GIS-Based Material Stock Analysis (MSA) of Climate Vulnerabilities to the Tourism Industry in Antigua and Barbuda

RASMI: Global ranges of building material intensities differentiated by region, structure, and function

The construction materials used in buildings have large and growing implications for global material flows and emissions. Material Intensity (MI) is a metric that measures the mass of construction materials per unit of a building's floor area. MIs are used to model buildings' materials and assess their resource use and environmental performance, critical to global climate commitments. However, MI data availability and quality are inconsistent, incomparable, and limited, especially for regions in the Global South. To address these challenges, we present the Regional Assessment of buildings' Material Intensities (RASMI), a new dataset and accompanying method of comprehensive and consistent representative MI value ranges that embody the variability inherent in buildings. RASMI consists of 3072 MI ranges for 8 construction materials in 12 building structure and function types across 32 regions covering the entire world. The dataset is reproducible, traceable, and updatable, using synthetic data when required. It can be used for estimating historical and future material flows and emissions, assessing demolition waste and at-risk stocks, and evaluating urban mining potentials.

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.

Analyzing Socio-Metabolic Vulnerability: Evidence from the Comoros Archipelago

Small island developing states are often characterized as vulnerable owing to their unique geographies of smallness and remoteness, resource insecurity, and more recently from the impacts of climate change. These vulnerabilities are often manifested in resource insecurity, significant imports, poor waste management, and the inability to develop economies of scale. In effect, sustaining small islands in an era of global environmental change is a task both scholars and policy makers are increasingly grappling with. Can small islands be sustainable? This research examines the social metabolism of an island system, and introduces the concept of “socio-metabolic vulnerability”. As such, this research provides novel insights into the linkages between patterns of resource-use, systemic risks and vulnerability. Results from a local material and energy flow analysis (local-MEFA) for the island of Ndzuwani (Comoros) suggest a very low level of resource-use but at the same time heavy reliance on critical imports that cover vast distances, that are vulnerable to price and climate shocks. Informal activities in resource extraction play an important role in lending both vulnerability and resilience to Ndzuwani. This study adds to the scarce body of literature that argues that small island economies would need to leverage resource-use patterns to build system resilience, along with bold policies and institutions that support material circularity, engage communities and fosters frugal innovation.

Introduction: The Metabolism of Islands

This editorial introduces the Special Issue “Metabolism of Islands”. It makes a case why we should care about islands and their sustainability. Islands are hotspots of biocultural diversity, and home to 600 million people that depend on one-sixth of the earth’s total area, including the surrounding oceans, for their subsistence. Today, they are on the frontlines of climate change and face an existential crisis. Islands are, however, potential “hubs of innovation” and are uniquely positioned to be leaders in sustainability and climate action. We argue that a full-fledged program on “island industrial ecology” is urgently needed with the aim to offer policy-relevant insights and strategies to sustain small islands in an era of global environmental change. We introduce key industrial ecology concepts, and the state-of-the-art in applying them to islands. Nine contributions in this Special Issue are briefly reviewed to highlight the metabolic risks inherent in the island cases. The contributors explore how reconfiguring patterns of resource use will allow island governments to build resilience and adapt to the challenges of climate change.