Management of plastic wastes at Brazilian ports and diagnosis of their generation

Gobbi, Clarice Neffa; Sanches, Vânia Maria Lourenço; Pacheco, Élen Beatriz Acordi Vasques; Guimaraes, Maria; Freitas, Marcos Aurélio Vasconcelos de

doi:10.1016/j.marpolbul.2017.07.004

Cited by 19 publications

(4 citation statements)

References 15 publications

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“…Furthermore, construction activities may cause agricultural land loss, loss of soil, and air pollution 2 . Waste is generated as a result of mismanagement, and there are devastating environmental concerns about this waste which are being faced by all of us 3 . Due to the increasing amount of waste generated and its associated environmental impacts, there is an urgent need to accurately estimate the amount of construction waste so as to measure the cost of the urban renewal process 4 .…”

Section: Background and Summarymentioning

confidence: 99%

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Lin,

Huang,

Liu

et al. 2024

Sci Data

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Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

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Section: Background and Summarymentioning

confidence: 99%

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Lin,

Huang,

Liu

et al. 2024

Sci Data

View full text Add to dashboard Cite

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“…Both projects have been developed by EMLURB, which also provides paper bags for garbage collection on the Boa Viagem beach. However, discussions about new laws on disposable bans are still in the early stages and an interinstitutional articulation platform to improve planning is also an urgent need, as the region faces challenges in monitoring and implementing the goals foreseen in the regulatory devices [33].…”

Section: Political-institutional Dimension -Planning That Does Not Comentioning

confidence: 99%

Developing a Plastic Waste Management Program: From River Basins to Urban Beaches (Case Study)

Moura

Falcão

Silva

et al. 2020

J. Eng. Technol. Sci.

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“…The government, through the National Environment Management Authority, is the most critical player, since it is solely responsible for the formulation of policies. Studies exist on the effect of government regulations on plastic manufacturing and management [12,69,70]. Policies formulated by any government should be aimed at encouraging employment and investment [33].…”

Section: Key Partnershipsmentioning

confidence: 99%

Designing Business Solutions for Plastic Waste Management to Enhance Circular Transitions in Kenya

2018

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Least-developed countries face many challenges regarding their plastic waste management systems. In 2017, Kenya imposed a selective ban targeting manufacturers and consumers of plastic carrier bags. However, this selectivity does not avoid the continuous use of other plastic products. The present paper states that circular priorities, which have been defined to advanced economies, would not be entirely valid for the rest of the world. While high-income countries face only the impacts of their own consumption, developing nations must endure the externalities of these developed economies. Thus, the focus of the least developed part of the world must not be on reducing its relatively normal (or even low) consumption, but to manage its surplus material flow. According to the employed circular evaluation methodology (CEV-Circular Economic Value), the circularity level in Kenya's plastic material flow stands on a rather low stage with 32.72%. This result outlines the linear deficiencies of the plastic waste management system and urges the prevention of further material leakage (such as energy use). Through the Business Model Canvas (BMC) approach this study offers a holistic business solution which can improve the system's sustainability.

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Management of plastic wastes at Brazilian ports and diagnosis of their generation

Cited by 19 publications

References 15 publications

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Developing a Plastic Waste Management Program: From River Basins to Urban Beaches (Case Study)

Designing Business Solutions for Plastic Waste Management to Enhance Circular Transitions in Kenya

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