extraction and processing of natural resources has been closely linked to serious environmental issues, including climate change and biodiversity loss. On the same vein, as some cities around the world are also faced with the challenges of demographic decline and environmental sustainability, the management of existing building stocks has become a growing concern. The urban shrinkage in Japan and other developed nations requires data-driven strategies to balance development, maintenance of infrastructure, and material circularity (Haase et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response, the transition toward a circular society, which aims to minimize the ecological burden arising from natural resource use by promoting resource circulation and suppressing waste generation, has become an urgent policy challenge. In Japan, the \u003cem\u003eFourth Basic Environment Plan\u003c/em\u003e was formulated to promote the proper management of material flows, establishing three key indicators: \u0026ldquo;inputs,\u0026rdquo; \u0026ldquo;circulation\u0026rdquo; (on both the input and output sides), and \u0026ldquo;outputs.\u0026rdquo; These indicators have shown substantial improvement since the 1990s; however, in recent years, the recycling rate and the volume of final disposal have exhibited signs of stagnation (Ministry of the Environment,2025). To further improve material flow indicators, it is essential not only to focus on suppressing and streamlining flows but also to pay attention to the vast amount of \u0026ldquo;stock\u0026rdquo; already accumulated within society and to utilize it effectively (Pauliuk \u0026amp; M\u0026uuml;ller, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In other words, a transition toward a \u0026ldquo;stock-type society,\u0026rdquo; in which high-quality goods are valued and used over the long term, is indispensable for building a sustainable society in the future (Okamoto,2010).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, Material Flow Analysis (MFA) and Material Stock and Flow Analysis (MSFA) have attracted increasing attention as effective methodologies for understanding the patterns of resource input, accumulation, and disposal in urban contexts, and for revealing the structure of material flows and stocks (Fischer-Kowalski \u0026amp; H\u0026uuml;ttler, 1999). These approaches contribute to the quantitative assessment of current conditions and the evaluation of policy impacts in pursuit of a circular society, and they have played a significant role in material management at both national and regional levels. However, existing studies also exhibit several limitations. Most existing studies on MFA and MSFA have been conducted at administrative levels such as national or prefectural scales (Hashimoto et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Heinz et al., 2008; Fishman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wiedenhofer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and in some cases at the municipal level (Inada et al., 2022; Guo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, these studies generally lack consideration of intra-municipal variations, such as differences among land use zones or the impacts of urban development within municipalities.\u003c/p\u003e\u003cp\u003eTanikawa et al. (2010) employed a 4d-GIS-based MSFA approach to estimate the lifespan of buildings by land use zone, highlighting the possibility that differences in zoning have a significant impact on building longevity. Incorporating detailed information at the individual building level could improve analytical accuracy and potentially provide valuable insights for small-scale urban interventions, such as community-led town planning initiatives and private-sector urban regeneration projects.\u003c/p\u003e\u003cp\u003eHowever, analyses utilizing 4d-GIS often entail considerable effort and cost in data construction (Marcellus-Zamora et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kleemann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bradshaw et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which tends to limit the scope of study areas and makes it difficult to secure enough samples. Given these challenges, the development of a methodology capable of producing stable parameter estimates even with small sample sizes is a critical research issue.\u003c/p\u003e\u003cp\u003eIn estimating future material stocks and flows, the survival rate of buildings serves a critical role as it directly influences the timing and magnitude of material outflows from the built environment. Accurate estimation of building survival rates allows for better projections of demolition timing, recovery of resources, and future demand for construction materials. Generally, methods for setting building survival rates can be classified into three main categories. First, survival rates can be adopted from values reported in existing literature such as the work of Komatsu et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), providing standard survival rate assumptions based on historical building data. Second, an alternative method involves assigning the inverse of the legally defined service life of buildings as the annual demolition rate. This approach offers a straightforward estimation based on policy or regulatory lifespans, though it may not fully capture real-world variability in building longevity. Third, more detailed survival rates can be derived using empirical data by aggregating demolition and survival records from 4d-GIS (three-dimensional spatial combined temporal information). This data can be analyzed by structure type or geographic region and used to fit statistical approximation curves that describe the probability of survival over time (Tanikawa et al., 2010; Miatto et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eHowever, each of these approaches has its own limitations. Methods (1) and (2) rely solely on the structural classifications of buildings and therefore fail to account for geographical characteristics such as land use zoning or urban redevelopment pressures, that can significantly influence building lifespans. Method (3), while more flexible and capable of incorporating arbitrary conditions such as geographic area, zoning category, or building use, it faces the challenge of data scarcity. As more detailed criteria are applied, the number of samples in each group tends to decrease, which can compromise the statistical reliability and accuracy of the estimated survival parameters.