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Dataset of socio-economic and waste collection indicators for Portugal at municipal level
This data article presents demographic, socio-economic and waste-related data at municipal level for Portugal. The dataset includes raw data collected from 4 main sources: (i) the annual reports of waste management companies; (ii) the database of the Portuguese water, sanitation and waste regulatory...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327732/ https://www.ncbi.nlm.nih.gov/pubmed/30671514 http://dx.doi.org/10.1016/j.dib.2018.12.069 |
Sumario: | This data article presents demographic, socio-economic and waste-related data at municipal level for Portugal. The dataset includes raw data collected from 4 main sources: (i) the annual reports of waste management companies; (ii) the database of the Portuguese water, sanitation and waste regulatory entity; (iii) the Portuguese Environmental Agency; and (iv) national statistical data. Relevant indicators for waste generation and for the separate collection of waste are proposed and calculated using the raw data. The dataset comprises municipalities with high, medium and low separate collection yields, providing socio-economic and waste infrastructures data that can be used for benchmarking. The dataset can also be used to define a baseline against which the progress of the collection of packaging waste can be assessed over time, or else serve as input to mathematical models predicting waste generation and collection. Moreover, data can serve as the base to calculate new waste-related indicators. In addition to being a valuable input to the waste topic, the dataset can also be used in a large range of other topics where demographic and socio-economic parameters are relevant. The data presented herein are associated with the research articles “Model for the separate collection of packaging waste in Portuguese low-performing recycling regions” [1] and “Artificial neural network modelling of the amount of separately-collected household packaging waste” [2]. |
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