23

An Approach to Quantifying Narrative Scenarios for Sustainable
Consumption and Production Using Participatory Backcasting
Sota ONOZUKA1, Yusuke KISHITA1*, Mitsutaka MATSUMOTO2, Michikazu KOJIMA3and Yasushi UMEDA1
1

School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
2
Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology,
1-2-1 Namiki, Tsukuba-shi, Ibaraki 305-8564, Japan
3
Institute of Developing Economies, Japan External Trade Organization,
3-2-2 Wakaba, Mihama-ku, Chiba-shi, Chiba 261-8545, Japan*E-mail: kishita@pe.t.u-tokyo.ac.jp

Abstract
More attention has been paid to sustainable consumption and production (SCP) to explore the desirable
linkage between consumers and producers for sustainability. To envision SCP, the authors have developed aworkshop-based method for designing backcasting scenarios. The idea is to incorporate expertise, opinions andlocal knowledge that experts and stakeholders have into the scenario design process. While scenarios are
generally described in narrative format, it is necessary to quantify described scenarios to examine how to bridge
the gap between the current situation and predetermined goals for SCP. However, it is not easy to quantify
backcasting scenarios because there are no systematized methods available in existing studies. This paper thus
aims to develop a workshop-based process for undertaking a quantitative evaluation of backcasting scenarios innarrative format.
To develop the quantification process, we conducted a literature review and held experimental workshops.
Making scenario quantification feasible and efficient during the workshop, the scenario designers deal with thefollowing two things before the workshop – (1) setting tentative input values and the rationales for them and (2)suggesting discussion points in quantification workshops. In a case study, we evaluated one of the describedscenarios for Vietnam, inviting experts to discuss it. The SCP goal was assumed to halve CO2 emissions related
to passenger cars in 2050 from the level of the Business-as-Usual (BaU) scenario. Through the workshop, theexperts reached a consensus on the quantification results, which showed that the CO2 emissions were reduced
60% when half of car users shared a car with 10 users.
Key words: backcasting, scenario design, scenario quantification, sustainable consumption and production,
workshop

1. Introduction
As stated in Goal 12 of the Sustainable Development
Goals (SDGs), much attention has been paid tosustainable consumption and production (SCP), whichrefers to the use of services and related products to bring abetter quality of life while minimizing the use of natural
resources (UNEP, 2015). To clarify the desirable linkage
between consumers and producers, a five-year research
project (2016–2020) called “Policy Design and
Evaluation to Ensure Sustainable Consumption and
Production Patterns in Asian Region (PECoP-Asia) wasestablished (PECoP-Asia, 2016).” The focus was on
Global Environmental Research
25/2021: 023–030
printed in Japan

Southeast Asia because resource and energy consumption
is projected to increase drastically in response to future
economic growth (Bao et al., 2017). Through this project,
the authors developed a method for designing scenarios
using participatory backcasting to envision SCP, which is
far different from our current state of society (Kishita etal., 2019).
Participatory backcasting aims to envisage andexplore system innovations and transitions by inviting
stakeholders and/or experts to include various knowledge,
values, and opinions in backcasting processes, usually toaddress sustainability problems (Quist et al., 2011).
Backcasting first defines a desirable future endpoint (i.e.,
2021 AIRIES24