\u003c/p\u003e\u003cp\u003eFurthermore, a common limitation across all three methods is the lack of explanatory variables beyond building age in modeling survival rates. As a result, the differential impacts of spatial characteristics cannot be evaluated. This trade-off between specificity and data sufficiency remains a key methodological consideration in advancing more spatially nuanced modeling of building survival and material flow dynamics. This allows considerable room for improvement in MSFA methodologies to better account for spatial characteristics.\u003c/p\u003e\u003cp\u003eTo address the limitations of existing methods, this study aims to develop a disaggregate modeling approach for estimating building demolition probabilities that utilizes building-level data. In contrast to aggregated models, disaggregate approach treats demolition and survival outcomes as discrete data, allowing for a more precise analysis of individual building lifespans. By analyzing observations at the building level, this study seeks to construct mathematical models that incorporate a wide range of explanatory variables. By incorporating these detailed variables, the model aims to produce more accurate, spatially explicit estimates of building lifespans, thereby enhancing the reliability of projections related to future material stocks and flows.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe study area, Kitakyushu City, is a government-designated city located at the northernmost tip of Kyushu Region, Japan. Historically, it developed as a typical industrial city centered on heavy industry, and to this day, various industrial zones based on manufacturing remain distributed throughout the city.\u003c/p\u003e\u003cp\u003eThe city is served by multiple railway lines, with Kokura Station functioning as the primary hub for both transportation and commercial activity. The area surrounding Kokura Station hosts a concentration of administrative offices, medical institutions, and large commercial facilities, indicating a high degree of urban functionality. In contrast, peripheral areas of the city face challenge such as the emergence of vacant houses due to an aging population and unfavorable geographic conditions, including steep terrain. Against this backdrop, Kitakyushu City is required to shift toward a more compact urban structure in response to rapid population decline and demographic aging, to ensure the sustainable delivery of public services. A detailed map of the study area is provided in Supporting Information (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address these challenges, the city formulated the Kitakyushu City Location Optimization Plan in September 2016. It aims to promote a \"Compact and Networked Urban Structure,\" and positions the inducement of residential population into Residential Induction Zones, designated areas surrounding key urban hubs, as a central pillar of urban policy (Kitakyushu City, Location Optimization Plan).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Flow of this study\u003c/h2\u003e\u003cp\u003eThe overall research framework adopted in this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This framework was designed to systematically estimate building-level demolition probabilities and to quantify the associated material output and building lifespan. This approach combines spatial analysis, statistical modeling, and geospatial data integration to address variations in building dynamics.\u003c/p\u003e\u003cp\u003eTo identify which buildings were demolished within the study period, a building-level Geographic Information System (GIS) datasets from 2010 and 2018 were overlaid. By comparing the attributes across these two time points, the demolition status of each building in 2018 was identified. The demolition judgment followed the approach proposed by Ota et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the next steps, a range of spatial attributes and demographic attributes was integrated into the building-level dataset to serve as explanatory variables. These attributes included urban planning zones (such as commercial, industrial, residential), Residential Induction Zones (RIZs), population distribution by age group (e.g., proportion of elderly), and average slope angle derived from digital elevation models. Using the assembled dataset, a statistical model was developed to estimate the probability of building demolition. The Accelerated Failure Time (AFT) Weibull model was employed to estimate the probability of building demolition based on structural attributes and spatial conditions.\u003c/p\u003e\u003cp\u003eFinally, the developed model was utilized to estimate the future demolition probabilities for the building stock, demolition, and the associated material output volumes. This application extends the analysis beyond the observed data period, enabling projection of urban material flows and potential waste generation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Preparation of Input Data and Data Sources\u003c/h2\u003e\u003cp\u003eThe dataset includes attributes for each building\u0026mdash;such as structure type (e.g., wooden, steel-reinforced concrete), building usage (e.g., residential, commercial), year of construction, and location. Additionally, each building record includes a binary indicator reflecting whether the building was demolished during observation, determined through a comparison of building status between 2010 and 2018.