Zero waste (ZW) concept was initially defined in 1973 and its application was to remove chemical contamination from waste. This concept has started to become a growing movement since the end of the 20th century [1
]. Among numerous solutions related to waste management, waste disposal to landfills are known as the conventional method, which is detrimental to the environment in many ways. Harmful chemicals and gases from landfill waste pollute the soil and groundwater. The construction of new landfills, which are caused by the rapid growth of population, destroy the natural landscape. In contrast, recycling offers many environmental-friendly solutions. While waste that ends up in landfill is useless and hazardous, waste recycled properly can become a new product and moreover, the recycling process consumes much less energy and resources than the conventional production of that product. Other than the environmental issues, governments all over the world are also facing a shortage of landfill capacity in urban space, and they must seek other effective and eco-friendly solutions [3
]. From those considerations, the implementation of the ZW concept has been an alternative waste management strategy that helps to reduce waste diverted to landfills. A significant number of policymakers in several countries have involved the ZW concept in urban development planning, and this concept is described as the most holistic innovation of the 21st century for achieving sustainable waste management systems [4
]. Nevertheless, the successful implementation of this concept in waste management is still hindered. Leading countries in the field of environmental protection have introduced their own strategies to achieve ZW; however, majority of them are currently at the beginning stage and are struggling to reach their targets within the set timeframes. The reasons behind those difficulties could possibly be rapid population growth, unforeseeable changes in residents’ lifestyles and behaviors, and the variability of the waste market.
In Australia, a low plastic waste recycling rate of only 9.4% was recorded in 2017–2018 [5
]. The country recycled only 12% of the total produced waste in 2019 [6
]. The state of Victoria in Australia is one of the states that is facing recycling issues the most due to various factors, including a sudden termination of its major plastic waste collection partner [7
]. On the other hand, the state exported a significant amount of plastic waste (62%) overseas for reprocessing [8
]. The strong dependence on global waste exportation of recovered plastics created a sort of mini-crisis after the ban of plastic waste imports into China and Malaysia [9
]. The state government responded to this crisis by developing a new 10-year policy and waste management plan as an effort to reduce waste production, divert more waste from going to landfills, and enhance the recycling rate by 2030 [11
The aim of this research is to study Victoria’s current 10-year waste management plan and to develop a simulation model for assessing the feasibility of achieving 80% diversion rate by 2030. The study will also assess the feasibility of achieving zero plastic waste in Victoria by 2035. In this direction, a review of cities around the world that have successfully implemented ZW programs has been undertaken to gain an understanding of the challenges, obstacles, and uncertainties involved in achieving the ZW target. The lessons learnt from several cities leading in ZW implementation will form a strong foundation for ensuring the accuracy and reliability of the proposed model.
This paper is structured as follows. Section 2
presents a background to plastic waste management globally, in Australia and in the state of Victoria. This section also presents three selected case studies of cities from around the world that have successfully implemented ZW programs. The methodology and the simulation model developed in this study to evaluate Victoria’s current 10-year plan and assess its feasibility to achieve the set targets is presented in Section 3
. The results and analysis of the outputs from the simulation model are presented in Section 4
. Section 5
presents a detailed discussion of the outputs from the simulation model and finally conclusions drawn from this study are provided in Section 6
To assess the feasibility of Victoria achieving zero plastic waste by 2035, a simulation model was established using the general baseline prediction approach with a slight modification to include more than one use of the term ‘baseline’. The method is thus called the ‘double baseline method’. This method was developed to address the limitation of data availability for the model development. Since the latest audit on plastic recycling was in 2018, the year is taken as a baseline year or a reference point, which, later in the results, the predicted effort and cost are based upon. This baseline year is embedded in all scenarios, including the baseline scenario, which similarly acts as a reference point for comparison. The ‘baseline year’ and the ‘baseline scenario’ are implemented together, hence the method is called the ‘double baseline method’. This model is adopted from a conceptual approach to zero waste simulation modelling by Krystyna A. Stave of University of Nevada, Las Vegas [43
The key lessons, gaps, and potential improvements from early reviews will be used to form the main input factors. The inputs include product recyclability, packaging and non-packaging polymer consumptions, processing facilities’ capacity, recycling option efficiency, reuse/end-of-life proxy rate, and sorting efficiency. These input factors represent components that form the recycling system and will be used as the simulation model’s inputs. Scenarios are proposed in the model in an effort to assess different aspects of the recycling system, thus, alterations in the values of input factors in each scenario are made accordingly. An overall research methodology adopted in this study is presented in Figure 5
3.1. Scenario Identification
The model is run using 4 scenarios, including one from Victoria’s current 10-year plan called Recycling Victoria: A new economy [44
]. These scenarios are formed based on projections from available data in an effort to study their effects on plastic waste recycling performance as well as looking for possible alternatives to ZW. Details about each scenario are presented in Table 2
and discussed in detail in this sub-section.
