Interventions in the existing building stock — retrofit, adaptation, reuse — act on specific components of specific buildings, but the construction details required to assess the environmental consequences of these strategies are unrecorded for most of the urban stock. Scalable stock models therefore rely instead on deterministic archetypes, which neglect variations due to this missing information. We show that component-level stock estimation at urban scale is nevertheless possible when the missing information is treated probabilistically: the stock estimate becomes a probability distribution, built from only accessible database attributes, and ontology-based construction knowledge. Three uncertainty modules characterise database label reliability, the unknown choice of construction technologies and specifications, and unobserved component dimensions. Applied to 124,291 low-rise residential buildings in Bristol, UK, the framework produces component- and material-resolved stock distributions and attributes their variance to sources: structural stocks are dominated by construction-technology uncertainty, envelope walls and openings by dimensional uncertainty, and services by age-label uncertainty — identifying which data would most improve which estimates. The distributions differ: stone and concrete have similar central estimates, but stone's uncertainty interval is more than three times as wide. Propagated into a city-scale vertical-extension assessment, the framework shows current performance against UK embodied-carbon targets is robust across the full stock uncertainty range, but that stock uncertainty causes large uncertainties in when the cross-over point will come as targets tighten, between 2027 and 2030. Incomplete building data thereby becomes a quantified input to environmental impact assessment, rather than a barrier to it.

Quantifying the material intensity of buildings (MIB) is fundamental for built environment stock accounting, construction resource and waste management, and embodied carbon assessment. However, existing MIB data reported in the literature are often sparse, heterogeneous, and scattered across archetypes, which hinders comparability, quality checks, and harmonization. Here, we compiled a global MIB database containing 3051 MIB records in a unified form measured in kg/m2 for 31 types of construction materials, based on both secondary and primary data from multiple sources. Applying a mean-absolute-deviation (MAD) rule to generate archetype-specific general MIBs, we revealed that the upward pressure on MIB from increases in floor area and building height has been partly offset by the use of light-weight materials, yielding a current aggregate MIB of 1464.3 kg/m2 that is comparable to the pre-1920 levels. Global building material composition shifted markedly away from brick and wood and toward higher shares of steel, cement, sand, and stone, alongside sizable heterogeneity across archetypes, regions, and periods. This expanded, standardized, and harmonized global MIB database can help inform material efficiency targets, embodied carbon baselines, and stock-aware planning for selective demolition, procurement, and renovation in a circular and low-carbon construction transition.

The anthropogenic material in-use stocks accumulated in products, buildings, and infrastructure are essential for satisfying basic human demands and ensuring well-being. They drive global resource demand and environmental impacts while representing valuable resource reservoirs for potential recycling through urban mining. A high-resolution understanding of global material in-use stocks was achieved by integrating reconciled night-time light imageries with national stock data on primary construction materials, including steel, aluminum, and cement. The integration enabled the estimates of global stocks from 2000 to 2019 at a 500 × 500 m grid resolution. The updated dataset mitigated saturation and blooming effects in prior satellite data compared to previous datasets, offering refined temporal and geographical representations despite some regional variations. The refined results systematically elucidate the spatiotemporal dynamics of material accumulation worldwide, highlighting distribution discrepancies between and within cities. The comprehensive database serves as a helpful resource for supporting waste management, circular economy, spatial planning, urban sustainability, and climate change mitigation efforts across various geographical scales.

The dissemination of green consumption information is essential for promoting sustainable behaviors and guiding environmental policymaking. However, the underlying dynamics of its dissemination across social networks and geographic regions remain insufficiently explored. In this study, the spatiotemporal evolution of green consumption information dissemination on social media was analyzed, focusing on six key aspects: clothing, food, housing, transportation, products, and tourism. A comprehensive analytical framework was constructed by integrating network and spatial perspectives. Social network analysis and exploratory spatial data analysis were applied to data collected from Sina Weibo, China's leading social media platform, enabling an investigation into the multifaceted nature of green information dissemination. The results reveal that green consumption social networks exhibit clustering and tightening trends, while maintaining random network properties. Key nodes, particularly news media and environmental protection accounts, act as influential centers. Spatially, the network displays small-world characteristics, with Beijing, Shanghai, Guangdong, Jiangsu, and Zhejiang serving as critical information hubs. Additionally, geographical imbalances in information dissemination are evident, with high-value clusters concentrated in economically developed regions. Based on these findings, three targeted strategies are proposed: a network optimization-oriented guidance strategy to enhance dissemination efficiency and structural connectivity; a community integration-oriented guidance strategy to activate grassroots engagement and diversify participation; and a regional linkage-oriented guidance strategy to reduce spatial imbalances and promote cross-regional synergy. This research provides valuable insights for policymakers and stakeholders in promoting green consumption practices and environmental awareness across diverse areas.

