Journal of the Knowledge Economy
https://doi.org/10.1007/s13132-024-02101-w
Circular Economy Advances with Artificial Intelligence
and Digital Twin: Multiple‑Case Study of Chinese Industries
in Agriculture
Ali Zain Anwar1,2 · Mahreen Zain3 · Raza Hasan4 · Hussain Al Salman5
Bader Fahad Alkhamees6 · Faisal Abdulaziz Almisned6
·
Received: 1 November 2023 / Accepted: 14 May 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2024
Abstract
The population growth is drastically surging in demand for food and water and
uplifting consumption and waste resulting in overburden of society and the environment. Urgent actions are required to address these emerging global issues. Therefore, adopting a circular economy (CE) is essential to sustain the consumption rate
and accommodate the ever-increasing demand. Moreover, the CE practices accelerate the progress on sustainable development. From this perspective, digital technologies are playing driving roles in the successful implementations of CE practices and
achievements of the United Nations’ (UN) sustainable development goals (SDGs).
Among various emerging digital technologies, artificial intelligence (AI) and digital
twin ((DT) are the promising ones. This paper aims to understand and explore how
both technologies facilitate the CE transitions and attain SDGs in the agriculture
domain. To this end, we provide insights into the concepts of CE, AI, and DT with
preliminary and current research status. This research evaluates the contributions of
global organizations for CE transitions. We elaborate on the significant contributions
of AI and DT in the transition towards CE and identify some challenges that hinder
the adoption of these technologies. Besides expanding knowledge, concise multiple
case studies are also presented as evidence to depict how companies in China are
deploying these technologies to digitize various operations and create solutions for
waste management, sustainable resource consumption, renewal energy, water conservation, etc. Findings reveal that these companies successfully attain many SDGs
of 1, 2, 6, 7, 9, 11, 12, 13, 14, 15, and 17. This paper enormously contributes to the
emerging research domain of integrating CE, AI, DT, and agriculture.
Keywords Circular economy (CE) · Sustainable development · Sustainable
development goals (SDGs) · Artificial intelligence (AI) · Digital twin ((DT) · China
Extended author information available on the last page of the article
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Abbreviations
3DThree-dimensional
5GFifth Generation
AIArtificial intelligence
AIGCArtificial intelligence generated content
AIoTArtificial Intelligence of Things
AIPAgro-industrial park
ANNArtificial neural networks
BDABig data and analytics
BIPPBiomass intermediate pyrolysis poly-generation
CECircular economy
CEECircular economy eco-city
ChatGPTChat Generative Pre-Trained Transformer
COFCOChina Oil and Foodstuffs Corporation
CSVCreating shared value
DEMATELDecision making trial and evaluation laboratory
DLDeep learning
DMEData mid-end
DTDigital twin
ESGEnvironmental, social, and governance
EUEuropean Union
FAOFood and Agriculture Organization
FL-IFMMImproved fuzzy min-max neural network coupled with fuzzy
lattice inclusion measure
GHGGreenhouse gas
Grey-DEMATELGrey-decision-making trial and evaluation laboratory
IoTInternet of Things
LCALifecycle analysis
LEDLight emitting diode
MLMachine learning
MVSMultiview stereo
NISTNational Institute of Standards and Technology
NLPNatural language processing
NNNeural networks
NRELNational Renewable Energy Laboratory
OECDOrganisation for Economic Co-operation and Development
SDGsSustainable development goals
STEMScience, Technology, Engineering, and Mathematics
US EPAUnited States Environmental Protection Agency
UNEPUnited Nations Environment Programme
VRVirtual reality
WCEFWorld Circular Economy Forum
YTOYituo
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Introduction
Agriculture has received more attention since the post-epidemic era. The agricultural industry in China contributes majorly to the gross production. In 2021,
the value of agriculture production is nearly 7.83 trillion yuan. According to the
Food and Agriculture Organization (FAO), China produces one-fourth of the gross
world’s grain (FAO, 2023). However, the agriculture sector in China is producing
280,0711 million tons of waste and evolving 24% of total greenhouse gas (GHG)
emissions (Li, 2022). Today, agriculture is considered one of the most energy-intensive and resource-intensive sectors in many countries. The main reason is the adoption of a linear economy that is comprised of extraction, production, consumption,
and disposal practices. The prime challenge for all the stakeholders is not only to
respond to the food and agricultural requirements of the growing population but also
to reduce the resulting environmental impacts. Therefore, a transition from a linear
economy to a circular economy (CE) is essential to overcome resource depletion,
greenhouse gas emissions, and waste creation (Silvestri et al., 2022).