\u003c/p\u003e\u003cp\u003eThese input variables serve as essential explanatory factors for modeling demolition probability, as they capture both physical characteristics and contextual information relevant to building lifespan and urban transformation. A detailed summary of these input data is provided in Supporting Information (Table S2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Demolition Probability Model\u003c/h2\u003e\u003cp\u003eIn this study, a Weibull Accelerated Failure Time (AFT) model was employed to develop a probabilistic model of building demolition. The Weibull AFT model is a parametric approach within the filed of survival analysis, used to model the time until the occurrence of an event \u0026ndash; such as mechanical failure, disease progression, or death \u0026ndash; based on a set of explanatory covariates (Carroll, K. J., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Among the available parametric distributions, the Weibull distribution is flexible, as it can accommodate increasing, decreasing, or constant hazard rates over time (Klein \u0026amp; Moescberger, 2003). This flexibility makes it especially suitable for modeling the variable life spans of buildings, which may be influenced by diverse structural, spatial, and socio-demographic factors.\u003c/p\u003e\u003cp\u003eThis study adopts a survival analysis framework to building stock dynamics by modeling the time until demolition as a function of multiple explanatory variables. The duration until demolition serves as the dependent variable, modeled as a function of multiple covariates. These covariates include structural attributes (e.g. building type, year built, and usage), spatial variables (e.g. slope angle and urban planning zones), and demographic indicators at the regional level (e.g., aging rate, working-age population), which capture broader socio-economic conditions that affect decisions regarding building retention or replacement. These factors were quantitatively evaluated to assess their influence on the timing of building demolition, providing insights into the drivers of urban transformation and material turnover.\u003c/p\u003e\u003cp\u003eThe cumulative distribution function (CDF) of the Weibull AFT model is formulated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:F\\left(t\\right)=1-{e}^{-\\lambda\\:\\:{t}^{\\alpha\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe parameters are defined as:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:=\\frac{1}{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e , \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:={e}^{-\\left({\\beta\\:}_{1}{x}_{1}+{\\beta\\:}_{2}{x}_{2}+\\cdots\\:+{\\beta\\:}_{p}{x}_{p}+\\mu\\:\\right)/\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eF(t)\u003c/em\u003e denotes the cumulative demolition rate, \u003cem\u003et\u003c/em\u003e is time, \u003cem\u003eλ\u003c/em\u003e is the scale parameter related to failure time, \u003cem\u003eα\u003c/em\u003e is the shape parameter, \u003cem\u003eσ\u003c/em\u003e represents the scale of the error term, and \u003cem\u003eβx\u003c/em\u003e denotes the linear predictor composed of covariates \u003cem\u003ex\u003c/em\u003e and their corresponding coefficients \u003cem\u003eβ\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBy using the Weibull AFT model, the timing of building demolition can be explicitly modeled along the time axis. Compared to conventional models such as logistic regression, this approach has the advantage of more accurately reflecting time-dependent behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Material Intensity\u003c/h2\u003e\u003cp\u003eBoth material outputs from demolition and material inputs during construction were estimated using a consistent methodology based on material intensity values. The material output was calculated by multiplying the total demolished floor area\u0026mdash;estimated using the Demolition Probability Model\u0026mdash;by the corresponding material intensity. Similarly, the material input was derived by applying the same material intensity to the newly constructed floor area.\u003c/p\u003e\u003cp\u003eThe material intensity values were calculated using unpublished microdata from the \u003cem\u003eFY2018 Survey on the Actual Conditions of Construction Materials and Labor\u003c/em\u003e, provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) through a special data-sharing agreement for academic use. These values were obtained by dividing the amount of each construction material by the associated building floor area. A summary of the material intensity values is provided in Supporting Information (Table S3).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Material Input and Output of Construction Materials in Kitakyushu City\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the spatial distribution of material input and output related to construction activities in Kitakyushu City, Japan between 2010 and 2018. The data reveal that both material inflows (associated with new construction and building renovations) and outflows (primarily from building demolitions) were predominantly concentrated in the central urban area, particularly in the vicinity south of Kokura Station and its surrounding neighborhoods. This pattern suggest that these central districts experienced significant construction turnover during the study period, likely due to urban redevelopment, population density, and land use demand. Additionally, a distinct concentration of material input was observed in the coastal industrial zones, indicating ongoing development or expansion activities in these areas. These zones, typically characterized by large-scale infrastructure and industrial facilities, may have required substantial quantities of construction materials for upgrades, maintenance, or new installations. The spatial patterns shown in the figure highlight the importance of both functional land use and urban form in the material flow dynamics within the city.