Building stock modeling is a vital tool for assessing material inventories in buildings, playing a critical role in promoting a circular economy, facilitating waste management, and supporting socio‐economic analyses. However, a major challenge in building stock modeling lies in achieving accurate component‐level assessments, as current approaches primarily rely on archetype‐based statistical data, which often lack precision. Addressing this challenge requires scalable methods for estimating the dimensions of interior components across large building stocks. In this study, we introduce the UKResi dataset, a novel dataset containing 2000 residential houses in the United Kingdom, designed to predict interior wall systems and room‐level spatial configurations using exterior building features. Benchmark experiments demonstrate that the proposed approach achieves high predictive performance, with an score of 0.829 for interior wall length and up to 0.880 for bedroom counts, 0.792 for lounge counts, and 0.943 for the kitchen counts. Contributions of this work also include the introduction of a multi‐modal approach into the field of building stock modeling, integrating exterior features and facade imagery. Furthermore, we analyze the driving factors influencing wall length and room predictions using permutation importance and SHapley Additive exPlanations values, providing insights into feature contributions, especially facade opening information being a critical driving factor of modeling interior features. The UKResi dataset serves as a foundation for future component‐level building stock modeling, offering a scalable and data‐driven solution to assess building interiors. This advancement holds significant potential for improving material inventory assessments, enabling more accurate resource recovery, and supporting sustainable urban planning.

Sand and gravel, providing essential physical foundations for modern societies, are facing increasing demand due to urbanization and rural construction. This surge stems from the widespread use of these materials in building, roads, and other infrastructure, which has raised increasing concerns about the “sand crisis” and environmental damages from overexploitation. Addressing such concerns requires understanding patterns of global and national sand and gravel cycles, yet this remains hitherto unexplored. Here, we quantified historical stocks and flows of sand and gravel in buildings and infrastructure in 184 world countries from 1970 to 2019. We show that global gravel consumption is more than twice that of global sand consumption, albeit with a more stable growth rate. However, in international trade, sand dominates, with trade volumes 1.9 times larger than those of gravel, and Singapore is the leading importer. This suggests that sand supply is more vulnerable to geopolitical and market fluctuations. Over the past decades, per capita sand in-use stocks have increased in nearly all countries, whereas per capita gravel in-use stocks have saturated or even declined in many industrialized countries. Asia accounted for half of global sand in-use stocks and China has a large share of gravel stocks in residential buildings, both reflecting the impact of urbanization mode. These insights can inform policies for securing sustainable aggregate supply chains, improving resource efficiency, and mitigating environmental risks associated with overexploitation.

In the context of growing attention to environmental crises and sustainable development, the emotional expressions of social media users not only shape the formation of green consumption concepts but also facilitate the spread of related products and ideas. Understanding how information characteristics influence emotional expression and dissemination can enhance public guidance and emotional support for green consumption, thereby fostering a more effective green consumption culture. This study applies emotional contagion theory to conduct a comparative analysis of the dissemination mechanisms of emotional information across six domains: green clothing, green food, green housing, green transportation, green products, and green tourism. Using deep learning models, a cognitive-emotional evaluation framework, an extended Maslow's hierarchy of needs model, and social network analysis, the study extracts indicators such as sentiment classification, information demand hierarchy, and information presentation from textual data. These indicators are used to examine the breadth, depth, and influence of public sentiment in various green consumption networks. The results show that positive emotional information generally has a broader reach and stronger influence than neutral emotional information. Additionally, greater cognitive homogeneity tends to enhance the effectiveness of positive emotional information dissemination. The study also finds that dissemination behaviors vary across different green themes and information formats. Based on these findings, strategies for promoting positive sentiment and managing negative sentiment are proposed.

