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. Focusing on municipal-level emissions, the Logarithmic Mean Divisia Index approach was used to decompose spatiotemporal variations and identify the contributing factors. Results indicate that carbon emissions from Japan's construction and demolition activities fell by more than 50% between 2005 and 2020, largely due to declining new/demolished-to-stock ratio, suggesting a transition to a stock-based society. Central cities' reliance on carbon-intensive buildings positively contributed to spatial variations in their construction emissions, underscoring the importance of sustainable materials and timber designs. Differences between prefectures in demolition emission intensity highlighted the strategic placement of recycling facilities in key regions to curb transportation-related emissions. Overall, these findings provided data reference for local governments to devise tailored policies for managing construction and demolition emissions.

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. However, some of their limitations, especially blooming/saturation effects, have hampered performance in estimating building stocks. In this study, we experimentally proposed and trained a convolution neural network (CNN)-based building stock estimation (CBuiSE) model and applied it to major Japanese metropolitan areas to estimate building stocks using NTL data. The results show that the CBuiSE model is capable of estimating building stocks at a relatively high resolution (approximately 830 m) and reflecting spatial distribution patterns, although the accuracy needs to be further improved to enhance the model performance. In addition, the CBuiSE model can effectively mitigate the overestimation of building stocks arising from the blooming effect of NTL. This study highlights the potential of NTL to provide a new research direction and serve as a cornerstone for future anthropogenic stock studies in the fields of sustainability and industrial ecology.

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. This study examined the historical changes and progression patterns of the road network across Japan from 1965 to 2020 through material flow and material stock analysis. By using the road MS time series, along with explanatory socioeconomic variables, several models including Autoregressive Integrated Moving Average with explanatory variables (ARIMAX), Support Vector Regression (SVR), hybrid ARIMAX-SVR, Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF) were compared. After comparison analysis, ARIMAX and hybrid ARIMAX-SVR models were employed to forecast expected road MS in each prefecture of Japan by 2050 based on national shared socioeconomic pathways (SSP) scenarios. The study found that the total road MS of Japan increased 5.5-fold over 55 years. Aggregate was the dominant material, comprising over 70 % among the four materials of the total road MS. The forecast results for each prefecture were classified into three different patterns. Expected MS in most prefectures still displayed increasing trends in the five scenarios, but the projection of road MS in eight prefectures revealed a notable downward trend across each SSP scenario. For most prefectures, SSP5 displayed the highest expected road MS, followed by SSP1. SSP3 was the scenario with the lowest MS. This approach provided a more thorough understanding of the likely evolution of road MS across different SSP scenarios and could help inform decisions for resource allocation and policy formulation concerning road infrastructure management.

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. The objectives of this paper are (1) to predict land-use change in the province based on trends, market-based and conservation scenarios, (2) to quantify biodiversity, and (3) to identify biodiversity hotspots at greatest risk for future deforestation. This study used a dynamic land-use change model (Dyna-CLUE) to allocate aggregated land demand for three scenarios and employed FRAGSTATS to determine the spatial pattern of land-use change. In addition, the InVEST Global Biodiversity Assessment Model framework was used to estimate biodiversity expressed as the remaining mean species abundance (MSA) relative to their abundance in the pristine reference condition. Risk of deforestation and the MSA values were combined to determine biodiversity hotspots across the landscape at greatest risk. The results revealed that most of the forest cover in 2030 would remain in the west and east of the province, which are rugged and not easily accessible, as well as in protected areas. MSA values are predicted to decrease from 0.41 in 2009 to 0.29, 0.35, and 0.40, respectively, under the trends, market-based and conservation scenarios in 2030. In addition, the low, medium, and high biodiversity zones cover 46, 49 and 6% of Nan Province. Protected areas substantially contribute to maintaining forest cover and greater biodiversity. Important measures to protect remaining cover and maintain biodiversity include patrolling at-risk deforestation areas, reduction of road expansion in pristine forest areas, and promotion of incentive schemes for farmers to rehabilitate degraded ecosystems.