NetLogo User Community Models

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## WHAT IS IT?

The model aims to simulate a residential composting program in an urban area to gather data on waste diversion, compost, and recycling weights. A composting knowledge intervention is introduced prior to the residential composting program and spillover from composting to recycling is modeled.

## HOW IT WORKS

Households generate waste which they then sort into the garbage or recycling initially. Pro-environmental intentions are used to operationalize the different quantity of recyclable waste that households identify as recyclable. The intentions are randomly assigned at each time step to vary the quantity of waste that is recycled for each household weekly. Additionally, the intention ranges for households that are high versus low in environmental concern differ to capture the higher quantity of recycling that is generated by the high concerned households (Lansana, 1993; Nigbur et al., 2010; Schultz & Oskamp, 1996). This is repeated for 52 time steps (1 year), to simulate a residential garbage and recycling program prior to the introduction of the residential compost program.

Following one year of the residential garbage and recycling program running, a composting knowledge intervention is disseminated. The knowledge intervention leads to an increase in all households’ pro-environmental intentions, but larger increases are seen for the high environmental concerned households compared to the low concerned households. The residential composting program is initiated leading households to sort their waste into the garbage, recycling, or compost bin for weekly pickup. These new pro-environmental intentions are then used to operationalize the different quantity of organic waste that households identify as compostable. The intentions are randomly assigned at each time step to vary the quantity of waste that is composted for each household weekly. Additionally, the intention ranges for households that are high versus low in environmental concern differ to capture the higher quantity of compost that is generated by the high concerned households (Janmaimool & Khajohnmanee, 2019; Lansana, 1993; Nigbur et al., 2010; Nilsson et al., 2017; Schultz & Oskamp, 1996; Sintov et al., 2019; Spence et al., 2018).

Now that a residential composting program is in place, we expect to see effects on households recycling weights via spillover. If households display positive spillover, there is an increase in their recycling weight for that week. Alternatively, if households display negative spillover, there is a decrease in their recycling weight for that week. The spillover mechanisms used to operationalize positive and negative spillover were cognitive dissonance and moral licensing respectively. Specifically, when an agent does not commit to composting after acquiring knowledge, tension may arise. To alleviate the tension cognitive reduction strategies are used, leading the agent to increase their recycling (Thøgersen & Crompton, 2009). Alternatively, when an agent does commit to composting, they may feel morally satisfied by the quantity of materials they composted, which leads them to decrease their recycling (Blanken et al., 2015; Truelove et al., 2014). Therefore, the model first starts by assigning spillover intentions to each household, before calculating their recycling weight.

Spillover intentions are determined based on whether a household has committed to composting leading them to meet or surpass the composting threshold for that week. As high concerned households compost and recycle a greater percentage of materials they have a higher spillover intention range than their low concerned counterparts. Similar to the pro-environmental intentions, the spillover intentions are randomly assigned at each time step to vary both the quantity of recyclable waste that is recycled weekly, as well as vary their display of positive and negative spillover. Overall, high concerned households that display positive spillover will possess the highest recycling weights, followed by low concerned households displaying positive spillover, high concerned households displaying negative spillover, and finally low concerned households displaying negative spillover.

## HOW TO USE IT

To setup the model, the quantity of households that are high and low in environmental concern needs to be specified. Additionally, the Landfill parameter should be altered to reflect the average weekly quantity of garbage produced per household for the urban city you wish to model. Furthermore, the recycle-percentage and foodwaste-percentage parameters should also be altered to reflect the average percentage of residential waste that is recyclable and organic for the urban city you are modeling. Based off the foodwaste-percentage, the high-concerned-spillover-threshold and low-concerned-spillover-threshold parameters will need adjustment.

While the model is running the quantity of total diverted waste, as well as the quantity of compost and recycling for the high and low concerned households separately, is displayed via monitors in kilograms per time step (each week).

Quantity of diverted waste is plotted overtime, where recycling and compost is parsed out, as well as the difference in these waste diversion methods for high and low environmental concerned households. The number of time steps (weeks) is found on the x-axis and the quantity of diverted waste in kilograms is found on the y-axis. Low environmental concerned households compost weights are plotted in yellow. High environmental concerned households compost weights are plotted in magenta. Low environmental concerned households recycling weights are plotted in orange. Lastly, high environmental concerned households recycling weights are plotted in pink.