S. ONOZUKA et al.

vision) and then looks backwards from the vision to thepresent to connect the gap between them (Dreborg, 1996;
Vergragt & Quist, 2011). In participatory backcasting,workshops are usually used to describe a sustainablevision in a narrative format.
Based on the concept of participatory backcasting,the scenario design process developed in the PECoP-Asia
project consists of the following two steps (Kishita et al.,2018):
1. Describe future endpoints (i.e., visions) that achieve
SCP and pathways to connect with those visions,presenting them in narrative format taking into
account the regional characteristics of Southeast Asia.
2. Quantify narrative scenarios (i.e., stories describing
visions and pathways) to test if the scenarios achieve
the predetermined goals (e.g., carbon neutrality in2050) from an environmental viewpoint.
Note that the above steps should be iterative todevelop plausible scenarios. These steps are executed atexpert workshops to enhance collection of diversified
knowledge and views on SCP for Southeast Asia.
In the project, the authors described narrative
scenarios at the expert workshops (Step 1) as shown in
Table 1 (Kishita et al., 2019). Based on the experts’discussions in the scenario design process, various
measures to achieve SCP came to light, such as sharing,
remanufacturing and recycling. The focus of this paper ison Step 2, but the problem is that it is not easy to reflect
participants’ knowledge and opinions sufficiently in the
scenario quantification process, particularly when
determining plausible values of parameters in a simulation
model. This is because no systematized methods forquantifying backcasting scenarios in narrative format areavailable. Quantification is based on experts’ knowledge,
which tends to be implicit and not easily transferrable toother cases (e.g., Uwasu et al., 2020; Vita et al., 2019).
To make quantitative scenario creation based on
predefined narratives more efficient, we formulated theresearch question of this study as “How should narrative
scenario quantification processes using participatory
backcasting be developed and facilitated, particularly in
an SCP context?” To address this question, we aimed todevelop a workshop-based method for undertaking
quantitative expression of backcasting scenarios innarrative format. We assumed that workshop participants
using our method would be experts such as researchers
because diverse expertise is needed in SCP. In this paper,

we develop this method by taking an experimental
approach, i.e., prototyping and revising the quantification
process using SCP narrative scenarios, which aredescribed by the authors in PECoP-Asia (Kishita et al.,2019).
The rest of this paper is organized as follows:
Chapter 2 provides a literature review of participatory
backcasting and explains the problems of quantification of
narrative scenarios using participatory backcasting.
Chapter 3 proposes a method for quantifying narratives in
an SCP context. Chapter 4 presents a case study todemonstrate the proposed method using a narrative
scenario for Vietnam. Chapter 5 discusses theeffectiveness and challenges of the proposed method
based on case study results. Chapter 6 concludes thispaper.

2. Literature Review
2.1 Participatory Backcasting for Sustainability
Participatory backcasting, which is a normative
approach to foresight using desirable or alternative futures
involving experts or stakeholders, has grown into an
adequate approach to explore system innovations andtransitions towards sustainability (Quist et al., 2011).
There is an increasing number of scenario projects using
participatory backcasting in various domains, such as
household heating (Doyle & Davis 2013), SCP (Kishita etal., 2019), and food (Quist & Vergragt 2006).
Methodologies for participatory backcasting also havebeen developed based on such case studies. For example,
Quist et al. (2011) proposed a five-step process forparticipatory backcasting, including strategic problem
orientation; future vision development; backcasting
analysis; future alternative elaboration and follow-up
agenda definition; and embedding of the results andagenda, and follow-up stimulation, (Quist, 2007; Quist etal., 2011). Nikolakins (2020) analyzed how participatory
backcasting enabled communities to produce a range of
normative scenarios.
As mentioned above, many researchers have paid
attention to participatory backcasting. Most of them focus
on creating narrative scenarios to describe sustainablevisions. On the other hand, narrative scenarios must be
quantified for supporting decision-making towards
sustainability such as policymaking. This quantification
helps to clarify the gap between sustainable visions andTable 1 Example of SCP narrative scenarios (not exhaustive) (Kishita et al., 2019).
Title
Storyline
A: BICS Society (BICS:
 Because sharing services are already popular, B2B leasing and B2C sharing are widely used.
Business-Individual-Customer-Sharing) Some people become prosumers to satisfy individual needs.
B: Beauty is only skin deep
 Products are designed by coupling a generalized part and customized part.
 Because Vietnamese people like new products, customization services are provided using
augmented reality (AR) and virtual reality (VR).
C: Infrastructure innovation 2.0
 Sharing and replacement is accelerated by visualizing information for consumers.
 An authorization scheme is introduced to improve repair skills in local industry.

Quantify Backcasting Scenario with Workshop

the present as well as to discuss how to cross the gap bytaking various technological and political measures.
Although research on the quantification of narrative
scenarios using participatory backcasting has not beensystematized so far, a few relevant studies are available asfollows. Vita et al. (2019) created sustainable lifestylescenarios in European countries with various stakeholders.
Based on the described scenarios in narrative format, theyevaluated changes in environmental impacts (e.g., CO2
emissions or water footprints) when various measures
assumed in the scenarios were taken using expert andnon-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 toreflect 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 todetermine 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.

