People-Centred Development of a Smart Waste Bin - PMC

The survey was completed by 194 respondents. Of these, 71% were women, 27% were men and 2% did not wish to identify themselves by gender. In terms of age structure, the majority of respondents were aged between 41 and 60 years old with 39%, followed by those aged between 21 and 40 years old with 33%, those aged 61 and over with 19% and those aged under 20 years old with 8%. In terms of educational structure, the majority of respondents were highly educated, with 84% having a Master&#;s or PhD degree, a post-secondary education, a college or a university degree, while only 8% having a secondary education, 1% having a vocational education and 7% having a primary education. The survey was mainly responded to by the active population, i.e., employed or working people.

You will get efficient and thoughtful service from qunfeng.

5.2. Survey Results

In the research, we were interested in the extent to which participants were willing to act in an environmentally friendly manner and to what extent they actually do so. The results showed that participants at the declarative level showed a high level of environmental awareness and were very much willing to handle waste appropriately, where the mean value was 4.56. This was not surprising, since people often express a high level of awareness (or, as Beckmann [32] wrote, &#;who would actually dare to admit a lack of interest in environmental problems or an environmentally unfriendly attitude?&#;). One of the proofs of this arose quickly&#;the impression of a positive attitude towards the environment began to fade when participants were asked about actual environmental action. The average value of the demonstrated actual environmental position was 4.31. The transition to one&#;s own active participation in solving environmental problems thus expressed the gap between those who expressed their commitment to environmental protection only at the level of attitudes and those for whom caring for the environment actually held value ( ). This result is largely consistent with the results of similar research to date on the gap between environmental awareness and behaviour [4,33,34]. The shows the willingness to behave in an environmentally friendly manner on a hypothetical and active level where the respondents (N = number of respondents) answered how much they agreed with the statement with the help of a Likert scale (1&#;completely insignificant, 2&#;insignificant, 3&#;medium, 4&#;important, 5&#;very important).

Table 1

StatementNMean ValueStandard Deviation&#;I am willing to handle waste even more conscientiously&#;.5%0%8.3%24.9%66.3%4.560.69&#;I regularly separate waste in my household and I strive to produce as little as possible.&#;.5%1.0%9.3%44.9%44.3%4.310.726Open in a separate window

The results of the survey ( ) showed that people rate the waste bin with normative information as more suitable for waste management with 59%, and the waste bin with emotional information as less suitable by 41% of correspondents.

Based on the t-test of independent samples, we found that responses of the study participants regarding the choice of normative (index 1) and emotional (index 2) intervention in terms of their gender, age and educational structure did not differ statistically significantly, however, certain interesting differences between the individual groups of participants were indicated. Regarding the gender structure, both genders evaluated the choice of the type of intervention practically equally. Both genders largely preferred a normative intervention with a mean value of 1.40 (mean of the indexes 1 and 2) for women and 1.41 for men ( ).

Table 2

GenderNMean ValueStandard Deviationfemale531.400.494male.410.494Total.410.494Open in a separate window

However, younger respondents rated higher the waste bin with the emotional (index 2) and more personal appeal, while older respondents rated higher the one with the normative appeal (index 1). Under 20-yearsold respondents rated the waste bin with the emotional intervention as more appropriate for promoting waste management, on an average 1.56 (mean of the indexes 1 and 2). Those aged 21 to 40 rated the normative intervention as more appropriate on an average 1.44. Participants aged 41 to 60 (on an average 1.37), and over 61 (on an average 1.38) rated the normative version of the waste bin higher than the emotional version ( ).

Table 3

AgeNMean ValueStandard Deviationto 20 years old161.560.512from 21 to 40 years old641.440.500from 41 to 60 years old761.370.486more than 61 years old371.380.492Total.410.493Open in a separate window

In terms of education, those with a university or higher education rated the normative waste bin higher than the older ones, with on average 1.38 (mean of the indexes 1 and 2). Those with a secondary education rated the emotional appeal more highly with on average 1.60. Those with a vocational education were completely unanimous in their preference for the emotional appeal waste bin, while those with a primary education also rated the emotional appeal higher than the normative one, with an average of 1.57 ( ).