## THINGS TO NOTICE

When downloading the data you will notice that each household will produce a different amount of waste, recycling, and compost to capture the heterogeneity found within populations. Additionally, the amount of waste, recycling and compost will change from time step to time step (week to week). Stemming from this, households will participate in positive spillover during some time steps, but negative spillover during other time steps as the type of spillover displayed relies on their commitment to composting during the previous week.

Households that are high in environmental concern will recycle and compost a larger portion of waste, when compared to their counterparts that are low in environmental concern.

## THINGS TO TRY

Try changing the proportion of the population that is high and low in environmental concern to assess how mobilizing a larger proportion of the population to be high in environmental concern increases the quantity of waste that is diverted annually from the landfill. For example, if you want to simulate a population of 600 households, you could first simulate 420 high concerned households and 180 low concerned households to have a 70/30 proportion. During the next run of the mode, you could then simulate 300 high concerned households and 300 low concerned households to have a 50/50 proportion.

## EXTENDING THE MODEL

There are many routes for the extension of this model.

Firstly, other pro-environmental behaviours could be assessed for spillover, such as spillover from composting to backyard composting.

Secondly, there are many measures for environmental concern, and many are multidimensional. Therefore, rather than classifying households as high or low in environmental concern, changes could be made to classify them as high or low in specific dimensions of environmental concern.

Thirdly, the quantity of by-product produced from composting could be calculated within the model, as well as how much money would be made from selling it. This would also allow for the assessment of how long it may take municipal governments to re-coop the large costs associated with initiating and running residential composting programs.

Lastly, this model was created to simulate residential composting programs in urban areas. Indigenous peoples are affected to a greater extent by climate change than populations residing in urban areas. A variation of the current model could be made, with the input of indigenous peoples, to create a simulation they may benefit people living in reserves.

## NETLOGO FEATURES

No information to report.

## RELATED MODELS

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## CREDITS AND REFERENCES

Blanken, I., van de Ven, N., & Zeelenberg, M. (2015). A meta-analytic review of moral licensing. Personality & Social Psychology Bulletin, 41(4), 540–558. https://doi.org/10.1177/0146167215572134

Janmaimool, P., & Khajohnmanee, S. (2019). Roles of environmental system knowledge in promoting university students’ environmental attitudes and pro-environmental Behaviors. Sustainability, 11(16), 4270-. https://doi.org/10.3390/su11164270

Lansana, F. M. (1993). A comparative analysis of curbside recycling behavior in urban and suburban communities. The Professional Geographer, 45(2), 169–179. https://doi.org/10.1111/j.0033-0124.1993.00169.x

Nigbur, D., Lyons, E., & Uzzell, D. (2010). Attitudes, norms, identity and environmental behaviour: Using an expanded theory of planned behaviour to predict participation in a kerbside recycling programme. British Journal of Social Psychology, 49(2), 259–284. https://doi.org/10.1348/014466609X449395

Nilsson, A., Bergquist, M., & Schultz, W. P. (2017). Spillover effects in environmental behaviors, across time and context: a review and research agenda. Environmental Education Research, 23(4), 573–589. https://doi.org/10.1080/13504622.2016.1250148

Schultz, P. W., & Oskamp, S. (1996). Effort as a moderator of the attitude-behavior relationship: General environmental concern and recycling. Social Psychology Quarterly, 59(4), 375–383. https://doi.org/10.2307/2787078

Sintov, N., Geislar, S., & White, L. V. (2019). Cognitive accessibility as a new factor in proenvironmental spillover: Results from a field study of household food waste management. Environment and Behavior, 51(1), 50–80. https://doi.org/10.1177/0013916517735638

Spence, E., Pidgeon, N., & Pearson, P. (2018). UK public perceptions of Ocean Acidification – The importance of place and environmental identity. Marine Policy, 97, 287–293. https://doi.org/10.1016/j.marpol.2018.04.006

Thøgersen, J., & Crompton, T. (2009). Simple and painless? The limitations of spillover in environmental campaigning. Journal of Consumer Policy, 32(2): 141–163. https://link.springer.com/article/10.1007/s10603-009-9101-1

Truelove, H., Carrico, A., Weber, E., Raimi, K., & Vandenbergh, M. (2014). Positive and negative spillover of pro-environmental behaviour: An interview review and theoretical framework. Global Environmental Change, 29, 127-138. https://doi.org/10.1016/j.gloenvcha.2014.09.004