CE foundations can be traced to various schools of thought, for instance, R-principles, industrial ecology, cradle-to-cradle, biomimicry, and many others (VelascoMuñoz et al., 2022). All these theoretical foundations have the essence of creating
a regenerative economy by reducing resource flows, energy leakages, and waste
through four strategies. These strategies are narrowing, slowing, closing, and regenerating the loops. Narrowing the loops curtails the resource inputs through efficiencies in the design and operational optimizations. Slowing the loops aims to keep the
resources in use to the maximum extent and avoid unessential consumption through
reversible design, maintenance, repair, and reuse. Closing the loop intends to close
the resource cycle through reusing and recycling. Contrarily, regenerating the loop
focuses on promoting society, the economy, and the environment to a better position through non-toxic, renewable, and bio-based resource usage and biodiversity
improvement (Yaqot et al., 2023). Thus, CE strengthens sustainable development
and sustains our present and future generations. Academics, industries, policymakers, and governments have a growing interest in CE. Various organizations, such as
the Ellen MacArthur Foundation, have promoted the CE concept, which is why it
is extensively adopted from business goals to international policies. The European
Union’s Circular Economy Action Plan and China’s Circular Economy Promotion
Law are some examples (Rotolo et al., 2022).
Digital technologies are potential tools for a paradigm shift to CE. Besides CE
transitions, digital transformation privileges people and businesses while supporting the CE and climate-neutral transition. Digitalization and innovation in the
CE action plans are observed as crucial drivers to track, trace, and map resources
and dematerialize the economy for less dependency on natural resources. Hence,
a clear relationship exists between CE and digitalization (Puntillo, 2023). Moreover, various scholars sought to identify appropriate digital technology for CE
transition; however, this research is still in its infancy. According to a study by
Trevisan and Formentini (2023), artificial intelligence (AI), big data and analytics (BDA), internet of things (IoT), blockchain, additive manufacturing, digital
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twin (DT), and many others have shown potential in academics including real-life
applications in this context.
The contributions of AI for CE transitions are remarkable for the agriculture
sector. Andono et al. (2022) employ AI and IoT for end-to-end CE in Indonesia. This strategy optimizes the value chain of onions and improves food security.
A study by Mohammed et al. (2023) integrates artificial neural networks (ANN)
with feature fusion for automatically sorting and classifying waste according to
recycling requirements. Nevertheless, most AI applications are based on massive
amounts of data that result in cost issues in terms of time and data. To overcome
this challenge, DT is a potential tool (Ahmed et al., 2023). In this study, DT is
suggested for preserving cost competitiveness through operational expenses and
low maintenance in the energy sector. Findings reveal that DT supports green
design for energy consumption. This study signifies that data and time costs are
no more challenges and barriers to CE and progress. Moreover, the prime objective of DT technology is to construct informative mirror models employing computerized virtual reality (VR) in the hyper-real world. Correspondingly, a digital
model of a plant can be built that will transform the physical characteristics into
digital information, then this digital information is employed to grow this plant
in the hyper-real world. DT assists in introducing and conducting tests for new
technologies in VR. To illustrate this, Maksimović (2023) proposes DT to foretell the technical flaws, performance results, and enhancements of future systems.
This study concludes that DT assists industries and organizations to produce and
implement more sustainable processes, products, and workflows. Thus, DT has
become an integral research direction.
The promising potentials of AI and DT in CE transitions remain underexplored
(Vazhenina et al., 2023). The literature claims these technologies are playing
driving roles in the CE shift despite this, it is based on theoretical concepts only.
This claim does not show real-life implementations. One reason is resistance to
technological change and adoption. Besides, a knowledge gap exists on the intersection of AI and DT with CE. The novelty of this work lies in the integration of
four research domains CE, AI, DT, and agriculture. Empirical studies, for example, case studies, are essential for enlightening scientific knowledge, promoting
circular practices, and exploring the challenges that emerge when these technologies are employed. The prime objective of this research is to address these
gaps in two manners. First, by expanding knowledge while evaluating theoretical
concepts and literature review. Second, by conducting multiple case studies that
explore how industries in China deploy AI and DT to promote CE practices in
agriculture, resulting in sustainable development. The contributions of this paper
are as follows:
• Evaluating the concepts of CE, AI, and DT with research evidence.