Table 4

EducationMean ValueNStandard Deviationprimary school1..514vocational education1.001 secondary education1..507university or higher education1..487Total1..493Open in a separate window

In studying the role of individual constructs of psychological variables, values, attitudes, personal norms, subjective norms and perceived behavioural control in the choice of normative or emotional intervention, we found that within the values, altruistic and biosphere values were those that influenced the choice of normative intervention. Conversely, hedonic and biospherical values were the ones that most influenced the choice of emotional intervention. This result is not surprising, as hedonic values are those that are reflected in positive and emotionally related orientations, such as a joy of life, comfort and enjoyment [35]. Biosphere values, on the other hand, are linked to orientations such as a coexistence with nature, environmental protection, and are thus surprisingly positively linked to appropriate waste management, regardless of the intervention we used, whether emotionally or normatively. On the other hand, altruistic values, which are reflected in orientations such as equality and justice, are more closely linked to social and personal norms, collective social orientations and concern for the well-being of all people in the world. According to attitudes, there were no significant differences in their impact on selection. On the other hand, personal and subjective norms had a more visible influence on the selection. Thus, those who preferred normative intervention valued personal norms and subjective norms to a greater extent. Which is not surprising, since human norms are reflected in an individual&#;s sense of moral duty to act in an environmentally friendly manner and relate to their perception of what is &#;appropriate&#; in a given situation [36]. The participants also highly valued perceived behavioural control, which was reflected in their own ability to act in an environmentally friendly manner. The reason for this may be that normative intervention, when communicating certain information to participants, in our case the amount of separately collected waste in Ljubljana, encourages people to feel their own ability and a sense of higher capacity for environmentally friendly behaviour ( ). In terms of intention and actual behaviour, the results show that those with a higher intention to act environmentally friendly and actually behave in this way were more likely to choose the normative intervention.

Table 5

ConstructsNMean ValueStandard Deviationaltruistic valuesnormative intervention.880.29emotional intervention794.750.43egoistic valuesnormative intervention.720.72emotional intervention792.800.68hedonic valuesnormative intervention.140.69emotional intervention794.350.61biospheric valuesnormative intervention.620.56emotional intervention794.660.56attitudesnormative intervention.590.47emotional intervention794.600.46personal normnormative intervention.450.59emotional intervention794.210.76subjective normnormative intervention.070.86emotional intervention793.780.72perceived behavioural controlnormative intervention.210.58emotional intervention794.120.59actual behaviournormative intervention.320.72emotional intervention794.290.74behavioural intentionnormative intervention.570.73emotional intervention795.560.63Open in a separate window

Generating momentum in the circular economy means we must manage our waste smarter. As the world&#;s population grows, so does its waste generation. In places where people spend a lot of their time, like university campuses, mitigating waste is critical to achieving sustainability goals.

Higher education institutions are perfect candidates for introducing innovative solutions to tackle environmental challenges. Their ongoing research, development of new technologies, and presence and authority in the community make colleges and universities the ideal places to disrupt traditional waste practices and demonstrate their commitment to intelligent solutions.

Education Begins with Responsibility

Universities and other educational settings provide an excellent growth opportunity for waste technologies as they represent environments where the population naturally generates a large amount of waste, including food waste. In its most recent waste audit, Toronto Metropolitan University found that over 60% of all waste sent to landfill could be composted or recycled. The University of Richmond, which has set an ambitious goal of 75% waste diversion by , found in a audit that as much as one&#;third of the campus waste sent to landfill was compostable.

Millennials and Gen Z, groups that make up the majority of college students, have reported a deeper sense of responsibility for fighting climate change. Their commitment to prioritizing sustainability makes them the ideal audience to support and help implement new strategies.

Making a Complex Process More Efficient with ArtificiaI Intelligence

Enhanced waste management depends on finding and separating reusable material from vast quantities of refuse. Traditional bin systems place the burden on the user to sort their waste into the correct bin; however, even with adequate signage, humans get this right only 30% of the time. Confusing recycling rules and a lack of education result in high contamination rates and poor diversion.

