Exploring the influence of contextual and sociodemographic factors on waste prevention behaviour - the case of Campinas, Brazil

Rodrigues, Evelin Ribeiro; Bortoleto, Ana Paula; Fracalanza, Bruna Costa

doi:10.1016/j.wasman.2021.09.002

Cited by 7 publications

(9 citation statements)

References 33 publications

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“…These bands come from the action's difficulty level brought by the GEB. Note that agents with EM≥1.67 never use PB, as it is the difficulty value of the "PB avoidance" action measured in Ribeiro- Rodrigues et al (2021). Agents with EM<0.89 may be subject to the SN influence within their context.…”

Section: Resultsmentioning

confidence: 99%

“…15,340 resident agents were simulated, considering the same number of households in the Barão Geraldo district. The proportionality of the agents' characteristics considered accurate data provided by the prefecture and census (e.g., spatial distribution, lots, income, family size) and previously col-lected (e.g., environmental motivation, Ribeiro- Rodrigues et al 2021). Household supplies stock dynamics (pantry) are defined by equations that account for basic nutritional needs, purchase frequency, number of residents, and randomness.…”

Section: Standard Scenario For Waste Preventionmentioning

confidence: 99%

“…Here, the questionnaire was widely disseminated (April 2019 -June 2020) through printed posters in educational institutions, bus stops, and shops; via social networks (Facebook®, Instagram®, and Whatsapp®), e-mails, website (www.campinasrepense.wordpress.br), and also verbal communication with passers-by on some streets in the city center during one week. This strategy resulted in a representative sample of Campinas with 888 responses with a 95% confidence level and a margin of error of 3.29% (see Ribeiro-Rodrigues et al, 2021). Other data such as gender, age, marital status, neighborhood, level of education, type of housing, participation in selective recyclable collection, source of information on environmental issues, diet (vegetarianism, veganism, and omnivores), professional participation/ volunteer in environmental organizations and perception of environmental action in the surroundings were also collected.…”

Section: Data Collection Proceduresmentioning

confidence: 99%

“…To infer PEB, Kaiser & Wilson (2004) developed the General Ecological Behavior (GEB), a tool to rank PEB actions in a specific population according to their engagement level (i.e., intrinsic motivation and difficulty) related to the activities. The GEB is also an instrument to formulate public policies regarding behavioral change toward environmental conservation (see Ribeiro-Rodrigues et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Analyzing waste prevention behaviors by applying an ABMS framework

Rodrigues

Bortoleto

2022

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Nature and society have undergone fast and intense changes in the last decades. Consumption is expanding at a hyperbolic rate. Technological innovation has bypassed some environmental problems, but it is hardly sufficient to solve them. As a result, understanding the factors related to people's behavior is imperative when finding a novel approach for intervention policies that can effectively alleviate environmental impacts caused by human activities. A promising alternative to designing waste prevention (WP) policies is to develop strategies to endure behavioral change through collective actions. This paper briefly reviews some WP status worldwide and highlights the possibility of using agent-based modeling and simulation (ABMS) to plan WP policies and programs. ABMS enables a more in-depth analysis since experiments with a large sample in real situations are financial, temporal, and social cost-demanding. Preliminary results show an influence of the social norm on the adoption of reusable bags by individuals with medium and lower pro-environmental motivations. Understanding these dynamics relations in which WP policy is embedded makes it possible to forecast future waste generation and composition scenarios. Also, a framework for planning WP with ABMS is proposed.

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Section: Resultsmentioning

confidence: 99%

Section: Standard Scenario For Waste Preventionmentioning

confidence: 99%

Section: Data Collection Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing waste prevention behaviors by applying an ABMS framework

Rodrigues

Bortoleto

2022

Detritus

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“…The perpetuation of practices that generate waste has led to an unprecedented amount of waste being produced at a global scale, with an estimated 3.3 million tons generated each day [12]. This unprecedented level of waste production has presented numerous challenges for countries around the world in their management and mitigation of this issue [13].…”

Section: Introductionmentioning

confidence: 99%

Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application

Al Amin,

Setyadarma,

Wibisono

2023

RIA

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The surge in global population and economic activity has precipitated a significant escalation in waste generation, with projections indicating potential daily global waste levels of 11 million tons by the century's conclusion. This intensification presents a formidable challenge requiring innovative solutions, one of which is the utilization of machine learning algorithms for automated waste categorization. This study explores the integration of the Faster R-CNN algorithm within an Android application, aimed at streamlining waste management through the identification and categorization of recyclables. The proliferation of smartphone usage-specifically Android applicationsprovides an accessible platform for mass education on waste management, thereby contributing to potential waste reduction. This study deployed a visual testing approach to evaluate the application's performance across diverse waste categories, including cardboard, glass, plastic, and paper. During each testing session, images of each waste type were captured and the objects were methodically rotated by approximately 20 degrees, enhancing the robustness of the machine learning model. The implementation of the Faster R-CNN algorithm within an Android environment, as exemplified in this study, has demonstrated noteworthy potential to revolutionize waste management. An impressive accuracy rate of 98.106% was achieved in the detection and classification of various waste types. Such a technological innovation can augment the efficiency of waste categorization and recycling efforts. Thus, the study contributes to the development of a more sustainable and environmentally responsible waste management system.

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