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Citation: Zhang, J.; Yang, H.; Xu, X.
Research on Service Design of
Garbage Classification Driven by
Artificial Intelligence. Sustainability
2023,15, 16454. https://
doi.org/10.3390/su152316454
Academic Editors: Wei Liu and
Chia-Huei Wu
Received: 16 October 2023
Revised: 19 November 2023
Accepted: 23 November 2023
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sustainability
Article
Research on Service Design of Garbage Classification Driven by
Artificial Intelligence
Jingsong Zhang 1, Hai Yang 2,* and Xinguo Xu 1
1School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310014, China;
kingsun@neocross.cn (J.Z.); incamike@zjut.edu.cn (X.X.)
2Hangzhou Zhongwei Ganlian Information Technology Co., Ltd., Hangzhou 310023, China
*Correspondence: hyang@zwglink.com
Abstract:
This paper proposes a framework for AI-driven municipal solid waste classification service
design and management, with an emphasis on advancing sustainable urban development. This
study uses narrative research and case study methods to delve into the benefits of AI technology in
waste classification systems. The framework includes intelligent recognition, management strategies,
AI-based waste classification technologies, service reforms, and AI-powered customer involvement
and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-
effectiveness in waste classification, contributing to environmental sustainability and public health.
However, the effectiveness of AI applications in diverse city contexts requires further verification.
The framework holds theoretical and practical significance, offering insights for future service designs
of waste management and promoting broader goals of sustainable urban development.
Keywords:
AI; municipal solid waste classification; garbage classification; service design; intelligent
recognition; management strategy
1. Introduction
Since 2019, Chinese provinces and prefecture-level cities have advocated solving prob-
lems of “garbage siege” and carrying out the environmental protection policies like the
“Ban on Free Plastic bags”, both of which have put forward the overall requirements of
“reduce, reuse, and recycle” for municipal waste classification [
1
,
2
]. Compared with other
countries, there are big differences in the treatment modes and actual results of garbage
classification due to different economic strengths, resource demands, technical levels, and
legal policies. With the reuse, superimposition, and crossover collaboration of “Internet
plus”, “AI plus”, big data, Internet of Things, and other technologies in recent years, the
exploration of the technology and service changes in garbage classification, a systematic
project, is particularly prominent and important in the round of the comprehensive man-
agement of garbage classification in major cities of China [
3
]. This paper, based on the
scientific concept of service design, studies the latest development trends of AI 2.0 technol-
ogy and the driving factors in combination with waste classification service management.
It proposes a solution for the assistant tool for urban solid waste classification management
in the project practice in Hangzhou, China [
4
,
5
]. At the same time, it introduces the case
of the ZRR2 waste classification robot used in the waste classification processing plant
in Barcelona [
6
]. By comparing the two cases, it more prominently highlights the huge
potential of AI in waste management strategies.
The main research outcome of this paper is the proposal of a framework model for
AI-driven waste classification service design and management. The results of the project
practice summarize several forward-looking strategic plans for AI-driven service design
and management in future urban solid waste management. In addition, this paper dis-
cusses the environmental constraints, challenges faced, driving factors, and future research
Sustainability 2023,15, 16454. https://doi.org/10.3390/su152316454 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 16454 2 of 16
perspectives on future urban waste management. This research helps in understanding the
potential application of AI technology in the field of waste classification management and
provides practical guidance for the development of future waste classification policies and
initiatives. This study also reveals the importance of community service co-creation and
value network systems in environmental management under the concept of service design,
especially in the context of promoting sustainable urban development.
2. Literature Review
Municipal solid waste (MSW) classification management comprises the activities
associated with the collection, transfer, treatment, recycling, resource recovery, and disposal
of solid waste generated within urban locales [
7
]. The process extends from the point of
waste generation to its ultimate disposal, with efficiency and productivity being critical
considerations. Artificial intelligence (AI) technology has emerged as a promising tool in
bolstering the efficiency and precision of MSW classification. Artificial intelligence-based
technologies like smart garbage bins, garbage sorting robots, predictive models for waste
production, and optimizing the performance of waste processing facilities. The details are
shown in (Table 1). Notably, Finland, Japan, and the United States have pioneered the R&D
of automatic garbage-sorting robots. ZenRobotics Recycle system (ZRR) from Finland, the
first garbage classification robot globally, can efficiently differentiate mixed waste, useful,
and non-useful waste within MSW. Japan’s FANUC garbage sorting robots utilize the AI
vision analysis system to analyze wood quality and discriminate polymer from plastic.
The Computer Science and AI Laboratory at the Massachusetts Institute of Technology
has developed ‘Rocycle’, a garbage recycling and sorting robot that can distinguish paper,
metal, and plastic by touch [6].
The application of AI in waste management transcends waste classification. AI mod-
eling methods can accurately predict waste generation quantities, facilitating the design
and operation of effective waste management systems [
8
]. AI has also been instrumental in
forecasting waste generation, managing construction waste, and optimizing landfill site
selection [
9
]. Other studies have employed non-parametric models to track the tempo-
ral productivity changes in waste management services, considering both economic and
environmental aspects [10].
In essence, AI technology, in conjunction with the Internet of Things (IoT), plays a vital
role in the classification and management of MSW. These technologies enable precise waste
classification, waste collection and transportation optimization, and the establishment of
efficient waste management systems. AI modeling and DEA-based models are employed
to measure productivity from various perspectives, thus providing comprehensive insights
into the performance of waste management services in terms of productivity and eco-
productivity [10].
In China, the government initiated a new MSW classification strategy in 2017, with
Hangzhou being one of the first pilot cities. The policy aims to ensure effective MSW
classification implementation via measures like AI- and computer vision (CV)-based ap-
proaches [
11
]. The implementation of IoT technology in MSW classification and manage-
ment can enhance the classification level and optimize waste collection and transportation,
thereby reducing costs and environmental pollution. The COVID-19 pandemic has am-
plified the challenges of MSW management due to the surge in medical and household
waste [
12
]. This scenario has highlighted the criticality of waste collection, recycling, treat-
ment, and disposal services. Concurrently, the pandemic has exposed the complexities
of waste management, emphasizing the importance of efficiency, health considerations,
and customer satisfaction. In this context, factors such as waste collection frequency, age,
educational status, and family size play a crucial role in customer satisfaction [13].
Sustainability 2023,15, 16454 3 of 16
Table 1. The main application of artificial intelligence to waste management [14].
Type of AI
Technology Types of Waste Measures of AI Key Information Results/Benefits References
Smart
garbage bin
Solid waste Sensor network
1. Garbage bin monitoring
2. Collect data
3. Analyze information
Used to collect
municipal waste
Khan et al. (2021)
[15];
Ghahramani et al.
(2022)
[16]
Solid waste Ultrasonic sensors
1. Garbage will not overflow
2. The lid will open automatically
3. Automatic detection of garbage
Digital garbage bin
Wijaya et al. (2017)
[17];
Praveen et al. (2020a)
[18]
Solid waste Ultrasonic sensors
Red external sensor
1. Identify garbage
2. tracking the vehicle and IR
sensors
3. Garbage level monitoring
Instantly detection
of the status of Bins:
Filled or Empty
Pawar et al. (2018);
[19]
Garbage-
sorting
robot
Reusable garbage
Computer vision
Robotic framework
1. Gripping
2. Motion control
3. Material categorization
Success rates:
glass: 79%
plastic: 91%
Wilts et al. (2021) [8];
Kshirsagar et al.
