Building Floor Area Research Articles

Urban revitalization pathways toward zero carbon emissions through systems architecting of urban digital twins

Recognizing the critical role of cities in mitigating greenhouse gas emissions, many cities are adopting carbon neutrality goals as part of their climate action strategies. The efficacy of these initiatives, however, has been undermined by complexity of systemic problems, ineffectiveness in planning implementation, and lack of stakeholder engagement. Urban and community-level carbon reduction should transcend urban design and systems optimization to incorporate multi-faceted dimensions in urban contexts. To address these challenges, this paper proposes a framework of urban digital twins that includes digital representation, performance modeling, design interventions and interactive platform for decisions over temporal processes. The CANVAS, or Carbon Neutrality Architecting New Visions for Architectural Systems, is a systems architecting approach to modeling the process of urban revitalization for achieving carbon neutrality by 2050. The developed workflow integrates multidisciplinary approaches for carbon mapping, gap identification, alternative generation, Urban Building Energy Modeling (UBEM) simulation, evaluation, and decision-making to demonstrates applicability of the proposed framework through a case study of the Nihonbashi district in Tokyo. The approach revealed that Energy Use Intensity (EUI) can be decreased by 99 kWh/m2/y through reconstruction and operational improvements. Emerging photovoltaic technologies can further cut EUI by an average of 42.5 kWh/m2/y, although results vary significantly in respect to building characteristics, particularly geometry and floor area. The incremental, cyclical systems architecting approach revealed that a 97% reduction in carbon emissions could be achieved by the seventh cycle through stakeholder-centric system interventions. This paper contributes to the development of urban digital twin methodologies by integrating systems architecting concepts with UBEM as transformative tools for carbon neutral urban design and development.

Performance-based evaluation on egress safety of disabled welfare facilities

ABSTRACT With the increasing number of fire incidents, efforts are being made to quantitatively evaluate the egress safety of buildings. In particular, studies that reflect the behavioural characteristics of occupants when evaluating egress safety are being conducted. However, the research on the performance design and safety evaluation of facilities where individuals vulnerable to disasters (i.e. those who have difficulty evacuating in the event of a fire) reside remains insufficient. In this study, fire evacuating simulations were performed on welfare facilities for disabled individuals with mobility issues, and the egress safety was evaluated. The fire simulations were performed using the capacity of the smoke exhaust system as a variable. Meanwhile, the variations in temperature and toxic gases according to fire duration were analyzed to calculate the available safe egress time (ASET). The egress simulations were performed using the egress delay time and the number of egress guides as variables. Then, the required safe egress time (REST) was calculated. The results show that increasing smoke exhaust system delays the attainment of tenability limits, improving ASET. Egress simulations revealed an optimal number of egress guides. Based on these findings, general criteria have been developed to assess egress safety, considering the building floor area, smoke exhaust capacity, and occupant numbers. These criteria can be applied to ensure efficient and economical safety designs for typical welfare facilities for the disabled. HIGHLIGHTS Egress safety of disabled welfare facilities was evaluated by FDS. ASET was derived based on smoke exhaust capacity and fire duration. RSET was derived by considering evacuation delays and instructions. Findings support efficient, cost-effective safety design for disabled care facilities.

The diffusion of prefabrication technology and its potential for CO2 emissions reduction in China: A combined system dynamics and agent-based study

The building sector is one of the largest CO2 emitters. Prefabrication technology (PT) shows remarkable potential for reducing CO2 emissions during the material and construction phases of the building life cycle. However, the diffusion of PT in China is still in its infancy, and more targeted policies are necessary to accelerate the adoption of PT. This study proposes a combined system dynamics and agent-based modeling approach to include the technology adoption behavior of enterprises at the micro level, and the market effects and policies at the macro level. This approach provides a more comprehensive modeling approach and analysis for technology diffusion. Results show that the diffusion of PT has experienced three phases: (i) an initial phase that lasted until 2016, (ii) a mandatory adoption phase from 2016 to 2025, and (iii) a mandatory and market adoption phase that will last from 2025 to 2035 under appropriate policy mixes. A policy mix with moderate intensity is the most efficient in terms of balancing the policy effect and feasibility. The potential of CO2 reduction depends on both the diffusion rate of PT and the floor area of new buildings. Mandatory adoption is more effective than incentive-based policies in reducing CO2 emissions.

