the present as well as to discuss how to cross the gap by taking various technological and political measures. In this chapter, we develop a quantification process based on a literature review and experimental workshops. in literature the following review to prototype Quantify Backcasting Scenario with Workshop three experimental workshops online, varying the conditions of the workshops as shown in Table 2. input values without the information in 25 3.1 Approach Although research on the quantification of narrative scenarios using participatory backcasting has not been systematized so far, a few relevant studies are available as follows. Vita et al. (2019) created sustainable lifestyle scenarios in European countries with various stakeholders. Based on the described scenarios in narrative format, they evaluated changes in environmental impacts (e.g., CO2 emissions or water footprints) when various measures assumed in the scenarios were taken using expert and non-expert decisions with Environmentally Extended Multi-Regional Input-Output analysis. In Vita et al. (2019), it should be noted that quantification of narrative scenarios was accomplished after the stakeholder workshops. Uwasu et al., (2020) developed low-carbon energy scenarios with citizen workshops. Attempting to reflect citizens’ opinions in the quantification process, they evaluated CO2 emissions under the various narratives during one citizen workshop using a simplified simulation model. 2.2 Problems to be addressed As described in Section 2.1, quantification of narrative scenarios using participatory backcasting is becoming important. It is not easy, however, to quantify narrative scenarios during workshops for two main reasons. First, the process of quantification using workshops has not been clearly developed in previous research. Although Vita et al. (2019) and Uwasu et al. (2020) presented their quantification results, they did not clearly describe detailed processes such as how to determine input parameter values for quantification. Second, the quantification process is time-consuming because it is not easy to quantify a narrative scenario while securing internal consistency within it. For example, in Uwasu’s study (2020), at least 4 hours were spent on quantification. We took an experimental approach to developing the two steps: quantification process a performing quantification process and conducting experimental workshops to verify, modify and update the prototyped process. At the workshops, narrative scenarios describing sustainable consumption and production developed by members of PECoP-Asia (Kishita et al., 2019) were quantified by involving several members of the project as workshop participants along with a scenario designer, who determined the quantitative expression of each target a narrative scenario by organizing the opinions of the workshop participants and calculating target indices such as temperature or CO2 emissions. Here, the predetermined goal of SCP was assumed to halve CO2 emissions for consumer durables, such as cars, in urban areas compared with the Business-as-Usual (BaU) situation (Bao et al., 2017) in 2050. Such experimental workshops were also used to verify the method proposed in Chapter 5. The details are given below. 3.1.1 Prototyping a Quantification Process We developed a quantification process based on the literature review presented in Chapter 2. Quantifying backcasting scenarios described in a narrative format at workshops entails three tasks, i.e., (i) selecting or developing a simulation model to enable quantification of narrative scenarios, (ii) determining the input values for the simulation model, and (iii) discussing and validating the simulation results among workshop participants. 3.1.2 Experimental Workshops We held In the first experiment, we focused on a BICS Society (BICS: Business-Individual-Customer-Sharing) scenario, where sharing services were widely used to reduce CO2 emissions. The target products were defined by the scenario designers considering the content of the scenario. At the workshops, the participants established input values without referring to any external information. The participants quantified the scenario by changing the input parameters in a trial-and-error manner. For example, the penetration rate of electric vehicles was set at 100%. During the workshop, the participants confirmed a series of input values to achieve the goal of halving CO2 emissions but the input values were not convincing because of a lack of rationales. For example, participants assumed that all car users no longer owned cars in the scenarios, without a clear rationale. Moreover, in terms of efficiency, the workshop included much repetitive effort because the participants had to check their results each time they changed an input value. In the second experiment, the scenario designer conducted a sensitivity analysis of two parameters that were most relevant to the content and had a relatively larger impact on quantification results. At the workshop, the scenario designer showed the results of the sensitivity analysis to the workshop participants. The workshop participants could check the quantification results and get a grasp of the situation involving two parameters for halving CO2 emissions by referring to the results of the sensitivity analysis. Because it had been difficult to determine the previous workshop, the scenario designer also collected information relating to the scenario’s contents, such as the current state of target country or results of questionnaire surveys on sharing services. One example 3. Methodology
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