(2022) [20]
Solid waste
Computer vision
simultaneous
localization and
mapping
1. Automatic navigation
2. Garbage recognition
3. Pick up automatically
Recognition
accuracy is 94%,
even without path
planning
Bai et al. (2018) [21];
Lee, K.-F. (2023) [6]
Seven types of
garbage
Skin-Inspired Tactile
Sensor
1. Quadruple tactile sensing
2. Object recognition
3. Garbage classification
Recognizing 7
types of garbage,
accuracy of 94%
Li et al. (2020) [22];
Lee, K.-F. (2023) [6]
Predictive
model for
waste
production
Hazardous
waste,
construction site
waste
Genetic
algorithm-adaptive
neuro-fuzzy
inference system
1. Defining targets for waste
production
2. Optimizing resources
3. Reporting and conducting
inspections
4. Compared with different AI
prediction models
Raised proposed
measures for waste
reduction
prediction
Haque, M.S. et al.
(2021) [12];
Bang et al. (2022) [
10
]
Solid waste Proximate analysis
1. Generation rate and waste
composition
2. Quantified, characterized, and
evaluated energy potential and
nutrient value of solid waste
Reduce tons of
carbon dioxide
equivalent
greenhouse gas
emissions.
Fetene et al. (2018)
[13]
Solid waste Eco-Productivity
Analysis
1. DEA-based models
2. Sampling and characterization
3. Carbon emissions evaluation of
MSW disposal system
Decline of daily
carbon emission in
MSW disposal
system after waste
sorting.
Lo Storto, C. (2017)
[9]
Wang, Y. et al. (2021)
[11]
Therefore, it is imperative to conduct further research to unravel the multiple roles of
AI technology or AI agents in waste classification management systems. These technologies
function not only as technical components of the service system but also as collaborative
agents alongside human operators [
23
]. The aim is to explore how AI influences service
design and management practices for MSW from different perspectives. This investigation
would provide constructive insights for future service ecosystem innovation, preparing us
to better handle unforeseen events and navigate the multifaceted challenges of our evolving
social environment.
Service design models the social, material, and relational elements that support the
customer experience [
24
,
25
], integrating the various silos of the organization into a coordi-
nated service offering [
26
]. It applies human-centered and collaborative methods to explore
the experiences of different stakeholders. This ability to integrate stakeholders enables
service design to develop solutions that are relevant to customers while considering the
structural context of the organization [
27
,
28
]. However, the relationship between artificial
intelligence and service systems is a complex and evolving new one [
29
]. Some believe a
key aspect of the relationship between AI and service systems is that AI has the potential
Sustainability 2023,15, 16454 4 of 16
to complement rather than completely replace human labor [
30
]. While AI can automate
certain tasks, it also amplifies the comparative advantages of human workers in areas
such as problem solving, adaptability, and creativity. To fully leverage the potential of AI
in service systems, it is crucial to study and implement strategic frameworks [
31
]. This
framework should consider the nature of service activities, service processes, and other
specifics [32].
Consistent with all service-dominant logic premises, the AI-driven service system
guides the description of the situation at three levels: Value constellation, whole picture
view of the system, service activities, and other specifics. According to Alter [
32
], there
are three frameworks for service systems, namely the Work System Framework, the Ser-
vice Value Chain Framework, and the Service Lifecycle Framework. The Work System
Framework provides a systematic perspective for understanding and analyzing any system
that performs work within or between organizations. The Service Value Chain Framework
extends the Work System Framework by introducing functions that are particularly relevant
to services. The Service Lifecycle Framework emphasizes the evolution of service systems,
including the creation, operation, and planned and unplanned changes to services. There-
fore, AI technologies or AI agents are set to become vital focal points in the construction of
new service systems, interactive experiences, and value co-creation [32,33].
3. Materials and Methods
This research proposes a propositional AI-driven waste classification service design
and management framework (hereafter referred to as AI-MSWSS). The research strategy
combines literature review, case study, and Practice-oriented Design Research (PDR) [
34
].
The focus of the research process is on summarizing reflections or conducting empirical
analysis from Municipal Solid Waste (MSW) service design practice, aiming to uncover
innovative findings and new theoretical frameworks.
Literature Review: A propositional literature review was conducted to identify existing
theories and practices related to AI-MSWSS. This process helped establish a theoretical
foundation in service design and artificial intelligence and identified the undefined role
of AI in service design and management methodologies, as well as the opportunities and
challenges AI technology poses for service design [
14
,
24
]. These gaps in knowledge are
addressed in this research. We utilized academic databases for sourcing relevant literature
and critically analyzed the collected articles, books, and reports to understand the current
state of the field.
Case Study: We conducted in-depth research on selected cases, specifically selecting a
case of a waste classification helper, and a case garbage sorting robot [
8
], to gain a deeper
understanding of the practical applications of AI-MSWSS and to facilitate the comparison
and evaluation of relevant design and management experiences. Solutions were iteratively
refined based on the challenges encountered in practice. The goal was to derive practical
insights and lessons that could be generalized to other similar contexts.
Practice-oriented Design Research (PDR): In this research strategy, the focus is on gen-
erating new knowledge through service design practice. In our study, we implemented the
PDR process via four stages: problem framing, understanding and defining, designing the
service concept and architecture, and evaluation based on results [
35
]. Knowledge artifacts
and practical experiences gathered during the PDR process were critically reflected upon and
analyzed. The aim was to generate innovative knowledge and theoretical insights that could
contribute to the establishment of AI-MSWSS and contribute to the broader field of practice.
The research process placed significant emphasis on empirical analysis and reflection
from practice. The overall goal was to extract innovative findings and develop a new
theoretical framework that can enhance the practical understanding of AI-MSWSS and
enrich the knowledge construction in the interdisciplinary field of service design and
artificial intelligence.
Sustainability 2023,15, 16454 5 of 16
4. Case Study: Service Design and Management of MSW Classification Based on
AI Technology
4.1. Case Study 1: BinBin Helper
The BinBin Helper applet, primarily developed to aid in waste classification and
management, serves a crucial role in the urban management of Hangzhou’s Binjiang
district in China. This project, commissioned by the Urban Management Bureau, is an
embodiment of the effective use of artificial intelligence (AI) and Internet of Things (IoT)
technologies in managing municipal solid waste (MSW).
As shown in Figure 1, the applet’s user interface (UI) is designed with simplicity in
mind, utilizing a unique shade of blue as its main color and featuring intuitive visual icons
of sorted trash cans. These design elements aim to assist citizens and community workers
in navigating and using the applet effectively. One of the applet’s noteworthy features is its
use of AI technology. With capabilities such as image recognition, speech recognition, and
text search, the applet assists users in accurately classifying waste with an average accuracy
rate of 92%, which is 9.5% higher than the competing control group. This not only helps
users improve their waste classification practices but also aids the Urban Management Bureau
in the data sampling of garbage bins and waste transfer stations, thereby enhancing the
efficiency of urban environmental sanitation work. Another major component of the project is
the creation of an IoT monitoring platform. Based on Geographic Information System (GIS)
maps, the platform oversees garbage disposal and urban administration patrol management.
During the COVID-19 pandemic, the platform was used to develop a map and supervision
system for discarded mask drop-off points in the Binjiang district. This initiative helped
alleviate environmental pollution and health concerns among the residents, demonstrating
the responsiveness and adaptability of the system to unexpected challenges.
Sustainability 2023, 15, x FOR PEER REVIEW 6 of 17
Figure 1. AI-related features and UI design of BinBin Helper applet.