Sustainability of column‐supported slabs for buildings: A multi‐criteria assessment

AbstractThe global construction industry is experiencing significant growth, driving the demand for building floor area. However, this expansion comes with substantial environmental consequences, including high‐energy consumption and greenhouse gas (GHG) emissions, together with economic and social impacts. To address these challenges, this research aims at providing designers and decisions‐makers with an approach that will aid the quantification of those impacts relevant for the sustainability performance of flooring systems. A holistic sustainability assessment was performed considering economic, environmental, and social aspects using the Integrated Value Model for Sustainability Evaluations. This study focused on various flooring typologies, including reinforced concrete (RC) and fiber‐RC (FRC) slabs with solid and waffle (for RC solution) configurations. The most representative criteria and indicators of sustainability for concrete column‐supported slabs were identified, measured, and weighted—aggregated in a decision‐making tree—in order to obtain a representative a sustainability index (SI) for each alternative. The results of the analysis evidence that the RC solid slab has a higher overall SI than the other alternatives, the FRC alternatives performing with similar SI of that quantified for the RC solid slab solution and the RC waffle slab being that with the lower sustainability performance among those flooring systems considered in this study. The results of a sensitivity analysis showed that those alternatives using FRC have potential for improving the overall sustainability performance.

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.

Assessing embodied carbon emissions from material consumption in Hong Kong's building sector from 2012 to 2050 under uncertainty

Material consumption in building construction contributes to numerous embodied carbon (EC) emissions, and thus assessing this aspect of EC is crucial for tracking the climate change impacts of construction activities. The existence of multiple uncertainty factors leads to deviations of assessment results from reality. This study developed a systematic methodology to assess the material consumption-related EC emissions in the building sector under uncertainty at multiple levels, and applied it in Hong Kong's building sector from 2012 to 2050. Four types of uncertainty factors, namely, floor area of new buildings, material consumption intensity, carbon emission factor, and material recycling quantity, were considered, with their fluctuation intervals determined based on data sources and precision. The Sobol’ sensitivity analysis method was adopted to identify the sensitive factors affecting the EC. Results showed that the annual EC from material consumption peaked at 2.61 Mt in 2018, equivalent to 6.4% of the carbon emissions in Hong Kong. The emissions are estimated to have a rapid decline until 0.02–0.24 Mt in 2050 under different scenarios of accommodation floor area and material recycling. Concrete and steel account for over 80 % of the EC throughout the study period. The uncertainty factors lead to considerable variations in the EC assessment results, with fluctuations ranging up to ±83.7 %. The building sector scale was identified as the dominant factor affecting the EC results in the long term, while the significance of material demand and emission factors diminishes progressively. Based on these findings, we suggest establishing a carbon footprint tracking system for building materials, and implementing a dynamic update and modification mechanism for the decarbonisation targets in Hong Kong's building sector.

Wood-based policies as a driver for the climate transition

Abstract In the context of global accelerating urbanization, the total floor area of buildings is expected to double by 2060, with a significant impact on the environment. While cities made consistent progress towards increasing energy efficiency, accounting for, and reducing embodied emissions for new and renovated constructions represents one critical priority for mitigating this impact and reaching genuine carbon neutrality. Wood is a renewable and carbon-positive material, which can have a meaningful contribution to changing paradigms on net-zero carbon ambitions of cities, but still faces the challenge of an immature integration into urban development policy framework. Through the Horizon 2020 “Build-in-Wood” project we addressed the challenge of translating novel wood building systems, pilots, wood value chains for the construction of multi-storey wood buildings, and promising practices into policy. As a result, we formulate transferable lessons learned, recommendations, and criteria that can serve as guidance for municipalities and policymakers who aim to channel their efforts toward creating a more sustainable built environment by prioritizing wood, either implicitly or explicitly. We achieved this by working on understanding the real needs of cities and territories, the drivers, and the key entry points and preconditions for new policies supporting multi-storey wood buildings and genuine carbon neutrality. Specifically, the findings are based on the close collaboration with seven affiliated cities (Early Adopter Cities in Europe), outlining and integrating the results of (i)Multi-criterial urban planning, strategic and regulatory context analysis, (ii)Participatory and co-design approaches implemented with their local stakeholder ecosystem from the wood construction value chains and (iii)Research of different best practice policies at national, regional and local level from different parts of the globe, that incentives through informative, financial, research or regulatory instruments building with wood. The entire process is presented in the form of a Build-in-Wood Policy Catalogue [1], offering insights on working with Early Adopter Cities and guidance for improving policy, governance, and the decision-making process for subnational governments. The research was not intended to be exhaustive in documenting all existing policies because of the field’s dynamics, but rather to provide an overview and understanding of different policy instruments’ applicability and potential impact.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines

Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the historical evolution and project future scenarios of building material stock (MS) and their spatial distribution across Japan's three major metropolitan areas. Through a comprehensive material flow and stock analysis, the historical accumulation of building materials from 2009 to 2020 was quantified, revealing a dominance of concrete and an increasing overall stock. The contributions of various driving factors to changes in construction areas were explored, identifying population dynamics as the predominant influence. Leveraging shared socioeconomic pathway scenarios (SSPs), this research forecasted building floor area and MS until 2050 under five distinct SSPs. The results indicated an overall reduction across all scenarios, yet with a continued concentration in high-density urban cores. The substantial gap between the highest and lowest projected MS scenarios highlighted opportunities for material conservation and emission reductions through sustainable practices. Sustainable urban development in densely populated regions necessitates a balance between infrastructure provision and environmental conservation, while in sparsely populated areas, the focus shifts to the efficient management and utilization of vacant properties and materials to cope with the impacts of significant population declines. By offering insights into the building floor area and MS implications of Japan's demographic changes, this study underscores the necessity of sustainable urban planning and resource management strategies to navigate the challenges posed by demographic shifts, ultimately contributing to sustainable development and environmental conservation goals.

Spatiotemporal Population Projections within the Framework of Shared Socioeconomic Pathways: A Seoul, Korea, Case Study

Despite evidence of the growing importance of shared socioeconomic pathways (SSPs) in addressing climate change globally, there is a gap in research concerning the prediction of regional SSP populations. This study aims to project Seoul’s population from 2020 to 2100 under various SSPs and to interpolate this population through a spatiotemporal approach. Utilizing data from the Korea National Statistical Office and international socioeconomic scenario data, we applied a regression model for predicting population growth. This was supplemented with population projections derived from cohort modeling to enhance accuracy. Population allocation within each grid was determined based on the total floor area of residential buildings. To reflect shifting population demands, we adjusted long-term population trends using observed building completion dates from 2010 to 2020. By 2100, SSP3 is projected to have Seoul’s lowest population at 2,344,075, while SSP5 is expected to have the highest at 5,683,042. We conducted an analysis of grid population characteristics based on SSPs and verified the accuracy of our findings. Our results underscore the importance of refined population estimates for sustainable urban planning, indicating the potential for extending grid population estimates to other regions.

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

Quantifying Urban Spatial Morphology Indicators on the Green Areas Cooling Effect: The Case of Changsha, China, a Subtropical City

Green city areas are crucial in mitigating the Urban Heat Island Effect (UHI). However, the cooling effect of green city areas can be influenced by the surrounding complex urban spatial environment. This study focuses on Changsha, a subtropical city in China, where 40 green city areas were screened and analyzed. The study aims to quantify the specific impact of urban spatial morphology on the cooling effect of green city areas. Through statistical correlation and regression analysis, this study focused on six urban spatial morphology indicators: building density (BD), building floor area ratio (BFR), building volume density (BVD), building evenness index (BEI), building average height (BH), and building height standard deviation (BSD). The results indicate that the cooling effect of green city areas could be influenced by urban spatial morphology. Factors such as BD, BFR, BH, and BSD were found to be significantly correlated with the cooling effect of green city areas, with BH showing the strongest influence. BD and BFR were negatively correlated, while BH and BSD were positively correlated. The range values of BD, BFR, BH, and BSD were determined to achieve the optimal conditions for the cooling effect of green city areas. Additionally, the relative position of the green city areas in the neighboring urban areas affects the cooling effect of the green city areas. The cooling effect is most pronounced in the urban area situated to the south of the green city areas. These findings provide a solid foundation for urban planning around green city spaces and offer scientifically sound evidence for mitigating the UHI.