3. Methodology
In this chapter, we develop a quantification process
based on a literature review and experimental workshops.
3.1 Approach
We took an experimental approach to developing thequantification process in the following two steps:
performing a literature review to prototype aquantification process and conducting experimental
workshops to verify, modify and update the prototyped
process. At the workshops, narrative scenarios describing
sustainable consumption and production developed bymembers of PECoP-Asia (Kishita et al., 2019) were
quantified by involving several members of the project asworkshop participants along with a scenario designer,
who determined the quantitative expression of each target

25

narrative scenario by organizing the opinions of theworkshop participants and calculating target indices suchas temperature or CO2 emissions. Here, the predeterminedgoal of SCP was assumed to halve CO2 emissions for
consumer durables, such as cars, in urban areas comparedwith 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 theliterature review presented in Chapter 2. Quantifying
backcasting scenarios described in a narrative format atworkshops entails three tasks, i.e., (i) selecting ordeveloping a simulation model to enable quantification of
narrative scenarios, (ii) determining the input values forthe simulation model, and (iii) discussing and validating
the simulation results among workshop participants.
3.1.2 Experimental Workshops
We held three experimental workshops online,
varying the conditions of the workshops as shown in
Table 2.
In the first experiment, we focused on a BICS
Society (BICS: Business-Individual-Customer-Sharing)
scenario, where sharing services were widely used toreduce CO2 emissions. The target products were definedby the scenario designers considering the content of the
scenario. At the workshops, the participants establishedinput 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 ofefficiency, the workshop included much repetitive effort
because the participants had to check their results eachtime they changed an input value.
In the second experiment, the scenario designer
conducted a sensitivity analysis of two parameters thatwere 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 geta grasp of the situation involving two parameters forhalving CO2 emissions by referring to the results of thesensitivity analysis. Because it had been difficult todetermine input values without information in theprevious workshop, the scenario designer also collected
information relating to the scenario’s contents, such as thecurrent state of the target country or results ofquestionnaire surveys on sharing services. One example

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S. ONOZUKA et al.

Table 2 List of experimental workshops.
Narrative scenario
quantified (see Table 1)
Goal to be achieved
Overview of thequantification process
Persons involved in theprocess
Information provided toworkshop participantsby scenario designers
Discussion points
Examples of chenged
parameters (discussion
points) (not exhaustive)
Outcomes
Feedback from theparticipants

Experimental workshop 1
Experimental wokshop 2
A: BICS Society (BICS: Business-Individual-Customer-Sharing)Experimental workshop 3
B: Beauty is only skin deep

To halve CO2 emissions relating to passenger cars in 2050
compared with those in the BaU scenario
Participants determined input
Participants determined two input
parameter values without any
parameter values proposed by theadditional information andscenario designer by checking thechecked their results
results of sensitivity analyses
One scenario designer and four participants

To halve CO2 emissions relating torefrigerators
Participants determined two input
parameter values by checking the resultsof sensitivity analyses

Scenario storyline

Scenario storyline, information on current situations, future forecasts and theresults of sensitivity analyses

Participants chose discussion
points by themselves

Scenario designers chose
discussion points

Penetration rate of electricvehicles,
How many people share a car
Confirmed a series of values
for realizing SCP but without
clear rationales(+) Participants could discuss
whatever they wanted to(₋) Too much repetition
during the process

Penetration rate of car sharing
services,
Lifetime of shared cars
Confirmed the state of two
parameters needed to achieve SCP(+) Fewer reiterations in theprocess because it was easy tounderstand how the resultschanged when the two values
were changed(₋) Participants could not propose
other discussion points

Scenario designers proposed discussion
points and participants selected fromthemElectricity consumption of a refrigerator
Quantification unfinished because theparticipants determined the values(+) Participants could choose discussion
points(+)Fewer reiterations in the process
because it was easy to understand how
the results changed when the two values
were changed(₋) The discussion stopped when the
scenario desiger operated the simulation
model or serached for information on
the model(₋) Difficult to determine the values and rationales*(+) indicates positive feedback from the workshop participants and (₋) indicates negative or constructive criticisms.