4.2. Case Study 2: ZRR2 Robot Applied in Garbage Sorting
[8]
As shown in Figure 2, in Barcelonas Ecoparc4 waste treatment facility, a ZRR2 robot
from ZenRobotics was utilized to explore its potential in solid waste sorting, aimed at
testing and evaluating the automation of municipal waste sorting plants by supplement-
ing or replacing manual sorting. The objectives were to increase the current recycling rates
and the purity of the recovered materials, to collect additional materials from the current
rejected flows, and to improve the working conditions of the workers, who could then
concentrate on, among other things, the maintenance of the robots.
The ZRR2, equipped with dual mechanical arms, advanced sensors, and deep-learn-
ing software, could identify and sort a variety of waste materials, including metal, plastic,
and cardboard. Researchers trained and tested the robot using 13 different types of resi-
dential and industrial waste. Post-training, the robot demonstrated the ability to accu-
rately classify different waste materials, achieving over 90% purity in most categories. In
the next phase, the throughput was gradually increased until the desired throughput for
production was achieved. In some cases, a monolayer of the material could no longer be
guaranteed, objects began to overlap, touch, and cover each other. Under such conditions,
the ZRR2 robot showed increasing difficulties in detecting and handling objects. Although
the recovery rate test results for ZRR2 were not satisfactory, averaging only 67%. This case
study provides initial evidence that robots and artificial intelligence can potentially en-
hance traditional urban waste sorting processes, improving precision and efficiency.
However, it also identified some challenges in optimizing the entire system for robotic use
[8].
Figure 1. AI-related features and UI design of BinBin Helper applet.
4.2. Case Study 2: ZRR2 Robot Applied in Garbage Sorting [8]
As shown in Figure 2, in Barcelona’s Ecoparc4 waste treatment facility, a ZRR2 robot
from ZenRobotics was utilized to explore its potential in solid waste sorting, aimed at
testing and evaluating the automation of municipal waste sorting plants by supplementing
or replacing manual sorting. The objectives were to increase the current recycling rates
and the purity of the recovered materials, to collect additional materials from the current
rejected flows, and to improve the working conditions of the workers, who could then
concentrate on, among other things, the maintenance of the robots.
Sustainability 2023,15, 16454 6 of 16
Sustainability 2023, 15, x FOR PEER REVIEW 7 of 17
Figure 2. ZRR2 robot applied in garbage sorting [35].
4.3. Comparative Study: The BinBin Helper vs. the ZRR2 Robot
This case study primarily focuses on the innovative design and management of waste
classification services regardless of the project type. It represents an evolution and inte-
gration of previous service design methodologies, realized via three distinct stages: prob-
lem framing, understanding the experiences and goals of multiple actors, and ideation for
the design of service concepts and architecture [36].
The first stage, problem framing, involves mapping the value network and identify-
ing all actors in the waste classification service, and their inter-relationships. The second
stage delves into the goals and experiences of various actors. For instance, during the ini-
tial promotion of waste classification in Shanghai, residents were required to deposit
waste at fixed times and locations. This approach required significant community human
and material resources for classification supervision [11]. Recognizing these issues and the
fact that Hangzhou did not have the same level of resources to invest, Hangzhou im-
proved its regional implementation methods. This resulted in a more resource-efficient
solution such as a digital assistant tool. This multi-actor perspective provides a compre-
hensive view of the service ecosystem and identifies areas of the service encounter that
require improvement.
The third stage involves ideation to create a service blueprint for waste classification.
This blueprint includes the determination of the service interface and process, ensuring a
balance of different actors goals, and supporting real-time network interactions. These
architectures are not rigid representations with formal language; instead, they provide a
flexible visual experience for a collaborative and iterative service design process. Using
ZRR2 as an example, the design of the service blueprint must consider the differences
between actual working environments and optimal experimental environments [35].
Therefore, understanding how AI technology integrates with software and hardware fa-
cilities is crucial, which includes corresponding improvements to process design. In real
working environments, there may be various unforeseen challenges and constraints that
require adjustments and optimizations in the service design so that AI technology can
integrate more effectively with facilities to achieve optimal results.
In comparing the BinBin Helper and the ZRR2 robot, it is clear that both solutions
utilize AI technology to tackle waste management, albeit in different ways and contexts.
Their respective service designs and management strategies reflect the unique demands
of their environments.
BinBin Helper integrates AI technologies into a user-friendly applet. The design fo-
cuses on user experience, with an intuitive UI that employs recognizable icons and an
appealing color scheme. This design choice encourages user interaction and aids in the
proper classification of waste. The ZRR2 robot, designed for an industrial seing, priori-
tizes operational efficiency and accuracy. It incorporates AI and advanced sensors to au-
tonomously sort various types of waste materials [35]. The management strategy of BinBin
Figure 2. ZRR2 robot applied in garbage sorting [35].
The ZRR2, equipped with dual mechanical arms, advanced sensors, and deep-learning
software, could identify and sort a variety of waste materials, including metal, plastic, and
cardboard. Researchers trained and tested the robot using 13 different types of residential
and industrial waste. Post-training, the robot demonstrated the ability to accurately classify
different waste materials, achieving over 90% purity in most categories. In the next phase,
the throughput was gradually increased until the desired throughput for production was
achieved. In some cases, a monolayer of the material could no longer be guaranteed, objects
began to overlap, touch, and cover each other. Under such conditions, the ZRR2 robot
showed increasing difficulties in detecting and handling objects. Although the recovery
rate test results for ZRR2 were not satisfactory, averaging only 67%. This case study
provides initial evidence that robots and artificial intelligence can potentially enhance
traditional urban waste sorting processes, improving precision and efficiency. However, it
also identified some challenges in optimizing the entire system for robotic use [8].
4.3. Comparative Study: The BinBin Helper vs. the ZRR2 Robot
This case study primarily focuses on the innovative design and management of
waste classification services regardless of the project type. It represents an evolution and
integration of previous service design methodologies, realized via three distinct stages:
problem framing, understanding the experiences and goals of multiple actors, and ideation
for the design of service concepts and architecture [36].
The first stage, problem framing, involves mapping the value network and identifying all
actors in the waste classification service, and their inter-relationships. The second stage delves
into the goals and experiences of various actors. For instance, during the initial promotion of
waste classification in Shanghai, residents were required to deposit waste at fixed times and
locations. This approach required significant community human and material resources for
classification supervision [
11
]. Recognizing these issues and the fact that Hangzhou did not
have the same level of resources to invest, Hangzhou improved its regional implementation
methods. This resulted in a more resource-efficient solution such as a digital assistant tool.
This multi-actor perspective provides a comprehensive view of the service ecosystem and
identifies areas of the service encounter that require improvement.
The third stage involves ideation to create a service blueprint for waste classification.
This blueprint includes the determination of the service interface and process, ensuring
a balance of different actors’ goals, and supporting real-time network interactions. These
architectures are not rigid representations with formal language; instead, they provide a
flexible visual experience for a collaborative and iterative service design process. Using
ZRR2 as an example, the design of the service blueprint must consider the differences
between actual working environments and optimal experimental environments [
35
]. There-
Sustainability 2023,15, 16454 7 of 16
fore, understanding how AI technology integrates with software and hardware facilities is
crucial, which includes corresponding improvements to process design. In real working
environments, there may be various unforeseen challenges and constraints that require
adjustments and optimizations in the service design so that AI technology can integrate
more effectively with facilities to achieve optimal results.