Building stock modelling underpins energy and environmental assessments of the built environment. Material Intensity (MI), representing material mass per unit dimension, is vital for bottom-up estimation of building material stocks. However, reliance on sparse or uniform MI data can lead to significant inaccuracies due to intra-archetype variability, often stemming from differing building morphologies. This paper develops a geometry-informed MI (GIMI) method to characterise MI variability using machine learning and morphology features, applied to four materials—brick, concrete, mortar, and stone—in Sheffield, UK. Results indicate that GIMI reduces potential material uncertainties by up to 18% compared to conventional unitary MIs. This approach enhances bottom-up building mass accounting, advancing a circular economy and low-carbon building sector.

The relationship between political economy and green innovation has been one of the most dynamic research in the field of green development recently. In this study, we conduct a scientometric analysis of 117 academic literature related to the field published from January 2005 to September 2023 using CiteSpace. The findings show an explosive growth trend of literature on this topic since 2020. Most of the literature used panel data to empirically explore the relationship between political economy and green innovation. Researchers have not only investigated the impact of political connections on green innovation, but also studied the mediating effects of these variables. The majority opinion is that political connections are detrimental to corporate green innovation. From the perspective of the sources of the literature, Chinese universities and scholars pay more attention to this field. Finally, we find that the literature related to political economy and green innovation is mainly published in energy-related journals.

Developing and transition countries merit more attentions on resource monitoring and circular economy implementation to improve global sustainability. With four Eastern European countries, Bulgaria, Croatia, Poland, and Romania, as cases, we integrated economy-wide and dynamic material flow analysis principles to track multiple material flows and stocks during 1990-2019 and investigate circularity performance and decoupling status throughout all life cycle stages of their entire socioeconomic system. Although the absolute stocks presented different trajectories in these countries, they all have witnessed a progressive growth in per capita stocks, reaching 390 t/cap (Bulgaria), 383 t/cap (Croatia), 239 t/cap (Poland), and 306 t/cap (Romania) in 2019, dominated by minerals. Their material use along all life cycle stages has been identified as being in a strong coupling or a relative decoupling with economic outputs and thus further stock expansion is foreseeable. However, their socioeconomic circularity remained at a low level, ranging from 7 % to 14 %. Such sociometabolic patterns affirm demand-side strategies for manufacturing streams close to service provision are required to reduce resource extraction. Proper waste management systems and policy enforcement are needed to maximize recycling and increase circularity, particularly, in Bulgaria and Romania. We call for more bottom-up studies to improve sectoral resolution, zoom in key life cycle stages, and provide tailored insights towards circular economy implementation in such transition countries.

Achieving the goal of carbon neutrality and carbon peak as scheduled puts forward new demands for the green transition of low-carbon lifestyle in Chinese society. In-depth practice of green consumption (GC) behavior can effectively promote the supply-side and consumption-side emission reduction work, but the phenomenon of “high awareness, low practice” is widespread in GC. The causes of consumers' low practice of GC need to be analyzed from the perspective of time and space from the actual media data. Furthermore, this process assists policymakers and stakeholders to understand the general attitude of the public towards GC, clarifying the propagation path of public emotions and the source of negative emotions. Based on the data from Sina Weibo, this paper applied text mining, a hybrid model of convolutional neural network and long and short-term memory neural network to analyze the public's attention, sentiment tendency and hot topics on GC. The results show that the vast majority of the Chinese public has a positive attitude toward GC; women and economically developed regions are more concerned about GC; the drivers of positive public sentiment toward GC include environmental awareness education, air pollution prevention and control, and online shopping; high green product prices, excessive time costs, chaotic sharing economy and one-size-fits-all solutions lead to negative public sentiment toward GC. By providing public sentiment analysis of GC, this research would assist decision-makers to understand the dissemination mechanism of public will in social media and clarify targeted solutions, which is of great significance for policy formulation and improvement.