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u0026copy; OpenStreetMap contributors, \u0026copy; CARTO\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Spatial distribution of Material Flow between 2010 and 2018:\u003c/p\u003e\u003cp\u003ea) Material INPUT and b) Material OUTPUT\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the aggregated material input and output of construction materials by land use zone and building use type. In residential zones, material input and output were predominantly associated with detached houses, indicating that most construction and demolition activities in these areas involved low-rise, single-family dwellings. In industrial zones, both material input and output were heavily concentrated around factory buildings, reflecting large-scale industrial development and potential site renewal or expansion. In commercial zones, a wide variety of building uses were observed, indicating a more complex and heterogeneous building landscape. This diversity is likely due to the commercial zones\u0026rsquo; transitional geographical position between the coastal industrial areas and the mountainous residential zones. As a result, commercial zones exhibit characteristics of both adjacent land use types, supporting a blend of residential, industrial, and service-oriented structures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ea) Material Input and b) Material Output\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Parameter Estimation and Validation\u003c/h2\u003e\u003cp\u003eIn this study, a Weibull Accelerated Failure Time (AFT) model was developed to examine how building structure, usage, locational attributes, and regional demographic characteristics influence building lifespan (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main estimation results and their interpretations are summarized below.\u003c/p\u003e\u003cp\u003eOne of the most notable findings is the effect of demographic aging. The aging rate showed a significant positive effect on building longevity, with an estimated coefficient of \u0026minus;\u0026thinsp;1.890 and an Expected Time Ratio (ETR) of 1.99. This indicates that a 100% increase in the aging rate nearly doubles the expected building lifespan. This result is highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that demographic aging may lead to delayed building replacement and prolonged use of existing structures, but at the same time increase in vacant houses.\u003c/p\u003e\u003cp\u003eRegarding building use, apartment buildings demonstrated an ETR of 1.30, implying a lifespan approximately 30% longer than that of the reference category. In contrast, commercial and public facilities had slightly shorter expected lifespans, with ETRs around 0.96.\u003c/p\u003e\u003cp\u003eFor structural characteristics, wooden buildings showed a modestly extended lifespan (ETR\u0026thinsp;=\u0026thinsp;1.07), while steel structures were associated with shorter durability, with an ETR of 0.78.\u003c/p\u003e\u003cp\u003eIn terms of urban planning zones, buildings located in commercial and industrial zones exhibited longer lifespans, both with ETRs of 1.22, indicating lifespans over 20% longer than those in the reference zone. Meanwhile, buildings in the unclassified zone had a slightly increased lifespan (ETR\u0026thinsp;=\u0026thinsp;1.06). However, since unclassified zones often include hazard-prone or under-serviced areas, the extended durability of buildings in such zones may contribute to the increased number of vacant houses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated Parameters of the Weibull AFT Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eETR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e392.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebuilding area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3e-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWooden structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSteel structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-45.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApartment use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.3e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecial use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area: Commercial zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area: Industrial zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area: Unclassified zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean slope angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential Induction Zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epopulation aging rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking-age population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eσ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-474.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLog-likelihood\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;577,219.1; Likelihood ratio test\u0026thinsp;=\u0026thinsp;18,592.8 on 16 df, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003eModel fit was significantly improved compared to the null model (intercept only).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Estimation of Future Demolition Probability\u003c/h2\u003e\u003cp\u003eThe estimated building-level demolition probabilities for the year 2040 are presented below. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the central urban area surrounding Kokura Station, the primary transportation and commercial hub of Kitakyushu City.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the area surrounding Kokura Station, most buildings exhibit relatively low demolition probabilities; however, a concentrated cluster of buildings with probabilities exceeding 80% is observed near the station core. These high-probability buildings tend to be spatially grouped at the block level, suggesting that while surrounding areas have undergone planned redevelopment, this particular zone may have been excluded or left behind.\u003c/p\u003e\u003cp\u003eBy projecting demolition probabilities at the individual building level, this analysis allows for the visualization of potential future patterns of building removal across different urban contexts. Such spatially detailed forecasts provide a foundation for more informed and rational planning related to infrastructure renewal, land-use reconfiguration and urban development. Moreover, overlaying these projections with Residential Induction Zones (RIZs) can enhance the strategic alignment of urban development strategies. This approach offers a valuable decision-support tool for policymakers and serve as a valuable tool for supporting policy formulation.