Victoria’s current plan (baseline scenario): This scenario is based on the new policy called Recycling Victoria, which is the Victorian Government’s 10-year policy and action plan for waste and recycling [45
]. Since the policy has already been rolled out and is in place, this plan will be used as a baseline scenario to compare with scenarios 1, 2, and 3. While it is not mentioned in the publication by how much the government planned to improve those key input factors, the values in the table are approximations only and not without flaws. Nevertheless, each value was reasonably estimated by considering the goals established by the Victorian government. For example, the 2020 Recycling Victoria: A new economy package, proposes a plan to achieve a 15% decrease in consumption per capita and 80% diversion rate by 2030, with an interim rate of 72% by 2025. This means the diversion rate goal is achieved by 90%. Two-thirds of the targeted diversion rate, which is defined as the amount of waste not being disposed into landfills, is contributed by reducing waste, that would make a consumption rate at around 10% fall in the first 5 years. Assuming the rate peaks at a constant rate of 1% annually, reduction in consumption rate will be at 15% by 2030 and 20% by 2035. The two-thirds of diversion rate achieved by waste reduction is based on the fact that diversion rate in Victoria heavily relies on consumption compared to recycling capacity and the package allocates only one-sixth of the fund to facilities investment, which adds up to the first argument.
Scenario 1 focuses on changes made in values of plastic consumption and recycling capacity, and looks at its impact on the overall performance, without changing the values of the other input factors. Hence, this scenario is called ‘consumption and capacity analysis’, as indicated in Table 2
Scenario 2 is the reverse of scenario 1, where factors related to consumption and capacity are kept the same as that in the baseline scenario and factors related to recycling capacity are changed. Rather than asking consumers to reduce their consumption and build more recycling facilities, the state government provide education and promote the recyclability of products, reuse, and better sorting efficiency. The values under scenario 2 in Table 2
are proposed to meet the highest efficiency, as seen in Kamikatsu, Japan, where plastics are sorted into 6 categories with the highest recyclability rate. Hence, this scenario is called ‘recycling capability analysis’ in Table 2
Scenario 3 compliments Victoria’s current plan and is designed in such a way that zero plastic waste is ensured to be met by 2035. Hence, this scenario builds upon the State’s current plan and uses the knowledge gained from scenarios 1 and 2.
Below are the explanations of the input factors and their values used in Victoria’s current plan (baseline scenario) from Table 2
Product recyclability (×3)
Regardless of how recyclable plastic is, if there is no improvement to consumers’ sorting efficiency, plastic waste will still end up as a mixed contamination batch. Therefore, the rate of product recyclability should be increased alongside sorting efficiency, that is, by 3 times (see sorting efficiency below).
Plastic consumption (−20%)
Plastic consumption used in this study is simplified by categorizing into packaging and non-packaging plastic. As described earlier, the reduction in consumption rate of 20% is estimated by interpolating values based on targeted achievement and required effort.
Processing capacity (×3)
Diversion rate is the rate of recovery over consumption. The estimated values in 2020 are around 1,108,000 t (consumption) and 207,200 t (recovery). For the plan to achieve 80% diversion rate with 15% consumption reduction, the equation would yield recovery at 659,300 t or 3.18 times that in 2020. The model adopts this value as 3 instead of 3.18 due to some variations to accommodate errors in estimated values.
Recycling option efficiency (+10% and 0%)
According to the 2017–2018 Australian plastics recycling survey by O’Farrell [5
], there are 4 current recovery options in Australia. Mechanical recycling refers to the use of physical processes such as sorting, chipping, grinding, washing, and extruding to convert scrap plastics to a usable input for the manufacture of new products. Biological recycling is the recycling process through composting or anaerobic digestion. Feed stock recycling is the conversion of polymers back into a monomer or new raw materials by changing the chemical structure of the material and includes processes such as pyrolysis and gasification. Energy recovery is the process to recover energy from plastics through controlled combustion or conversion to a liquid fuel, which may be a good option for plastics that are not suitable for mechanical recycling, such as contaminated products [5
]. Each of these options contain two values that represent different periods of the plan.