In comparing the BinBin Helper and the ZRR2 robot, it is clear that both solutions
utilize AI technology to tackle waste management, albeit in different ways and contexts.
Their respective service designs and management strategies reflect the unique demands of
their environments.
BinBin Helper integrates AI technologies into a user-friendly applet. The design
focuses on user experience, with an intuitive UI that employs recognizable icons and
an appealing color scheme. This design choice encourages user interaction and aids in
the proper classification of waste. The ZRR2 robot, designed for an industrial setting,
prioritizes operational efficiency and accuracy. It incorporates AI and advanced sensors
to autonomously sort various types of waste materials [
35
]. The management strategy of
BinBin Helper involves aiding the Urban Management Bureau in waste management. The
applet not only enables users to classify waste accurately but also provides valuable data on
waste generation patterns, facilitating more efficient management and planning. The ZRR2
robot’s management strategy focuses on automation and enhancing operational efficiency
within waste treatment facilities. The robot also aims to improve working conditions by
taking over tasks usually performed by humans [8].
In conclusion, comparing these case studies provides valuable insights into the diverse
applications of AI technology in waste management. It highlights the importance of
tailoring the service design and management strategy to the specific context and user needs.
Despite the different approaches, both case studies (Table 2) demonstrate the immense
potential of AI in enhancing waste management efficiency and effectiveness, as shown
through the value networks of different service systems.
Table 2. Summary of the comparative case study on the BinBin Helper and the ZRR2 robot.
Project Case
Study
Types of
Artificial
Intelligence
Types of Waste
(Top 5–10)
Duration or
Frequency of
Use/Trial
Classification
Quality
Efficiency
Optimization References
1. Garbage sorting
helper in China
Hangzhou
Baidu
EasyDL
platform [37]
Mainly four-category
waste sorting, including:
Plastic bags, Milk
cartons, Sunflower seed
shells, Eggshells, Plastic
products
Total of users: 37,800+
Active Users: 6800+
Numbers of Queries:
1,000,279
Accuracy:
92%, compared
with control
group + 9.5%
\Yuan, J et al.
(2020) [38]
2. The Ecorparc4
municipal waste
sorting plant in
Barcelona [8]
ZenRobotics
ZRR2 [6]
Solid waste: PET bottles,
plastic films (LDPE),
aluminum, ferrous
metals, PE boxes, large
PE bottles,
paper/cardboard, PP,
(untreated) wood,
textiles, Tetra Pak and
vegetable substances
A trial period of
15–30 min, feeding
rate (about
1000 picks/h).
Average purity:
97%
Average
recovery:
67%
Wilts et al. (2021)
[8]
4.4. Findings and Further Study
The integration of AI in service design brings both opportunities and challenges, the
distinctiveness of AI-driven service design as a commodity and design object will bring
new design concepts and methods that revolve around service design. Co-creation in
service contact points now includes both common issues and complex uncertainty issues.
The traditional service co-creation process is an innovation process similar to participatory
and collaborative forms [
39
]. With the participation of AI, human–computer co-creation
has become a “recommend-select-feedback” cycle process, transitioning human–computer
interactions from “one-way dependence” to “two-way training” [
40
]. By introducing this
Sustainability 2023,15, 16454 8 of 16
kind of classification correction function into a waste classification inquiry and result pages,
both the accuracy of waste classification and user interaction can be improved.
Based on these findings, although the service system is not a new area of theoretical
research or social practice, we believe a new theoretical framework is needed to better
guide the design and management of service systems in the context of the AI era. The
following section will detail and explain this new theoretical framework, including its
theoretical basis, main components, and application scenarios. It will also discuss how it
helps us understand and address the problems encountered in this case study and provide
useful insights for more complete and complex service systems in the future.
5. Results: A Proportional Framework for AI-Driven Garbage Classification Service
Design and Management (AI-MSWSS)
Building upon the service design framework and stakeholder value network theory, the
aforementioned case study presents research findings on the key elements and stakeholders of
AI-MSWSS. Compared to traditional urban service systems, this new framework offers several
advantages, as it is designed to address the evolving complexities of waste classification and
management in urban environments. Beyond its immediate application in waste management,
this framework’s significance extends to addressing broader challenges in complex service
systems, challenges that have become increasingly prominent in the AI era [34].
5.1. A Proportional AI-Driven Service Design Framework
The service design basis for an AI-driven Municipal Solid Waste (MSW) service system
should be established using service design methodologies and stakeholder value network
concepts. Following the design thinking process as described by Brown [
41
] and Stickdorn
and Schneider (2010) [
28
], the construction of the entire service design framework can be
divided into four stages: problem framing, understanding, ideation, and implementation.
However, to effectively leverage AI technology and address the challenges of designing
services for value networks, it is necessary to explore the dual or multiple roles that
AI technology can play within the service system. This understanding should then be
translated into value network service concepts and service architectures.
Building upon the aforementioned case, the service design basis should not only
consider the tasks, processes, personnel allocation, interactive behaviors, and value ex-
changes at each stage of the waste management lifecycle but also how AI technologies
can continuously refine and optimize these elements. AI brings challenges to almost all
stages of a typical design process (Figure 3). As shown in the triangular colot block area
in Figure 3, the proposed AI design methods and tools have mostly focused on the two
ends of this creative process, either helping designers to understand what AI is and can do
generally or enhancing the evaluation of the final design [42].
Sustainability 2023, 15, x FOR PEER REVIEW 9 of 17
theoretical basis, main components, and application scenarios. It will also discuss how it
helps us understand and address the problems encountered in this case study and provide
useful insights for more complete and complex service systems in the future.
5. Results: A Proportional Framework for AI-Driven Garbage Classification Service
Design and Management (
AI-MSWSS
)
Building upon the service design framework and stakeholder value network theory,
the aforementioned case study presents research findings on the key elements and stake-
holders of AI-MSWSS. Compared to traditional urban service systems, this new frame-
work offers several advantages, as it is designed to address the evolving complexities of
waste classification and management in urban environments. Beyond its immediate ap-
plication in waste management, this frameworks significance extends to addressing
broader challenges in complex service systems, challenges that have become increasingly
prominent in the AI era [34].
5.1. A Proportional AI-Driven Service Design Framework
The service design basis for an AI-driven Municipal Solid Waste (MSW) service sys-
tem should be established using service design methodologies and stakeholder value net-
work concepts. Following the design thinking process as described by Brown [41] and
Stickdorn and Schneider (2010) [28], the construction of the entire service design frame-
work can be divided into four stages: problem framing, understanding, ideation, and im-
plementation. However, to effectively leverage AI technology and address the challenges
of designing services for value networks, it is necessary to explore the dual or multiple
roles that AI technology can play within the service system. This understanding should
then be translated into value network service concepts and service architectures.
Building upon the aforementioned case, the service design basis should not only con-
sider the tasks, processes, personnel allocation, interactive behaviors, and value exchanges
at each stage of the waste management lifecycle but also how AI technologies can contin-
uously refine and optimize these elements. AI brings challenges to almost all stages of a
typical design process (Figure 3). As shown in the triangular colot block area in Figure 3,
the proposed AI design methods and tools have mostly focused on the two ends of this
creative process, either helping designers to understand what AI is and can do generally
or enhancing the evaluation of the final design [42].
Figure 3. Mapping AI technology challenges onto a design process (based on Double Diamond
[43]).
The four stages that can be employed in AI-driven service design include the follow-
ing:
Stage 1. Ploing the Value Network
Figure 3.
