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• Understanding total in-use stock plays an important role in sustainable resource use and management. • Conventional dynamic material flow analysis (DMFA) was conducted to unveil the understanding of the total in-use material stock of developing country, Laos. • Several linear regression models were employed to investigate the correlation between Nighttime light (NTL) and the total in-use stock. • The best-fit model shows there is a possibility for the application of the NTL for assessing the total in-use stock in other developing countries. Global material extraction has been growing rapidly for several decades, accelerated by increasing population and expansion of the built environment. Understanding the total in-use stock is identified as an important pillar to support information for sustainable development. However, knowledge of the in-use stock in the economically developing world remains insufficient. Nighttime light (NTL) imagery has been used to address research questions in various fields when data are intermittent or lacking. This study was conducted to measure a correlation between the NTL and the total in-use in an economically developing nation: Laos. A dynamic material stock (MS) model is implemented to provide a conventional dataset of the total in-use stock. Various regression models are then implemented to assess the correlation of the NTL and the total in-use stock. Results of MS model demonstrate that, during 1990–2015, the total in-use stock expanded almost seven-fold, increasing from 75 million tonnes to 490 million tonnes. The result from the best regression models of this study shows the correlation between the lit area and in-use stock is 0.94, implying that if the lit area increases by 1%, the total in-use stock of Laos will increase by about 0.67%. The NTL distribution shows that most of the in-use stock is currently accumulated in cities and urban areas such as Vientiane (the capital), Pakse, and Kaysone Phomvihane city. The proposed methodology can be used as a proxy to asset material stocks in other developing countries when data are not available for a conventional material stock model. [Display omitted] [ABSTRACT FROM AUTHOR]

Environmental impacts from the mining is so diverse in nature that it requires different tools to quantify. Life Cycle Assessment (LCA) is one of the popular tools that can assess the impact of mining activities. Remote sensing (RS) based imagery analysis helps to explore the change in earth surface by providing long-term historical data which is very useful for mining induced environmental impact assessment. Therefore, we used the knowledge of RS based image analysis to analyze the direct footprint of mining that is the land use change. Besides, we also used LCA to quantify the environmental impacts focusing mainly on Global Warming Potential (GWP) and Gross Energy Requirement (GER). The case study site was a copper-gold-silver mine located in Laos, called Phu Kham mine. The RS based image analysis suggests that the land use change due to mining during 2007–2018 was approximately 3.98 km2 whereas the overall intensity of land use change in the same period was about 0.0052515 m2/kg of concentrate. We also analyzed the indirect CO 2 emission from vegetation lost by mining. The GER of the mine showed average value for an open-pit copper mine though the CO 2 emission was lower mainly due to the use of hydroelectricity. We also tried to quantify the productivity of the mine site. The findings from this study was also compared with existing literature. Though the impact of the mine was lower, there is room for improvement. We have tried to provide a few directions for that as well. [ABSTRACT FROM AUTHOR]