Applications of artificial intelligence (AI) in waste management help simplify a formerly complicated, expensive, and painstaking process. One of the key solutions AI offers is eliminating the user&#;s decision-making process at the source, which has positive ramifications down the line. AI technologies can impact the entire waste lifecycle, from sorting at the source to real-time monitoring and analysis, to predictive modeling, to increasing public engagement.

Related links:
The Rancher's Cattle Chute Shopping Guide
2024 Guide to Vacuum Capping Machine Efficiency
Top 5 Benefits of Using a Vacuum Capping Machine

Contact us to discuss your requirements of Smart Waste Sorting Equipment Factory. Our experienced sales team can help you identify the options that best suit your needs.

For campuses, AI can drastically impact the cost efficiency of waste collection. In addition to increased sorting accuracy, smart bins can deliver fullness alerts, so custodians only need to check the bins when they receive a notification. This helps to improve custodial efficiency and reduce unnecessary labor. The data collected by smart bins can also deliver expense projections and illuminate areas for improvement.

Waste data is highly effective at reflecting waste trends specific to a campus and its population. These insights are the key for universities that want to support their student population while reducing waste generation. Waste audits can reveal an overabundance of certain materials in the landfill bin, which can inform purchasing decisions. They can also illuminate knowledge gaps, such as if the bin notices recyclable bottles are thrown away with liquids still in them.

Many universities still conduct manual waste audits by transporting all the campus waste to one location and having volunteers sift through it by hand. Smart bins streamline this process by automatically recording data as waste is collected. High-quality analytics and on-demand audits can be accessed through a simple online dashboard. These analytics are critical to decision- making and improving policies.

Tech solutions that combine autonomous waste management and an enhanced user experience will have the most significant long-term impact. Smart bin systems using AI and robotics to sort waste at the time of disposal manage the process for the user. These systems can be calibrated to comply with local recycling rules and regulations, which means minimal or no contamination and the avoidance of fines.

AI bins also provide value through responsive recycling education. Despite their best intentions, humans often become the biggest barriers to recycling. Due to a gap in recycling education, people will throw containers with grease or other food contaminants into recycling bins. Wishcycling is also a phenomenon that causes significant issues for recycling systems. People put something in the recycling bin that they hope will be recycled, but the reality is that it contaminates the rest of the waste in the bin, and everything goes to landfill.

Oscar Sort and EvoBin, made in Canada and the U.S., provide gamification and respond to the user as they deposit their waste. TrashBot, also made in the U.S., uses AI and computer vision to recognize the waste deposited and, through a built-in screen, delivers context-specific education based on the waste disposed. The benefit of all these systems is that users can participate in the process and get tips on how to improve their recycling behavior. Recycling education is a critical link that connects people and their impact.

Machine learning provides an additional benefit to waste management, as it allows the system to get smarter over time. The more unique waste items a smart bin &#;sees,&#; the more waste it can identify and sort. As these bins recognize more unique waste items, their data tracking ability improves.

Benefits for Facilities

Using smart bin solutions for waste disposal can mean substantial benefits for a campus, including:

• Increased student engagement around waste reduction and recycling
• Customizable compliance, meaning more cost-effective recycling
• Digitization of waste management
• Real-time data and analytics
• Custom messaging and education delivered via a content management system
• Higher diversion rates and lower disposal fees
• Credits toward green certifications

Committing to sustainability is a lifelong endeavor, so college environments are critical to introducing and nurturing this knowledge. The next generation of thinkers, creators, and leaders are today&#;s students. Purposeful engagement and support of AI-powered recycling systems will help students retain these critical, sustainable behaviors once they leave the university setting and will ultimately drive a zero-waste future.

Rachel Whitener is PR and Communications Associate at CleanRobotics in Colorado. She can be reached at [ protected]. This is her first article for Facilities Manager.

Power Tools

Seeks to engage multiple voices from among FM ranks to bring fresh insight and real knowledge from the trenches on what&#;s working in planning and implementation for energy-related projects and innovations. To contribute, contact Kevin Willmann, FM Column Editor.
See all Power Tools.

For more information, please visit Smart Waste Sorting Equipment Manufacturing.