• Exploring the significance of AI and DT in CE transitions and challenges in the
adoption of AI and DT.
• Examining how the agricultural industries and technology provider companies
are deploying AI and DT in agriculture to promote CE practices and sustainable
development goals (SDGs) in China.
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The remaining research paper is comprised of various sections. The “Methodology” section displays the research methodology for data collection and case
selection. The “Circular Economy” section introduces the theoretical concepts
and literature review for expanding knowledge on CE, the contributions of global
organizations to advance CE, and the role of CE in addressing the challenges of
agriculture. The “Artificial Intelligence and Digital Twin for Circular Economy
Progressions and Sustainable Development Goals” section provides insights into
AI and DT and how these technologies transform CE and meet SDGs. It further
elaborates on some challenges that hinder the implementation of AI and DT. The
“Initiatives Taken in China Promoting Circular Economy in Agriculture” section
explains the current situation of CE in China, whereas the “Case Studies” section explores the selected case studies. The “Discussion and Conclusion” section discusses the empirical findings and concludes this research by evaluating
future research directions.
Methodology
This paper aims to expand theoretical knowledge on how CE advances with AI
and DT and to integrate the role of AI-driven CE and DT-driven CE in the agriculture industries of China. For this purpose, we have carried out research for
theory development and multiple case study design.
Multiple Case Study Design
The multiple case study design allows an in-depth analysis of all the individual
cases by providing an overview of the companies and start-ups, revealing steps
taken for CE and sustainability, and unearthing the role of AI and DT. This
helps in empowering the validity and stability of the research. All the cases were
selected by employing the following four criteria:
• Location: We focused on Chinese companies only as circulatory motives, and
regulations vary in all countries. Moreover, China is a pioneer in CE practices.
• Domain of companies: We concentrated on agricultural companies and tech-
nology provider companies that provide tools and solutions for precision agriculture, as similar companies will refine confidence in our findings.
• Circularity implementation: We selected those companies that implement the
R-principles of CE.
• Company scale: We focused on large-scale companies as they adopt technologies more likely than smaller companies.
According to these criteria, we explored the web sources and listed the potential cases, agriculture industries, and start-ups.
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Data Collection
For theory development, we conducted a literature review from the perspectives
of CE, AI, DT, and agriculture. Academic and grey literature was reviewed on CE
practices. Two search engines, Google and ScienceDirect were explored. Different
combinations of keywords, one from each group, were used for selecting relevant
academic papers.
• Group 1: Circular economy; reuse; reduce; recycle; recover; repair; renew;
remanufacture; redesign; regenerate.
• Group 2: Artificial intelligence; machine learning; deep learning; neural net-
works; drones; digital twin; digital twinning.
• Group 3: Agriculture.
After selecting the relevant research papers, the related papers were also searched.
In the screening and eligibility steps, the abstract analysis along with full-text analysis was also carried out. In the inclusion step, those papers were included, which
give comprehensive details regarding the selected four research domains. Papers
published from 2019 to 2023 were included. Moreover, grey literature was also
retrieved by searching some websites such as the Food and Agriculture Organization (FAO), Ellen MacArthur Foundation, United Nations Environment Programme
(UNEP), Organisation for Economic Co-operation and Development (OECD), and
World Circular Economy Forum (WCEF).
For selected cases, the prime source of the corporate data was the publicly available secondary sources. These documented pieces of evidence comprehended data
from companies’ websites, creating shared value (CSV) reports; annual reports;
financial reports; sustainability reports; environmental, social, and governance
(ESG) reports; and press releases. The data collected for theory development
and multiple cases for research triangulation improves the research precision and
validation.
Circular Economy
In the linear economy systems, resources and materials are transformed into products without focusing on their lifespan, sold, and finally incinerated after usage.
This results in running out of resources, eventually accumulating waste, incurring
expenses, and enhancing pollution (Zhu et al., 2019). Contrary to this, CE is a sustainable development approach that creates healthy and functional relations between
society and nature by efficiently closing the material flows in long cycles.
According to Ellen MacArthur, the CE is founded on three fundamental principles: elimination of waste and pollution, circulation of products and materials to
the maximum extent, and regeneration of nature (Velasco-Muñoz et al., 2021). The
CE strategies are formed on the basic R-principles (recycle, reduce, reuse, recover,
renew, regenerate, redesign, remanufacture, redesign). The recycling principle reinvolves the used materials and products in a cycle such that these materials and
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products serve as re-resources for new products and services. Thus, recycling activity in CE ensures a significant reduction in primary resource utilization and waste
dumps. However, recycling is not considered an attractive solution as the substitution processes, replacing primary materials with secondary ones, degrade the quality
of materials and require vigorous energy. The recycling principle is an outer cover
of the CE, whereas the restoring principle is the inner cover of the CE (Ren et al.,
2023).