The values of +10% represents a boost in the first 5 years of the period, while 0% represents the later 10 years. Existing annual growth rate is estimated based on increase in reprocessors from 2016 to 2018 to be 2% for the later 10 years. Since, in this model, processing capacity above is defined to have a direct relationship with recycling options efficiency, we could work backward to diminish the growth by 2%, which in doing so for that remaining after 10 years is worked out to be a 10% boost on top of it.
Reuse/end-of-life proxy rate (10%)
The government plans to allocate about $1.8 million of the budget towards improving reuse economy and charitable sector, which spend about $13 million and divert around 31,600 t of waste donation from landfill. Assuming a direct relationship, the grant could increase the sector’s diversion rate by 14%, resulting in end-of-proxy rate at 3.2%, which is 0.5% on top of 2020’s rate. If the rate is constant throughout a 15-year period, the rate should be projected to be at 10%.
Sorting efficiency (×3)
Since Victoria’s waste sorting system only sorts plastic in one recycled bin, the efficiency can be considered as ×1. The new plan separates glass into a new bin and introduces a container deposit scheme which effectively cuts down the process of segregating plastic and glass from carboard, metal, and other materials. The efficiency from this plan could arguably be considered to increase by 3 times.
3.2. Establishing Factor Relationships
The input factors, despite their simplicities, have tangible relationships with one another which require a thorough analysis to breakdown. Due to limitation of resources, this study only takes the important factors into consideration. The relationships of these factors are established and presented in Table 3
, where each input is configured interrelatedly to obtain certain factorized values defining their relationships and how changes in one factor would affect the others. The purpose of introducing this factor relationship is to project as realistic results as possible despite the limitation on resources and to better understand the relationship between areas of plastic recycling. Explanations on how each value in Table 3
is obtained are also provided below.
While there is no direct study on how much of an effect product recyclability has on other inputs, the values used in Table 2
are estimated based on the indirect relationship found in the following studies. One study suggests how promoting recyclable waste to be transformed into new products can increase recycling rates. Six tests were conducted with varying results, the least effective of which is an audit of two university residence hall waste collection stations where signage was placed to show how the recyclables can be made into products. Approximately 11% recorded an increase in recycling rate [46
]. Since human behavior has a direct effect on sorting and recycling efficiency, the value can be adopted as a factorized value. In Kamikatsu, Japan, the highest sorting categories for plastic is 6, where even products like water bottles are sorted separately from their caps. From this example, assuming water bottle and its cap are made from the same material and can be sorted into the same category, it would increase the sorting efficiency by one sixth, that is 15%.
Reuse/end-of-life proxy rate
Victorians have been very supportive of banning low-density plastics like plastic bags. This type of polymer represents roughly 10% of total plastic consumption in Victoria. Assuming products with low-density plastic are banned, plastic consumption can be cut down by around the same rate, that is 10%.
In facilitating terms, there are 4 recycling options in Victoria, each of which recycles different mixes of polymers according to its contamination. Thus, any alterations to sorting efficiency should influence recycling options by 25%.
Change in population and lifestyle
This is based on the population growth rate in Victoria. The rate varies from year to year, toping at 2.55% [45
], which could be rounded up to 3% for conservative purposes.
While there is no information on how much effort is required to conduct certain campaigns, all values are obtained through processes of ranking based on delivery plans laid out by the government and its allocated budget to each aspect.
Most of the resistance values are reflective of those of the required effort. The resistance values to consumption are based on the example of banning lightweight plastic bags, where public consultation was conducted and only 3% of individual respondents opposed (Victoria State Government, 2019). If banning other types of plastic for the same reasons and purpose, there is a high chance of receiving a similar response.
3.3. Model Development
(a) Input Factors
With input factors and their relationships being established (as presented in Table 3
), the development of the model can be started by using simple conditional formulae in MS Excel to combine the relationships. Explanations of how input factors are interlinked by the relationships are explained below.
Given the condition that if the selected year’s value is greater than that from its baseline year, that is 2018, the result must yield a product of the selected year’s value and behavioral resistance, otherwise it can be used as it is.
