This study assesses the quantity and composition of household solid waste (HSW) in the City of Da Nang and proposes a transparent and standardised method for its assessment through a combination of very-high-resolution (VHR) satellite imagery, field surveys, questionnaires, and solid waste measurements on the ground. This was carried out in order to identify underutilised resources and to obtain discrete planning values at city level. The procedure proved to be a suitable method for reliable data gathering. To describe HSW generation, 818 valid datasets, subdivided into five building types, and their location were used. The average HSW generation rate was 297 g per capita per day. Within a total of 19 subcategories, organic waste had a share of 62.9%. The specific generation and composition of HSW correlates positively with both the building type and the spatial location within the city. The most HSW (509 g per capita per day), by far, was generated in the ‘villa-type’ building while in the ‘basic-type’ building, this was the least (167 g per capita per day). Taking into account the number of individual buildings, the total HSW generation in Da Nang in 2015 was estimated between 109,844 and 164,455 tonnes per year, which corresponds to about one-third to one-half of the total municipal solid waste.
Data on electricity consumption is crucial for assessing and modeling energy systems, making it a key element of sustainable urban planning. However, many countries in the Global South struggle with a shortage of statistically valid, geocoded, and disaggregated household-level data. This paper aims to develop a generic methodology for the generation of such a database in terms of electricity consumption. The methodology was tested in Kigali, the capital city of Rwanda, with a focus on all single-family residential building types of the inner city. Discrete data on buildings is obtained through combined information products derived from very high resolution (VHR) satellite imagery, field surveys, and computer assisted personal interviewing. In total, 509 valid geocoded survey datasets were used to evaluate and model household electricity consumption, as well as electrical appliance ownership. The study's findings reveal that the arithmetic mean of specific electricity consumption was 3.66 kWh per household per day and 345 kWh per capita per year in 2015. By subdividing the data into distinct building types as well as their spatial location, and weighting the specific values according to their proportion in the study area, a more accurate mean value of 1.88 kWh per household per day and 160 kWh per capita per year was obtained. Applying this weighted mean to extrapolate household electricity consumption for the study area, in conjunction with the sample's precision level, resulted in an estimate of 126–137 GWh for the year 2015. In contrast, using the arithmetic mean would have led to values twice as high, even exceeding the total electricity consumption of the entire city, including multi-family and non-residential buildings. The study highlights the significance of on-site data collection combined with geospatial mapping techniques in enhancing of understanding of residential energy systems. Using building types as indicators to distinguish between households with contrasting electricity consumption and electrical appliance load levels can address the challenges posed by rapid urban growth in the Global South. This proposed method can assist municipal administrations in establishing a database that can be updated resource-efficiently at regular intervals by acquiring new satellite images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.