Restoring the industrial system is based on modifying, repairing, or reusing materials, which also reduces the loss of valuable materials. The objectives of CE are not
only to enhance a product’s lifecycle and waste reduction but also to reduce energy
consumption and control GHG emissions (Chen et al., 2022). In this context, secondary materials, by-products, and waste products are utilized as energy resources.
Furthermore, renewable energy like solar and wind energies and renewable products
like light-emitting diode (LED) bulbs play significant roles.
The CE concept is still developing and modifying with the usage of evolving technologies. The adoption of CE in industries brings appropriate balance and harmony
to different industrial links in the long term, which benefits the national economies
by saving material costs. De Keyser and Mathijs (2023) propose CE forms new businesses, markets, and jobs, turning to entrepreneurs’ and employees’ advantages. CE
creates shared prosperity by eradicating poverty and offering equal opportunities for
both genders. Cao and Solangi (2023) evaluate that CE safeguards the environment
by enhancing resource efficiency, advancing cleaner production, promoting green
industries, and supporting energy transitions and waste management. Thus, CE
practices and sustainable development are intertwined with a primary focus on all
three dimensions of sustainability, economic, social, and environmental.
Contributions of Global Organizations to Advance Circular Economy Practices
China was the first to embrace CE to recover resources from waste in 2008. Later,
the European Union (EU) also implemented the CE with the objectives of reusing
water, food, and plastics in 2015 (Ma et al., 2023). Developed countries are reducing their dependencies on raw materials from fossil fuels, minerals, and metals by
adopting CE. On the contrary, developing countries are striving due to a lack of
knowledge and technology. In developing countries, the need for material, energy,
and water is rising because of the growing population and increased demands for
industries and infrastructure (Smol et al., 2020).
Many organizations are working to overcome these mentioned challenges and
advance CE models not only in developed countries but also in developing countries (Feng & Lam, 2021). The Ellen MacArthur Foundation, United Nations Environment Programme (UNEP), United States Environmental Protection Agency (US
EPA), National Institute of Standards and Technology (NIST), National Renewable
Energy Laboratory (NREL), Organisation for Economic Co-operation and Development (OECD), World Circular Economy Forum (WCEF) and many others are its
representatives.
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Ellen MacArthur Foundation promotes CE ideas while working with key actors
across the globe. It conducts evidence-based research on advantages across sectors
and stakeholders and the contributions of CE for addressing global stability challenges. It supports individuals and organizations with advanced learning courses
and resources. Its vision is an economic system better for humans and the environment (Ellen MacArthur Foundation, 2023). UNEP is a leading global authority that
assists its members in living in harmony with nature, nurturing climate stability, and
generating a pollution-free future. It advises governments on the ways to achieve
SDGs and assists states in measuring their progress irrespective of national borders
(UNEP, 2023). The US EPA is creating strategies and action plans for developing a
CE for everyone. US EPA has designed a vision that transforms the waste management system with equity and highlights the necessity to address the climate crisis
(EPA, 2023).
NIST is collaborating with others to bridge the gaps in various domains such as
reference materials, new measurement sciences, infrastructure and data tools, documentary standards, measurement support, and regulatory strategies. NIST provides
this expertise to different organizations for analyzing their environmental impacts
(NIST, 2023). NREL employs innovative research credentials to develop a suite of
tools for CE models and clean energy products. NREL conducts regulatory, technical, social, and economic analyses of clean energy technologies along with their
supply chains (NREL, 2023). OECD offers support to various cities and regions for
the paradigm shift towards CE through measuring, learning, and sharing. Through
measuring, it develops a model for decision-making and defining CE approaches.
Through learning, it engages multiple standard dialogues for exploring opportunities
and challenges. Through sharing, it favors the best lessons, research, and practices
from international experience (OECD, 2023).
The WCEF provides the best CE solutions and collaborates with numerous
experts, policymakers, and business leaders. At Sitra, the Finnish Innovation Fund,
WCEF assists all its members with capabilities to achieve SDGs and leads them to a
new society, an economy with more jobs and businesses, and a carbon-neutral environment (WCEF, 2023).