Plastic consumption (packaging and non-packaging)
Given that if the selected year’s value is lesser than that of the baseline year of 2018, the result must include a product of the selected year’s value with its factorized values, reuse rate value, and behavioral resistance value, otherwise no behavioral resistance value is needed.
To simplify the process, processing capacity is set to be the product of total recycling option efficiency and the constant pre-calculated value of capacity per efficiency.
Recycling option efficiency
Because of the nature of its relationship, recycling option efficiency is affected by product recyclability and sorting efficiency. Given the condition that the two affecting values increase, the result must yield a product of the three values with their according factorized values, otherwise no factorized values are needed.
Reuse/end-of-life proxy rate
Given the condition that if the selected year’s value is greater than that from its baseline year, that is 2018, the result must yield a product of the selected year’s value and behavioral resistance, otherwise it can be used as it is.
Given the condition that the product recyclability value increases, the result must yield a sum of sorting efficiency value and the difference in increased recyclability along with its factorized values, otherwise it is equal to the efficiency value itself.
(b) Output Factors
Subsequently, the output section of the model is developed by formulating it according to their definitions. There are 6 outputs to be produced from the model: plastic consumption, plastic waste to landfill, diversion rate, recycling rate, relative effort, and relative cost. Each output is plotted in the form of a line chart against a timeframe from 2016 to 2035. There are 4 critical points along the timeframe, with year 2020 as the starting point and 2025, 2030, and 2035 as the next 3 milestones. It can be seen that only results from 2020 onward are the important ones. However, the decision to include results from years 2016–2019 was made because of the fact that 2018 is used as the baseline year; thus, it is practical to include a closest period of two years to bring consistency to the overall result. Below are explanations about the 6 outputs used in the modelling.
Plastic consumption (Total)
The sum of packaging and non-packaging consumptions.
Plastic waste to landfill
The deduction of plastic consumption and plastic recovered or processing capacity for simplicity.
Equal to plastic recovered (plastic consumption minus plastic waste to landfill) over plastic consumption.
For simplicity, yet not entirely dependable, recycling rate is the additional rate of reuse or end-of-life proxy to diversion rate.
Relative accumulative effort
Relative effort indicates combined effort required for implementation with reference point in year 2018. It is calculated based on required effort values, as shown in Table 3
, where the value is broken into percentage per unit. The percentage per unit values are then used to multiply by the units to get the original required effort values minus 100% to get 0% as a baseline value in 2018.
Relative accumulative cost
Relative cost shows the total cost required to carry out the plan in the scenarios with reference point in the year 2018 and is calculated based on the allocated budget of Recycling Victoria: A new economy plan, and the recently opened $
20 million facility that has capacity of about half of that in 2018 (Victoria State Government, 2020). The procedure is very much the same to that for relative effort. Table 4
shows one configuration example of product recyclability (input) and plastic consumption total (output). The configuration of remaining inputs and outputs are presented in the Supplementary Material section
3.4. Data Collection and Entry
Once the technical stages are well established, relevant data are collected and fed to the model. These data (raw data) are taken from multiple sources to form values that are used in input factors. Since the input factors are interlinked by their relationships, the model will then configure and adjust those values accordingly. These new adjusted data are then used to produce the outputs.
Since the latest audit on plastic waste was done in 2018, data from 2019 to 2020 could not be obtained. Therefore, a projection is necessary to be done at a conservative steady rate to complete the model. It is also critical to point out that since studies on plastic waste and waste audits possess uncertainties related to the data, the modelling is expected to encounter slight errors, which, regardless, is still viable to support the study.
highlights the overall performance of the proposed scenarios and provides the basis of this discussion. The rate of plastic consumption drop in the baseline scenario deserves a good explanation since it could be misleading based on the information provided previously. Initially, this scenario estimates that by 2035, consumption rate should reduce by 20% based on the assumption described in step 1. Since the government’s plan is a 10-year policy, where most of the programs are to be rolled out in the first 5 years, whereas the program will operate at a normal rate in the remaining years, it is obvious to expect a much stronger commitment in the early period. For this reason, the model applies the full target of a 15% reduction in the first 5 years, followed by 10% in the rest of the years, assuming that the targeted rate of 10% is achieved by 2025. The result is expected to be around 15–25% by 2025 because this high rate is achieved to accommodate consumption growth due to increase in the population. With this assumption in mind, the baseline scenario and scenarios 1 and 3 show similar changing patterns when plastic consumption is altered. The drop in the first 5 years are common, however, the latter part of the graphs vary.
