economic and infrastructure constraints. Thus, the following premise is advanced, based on findings in Paper IV:
Premise 5. It is possible to be more circular by optimizing the existing waste treatment system when changing it is not an option.
3.3 Assumptions, sensitivity analysis, and scenario
impacts, respectively), single-score output can streamline the result and render it more articulate.
Another significant assumption was used when calculating the environmental benefit from material produced during plastic recycling. In the waste treatment process, environmental benefits are obtained from products or energy, such as recycled material, biosolids, or energy. Ratio 1:1 is still commonly used to reflect the substitution of virgin material for recycled material (Laurent et al., 2014). It implies that secondary material has the same quality and acceptance as virgin material and could lead to overestimation of the environmental benefit (Gala et al., 2015). Paper III applied a market substitution factor that reflects the acceptance of the recycled material. Even after a careful attempt was made to avoid overestimating the benefits, different studies applied different values of substitution factors, ranging from 50–95% (Faraca et al., 2019; Gu et al., 2017;
Rigamonti et al., 2014). Thus, sensitivity analysis becomes important in examining the importance of this parameter relative to the LCA model.
These input parameters, assumptions, formulas, and methodological selection generated uncertainties. The studies were complemented with sensitivity analysis to address the issue. It aims to identify how the outcomes vary as a result of changing the input values (Bisinella et al., 2016). The analysis was conducted by increasing each input parameter by a certain percentage and holding the others the same as the baseline values. The analysis was conducted to gain understanding of ranking parameters corresponding to their sensitivity within a particular LCA or LCC model context. This information conveys insights and can be utilized by interested stakeholders. High-ranking parameters show their relative importance in affecting the outcomes. Thus, reassuring good quality of data regarding sensitive parameters is key. Moreover, when the LCA outcomes need to be adjusted, actors can target a few sensitive parameters. An example can be found in Paper IV, where optimized solutions could be attained by modifying operating parameters in the WtE plants. Six parameters were adjusted to generate optimized solutions.
Sensitivity analysis showed that the most sensitive parameter for plant efficiency, environmental impact, and economic impact was steam temperature entering and leaving the high-pressure turbine. Therefore, focusing on these two parameters can simplify the task instead of tuning all six parameters. A summary of the most sensitive parameters is shown in Table 6.
Table 6. The most sensitive parameters of different case studies
Paper Sub-case Most sensitive parameter
Environmental assessment Economic assessment
Paper II Source-separated biowaste
Fuel consumption rate (for the transportation), methane potential in waste (for the treatment)*
Waste quantity (for transportation), labor cost (for treatment)
Mixed waste (without the biowaste)
Fuel consumption rate (for the transportation), the fossil carbon content in waste (for the treatment)
Waste quantity (for transportation), CAPEX (for treatment)
Paper III Case 1 and case 2 Market substitution factor Market substitution factor
Paper IV The temperature of the steam coming into the high-pressure turbine**
The temperature of the steam leaving the high- pressure turbine
*The environmental assessment in Paper II refers to the environmental damage cost assessment
**The same parameter was also the most sensitive affecting the efficiency
The model robustness was then examined against the background system using scenario analysis; scenario analysis also answers questions regarding multiple alternatives available that should be compared. Furthermore, it can assist in directing future research or making a decision based on the possible pattern produced. Table 7 summarizes the scenarios and their outcomes in Papers III and IV.
Table 7. Scenario analysis in Papers III and IV
Paper Scenario Outcomes
III 1. Changing diesel to liquid natural gas (LNG) for the plastic waste collection 2. Changing the marginal energy source
to natural gas
1. The results showed relatively small environmental and economic benefits
2. The results varied—some worsening, some improving—depending on the impact categories
IV 1. Changing waste composition by decreasing and increasing the organic and plastic waste composition, respectively
2. Varying the marginal energy source into a more sustainable source (mix of wood, wind, nuclear) and fossil source (mix of nuclear, natural gas, and hard coal)
1. The cost of treating waste increased, the energy produced increased, and the single- score impact decreased (more overall environmental benefit)
2. The impact score increased for more sustainable source increased (less benefit than baseline) and decreased for fossil sources (more benefit than baseline) to LNG provided relatively small environmental and economic benefits
The scenarios in Paper III included changing the fuel type during the collection and switching the marginal energy, for both the electricity and heat source, into natural gas. Paper IV implemented different waste compositions and marginal energy sources for its scenarios. The analysis answered the what-if and offered evidence for more informed decision-making. In Paper III, there were small benefits in environmental and economic performance from LNG compared with diesel. This information can help direct the decision of whether to change fuel based on the interests of affected stakeholders, especially if the vehicle should be modified to run on LNG. Modifying incoming waste in Paper IV presented different results for environmental, energy, and economic aspects. The environmental aspect demonstrated improvement, as shown by the decrease in single-score value. Nevertheless, it is important to obtain more information regarding plastic waste treatment and to contextualize the study. The single score in Paper IV covered all environmental impacts, whereas interested stakeholders may wish to prioritize one impact above the others—specifically, when climate change impact is the highest priority, as Paper III, as well as Hou et al. (2018) and Wäger & Hischier (2015), deduced that recycling was better than incineration.
Papers III and IV included marginal energy analysis. When incineration is accompanied by energy recovery, it is inherently assumed that environmental benefits will be obtained. However, the benefits are relative to the source of marginal energy being substituted. For example, in the case of climate change impact, both Papers III and IV showed that the more sustainable the source of marginal energy, the lower the benefits obtained. The opposite applied when the benefits from fossil energy sources were calculated. The system expansion is performed by subtracting the impacts from the alternative system. Therefore, with a higher climate change impact generated by marginal fossil energy, more value is subtracted from the total impact caused by incineration. This result indicates that we should be cautious in generalizing one result to another, because the source of marginal energy in different contexts or countries tends to vary.
The scenario analysis also provided understanding regarding outsourcing certain processes in different countries because changing marginal energy showed different impacts caused by different energy sources. It could inform stakeholders such as government or private businesses when they plan to recycle waste in other countries or import goods from overseas, because the impact on the manufacturing level can be different, even if the process may be the same. Countries have different energy mix sources, and certain businesses may have greener contracts for their electricity consumption. Comparing several contractors through scenario analysis will produce a more comprehensive understanding.