Impact of changing urban typologies on residential vegetation and its climate-effects – A case study from Helsinki, Finland

Residential green spaces are an integral part of urban green infrastructure and its role in climate change adaptation and mitigation. Various urban typologies and changing planning practices affect the amount and structure of residential greenery, which has a direct impact on climate benefits. While urban green and its climate benefits have received increasing attention, there is still limited knowledge on how changing planning practices and related urban typologies impact residential vegetation and its capacity to deliver climate benefits. This paper aims to address this gap by determining the impact of planning practices on residential vegetation, focussing specifically on climate mitigation and adaptation. With the case study of Helsinki, characterized by a high share of green areas, the paper first examines how construction year and urban density affect the amount and structure of vegetation on residential properties. Second, it estimates the carbon sequestration and summer temperatures in the present-day climate. The paper applies spatial modelling and regression analysis to estimate the impact of construction year on the studied dependent variables, while controlling density via gross floor area of buildings. The study demonstrates that the average amount of residential vegetation, as measured using canopy and vegetation cover, has declined 15 percentage points from the 1970 s to early 2010 s and the canopy to low vegetation ratio has decreased constantly over the periods studied. The decline of the canopy cover in particular has reduced the climate benefits of residential vegetation. The paper highlights the significant impact of gross floor area and planning practices on urban vegetation cover and the climate benefits it provides. It also stresses the importance of ensuring sufficient tree cover and permeable surfaces in cities with progressive climate mitigation agenda throughout the chain of urban planning, construction, and subsequent property management stages.

Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage: Case study of China's 35 public buildings

For the significant energy consumption and environmental impact, it is crucial to identify the carbon emission characteristics of building foundations construction during the design phase. This study would like to establish a process-based carbon evaluating model, by adopting Building Information Modeling (BIM), and calculated the materialization-stage carbon emissions of building foundations without basement space in China, and identifying factors influencing the emissions through correlation analysis. These five factors include the building function type, building structure type, foundation area, foundation treatment method, and foundation depth. Additionally, this study develops several machine learning-based predictive models, including Decision Tree, Random Forest, XGBoost, and Neural Network. Among these models, XGBoost demonstrates a relatively higher degree of accuracy and minimal errors, can achieve the RMSE of 206.62 and R2 of 0.88 based on testing group feedback. The study reveals a substantial variability carbon emissions per building's floor area of foundations, ranging from 100 to 2000 kgCO2e/m2, demonstrating the potential for optimizing carbon emissions during the design phase of buildings. Besides, materials contribute significantly to total carbon emissions, accounting for 78%–97%, suggesting a significant opportunity for using BIM technology in the design phase to optimize carbon reduction efforts.

Aging City and House Prices: Impact of Aging Condominium Stock on the Housing Market in the Tokyo- Metropolitan Area

Based on an analysis of the housing market in the Tokyo metropolitan area, this study focuses on the external diseconomies of aging condominiums. It is possible that the quality of the condominium stock deteriorates more rapidly than that of ordinary housing. This deterioration in quality leads to a reduction in the quality of housing services received by residents. Furthermore, this worsening of the residential environment may lead to external diseconomies. We run hedonic models to identify the external diseconomies of aging condominiums in the residential market of the Tokyo metropolitan area. The results of our estimated models indicate that such external diseconomies occur for detached housing in areas where detached houses and condominiums coexist. These external diseconomies exert downward pressure on prices. Specifically, detached housing prices are lowered by around 3.2% for each 1% increase in the proportion of total building floor area in neighborhoods where condominiums were built before 1990. In other words, aging condominiums begin to generate diseconomies in their vicinities approximately 20 years after they were built.