The important point to note from this output is how the latter part of the graphs behave. The flatter the graphs, the more desirable they are considering how difficult it is to reduce plastic consumption with a constant increase in population. Scenario 2 demonstrates a perfect example of the said difficulty when no commitment is made towards decreasing plastic consumption. The final outcomes of these scenarios alone, despite indicating how feasible and successful each scenario is, could not provide full understanding of the recycling system. The baseline scenario and scenario 1, for example, show feasible results of 82% and 97.2%, yet their outcomes behave differently throughout the period. As the analysis of scenario 1 has shown, the differences between the two lines in the first 5 years of comparison are due to the effects of product recyclability, reuse rate, and sorting efficiency. These inputs significantly improve efficiency of the recycling options; thus, it increases plastic waste recovery, decreases waste to landfill, and improves diversion rate. Their effects can be estimated by examining 2025’s outcomes of all scenarios, except scenario 3, since the values are complemented. The baseline scenario and scenario 1 have a difference in value of 29.7% for diversion rate and 32% for plastic waste to landfill rate. Scenario 2 shows an increase in consumption due to population growth by 68%, but only sends 40% more of plastic waste to landfill, which yields a difference of 28%. Consequently, it is reasonable to conclude that in general, product recyclability, reuse rate, and sorting efficiency can improve recycling systems by about 30%, in addition to the commitments to reduce waste.
Victoria’s current 10-year plan or the baseline scenario presents a viable result of 77.5% averaged diversion rate by 2030. Although it is 2.5% behind the target, it is still a good step towards achieving ZW, considering some cities around the world took more than 10 years to achieve the same result. This achievement, at the same time, comes with a caution that the prediction does not consider any disruptive events that could happen along the way. Therefore, while it is considered achievable, the real outcome might fall a bit below the target. The fact that the current plan does not achieve ZW or 100% diversion rate by 2035 (which this study aimed to assess), however, indicates that improvements to the plan is desirable. Further development of this plan is recommended if it were to achieve zero plastic waste yet should be done with careful consideration with regards to effort and cost required and its long-term effect.
Scenario 1, which is the analysis of consumption and capacity, leads Victoria’s recycling system to meet zero plastic waste by the targeted year without having to exert as much effort as in the current plan, while keeping over-time expenses steady. This scenario, despite its statistical viability, is less likely to happen due to concerns about its long-term effect, its practicality, and increases in behavioral resistance. Any ZW approach should consider long-term effects into consideration. For example, encouraging people to reuse their plastic waste can save energy from having to recycle it, can reduce emission, and can reduce the overall consumption. On the same note, approaches without improving recycling capability-based factors like sorting efficiency, reuse rate, and recyclability are inefficient and impractical. The fact that scenario 1 reduces consumption and increases recycling capacity will result in an overwhelming increase in recovered material waiting to go for reprocessing. Consequently, if those recovered materials were to go into local reprocessing, the recyclability rate should increase on its own to accommodate the changes. To further support the argument, the fact that recycling capacity increases without increasing sorting efficiency and recyclability means that a huge number of the same technologically inefficient recycling facilities are being built, which again leads to the long-term environmental concern. Lastly, reducing plastic consumption at a certain rate over a long period of time might attract increase in behavioral resistance from consumers. The key to minimizing this concern is to take steps such as proper educational campaigns, strategical studies, and council coordination, which are not applicable in this scenario. This argument explains the projected low effort, where much of it only caters towards drafting policies on plastic consumption. Nonetheless, this scenario provides importance to how significant plastic consumption and recycling capacity are to recycling systems. It serves as an important piece of the puzzle towards the understanding of their relationships.