\u003c/p\u003e\u003cp\u003eAs a contrasting case, results for the area surrounding Edamitsu Station\u0026mdash;representative of a peripheral district with different urban dynamics\u0026mdash;are provided in Supporting Information (Figure S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the Orthoimagery from GSI Maps, provided by the Geospatial Information Authority of Japan (GSI).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Estimated Building Demolition Rate by 2040 in the Kokura Station Area\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Future Projection of Material Output\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the projected spatial distribution of material output resulting from building demolitions based on the estimated demolition probabilities. The results reveal notable spatial variation in material output, largely influenced by building use type and locational characteristics. Distinct distributional patterns emerge for each building category, highlighting the spatial heterogeneity of future material output within the Kitakyushu City, Japan.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMaterial output from detached houses and apartment buildings is projected to be widely distributed, with concentration in the mountainous southern areas extending from the railway corridors. This pattern reflects the historical expansion of low- to medium-density residential neighborhoods in less central, topographically complex area of the city. Commercial buildings, on the other hand, display a pronounced clustering of output around major transportation nodes, particularly in the vicinity of Kokura Station, the city's primary commercial and transit center.\u003c/p\u003e\u003cp\u003ePublic buildings exhibit spatial tendencies similar pattern to residential buildings, with demolition-driven material output concentrated along key railway corridors and in within southern parts of the city. In contrast, industrial buildings show a distinct spatial pattern with material output heavily concentrated in coastal zones, consistent with the Kitakyushu City\u0026rsquo;s industrial zoning and land use designations.\u003c/p\u003e\u003cp\u003eMoreover, it was confirmed that a certain level of material output is expected even in areas with relatively low accessibility, particularly those situated farther from railway stations. The average cumulative amount of material output per mesh from 2022 to 2040 was 55.2 thousand tons per 22 years within 500 meters of railway stations, while it was 22.3 thousand tons per 22 years in areas outside the 500-meter radius. These peripheral zones may face challenges in redevelopment and reintegration into the urban core. Accordingly, policy measures that promote residential relocation concurrent with demolition events could be strategically leveraged to support population consolidation in more accessible, transit-oriented areas. Such relocation strategies not only enhance land-use efficiency but also contribute to long-term urban sustainability by aligning material flow management with broader spatial planning and demographic objectives. In areas with low transportation accessibility, it is important to consider strategies for post-demolition land use and population redistribution. In particular, promoting residential relocation at the time of building demolition may serve as a policy opportunity to encourage population consolidation in more accessible, station-adjacent areas.\u003c/p\u003e\u003cp\u003e\u0026copy; OpenStreetMap contributors, \u0026copy; CARTO\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e: Material Output between 2018 and 2040 by building use-type by Spatial Mesh:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003ea) Residential and Apartment buildings, b) Commercial buildings and Special Purpose,\u003cbr\u003e\u003c/span\u003e\u003cspan\u003ec) public building and d) Industrial and Others\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe projected results for the future material output from building demolitions are summarized in Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, which respectively illustrate trends within and outside the Residential Induction Zones (RIZs). The comparative analysis highlights distinct differences in building composition and temporal dynamic of material output across these spatial categories. Within RIZs, material output is predominantly associated with apartment buildings and commercial buildings, reflecting the denser and more mixed-use character of these zones. In contrast, areas outside RIZs are characterized by a higher proportion of detached houses and industrial buildings, indicative of lower-density residential development and industrial zoning patterns.\u003c/p\u003e\n\u003cp\u003eIn terms of total material output over time, the RIZ areas are projected to reach their peak between 2041 and 2045, with a maximum estimated annual output of 1.143 million tons. Conversely, areas outside RIZs are projected to peak earlier, between 2036 and 2040, with an annual maximum output of around 749 thousand tons.\u003c/p\u003e\n\u003cp\u003eFurther disaggregation of building use types outside RIZs reveals that material output from detached houses is anticipated to peak between 2026 and 2030, reaching an estimated 233 thousand tons per year. In comparison, apartment buildings in the same area are projected to peak much later, between 2051 and 2055, with an annual output of 130 thousand tons per year. This temporal lag indicates that detached houses are likely to reach the end of their service life earlier than apartment buildings, reflecting differences in construction period, structural durability, and redevelopment cycles. Apartment buildings tend to occur later than that for detached houses.\u003c/p\u003e\n\u003cp\u003ea) In Residential Induction Zone, b) Outside the Residential Induction Zone\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Distribution of Building Lifespans\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the distribution of building lifespans, measured as Mean Lifetime (MLT), categorized by building use type and location relative to the Residential Induction Zone (RIZ). In this study, MLT is defined as the number of years at which the cumulative demolition probability reaches 50%, representing the expected median service life of buildings under prevailing conditions.