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.

Dynamic analysis of carbon emissions from construction and demolition activities in Japan: Revealed by high-resolution 4D-GIS modeling.

To meet the 2050 decarbonization target of the global buildings and construction sector, more attention is needed to reduce carbon emissions from construction and demolition. However, current national carbon accounting studies for these activities remain limited in spatial granularity and localized applicability. This study developed a bottom-up spatiotemporal database of carbon emissions from building construction and demolition in Japan via integrating a geographic information system-based building stock model, statistical data, and survey information.

Paving the way to the future: Mapping historical patterns and future trends of road material stock in Japan.

Roads are a fundamental component of societal infrastructure, whose decades-long lifespan has far-reaching implications for developmental decisions. The road construction and development have profound impacts on economic growth, social dynamics, and environmental sustainability. Therefore, comprehensive measurement of the current road material stock (MS) and the projection of expected future road scale based on regional socio-economic scenarios that can reflect unique local conditions are necessary.

Does Deep Learning Enhance the Estimation for Spatially Explicit Built Environment Stocks through Nighttime Light Data Set? Evidence from Japanese Metropolitans.

Built environment stocks have attracted much attention in recent decades because of their role in material and energy flows and environmental impacts. Spatially refined estimation of built environment stocks benefits city management, for example, in urban mining and resource circularity strategy making. Nighttime light (NTL) data sets are widely used and are regarded as high-resolution products in large-scale building stock research.

Land-Use/Land-Cover Change from Socio-Economic Drivers and Their Impact on Biodiversity in Nan Province, Thailand.

The rate of deforestation declined steadily in Thailand since the year 2000 due to economic transformation away from forestry. However, these changes did not occur in Nan Province located in northern Thailand. Deforestation is expected to continue due to high demand for forest products and increased agribusiness.