Mapping AI technology challenges onto a design process (based on Double Diamond [
43
]).
Sustainability 2023,15, 16454 9 of 16
The four stages that can be employed in AI-driven service design include the following:
Stage 1. Plotting the Value Network
As shown in Figure 4, the first phase focuses on plotting the value network of the
municipal solid waste (MSW) service system. This involves identifying all key stakehold-
ers and their respective roles in the system. These can include waste generators such as
individual households and businesses, waste collectors, waste treatment facilities, and
regulatory bodies supervising waste management [
44
]. Tasks in this phase include stake-
holder identification, role definition, and relationship mapping. Interactions in this phase
primarily involve decomposing the elements of the service system, understanding the
role positioning and network relationships of different participants, and establishing a
comprehensive understanding of the service ecosystem. AI can play a crucial role in this
phase, helping analyze the complex stakeholder network and the AI agent itself can also
become part of the network structure. For example, machine learning algorithms can iden-
tify waste generation patterns among different stakeholder groups, helping to highlight
key contributors or potential inefficiencies within the system.
Sustainability 2023, 15, x FOR PEER REVIEW 10 of 17
As shown in Figure 4, the first phase focuses on ploing the value network of the
municipal solid waste (MSW) service system. This involves identifying all key stakehold-
ers and their respective roles in the system. These can include waste generators such as
individual households and businesses, waste collectors, waste treatment facilities, and
regulatory bodies supervising waste management [44]. Tasks in this phase include stake-
holder identification, role definition, and relationship mapping. Interactions in this phase
primarily involve decomposing the elements of the service system, understanding the role
positioning and network relationships of different participants, and establishing a com-
prehensive understanding of the service ecosystem. AI can play a crucial role in this phase,
helping analyze the complex stakeholder network and the AI agent itself can also become
part of the network structure. For example, machine learning algorithms can identify
waste generation paerns among different stakeholder groups, helping to highlight key
contributors or potential inefficiencies within the system.
Figure 4. Ploing value network of the AI-MSWSS [44].
Stage 2. Understanding the Experiences and Interactions of Multiple Participants
As shown in Figure 4, the second phase involves empathizing with various stake-
holders to understand their experiences, needs, and pain points in the waste management
process. The means include qualitative research methods such as user interviews, surveys,
and observations (Table 3). Process tasks may include developing user personas, journey
mapping, and empathy mapping. Interactions mainly involve contact with stakeholders,
activities, interactions of multiple users, goals, and conflicts of multiple users, and the
value goal is to discover unmet needs and opportunities to improve the service [36]. AI
can provide assistance in this phase by analyzing large amounts of user feedback to iden-
tify common themes or sentiments. For example, user-generated content can be used for
social network analysis, survey feedback, media commentaries, and internet public senti-
ment about waste management.
Figure 4. Plotting value network of the AI-MSWSS [44].
Stage 2. Understanding the Experiences and Interactions of Multiple Participants
As shown in Figure 4, the second phase involves empathizing with various stake-
holders to understand their experiences, needs, and pain points in the waste management
process. The means include qualitative research methods such as user interviews, surveys,
and observations (Table 3). Process tasks may include developing user personas, journey
mapping, and empathy mapping. Interactions mainly involve contact with stakeholders,
activities, interactions of multiple users, goals, and conflicts of multiple users, and the
value goal is to discover unmet needs and opportunities to improve the service [
36
]. AI can
provide assistance in this phase by analyzing large amounts of user feedback to identify
common themes or sentiments. For example, user-generated content can be used for social
network analysis, survey feedback, media commentaries, and internet public sentiment
about waste management.
Sustainability 2023,15, 16454 10 of 16
Table 3. Mapping AI technology challenges onto the AI-MSWSS [36].
Actor’s Goal Subgoal Quotes
1. quality of garbage
classification
accuracy What the hell are dry batteries? Is it hazardous waste? (inspector)
complete Do we need to break the bag of perishable garbage? Under what
circumstances do not need to break the bag? (citizen)
understandability Why are the classification marks on the trash cans inconsistent? (city
management)
2. efficiency of garbage
reduction
source classification There are too many types of garbage, how to quickly memorize and identify
them? (company representative)
recycling efficiency There are so many types of garbage, how do you know which ones are
recyclable? (community management)
transfer and treatment
efficiency
We need to optimize the transportation routes for garbage collection to reduce
the time and cost of delivery to the landfill (city management)
labor saving Disposal supervisors are not doing a good job (inspector)
3. relationship among
actors
citizen centered Garbage classification is beneficial to the people rather than disturbing the
people (community management)
majority support Good garbage classification reflects the level of the community (community)
mostly value
recognition
If the garbage classification in the community is done well, the value of the
real estate will increase a lot (citizen)
4. information sharing
regulatory compliance We need to make sure that all stakeholders are aware of the regulations and
guidelines for garbage reduction (community property)
uniform standards The standard of classification in Hangzhou is different from that in Shanghai,
and the names are also different (city management)
easy to use We must first teach the elderly and children to sort garbage, and others will
naturally (community management)
Stage 3. Designing the Value Network Service Architecture
The third phase involves designing service prototypes and architecture based on
insights gathered from the previous two phases (Figure 5). This involves defining the
customer journey, touchpoint interfaces, and the service architecture that will deliver
value to the stakeholders. The research methods used are primarily participatory design,
service blueprinting, and prototyping. Tasks in this phase may include service blueprinting,
process modeling, and technology selection. Interactions here mainly involve collaborative
design work, ensuring the balance of customer value constellations and activities and
goals of multiple user roles. The goal of constructing the value network is to integrate
service concepts and architectures for different participants, thereby creating an effective
and efficient service process [
36
,
44
]. AI can play a significant role in this phase, helping
optimize the service process and integrate the technical architecture. For example, AI can
offer a model service to assist in computing how front-end waste classification can adjust
to fit the city’s MSW disposal scheme, ultimately achieving an optimal allocation scheme
with maximum economic benefits.
Stage4. Evaluation Based on Results
The final phase involves testing and evaluating the service design. This includes
prototype testing and field testing and involves measuring the performance of the service
system according to defined metrics, as well as a qualitative assessment of customer
satisfaction and other subjective indicators in the value network. The research methods
used can include the Analytic Hierarchy Process (AHP) and satisfaction surveys [
45
]. Tasks
in this phase may include prototype testing, field pilot testing, performance testing, and
satisfaction surveys. Interactions mainly involve testing and collecting feedback, and the
value goal is to validate the service design and identify areas for improvement. AI can
provide advanced analytical capabilities in this phase. For instance, machine learning
algorithms can analyze operational data and identify patterns or trends that may not be
Sustainability 2023,15, 16454 11 of 16
apparent to human analysts. This can help gain a deeper understanding of the service
system’s performance and provide recommendations for further optimization.
Sustainability 2023, 15, x FOR PEER REVIEW 12 of 17
Figure 5. Designing service prototypes and architecture of the AI-MSWSS [36].
Stage4. Evaluation Based on Results
The final phase involves testing and evaluating the service design. This includes pro-
totype testing and field testing and involves measuring the performance of the service
system according to defined metrics, as well as a qualitative assessment of customer sat-
isfaction and other subjective indicators in the value network. The research methods used
can include the Analytic Hierarchy Process (AHP) and satisfaction surveys [45]. Tasks in
this phase may include prototype testing, field pilot testing, performance testing, and sat-
isfaction surveys. Interactions mainly involve testing and collecting feedback, and the
value goal is to validate the service design and identify areas for improvement. AI can
provide advanced analytical capabilities in this phase. For instance, machine learning al-
gorithms can analyze operational data and identify paerns or trends that may not be
apparent to human analysts. This can help gain a deeper understanding of the service
systems performance and provide recommendations for further optimization.