\u003c/p\u003e\n \u003cp\u003eThe results reveal substantial variation in MLT across different building use types. Apartment buildings (APT) showed the longest lifespans, with median values ranging from 65.6 to 65.9 years. Detached houses (RES) had moderate lifespans, with median values between 50.7 and 52.9 years. In contrast, commercial (COM), industrial (IND), and other (OTH) buildings categories exhibited relatively shorter lifespans.\u003c/p\u003e\n \u003cp\u003eAccording to the survey conducted by Hagishima et al. (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) in Kitakyushu City, the average lifespan of wooden buildings was 55.7 years, while that of non-wooden buildings was 59.15 years. Since the average lifespan obtained in this study is close to these values, the results can be considered generally valid.\u003c/p\u003e\n \u003cp\u003eThe influence of RIZ designation on MLT appears to be varied use-type dependent. For APT, the median lifespan remained consistent at approximately 66 years, regardless of whether the building was located inside or outside a RIZ, suggesting that zoning designation has a limited direct impact on the longevity of multi-family residential structures. In the case of RES, a small difference of approximately two years was observed between RIZ and non-RIZ areas. However, this variation falls within the range of standard deviation (SD\u0026thinsp;\u0026asymp;\u0026thinsp;7\u0026ndash;8 years), suggesting that the observed difference is not statistically significant. These findings suggest that zoning designation alone does not strongly determine building lifespan. Instead, RES longevity is more likely influenced by factors such as distribution of construction years, regional redevelopment patterns, land market dynamics, and sociodemographic characteristics.\u003c/p\u003e\n \u003cp\u003eWhile the direct effects of RIZ designation on building lifespan appear limited, there maybe important indirect impacts that influence long-term urban form and material efficiency. In particular, RIZ designation may contribute to increased population density and redevelopment activity, especially through the replacement of detached houses with higher-density apartment buildings in central urban areas. These shifts not only extend the average service life of buildings, given the typically longer lifespan of apartments, but also enhance material efficiency by consolidating infrastructure and optimizing land use.\u003c/p\u003e\n \u003cp\u003eIn contrast, areas located outside RIZs face a different set of challenges. Declining population in these peripheral zones can lead to decreased housing demand, which in turn elevates the risk of underutilized or abandoned properties. This issue is especially acute for apartment buildings situated outside RIZs, where the rate of demolition may lag behind the pace of population decline. This vacancy concern is particularly pronounced for APT located outside RIZs, where demolition may not keep pace with population decline, potentially exacerbating the vacancy problem.\u003c/p\u003e\n \u003cp\u003eKitakyushu City\u0026apos;s Location Optimization Plan provides a policy framework aimed at addressing these concerns. The city has set a target to set to relocate approximately 7% of its population from areas outside RIZs to within RIZs over a five-year period. In line with this objective, the population outside RIZs is projected to decrease from around 250,000 in 2010 to approximately 180,000 by 2040. However, if building demolition proceeds only at its natural pace without targeted intervention, this demographic transition may result in a significant mismatch between population decline and building stock reduction. Such a disparity would likely lead to a substantial increase in vacant housing, further straining municipal resources and undermining the goals of compact, sustainable urban development. Therefore, future urban planning must incorporate proactive and coordinated strategies that synchronize demolition timing with demographic and spatial trends. This includes aligning housing stock reduction with patterns of population decline, prioritizing high-vacancy areas for strategic deconstruction, and integrating land-use reconfiguration into broader urban shrinkage policies. Only through such intentional planning can cities effectively manage the dual challenges of population decline and excess building stock, while promoting more resilient, efficient, and sustainable urban environments.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Relevance to Local Urban Planning\u003c/h2\u003e\u003cp\u003eThe method developed in this study presents a flexible and data-efficient framework for estimating building lifespans and projecting material output, thereby offering a valuable contribution to contemporary municipal planning. In contrast to conventional approaches, which often require extensive datasets and are limited in spatial resolution, the proposed model allows for reliable estimation even with a relatively small sample size. This makes it particularly well-suited for regions with limited data availability or for applications at smaller spatial scales.\u003c/p\u003e\u003cp\u003eA key advantage of this approach lies in its disaggregate structure, which estimates demolition probabilities at the individual building level. This enables highly flexible spatial aggregation, facilitating analysis across a range of spatial units, from traditional administrative boundaries and urban planning zones to more localized geographies such as elementary school districts, street blocks, or areas delineated by railway station catchments (e.g., east versus west sides). Such spatial granularity significantly enhances the model\u0026rsquo;s applicability to a diverse urban context and planning requirements.