of a determined input value is the lifetime of shared cars,
which was set at longer than seven years (Onozuka et al.,2021). In terms of the workshop’s efficiency, the numberof iterative processes was lower because the participants
were able to get a grasp of the situation to achieve thatgoal easily. However, the participants could not propose
other discussion points during the workshop because thescenario designer did not present the results of sensitivity
analyses of other parameters. Although the workshop
participants could see related information, they felt that it
was still difficult to establish plausible input values forwhat might occur in 2050.
In the third experiment, the target product waschanged from automobiles to refrigerators because thecontent of the target narrative scenario changed to sharing
an electric appliance among many people. To address thechallenges found in the second workshop, the scenario
designer prepared results of sensitivity analyses of ten
parameters in advance, considering the content of thenarrative scenario. At this workshop, the participantschose discussion points from candidate discussion points
proposed by the scenario designer such as the electricity
consumption of a refrigerator. Nevertheless, theparticipants could not determine the input values within
the workshop for two reasons. One was a lack of
information for determining plausible input parametervalues. This time, we quantified narrative scenarios interms of CO2 emissions relating to refrigerators. There
was less information about refrigerators in Vietnam thanabout passenger cars. The other reason lay in theworkshop’s facilitation. During the workshop, if the
scenario designer could not recall information, he would
try to find it in the simulation model or on the Internet,
leaving him unavailable to facilitate the discussion, so thediscussion would stop. This may have led to insufficient
discussion.
3.1.3 Problems Identified from the Experimental
Workshops
Through the experimental workshops, we identified
the following three problems: The rationales behind the input parameter values areimportant to consider for reasonably determining thefuture parameter values described in the target narrative
scenarios, e.g., for 2050. For this purpose, sufficient
information should be provided to the participants ofquantification workshops.
 It is more efficient and effective to limit the number ofdiscussion points, because not all parameters need to be
discussed and determined precisely at the workshop.
 It is difficult for scenario designers to facilitate theworkshop while operating the simulation model,
particularly in a virtual environment.

Quantify Backcasting Scenario with Workshop

3.2 Quantification Method for Pre-defined Narrative
Scenarios
We developed a quantification method to address the
problems found in the experimental workshops (seeSection 3.1.3).
To deal with the first problem, we presume that,when setting input parameter values for scenarios,
scenario designers need to prepare tentative parametervalues and the rationales for them in advance of workshop.
Here, rationales are developed mainly based on external
information, such as future forecasting reports or
statistical reports gathered by the scenario designers. The
scenario designers explain these parameter values andtheir rationales at the workshop to the participants.
To deal with the second and third problems, thescenario designers prepare key discussion points
considering storylines of the scenario and sensitivity
analyses of input parameters. They also divide the role ofscenario designer at quantification workshops between afacilitator and a technical assistant, who is responsible forrunning the model. Therefore, it is preferable to involve atleast two scenario designers.
Fig. 1 gives an overview of the proposed
quantification process. A workshop is combined withback-office work done by the scenario designers to makequantification during the workshop more effective andefficient. The process is composed of three phases andeach phase includes one or more steps.
Phase 1. Workshop preparation by the scenario designers(Steps 1–3 in Fig. 1)
Before running workshops, the scenario designers dosome preparatory work for quantifying the narrative
scenarios, which includes:
 Clarifying the problem definition for quantifying
narrative scenarios (e.g., scenario to be quantified,goals to be achieved, time horizon, target region, andtarget products) to share with the workshop
participants.
 Developing or selecting a simulation model for
quantifying the narrative scenario to makequantification results more convincing.
 Providing tentative input values and their rationales forthe simulation model to stimulate discussion among theparticipants.
 Providing key discussion points to focus on to facilitate

Fig. 127

workshop efficiency.
Here, we presume that the storyline of the narrative
scenario to be quantified has already been developed inadvance of quantification.
Phase 2. Discussion by workshop participants (Steps 5
and 6 in Fig. 1)
The workshop participants first check the results ofquantification with tentative input parameter values
suggested by the scenario designers. The participants thendiscuss the validity of the suggested input parametervalues and the results to modify and determine new input
values and their rationales. In this phase, the participants
mainly focus on the discussion points suggested by the
scenario designers, but the participants can add morediscussion points as they need.
Phase 3. Review and modification (Step 7 in Fig. 1)
After the workshop in Phase 2, the scenario designers
set the input values and quantify the narrative scenario.
The scenario designers may share the quantification
results with the participants to have them review thequantification results. Steps 5–7 are reiterated until theparticipants are satisfied with the quantification results. In
cases where the participants suggest modifying thesimulation model or request additional information to
make quantification convincing, the scenario designers
need to conduct part of the preparatory work for Phase 1
again.