5.2. A Proportional AI-Driven Service Management Framework
Consistent with all service-dominant logic premises, the AI-driven service system
guides the description of the situation at three levels: value constellation, whole picture
view of the system, service activities, and other specifics. According to Alters view [32],
there are three frameworks for service systems, namely the Work System Framework, the
Service Value Chain Framework, and the Service Lifecycle Framework. The Work System
Figure 5. Designing service prototypes and architecture of the AI-MSWSS [36].
5.2. A Proportional AI-Driven Service Management Framework
Consistent with all service-dominant logic premises, the AI-driven service system
guides the description of the situation at three levels: value constellation, whole picture
view of the system, service activities, and other specifics. According to Alter’s view [
32
],
there are three frameworks for service systems, namely the Work System Framework,
the Service Value Chain Framework, and the Service Lifecycle Framework. The Work
System Framework provides a systematic perspective for understanding and analyzing
any system that performs work within or between organizations. The Service Value
Chain Framework extends the Work System Framework by introducing functions that are
particularly relevant to services. The Service Lifecycle Framework emphasizes the evolution
of service systems, including the creation, operation, and planned and unplanned changes
of services. Therefore, AI technologies or AI agents are set to become vital focal points in
the construction of new service systems, interactive experiences, and value co-creation.
In integrating AI as a specific element in the Work System Framework, a marked
enhancement in the system’s ability to perform tasks efficiently is observed (Figure 6). This
elevation, attributed to AI technologies, presents in the form of intelligent automation, data
analytics, and predictive capabilities that significantly augment traditional infrastructure.
Sustainability 2023,15, 16454 12 of 16
This shift redefines the roles and responsibilities within the service system, where both
service providers and receivers find their functions facilitated. The former, in leveraging
AI’s data analysis and decision-making capabilities, can deliver more personalized and
efficient services. The latter, on the other hand, benefits from improved service experiences,
facilitated by AI’s ability to anticipate needs and provide timely solutions.
Sustainability 2023, 15, x FOR PEER REVIEW 13 of 17
Framework provides a systematic perspective for understanding and analyzing any sys-
tem that performs work within or between organizations. The Service Value Chain Frame-
work extends the Work System Framework by introducing functions that are particularly
relevant to services. The Service Lifecycle Framework emphasizes the evolution of service
systems, including the creation, operation, and planned and unplanned changes of ser-
vices. Therefore, AI technologies or AI agents are set to become vital focal points in the
construction of new service systems, interactive experiences, and value co-creation.
In integrating AI as a specific element in the Work System Framework, a marked
enhancement in the systems ability to perform tasks efficiently is observed (Figure 6).
This elevation, aributed to AI technologies, presents in the form of intelligent automa-
tion, data analytics, and predictive capabilities that significantly augment traditional in-
frastructure. This shift redefines the roles and responsibilities within the service system,
where both service providers and receivers find their functions facilitated. The former, in
leveraging AIs data analysis and decision-making capabilities, can deliver more person-
alized and efficient services. The laer, on the other hand, benefits from improved service
experiences, facilitated by AIs ability to anticipate needs and provide timely solutions.
Figure 6. A proportional AI-driven service management system framework (AI-MSWSS) [32].
The lifecycle of the service activities within this system, under the influential hand of
AI, undergoes continuous evolution and iteration. AI drives this process by promoting
efficiency, scalability, and adaptability, allowing the system to respond effectively to fluc-
tuating service needs and environments. The transformative effect of AIs inclusion in the
new service management framework is profound, enhancing operational efficiency while
empowering the creation of innovative service offerings. Its ability to co-create value by
amplifying the effectiveness of service delivery and experience underscores AIs pivotal
role. Conclusively, AI is a vital catalyst in the new service management framework, driv-
ing the evolution and iteration of service activities, and enabling role players to excel in
their responsibilities. As the importance of AI continues to gain recognition, its role in the
future of service management frameworks is set to increase, validating its significance.
6. Discussion
Artificial intelligence (AI) technologies are observed to play a crucial role in the new
service system of municipal solid waste (MSW) classification. They serve multiple roles,
necessitating careful consideration of their positioning. In the context of urban solid waste
Figure 6. A proportional AI-driven service management system framework (AI-MSWSS) [32].
The lifecycle of the service activities within this system, under the influential hand of
AI, undergoes continuous evolution and iteration. AI drives this process by promoting
efficiency, scalability, and adaptability, allowing the system to respond effectively to fluctu-
ating service needs and environments. The transformative effect of AI’s inclusion in the
new service management framework is profound, enhancing operational efficiency while
empowering the creation of innovative service offerings. Its ability to co-create value by
amplifying the effectiveness of service delivery and experience underscores AI’s pivotal
role. Conclusively, AI is a vital catalyst in the new service management framework, driving
the evolution and iteration of service activities, and enabling role players to excel in their
responsibilities. As the importance of AI continues to gain recognition, its role in the future
of service management frameworks is set to increase, validating its significance.
6. Discussion
Artificial intelligence (AI) technologies are observed to play a crucial role in the new
service system of municipal solid waste (MSW) classification. They serve multiple roles,
necessitating careful consideration of their positioning. In the context of urban solid waste
management, AI is viewed not merely as a technical tool, it is seen as an active participant,
automating and optimizing complex tasks. For example, AI-driven robots using advanced
sensors and cameras can effectively identify, classify, and separate waste, enhancing system
efficiency and reducing human exposure to hazardous waste.
However, it must be acknowledged that AI’s role in the entire new service system is
currently apparently local and not global. Some sorting tasks still have to be carried out
manually (manual picking), e.g., the quality control of recovered material and the manual
sorting of oversize and waste streams [
8
]. This raises a demand for setting how AI adapts
to existing workflows and environments. While the advent of AI 2.0 technologies offers
the possibility of AI replacing some human participants, particularly those that follow
logical structures and require minimal social interaction, this transition is recognized as
being not immediate and may face technical and implementation challenges. AI technology
Sustainability 2023,15, 16454 13 of 16
is also seen as assisting human participants in achieving their goals within the system.
Ideally, it serves as a co-pilot, sharing the objective of improving process efficiency and
user experience with humans. In certain circumstances, AI may turn the task of image
recognition into a bidirectional training process that continuously enhances learning [
42
].
For instance, in the waste classification recognition process of BinBin Helpler, human
intervention is still required when situations arise where multiple objects overlap and cause
confusion in image recognition. This is necessary to determine the correct target for sorting
and subsequent disposal actions.
In the commercial environment, services dominated by efficiency and utilitarianism
can be replaced with AI, as demonstrated in various automatic telephone services launched
by banks, airlines, and other operators [
46
]. It is expected that classification and sorting
services will also be gradually replaced with AI in the waste classification field. On the
other hand, the distinctiveness of AI-driven service design as a commodity and design
object will bring new design concepts and methods that revolve around service design.
With the introduction of service design, the concept of ‘co-creation’ has evolved.
Co-creation in service contact points now includes dealing with both common issues
and complex uncertainty issues [
39
]. The ultimate concern of service design is people’s
experience perception in value co-creation. Users complete value co-creation in the process
of service delivery and contact. The service’s value is embodied in the “enable” of the
platform, which provides various possibilities for participants. This concept of “enable
design” can be evaluated using methods like AHP and CSI satisfaction, calculating the
evaluation level of the sense of value created from service design [39,45].