\u003c/p\u003e\u003cp\u003eMoreover, the method extends beyond large-scale, city-wide master planning applications. Its adaptability makes it equally applicable to smaller-scale urban initiatives, including community-based planning initiatives and private-sector-led urban regeneration efforts. The model\u0026rsquo;s capacity to be implemented incrementally, starting from localized areas makes, aligns well with \u0026ldquo;small-start\u0026rdquo; planning approaches. This scalability enhances its practical utility and positions are method as a robust tool for flexible, data-informed urban formulation and land-use decision-making in a wide range of governance and planning settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Vacant and Retained Structures\u003c/h2\u003e\u003cp\u003eIn this study, demolition probabilities were estimated at the individual building level, allowing for the projection of both future remaining building stock and anticipated demolition volumes. However, the remaining stock, as defined in the current analysis, includes both buildings and those that are not\u0026mdash;such as vacant or abandoned structures. At present, the model does not differentiate between these two categories.\u003c/p\u003e\u003cp\u003eThe current discussion is primarily limited to referencing the relationship between building lifespans and the objectives outline in the Location Optimization Plan, as well considering the potential emergence and spatial distribution of vacant buildings. However, to more accurately evaluate the future composition and functional utility of the building stock, it is essential to distinguish between buildings in active use and those that are not. This differentiation has critical implications not only for estimating material flows and circularity, but also assessing broader environmental outcomes, such as energy consumption and greenhouse gas emissions associated with the built environment.\u003c/p\u003e\u003cp\u003eAs a prospective research direction, the integration of a vacancy rate estimation model as a sub-component of the existing framework could offer a more nuanced and comprehensive understanding of building stock dynamics, By incorporating qualitative attributes, specifically, the operational status of buildings, such as model extension would enhance the analytical precision and policy relevance of material stock analysis. This would, in turn, support more informed decision-making in the context of urban sustainability, infrastructure planning, and resource-efficient development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Importance of Determining the Year of Construction\u003c/h2\u003e\u003cp\u003eIn this study, access to year-of-construction data for structures within Kitakyushu City, Japan, enabled age-based analysis of the building stock. However, in many regions of Japan, commonly used building datasets\u0026mdash;such as Z-map\u0026mdash;often lack construction year information, which presents a key limitation when applying the same methodology to other regions.\u003c/p\u003e\u003cp\u003eBuilding age is recognized as a crucial variable in numerous analytical contexts, including future projection and causal modeling. This study, therefore, emphasizes the significance of incorporating year-of-construction information as part of essential building attribute data.\u003c/p\u003e\u003cp\u003eAlthough much of the relevant building data is managed by local government agencies, access and sharing remain limited due to privacy protection concerns. In addition, the relatively low level of precedent in applying such data\u0026mdash;both within government and in collaboration with external stakeholders\u0026mdash;represents a barrier to broader and more effective utilization.\u003c/p\u003e\u003cp\u003eThe results of this study may be positioned as a pilot example of how municipal big data can be effectively leveraged. It is hoped that this case will encourage the advancement of data-sharing practices and the strategic use of government-held datasets in the future.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study presented a building-level modeling framework to estimate future demolition probabilities and material output in the context of urban shrinkage, using Kitakyushu City as a case study. By applying a Weibull Accelerated Failure Time model to disaggregated geospatial and structural data, the analysis enabled the identification of spatially uneven demolition trends across the city. While centrally located buildings with high accessibility showed moderate demolition probability\u0026mdash;often associated with ongoing redevelopment\u0026mdash;peripheral areas exhibited higher retention rates despite declining population levels, raising concerns over future vacancy and underutilization.\u003c/p\u003e\u003cp\u003eOne of the key contributions of this study lies in its methodological flexibility. The proposed approach requires a relatively small number of input data inputs, yet it delivers spatially detailed, building-specific outputs that can be aggregated across a variety of planning units. This capacity makes the model well-suited for both top-down and bottom-up planning contexts, including neighborhood-scale initiatives and broader city-level strategies. Furthermore, its scalability and adaptability align well with incremental \u0026ldquo;small-start\u0026rdquo; urban planning approaches, facilitating localized and strategic land-use interventions.\u003c/p\u003e\u003cp\u003eThe study also underscored the importance of incorporating building age data, which serves as a key explanatory variable in modeling building life cycles and material stock dynamics. However, access to year-of-construction information remains limited in many Japanese municipalities due to privacy and institutional constraints. This work offers a pilot example of how municipal big data, when properly utilized, can generate valuable insights for urban analysis, potentially encouraging broader data-sharing practices and strategic use of government-held datasets.