4. Case Study
4.1 Overview
To demonstrate the proposed method, we organized
two online workshops. The first one was forquantification of narrative scenarios (around 1.5 hours)
and the second one was for the workshop participants toreview the quantification (around half an hour). Threeexperts from engineering and economics were invited to
the workshop as participants. Two scenario designers
were assigned to the first workshop, one to act as a
facilitator and the other as a model operator. A scenario in
Vietnam developed by members of the PECoP-Asia
Project was quantified. The scenario assumed that the key
measure to attain SCP would be sharing services tomitigate environmental impacts, while the predeterminedgoal was to halve CO2 emissions in 2050 compared with

Scenario quantification procedure using participatory backcasting (updated from Onozuka et al. (2021)).

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S. ONOZUKA et al.

Table 3 Examples of parameter values and their rationales established in the quantification workshop.
Tentative value
Value
Value
After
Parameter
set by scenario
Unit
Rationales(2020) (BaU)
workshop
designers
Penetration rate of
The same level as in Thailand. Reference: 45% incar sharing
0%
0%
50%
50%
Bangkok, Thailand at 2050 (Sekine et al., 2020).
services
-

-

50

10

-

Annual car
mileage

10000

10000

10000

5000

km /
year

the BaU situation. In this case study, we assumed that thetarget product and region of concern were consumer carsin urban areas of Vietnam. This is because cars have not
become widespread in Vietnam and controlling the
production volume of cars could have a big influence on
the country’s CO2 emissions.
4.2 Case Study of Narrative Scenario Quantification
The case study results are summarized as follows:
Phase 1. Workshop preparation by the scenario designers
1. The scenario designers decided to use a simple
simulation model called “product circulation model,”which was developed to evaluate the environmental
impact of product lifecycles, considering consumer
behaviors (Onozuka et al., 2021). This model was able
to estimate environmental impact changes whenvarious measures (e.g., sharing and remanufacturing)were considered.
2. The scenario designers set tentative input values andtheir rationales for the product circulation model. For
example, the penetration rate of car-sharing services
was set at 50% assuming that circumstances relating tocar-sharing services would be similar to those in
Bangkok, Thailand, referring to previous researchdone by other scholars. Table 3 gives some examples
of input values and the rationales for them set by the
scenario designers.
3. The scenario designers suggested a few discussion
points, which focused mainly how parameter values
would change from those in BaU scenarios. For
example, they raised a discussion point about how to
set the value of the parameter “Users of car-sharing
services per shared car” because it was strongly relatedwith the main measure (sharing services) of thenarrative scenario.
Phase 2. Discussion by the workshop participants
The workshop participants discussed how to modify
the input values based mainly on the discussion points
proposed by the scenario designers. For example, “Usersof car-sharing services per shared car” was modified from50 to 10 through discussion by participants: “It is assumed
in the narrative scenario that people will stop owning cars
and use sharing services instead. However, this input
parameter value refers to the current situation in adeveloped country, where people already own cars.

The same level as in Japan (assumed by the scenario
designers). Reference: Internet questionnaire.
Considering time required, 50 is not realistic(comment by participants).
Reduced because of lifestyle change caused by
COVID-19 (comment from workshop). In 2020,
mileage was almost halved in Vietnam.

CO
CO22 emissions
emissions

Users of car
sharing services
per shared car

Fig. 2 Quantification result of case study.

Considering the time a sharing-service user would use thecar in a day, 10 would be more appropriate than 50 tomake the value more consistent with the storylines.” Twoother qualitative statements were also discussed. One wason lifestyle changes due to COVID-19 - “we can assume
that the demand for mobility will decrease because remote
work is being promoted to address the COVID-19
situation.” This value was halved after considering amobility report (Apple, 2020). The other was on
renewable energy. One participant said “Renewableenergy should also be taken into consideration because it
has huge potential for reducing CO2 emissions.” In thisnarrative scenario, we assumed that renewable energy
would be used at the level of the Sustainable
Development scenario in the World Energy Outlook 2020
(International Energy Agency, 2020). Table 3 presents
some of the input parameter values and rationales
discussed at the workshop.
Phase 3. Review and modification
Based on the discussion by the participants, thescenario designers set input parameter values to estimate
the CO2 emissions and shared them with the workshop
participants. Figure 2 shows the quantification results,
which showed that CO2 emissions would be reduced by
60% compared with the BaU scenario in 2050. Note that
all the workshop participants agreed on the final
quantification results in the second workshop.