CLUSTER ANALYSIS FOR DISTRICT/CITY GROUPING BASED ON VARIABLES AFFECTING POVERTY IN ACEH PROVINCE USING AVERAGE LINKAGE METHOD

Poverty is an inability of a person/household to meet basic needs in everyday life. Aceh is one of the provinces in Indonesia which is still faced with the problem of poverty. In March 2021 the poor population in Aceh numbered 834.24 thousand people and in September 2021 the poor population in Aceh increased by 16 thousand people, a total of 850.26 thousand people. Therefore the authors are interested in classifying and looking at the characteristics of 23 districts/cities in Aceh Province based on 5 variables that affect poverty. This study uses data from SUSENAS processed from BPS Kota Langsa in 2021. The variables used are households with the type of floor of a residential building made of soil/bamboo (X1), households with a floor area of ​​a residential building < 10 m2 per capita (X2), households with residential walls made of bamboo/rumbia/wood (X3), households with a source of drinking water from unprotected wells/springs/rivers/rainwater (X5), and households whose head of household did not attend school/didn't finish primary school/only primary school (X8). This study uses the average linkage method, namely the distance between two clusters is measured by the average distance between objects in each cluster. Of the 23 regencies/cities, 3 clusters were formed, namely cluster 1 with the lowest poverty rate consisting of 17 regencies/cities. Cluster 2 with the highest poverty rate consists of 2 districts/cities. Cluster 3 with a moderate poverty level consists of 4 districts/cities. The characteristics of the clusters that are formed are in clusters 1, 2 and 3 the dominant poverty level is influenced by the variable X3, which means that there are still many households that have houses with inadequate wall types. In clusters 1 and 3 the poverty rate is not dominantly influenced by variable X1, which means that many households have houses with proper floor types. In cluster 2 the poverty rate is not dominantly influenced by variable X5, which means that many households consume drinking water from cleaner and more protected sources.

Equilibrating provincial carbon increments for residential buildings in China under carbon peaking constraints

The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings.

Embodied greenhouse gas emissions in structural materials for the German residential building stock — Quantification and mitigation scenarios

Embodied emissions in construction materials make a relevant contribution to carbon emissions worldwide. While this has been broadly recognised, only little attention has been paid to the role of load-bearing structures in this regard, and if so, mainly limited to assessments of individual structures. For analysing the global warming impact of engineering structures in a wider context, dynamic material flow analysis is deployed in this study. The future stocks and flows of structural materials and their associated embodied emissions in German residential buildings are quantified based on a mass-balance consistent multi-layer model, which relates the stocks in use, their inputs, outputs and determinants, such as the building lifetime, the population or the useful building floor area per capita. A scenario analysis under combination of different emission mitigation measures is performed, among them a gradual replacement of the comparatively large masonry structure stock share by timber structures, and a general downsizing of structural material quantities. The results show that when applied to a realistic extent, such measures could contribute with about 4% to 8% to the German average target mitigation rate required for achieving emission neutrality in 2045.

A general effectiveness-NTU modeling framework for membrane dehumidification systems

Selective membrane dehumidification is a promising technology for energy efficient air dehumidification in buildings and has been ranked as a top alternative technology by the Department of Energy. One critical area that needs to be addressed is system modeling that incorporates device mass transfer characteristics and sizing. This work develops, validates, and applies a simple effectiveness-number of transfer units (ε-NTU) modeling framework for dehumidification technologies that can be used to predict performance and determine required membrane area for a wide range of technologies, including energy recovery ventilators, vacuum membrane dehumidification, and desiccant dehumidifiers. Although the ε-NTU method is a well-established framework for sizing and modeling heat exchangers, past attempts to create εL-NTUL(meaning ε-NTU for latent loads) models for dehumidification (referred to as latent cooling): (1) don’t agree on the definition of NTUL, (2) are not fully analogous to the heat transfer definition of NTU, (3) cannot be easily applied across all technologies, (4) use humidity ratio, not vapor pressure, as the driving force, and (5) are often complex or lack a fundamental basis. In this work, we develop an effectiveness-NTU model for membrane humidity exchangers that is based on vapor pressure differences, employs a novel definition of the latent number of transfer units for dehumidification applications (NTUL) and is fully analogous to the heat transfer effectiveness-NTU models employed for sensible heat exchangers. The resulting εL-NTUL relationships are validated against experimental data with a maximum error of 1.2%. The overall methodology is applied in case studies for vacuum membrane dehumidification system sizing showing that the ratio of required membrane area per building floor area ranges between 0.1 and 3.5 depending on the building type and operating conditions. Finally, the framework is further generalized for applicability to any gas separation application.