In the lifecycle management of the new service system, AI also plays a critical role. If
AI widely replaces some participants, it can simulate human participants’ goal perception
and the system’s incentive for proactive behavior, enabling AI to exhibit capabilities of
reinforced learning and adaptability. This plays a crucial role in managing the lifecycle of
the service system. In large institutions, the role of AI can be further optimized to ensure
that AI agents continue iterating to meet an ever-changing user experience. As the system
evolves, AI can be trained and retrained to ensure system adaptability and durability.
For instance, when new types of waste are introduced, the AI model can be updated to
recognize and sort these new waste types.
However, it must also be noted that the application of AI in lifecycle management
needs to consider certain constraints, such as hardware durability, software scalability, and
the cost of AI updates and maintenance [
32
,
42
]. For example, predictive maintenance can
foresee potential breakdowns and schedule maintenance to avoid service interruptions, which
requires ample data support and highly accurate forecasting models. Additionally, AI needs
ongoing supervision and adjustment in adapting to changes in waste management regulations
or guidelines to ensure that services are always in compliance with legal requirements.
In discussing this topic, the possibility that AI challenges social ethics and economic
activity must also be considered. The increased use of AI agents may lead to a loss of jobs
requiring lower qualifications, which could obviously pose social and ethical challenges and
increase uncertainties in economic activity. Future research is needed to delve deeper into
this issue to ensure that the application of AI not only improves efficiency but also considers
social and economic impact. This could involve developing new policy frameworks to
protect workers who may be affected or providing new job opportunities through education
and training.
7. Conclusions
This framework is intended to offer profound insights into advancing AI technology in
waste classification management and pave a new path toward the goal of sustainable cities.
By automating tasks, optimizing processes, and analyzing large volumes of data, AI holds
the potential to significantly enhance the efficiency of waste classification and management,
thereby reducing the environmental impact of waste. Nevertheless, while utilizing AI
technology to achieve these goals, we also need to address a range of challenges including
Sustainability 2023,15, 16454 14 of 16
setting appropriate boundaries for AI, ensuring transparency and ethics, managing shifting
human–AI relationships, and designing people-centric services. Particularly in the process
of human–AI service co-creation, we need to find a way to align human and AI capabilities
for mutually beneficial collaborations.
To further advance the implementation and refinement of this framework, more
research is needed, including deepening community engagement, exploring solutions to
uncertainties in service co-creation, and investigating how to better integrate this framework
with the broader goals of sustainable urban development. We hope this framework can not
only drive improvements in waste management services but also provide new ideas and
tools for building more sustainable and environmentally friendly cities.
Author Contributions:
Conceptualization, J.Z. and H.Y.; methodology, J.Z.; software, H.Y.; validation,
X.X. and J.Z.; formal analysis, J.Z.; investigation, X.X.; resources, X.X.; data curation, X.X.; writing—
original draft preparation, J.Z.; writing—review and editing, J.Z.; visualization, X.X.; supervision, J.Z.;
project administration, J.Z. All authors have read and agreed to the published version of the manuscript.
Funding:
This research received no external funding, and the Article Processing Charge (APC) was
borne by the author.
Institutional Review Board Statement:
Ethical review and approval were waived for this study, as
it did not involve human or animal subjects.
Data Availability Statement:
No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Acknowledgments:
The authors would like to express their sincere gratitude to the Hangzhou Binjiang
Urban Management Department for their support and collaborative efforts in promoting the widespread
use of the Binbin Helpler Applet, which have been crucial preconditions for this research project.
Conflicts of Interest:
Author Hai Yang was employed by the company Hangzhou Zhongwei Ganlian
Information Technology Co., Ltd. The remaining authors declare that the research was conducted
in the absence of any commercial or financial relationships that could be construed as a potential
conflict of interest. The research team did not receive any financial benefits from the consulting
projects referred to in this study that could influence the results. There are no personal or profes-
sional relationships with the clients or other parties involved in the consulting projects that could
inappropriately affect the outcomes. The collection and analysis of data were conducted impartially
and were not influenced by any external factors related to the consulting projects.
References
1.
Li, X.; Zhang, C.; Li, Y.; Zhi, Q. The Status of Municipal Solid Waste Incineration (MSWI) in China and its Clean Development.
Energy Procedia 2016,104, 498–503. [CrossRef]
2.
Xiao, S.J.; Dong, H.; Yong, G.; Brander, M. An overview of China’s recyclable waste recycling and recommendations for integrated
solutions. Resour. Conserv. Recycl. 2019,134, 112–120. [CrossRef]
3.
Zhou, M.-H.; Shen, S.-L.; Xu, Y.-S.; Zhou, A.-N. New Policy and Implementation of Municipal Solid Waste Classification in
Shanghai, China. Int. J. Environ. Res. Public Health 2019,16, 3099. [CrossRef]
4. Pan, Y. Heading toward Artificial Intelligence 2.0. Engineering 2016,2, 5. [CrossRef]
5.
Liu, L.-Y. Release of the “Domestic Waste Classification Sign” Standard. Available online: https://www.mohurd.gov.cn/xinwen/
gzdt/201911/20191118_242752.html (accessed on 31 August 2023). (In Chinese)
6.
Lee, K.-F. Touch Recognition, Intelligent Sorting: How AI Empowers Waste Classification. Kai-Fu Lee’s Caixin Blog. Caixin.
Available online: https://likaifu.blog.caixin.com/archives/208221 (accessed on 31 August 2023). (In Chinese).
7.
Bernstein, J. Social Assessment and Public Participation in Municipal Solid Waste Management—Toolkit; Worldbank:
Washington, DC, USA, 2004; Available online: https://documents.worldbank.org/en/publication/documents-reports/
documentdetail/741051468340748098/-Social-assessment-and-public-participation- in-municipal-solid-waste-management-
toolkit (accessed on 31 August 2023).
8.
Wilts, H.; Garcia, B.R.; Garlito, R.G.; Gómez, L.S.; Prieto, E.G. Artificial Intelligence in the Sorting of Municipal Waste as an
Enabler of the Circular Economy. Resources 2021,10, 28. [CrossRef]
9.
Lo Storto, C. Eco-Productivity Analysis of the Municipal Solid Waste Service in the Apulia Region from 2010 to 2017. Sustainability
2021,13, 12008. [CrossRef]
10.
Bang, S.; Andersen, B. Utilising Artificial Intelligence in Construction Site Waste Reduction. EPPM—J.
2022
,12, 239–249.
[CrossRef]
Sustainability 2023,15, 16454 15 of 16
11.
Wang, Y.; Shi, Y.; Zhou, J.; Zhao, J.; Maraseni, T.; Qian, G. Implementation Effect of Municipal Solid Waste Mandatory Sorting
Policy in Shanghai. J. Environ. Manag. 2021,298, 113512. [CrossRef]
12.
Haque, M.d.S.; Uddin, S.; Sayem, S.M.d.; Mohib, K.M. Coronavirus Disease 2019 (COVID-19) Induced Waste Scenario: A Short
Overview. J. Environ. Chem. Eng. 2021,9, 104660. [CrossRef]
13.
Fetene, Y.; Addis, T.; Beyene, A.; Kloos, H. Valorisation of Solid Waste as Key Opportunity for Green City Development in the
Growing Urban Areas of the Developing World. J. Environ. Chem. Eng. 2018,6, 7144–7151. [CrossRef]
14.