5. Discussion
We have proposed a quantification method fornarrative scenarios using participatory backcasting.
Judging from the results of the case study, the proposed
method worked well in obtaining quantitative scenarios

Quantify Backcasting Scenario with Workshopthat achieve the predefined SCP goal (i.e., halving CO2
emissions in 2050 from the BaU scenario). Although thefocus of this paper was on SCP, the proposed method
could be applied to any discussion on sustainability. We
obtained some important insights into the quantification
process when comparing the results of the experimental
workshops.
First, the scenario designers’ preparation of tentative
input parameter values and rationales helped the experts
determine the input parameter values for 2050. This isbecause it was smoother to start a discussion on whether
suggested values were convincing or not, rather thandetermining input parameter values from scratch. Before
running workshops, it is important to gather a sufficientamount of information related to various SCP measuresdescribed in the target scenario in a local context (e.g., inSoutheast Asia) to facilitate discussions among theparticipants. The gathered information can be used informulating rationales to decide input parameter values.
Second, having the scenario designers raise the discussion
points leads to reduced time required for quantification by
guiding the discussion among the participants. Third,
separating the roles of the scenario designers into
facilitator and model operator (technical support staff) is
helpful toward efficiently quantifying the narrative
scenarios during two-hour workshops based on experts’discussions.
Table 4 compares three methods for quantifying
narrative scenarios using a participatory backcasting
approach. The originality of the proposed method is toprovide a formal process for conducting workshop-based
quantification aiming to obtain plausible sets of parametervalues. When compared with the quantification processes
by Vita et al. (2019) and Uwasu et al. (2020), our method
focuses more on interactions with workshop participants
to obtain direct feedback by asking them to give rationales
for determining parameter values. One characteristic of
the proposed method is to quantify narrative scenarios
based on participants’ discussions under workshops’ stricttime constraints (e.g., two hours per workshop).
In the proposed method, however, some problems
remain to be further addressed. One problem is that the29

possible range of quantification (e.g., which parameters
are considered at a workshop) is limited because thescenario designers choose the discussion points andpre-developed simulation models in advance of theworkshops. To deal with this limitation, in Phase 1, thescenario designers need to choose discussion points
carefully that are critical to achieving the predeterminedgoals. Another problem of how the proposed method
would support policy design for SCP has not yet been
examined. Therefore, engaging more real-world
stakeholders (e.g., policy makers) in the scenario design
process, including the quantification process, will beneeded in further research. For example, combining
participatory backcasting with a gamification approachshows a potential to motivate workshop participants’
action towards visions created in a participatory
backcasting process (Mandujano et al., 2021). The casestudy shown in this paper invited experts only, but itwould be ideal to involve local stakeholders as well. One
future task will be to have both researchers andstakeholders involved in testing the proposed methods.

6. Conclusions
We proposed a workshop-based method forquantifying narrative scenarios based on the concept ofparticipatory backcasting. To develop the method, we
conducted a literature review and held experimental
workshops. To verify the effectiveness of our method, wequantified a narrative scenario in Vietnam using expert
workshops. Through the experimental workshops and thecase study, we gained the following insights:
 To reduce time spent on quantification workshops, it isimportant to establish discussion points in advance.
 Tentative input parameter values and rationales
prepared by the scenario designers facilitate discussion
during the workshop.
 Separating the role of scenario designer into facilitatorand model operator assists smooth discussion.
Future work will include applying this process toother scenarios involving real-world stakeholders tofurther test the method.