A simple cost-benefit analysis would favor scenario 1 considerably since it requires less cost and effort to reach ZW when compared to the baseline scenario and would provide a similar effect towards boosting the economy through businesses and employment. However, this scenario lacks in practicality without taking much consideration of behavioral resistance and is only viable for short-term perspectives. This claim can be supported by the finding from Krystyna A. Stave of University of Nevada, Las Vegas, where the author suggests that increasing processing capacity and alternative disposal capacity could bring waste down to zero with lesser cost and effort; however, waste begins to increase eventually and further improvement to consumer diversion rate, waste in products, and consumption are needed to kept the waste at zero [43
Scenario 2 is the least economically viable since it requires significantly higher cost and effort and provides minimal benefits to the economy. However, it enables further understanding of input relationships in addition to what scenario 1 offers, which, to summarize indicates that plastic consumption and recycling capacity have greater impacts on the system compared to the other inputs. Similarly, in this scenario, where no alternations to the two inputs were made, the outcome is far from acceptable, as it falls behind in achieving zero plastic waste. However, it can demonstrate how these inputs can go hand in hand in recycling systems. The first indication to this claim can be found in the plastic to landfill figure for scenario 1, where it goes down at a slower rate than the baseline scenario due to the effects of product recyclability, reuse rate, and sorting efficiency. The same figure in scenario 2, where these three inputs are at maximum, shows no progress at all. This leads to the conclusion that while reducing consumption and increasing recycling capacity helps with diverting waste from landfill, the inclusion of product recyclability, reuse rate, and sorting efficiency can amplify the effect by a significant rate. Correspondingly, without altering plastic consumption and recycling capacity in the first place, product recyclability, reuse rate, and sorting efficiency are of little to no use to the recycling system. This point also adds up with the concerns of long-term effect, practicality, and behavioral resistance mentioned in scenario 1 above.
Scenario 3 compliments the baseline scenario in a way that agrees with what was discussed in the previous scenarios. By this stage, it is comprehensible to categorize the inputs into two groups, namely major inputs (plastic consumption and recycling capacity) and minor inputs (product recyclability, reuse/end-of-life proxy rate, and sorting efficiency). The changes made in scenario 3 on the major inputs are kept minimal and are enough to produce the desirable outcome, while having a priority of minimizing the chance of stirring up behavioral resistance as well as impacting the environment. Changes to the minor inputs are made with consideration of the source of plastic products rather than demanding more commitment from consumers, which again would have further concerns regarding behavioral resistance. Therefore, there are no additional changes made towards reuse rate and sorting efficiency and an increase of +1 is added to product recyclability. The difference in effort required from this approach is only 25% when compared to the baseline scenario. With the viable result and commitment to other considerations, this scenario is demonstrated to be worthwhile and could potentially lead Victoria’s recycling system to reach the ZW target by 2035.
The objective of this study was to access the feasibility of Victoria’s current waste management plan, which aims to divert about 80% of waste from landfills by 2030 and achieve zero waste (ZW) by 2035 through a simulation model. The model was run on 4 scenarios, including a baseline scenario of Victoria’s current 10-year policy and action plan for waste and recycling. The other scenarios were developed based on changes to the baseline scenario. The model outputs show that Victoria’s current plan (baseline scenario) to achieve 80% diversion rate by 2030 is possible. However, the state may not reach 100% diversion rate by 2035, which means zero plastic waste is less likely to happen. An improvement to the plan was proposed by introducing slight changes to the input factors (in scenario 3) to ensure that zero plastic waste is met by 2035. The input factors include product recyclability, packaging polymer consumption, non-packaging polymer consumption, processing facilities’ capacity, recycling options efficiency, reuse/end-of-life proxy rate, and sorting efficiency
Results from scenarios 1 and 2 have significance towards the understanding of how product recyclability, reuse, and sorting efficiency can impact the recycling system. The baseline scenario and scenario 3 reflect the aims and objectives of this study, namely assessing the feasibility of Victoria’s current plan of diverting 80% waste from landfill by 2030 and also if it could achieve ZW by 2035.
One of the outcomes of this study is to demonstrate how each input factor plays an important role in the recycling system. For example, some input factors like product recyclability, reuse/ end-of-life proxy rate, and sorting efficiency in scenario 2 might give an impression of not providing usefulness or that it might scale-up the expenses when used on their own. But, when these input factors are used in conjunction with other factors, as done in scenario 3, it leads to an increase in the efficiency.
Finally, the findings of this study conclude that Victoria’s current plans are feasible. This study has also presented opportunities for improvement, especially towards achieving zero plastic waste by 2035. Thus, the model developed in this study provides a useful prediction tool, which enables the possibility of detailed analysis of input and output factors to facilitate achieving ZW. Development of further scenarios (in addition to those presented in this study) can be done by using different values of the input factors to find the best possible outcomes in terms of achieving ZW.
While the developed simulation model can be a reliable tool to support the predicted targets, the progress is expected to meet challenges and unpredicted events that could cause disruption, which should be anticipated and embraced with all necessary precautions. Thus, by having a strong commitment to reducing plastic waste, a circular economy and a better and more reliable recycling system in Victoria can be achieved.