Fang, B.; Yu, J.; Chen, Z.; Osman, A.I.; Farghali, M.; Ihara, I.; Hamza, E.H.; Rooney, D.W.; Yap, P.-S. Artificial Intelligence for Waste
Management in Smart Cities: A Review. Environ. Chem. Lett. 2023,21, 1959–1989. [CrossRef] [PubMed]
15.
Khan, A.A.; Sajib, A.A.; Shetu, F.; Bari, S.; Zishan, M.d.S.R.; Shikder, K. Smart Waste Management System for Bangladesh. In
Proceedings of the 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka,
Bangladesh, 5–7 January 2021; pp. 659–663. [CrossRef]
16.
Ghahramani, M.; Zhou, M.; Molter, A.; Pilla, F. IoT-Based Route Recommendation for an Intelligent Waste Management System.
IEEE Internet Things J. 2022,9, 11883–11892. [CrossRef]
17.
Wijaya, A.S.; Zainuddin, Z.; Niswar, M. Design a Smart Waste Bin for Smart Waste Management. In Proceedings of the 2017
5th International Conference on Instrumentation, Control, and Automation (ICA), Yogyakarta, Indonesia, 9–11 August 2017;
pp. 62–66. [CrossRef]
18.
Praveen, A.; Radhika, R.; Rammohan, M.U.; Sidharth, D.; Ambat, S.; Anjali, T. IoT Based Smart Bin: A Swachh-Bharat Initiative.
In Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore,
India, 2–4 July 2020; pp. 783–786. [CrossRef]
19.
Pawar, S.S.; Pise, S.; Walke, K.; Mohite, R. Smart Garbage Monitoring System Using AVR Microcontroller. In Proceedings of
the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India,
16–18 August 2018; pp. 1–4. [CrossRef]
20.
Kshirsagar, P.R.; Kumar, N.; Almulihi, A.H.; Alassery, F.; Khan, A.I.; Islam, S.; Rothe, J.P.; Jagannadham, D.B.V.; Dekeba, K.
Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials. Comput. Intell. Neurosci.
2022
,2022, 2073482.
[CrossRef] [PubMed]
21.
Bai, J.; Lian, S.; Liu, Z.; Wang, K.; Liu, D. Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass. IEEE
Trans. Consum. Electron. 2018,64, 382–389. [CrossRef]
22.
Li, G.; Liu, S.; Wang, L.; Zhu, R. Skin-Inspired Quadruple Tactile Sensors Integrated on a Robot Hand Enable Object Recognition.
Sci. Robot. 2020,5, eabc8134. [CrossRef] [PubMed]
23. Huang, M.-H.; Rust, R.T. Artificial Intelligence in Service. J. Serv. Res. 2018,21, 155–172. [CrossRef]
24. Zomerdijk, L.G.; Voss, C.A. Service Design for Experience-Centric Services. J. Serv. Res. 2010,13, 67–82. [CrossRef]
25.
Wetter-Edman, K.; Sangiorgi, D.; Edvardsson, B.; Holmlid, S.; Grönroos, C.; Mattelmäki, T. Design for Value Co-Creation:
Exploring Synergies between Design for Service and Service Logic. Serv. Sci. 2014,6, 106–121. [CrossRef]
26.
Kimbell, L. Beyond Design Thinking: Design-as-Practice and Designs-in-Practice. In Proceedings of the CRESC Conference,
Manchester, UK, 1–4 September 2009.
27. Miettinen, S. An Introduction to Industrial Service Design; Taylor & Francis: Oxfordshire, UK, 2016.
28.
Stickdorn, M. Service Design. In The Routledge Handbook of Tourism Marketing; Routledge: London, UK, 2008; ISBN 978-1-315-
85826-5/978-1-317-93620-6/978-0-415-59703-6.
29.
Bock, D.E.; Wolter, J.S.; Ferrell, O.C. Artificial Intelligence: Disrupting What We Know about Services. JSM
2020
,34, 317–334.
[CrossRef]
30.
Autor, D.H. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. J. Econ. Perspect.
2015
,29,
3–30. [CrossRef]
31. Huang, M.-H.; Rust, R.T. Engaged to a Robot? The Role of AI in Service. J. Serv. Res. 2021,24, 30–41. [CrossRef]
32. Alter, S. Service System Fundamentals: Work System, Value Chain, and Life Cycle. IBM Syst. J. 2008,47, 71–85. [CrossRef]
33.
Alter, S. Metamodel for Service Analysis and Design Based on an Operational View of Service and Service Systems. Serv. Sci.
2012,4, 218–235. [CrossRef]
34.
Zimmerman, J.; Forlizzi, J.; Evenson, S. Research through Design as a Method for Interaction Design Research in HCI. In
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, 3 May–28 April 2007;
ACM: New York, NY, USA, 2007; pp. 493–502. [CrossRef]
35.
ZenRobotics Offers Three Products: Heavy Picker for Bulky Waste, Fast Picker for Lighter Waste, and ZenBrain to Make Them
Both Intelligent. Available online: https://www.terex.com/zenrobotics/robots/overview (accessed on 19 November 2023).
36.
Patrício, L.; De Pinho, N.F.; Teixeira, J.G.; Fisk, R.P. Service Design for Value Networks: Enabling Value Cocreation Interactions in
Healthcare. Serv. Sci. 2018,10, 76–97. [CrossRef]
37.
Baidu Brain. EasyDL Zero-Threshold AI Platform. Available online: https://ai.baidu.com/ai-doc/EASYDL/pk38n3odv
(accessed on 6 July 2021). (In Chinese)
38.
Yuan, J.; Nan, X.; Li, C.; Sun, L. Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural
Network. Math. Probl. Eng. 2020,2020, 5795976. [CrossRef]
39.
Xin, X.-Y.; Wang, X. Co-creation in service design and the uncertainty of service experience. Zhuangshi
2018
,4, 3. (In Chinese)
[CrossRef]
Sustainability 2023,15, 16454 16 of 16
40.
Zhang, C.-Y.; Zhang, X.-M. Challenges and application ideas of human-computer interaction technology driven by artificial
intelligence. Digit. Technol. Appl. 2018,5, 2. (In Chinese) [CrossRef]
41. Brown, T.; Wyatt, J. Design Thinking for Social Innovation. Dev. Outreach 2010,12, 29–43. [CrossRef]
42.
Yang, Q.; Steinfeld, A.; Rosé, C.; Zimmerman, J. Re-Examining Whether, Why, and How Human-AI Interaction Is Uniquely
Difficult to Design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA,
21–30 April 2020; ACM: New York, NY, USA, 2020; pp. 1–13. [CrossRef]
43.
Richard, E.; Design Council. What Is the Framework for Innovation? Design Council’s Evolved Double Diamond. 2004. Available
online: http://www.designcouncil.org.uk/news-opinion/design-process-what-double-diamond (accessed on 1 October 2023).
44.
Patrício, L.; Fisk, R.P.; Falcão e Cunha, J.; Constantine, L. Multilevel Service Design: From Customer Value Constellation to
Service Experience Blueprinting. J. Serv. Res. 2011,14, 180–200. [CrossRef]
45. Jhaveri, C.A.; Nenavani, J.M. Evaluation of eTail Services Quality: AHP Approach. Vision 2020,24, 310–319. [CrossRef]
46. Xin, X.-Y.; Cao, J.-Z. Positioning Service Design. Packag. Eng. 2018,39, 43–49. (In Chinese) [CrossRef]
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