Table 4 Comparison of quantification methods in participatory backcasting.
Scenario title
Goals to beachieved
Purpose of using
workshops

Quantification
process

Participants

Proposed method
Sustainable consumption and production
in Southeast Asian countries in 2050
To halve energy and resource consumption
compared with that in the BaU scenario
To determine a plausible set of parametervalues to achieve predefined goals basedon narrative scenarios

Vita et al. (2019)
Sustainable lifestyles in European
countries in 2030
To reduce footprints such as water orland.
To develop visions in narrative format

The quantification process was formalized
as illustrated in Fig. 1. While the scenario
designers chose discussion points andsimulation models used, workshop
participants (experts) determined theparameter values to achieve predefinedgoals based on their discussions.
Scenario designers and experts

With an input-output model, theresearch team performed quantification
based on narrative visions which weredeveloped at stakeholder workshops.
No feedback was provided by theworkshop participants to thequantification results.
Scenario designers, experts, andstakeholders

Uwasu et al. (2020)
Sustainable energy vision for a cityin Japan in 2050
To reduce CO2 emissions by 70% in
2050 compared with those in 1990
To develop visions and pathways innarrative format,
To determine plausible input
parameters to achieve predefinedgoals based on narrative scenarios
Workshop participants (citizens)
performed quantification, but theplausibility of the chosen parametervalues was not discussed orconfirmed during the workshop.
Scenario designers and stakeholders

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S. ONOZUKA et al.

Acknowledgments
This research has been supported by the Environment
Research and Technology Development Fund (JPMEERF
16S11600) of the Environmental Restoration andConservation Agency, and a Grant-in-Aid for Scientific
Research (No. 18K18233, 19H04338, 19KT0008) fromthe Japan Society for the Promotion of Science. The
authors would like to thank the PECoP-Asia project
members for their participation in the workshops.
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Sota ONOZUKA
Sota Onozuka is a master's student at the Schoolof Engineering of the University of Tokyo. He
received his Bachelor's degree in MechanicalEngineering from Kyoto University, Japan in
2019.

Yusuke KISHITA
Yusuke Kishita is an Associate Professor at the
University of Tokyo. Prior to his current
position, he worked as a Research Scientist at the
National Institute of Advanced Industrial Scienceand Technology (AIST) and Assistant Professor
at Osaka University. During the period of August
2019 to February 2020, he was a
guest
researcheratTechnische
Universität
Braunschweig, Germany and Visiting Academic
Fellow at University of Cambridge. With his background in life-cycle
engineering and design engineering, his research interests include
scenario design for sustainable futures, circular economy, roadmap design
and backcasting. He holds an M.Sc. and Ph.D. in Mechanical Engineeringfrom Osaka University, Japan.

Mitsutaka MATSUMOTO
Mitsutaka Matsumoto is group leader of theAdditive Processes and Systems Group at theAdvanced Manufacturing Research Institute(AMRI), National Institute of Advanced
Industrial Science and Technology (AIST).
His research interests include productremanufacturing, production systems, smart
manufacturing, additive manufacturing andsustainability. He holds a Bachelor’s andMaster’s degree in Electrical Engineering from Kyoto University and a
Ph.D. in Decision Science from the Tokyo Institute of Technology.

Michikazu KOJIMA
Michikazu Kojima is a chief senior researcher at
the Institute of Developing Economies, Japan
External Trade Organization (JETRO). He is amember of Theme 1 of the PECoP-Asia research
project for SCP in Asia. He has studied
environmental policies, waste management,
recycling, international trade of recyclable waste
and marine plastic issues in Asian countries. He
was also seconded to the Economic ResearchInstitute for ASEAN and East Asia from March 2018 to September 2020.
In addition, he has contributed to various international cooperation
programs, such as the Expert Working Group of Environmentally Sound
Management under the Basel Convention and Technical Expert
Committee for Green Industry Platform organized by the United Nations
Industrial Development Organization (UNIDO). He has also served as acourse leader in Japan International Cooperation Agency (JICA) training
courses on recycling policy. He holds a Master’s of Science inAgricultural and Resource Economics from the University of California,
Berkeley.

Yasushi UMEDA
Yasushi Umeda is a full professor in RACE
(Research into Artifacts, Center for Engineering)at the School of Engineering of the University ofTokyo, Japan. He holds a B.E., M.E. and Dr.
Eng. in Precision Machinery Engineering fromthe University of Tokyo. He has authored oredited 29 books and over 150 peer-reviewed
articles, and has 19 patents granted or pending.
Five of his papers won best paper awards inscientific journals and international conferences. His research interests
include sustainability science, smart manufacturing systems, design
theory, life-cycle design and eco-design; in particular, his current researchinterests include life-cycle design for sustainable resource circulation.

(Received 21 June 2021, Accepted 6 August 2021)