\u003c/p\u003e\u003cp\u003eFinally, the integration of vacancy modeling was identified as a crucial next step. The current model does not distinguish between retained buildings that are occupied and those that are vacant or obsolete. Differentiating between these categories would significantly improve the model\u0026rsquo;s utility for evaluating future building stock composition, estimating material flows, and assessing environmental impacts such as energy consumption and emissions. Expanding the model to account for building operational status would enhance its relevance for sustainable urban planning, infrastructure strategy, and circular resource management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSUPPORTING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupporting information is linked to this article on the \u003cem\u003eJIE\u003c/em\u003e website:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S1:\u0026nbsp;\u003c/strong\u003eThis supporting information provides Thematic Map of Kitakyushu City, Japan, Showing Key Urban Center and Sloped Area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S2:\u0026nbsp;\u003c/strong\u003eThis supporting information provides Input Data for Demolition Probability Model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S3:\u003c/strong\u003e This supporting information provides\u0026nbsp;Material Intensity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting Information S4:\u003c/strong\u003e This supporting information provides\u0026nbsp;Estimated Building Demolition Rate by 2040 in the Edamitsu Station Area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data used in this study contain personally identifiable information and are therefore not publicly available due to privacy and ethical restrictions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Environment Research and Technology Development Fund (JPMEERF20231005, JPMEERF20252RB1). This research was also supported by JSPS KAKENHI Grant Numbers JP23H00531,JP24K03140, JP25H01206, MEXT Grant Number JPJ010039,and JST Grant Number JPMJPF2204.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBradshaw, J., Jit Singh, S., Tan, S.-Y., Fishman, T., \u0026amp; Pott, K. (2020). GIS-Based Material Stock Analysis (MSA) of Climate Vulnerabilities to the Tourism Industry in Antigua and Barbuda. \u003cem\u003eSustainability\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(19). https://doi.org/10.3390/su12198090 \u003c/li\u003e\n\u003cli\u003eCarroll, K. J. (2003). On the use and utility of the Weibull model in the analysis of survival data. \u003cem\u003eControl Clin Trials\u003c/em\u003e,\u003cem\u003e 24\u003c/em\u003e(6), 682-701. https://doi.org/10.1016/s0197-2456(03)00072-2 \u003c/li\u003e\n\u003cli\u003eChen, C., Shi, F., Okuoka, K., \u0026amp; Tanikawa, H. (2016). 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Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data. \u003cem\u003eJ Ind Ecol\u003c/em\u003e,\u003cem\u003e 28\u003c/em\u003e(6), 1409-1421. https://doi.org/10.1111/jiec.13550\u003c/li\u003e\n\u003cli\u003eWiedenhofer, D., Steinberger, J. K., Eisenmenger, N., \u0026amp; Haas, W. (2015). Maintenance and Expansion: Modeling Material Stocks and Flows for Residential Buildings and Transportation Networks in the EU25. \u003cem\u003eJ Ind Ecol\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(4), 538-551. https://doi.org/10.1111/jiec.12216\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"4d-GIS, life span, waste management, industrial ecology, spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-7968259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7968259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a disaggregated modeling approach to estimate building-level demolition probabilities and project future material output. While existing Material Stock and Flow Analysis (MSFA) studies are typically conducted at national or municipal levels, they cannot often reflect differences in local geographic characteristics within cities. To address this issue, a Weibull Accelerated Failure Time (AFT) model was applied to building-level data in Kitakyushu City, Japan, incorporating structural, locational, and demographic variables.\u003c/p\u003e\u003cp\u003eDemolition status was identified using geospatial overlays of building data from 2010 and 2018 and linked with spatial attributes such as land use zones, slope angle, and aging rates. The model was used to estimate demolition probabilities and simulate material output through 2040. Results indicate that buildings in aging and less accessible areas are more likely to remain despite the population decline, suggesting a growing risk of vacancy. The framework provides flexible spatial aggregation beyond administrative boundaries and can be introduced incrementally, making it suitable for areas with limited data availability. The study also highlights the importance of including year-of-construction data in building inventories. It will support sustainable urban development and enhance the framework, allowing for more strategic planning in the context of urban shrinkage and circular resource management.\u003c/p\u003e","manuscriptTitle":"Building-Level Demolition and Material Output Forecasting Using 4d-GIS: A Case Study of Kitakyushu City, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:25:01","doi":"10.21203/rs.3.rs-7968259/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T11:19:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T05:34:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T10:38:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312730688180556162457663013953911854207","date":"2026-01-14T15:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249215208728753221265652655133697103854","date":"2025-12-17T04:17:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T04:11:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T06:23:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T06:20:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Industrial Ecology","date":"2025-10-28T11:03:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"979d7a9d-7c1d-4704-9269-37a4656f0ea4","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T12:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 17:25:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7968259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7968259","identity":"rs-7968259","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}