Circular Economy and Sustainability of Agriculture
Global Challenges of Sustainable Development of Agriculture
The agriculture industries contribute to social and economic development by assuring food security, reducing poverty, and generating income. This development is
achieved while increasing pressure on the natural environment. Rathore and Malawalia (2021) suggest that the current linear models of agriculture production affect
the natural resources influencing the ecological environment of a country. Numerous significant interdependencies exist that make the agricultural system helpless.
Interconnectivity in resources, water, land, and energy restricts bioenergy and food
production. Additionally, the rise in population urges an increase in food and agricultural production. The agriculture system is considered the most incompetent
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system because it cannot meet the surge in demand for feed, food, and fiber against a
reduced availability of cultivable land (Selvan et al., 2023).
The agriculture industries are compounded by various other challenges, such as
climatic impacts, pollution, waste, and biodiversity loss (Dahiya et al., 2020). Crops
and livestock are the prime contributors to global pollution, whereas the food sector
is the major contributor to waste production globally. According to Kusumowardani
et al. (2022), food waste and food loss throughout the supply chain and consumption, which declines overall quality of life, affects the environment and leads to
financial losses. All these challenges restrict sustainability and socio-economic
development. Therefore, all these global issues require efficient economic models and technical innovations in the agriculture industrial activities for sustainable
renewable resources usage, environmental damage depletion, and non-renewable
resources reduction.
Role of Circular Economy for Sustainability of Agriculture
CE models and strategies solve systemic and embedded issues efficiently. Therefore, the agriculture sectors implement the two circular cycles of CE, technical and
biological (Kara et al., 2022). The technical cycle is implemented via reuse, renew,
remanufacture, repair, and recycle strategies, resulting in agriculture production efficiency with minimum waste and a rise in cost savings. It is also applicable to nonnatural sources of packaging with a preference for recycling and reusing concepts.
Such as the recent ban on single-use plastic bags has pushed food brands to rethink
their packaging materials. This opened opportunities for entrepreneurs to consider
renewable packaging that can be used repeatedly. The biological cycle is used in the
system of recapturing value from waste via reusing food, utilizing food waste and
by-products, and recycling nutrients. In this way, waste evolves into a new product,
material, or bio-product, which acts as an input for the manufacturing of new products supporting energy, the production of crops, and the processing of food. Closing loops of input reduces discharges and resource demands and enhances resource
efficiency, leading to circularity in the practices of agriculture and food sectors. Furthermore, CE increases the end-of-life by developing new economic returns with
resource usage optimization and environmental damage ease (Popescu et al., 2022).
“Waste prevention” in CE practices refers to the utilization and recycling of water
and food, agriculture, organic, and household waste along with the recovery of
energy, organic compounds, and nutrients. CE with waste management reduces the
growing crisis of waste. Rathore and Malawalia (2021) explore the CE transformations and suggest these lead to sustainable consumption and production that does not
overload society and the environment. Moreover, greenhouse gas emissions are primarily blamed on industrial waste. Many industries around the world try to reduce
the impacts of greenhouse gas emissions in their global supply chains. Maintaining
the circularity of materials minimizes these impacts, whereas recovering resources
such as water and energy by extending product life significantly reduces pollution.
Ogunmakinde (2019) conducts a review to analyze the CE models in China, Japan,
and Germany. Findings reveal that in China, the agriculture industry has surpassed
being a zero source of pollution. This is achieved by promoting a green, ecological
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environment and implementing CE for it. Similarly, many European countries are
recycling agricultural waste into different energies.
In the field of CE, “biomass” includes several goods such as wood, crops, fruits,
manure, garbage, litter, landfill gas, and many others. CE practices use these goods
for energetic purposes and renewable resources via anaerobic digestion, composting,
and others. Alzate Acevedo et al. (2021) analyzed that large quantities of banana
biomass are wasted after harvest. They propose the recovery of banana wastes for
CE transitions and suggest their usage for obtaining fertilizers, bioplastics, and biofuels and treating wastewater and other processes. In the field of CE, “manufacturing” is the production of products from renewable and non-renewable raw materials. For example, European countries are adopting a mitigation strategy for climate
change by manufacturing bio-based packaging, bio-based plastics, and biochemical.
Similarly, to tackle “resource scarcity,” CE aims to regenerate resources by drawing and procuring their possible value at the maximum level. Pathan et al. (2023)
discuss the initiatives of the Irish government to construct a CE framework, evaluate CE as a restorative, regenerative, and sustainable solution, and explore the significant contributions of AI towards CE. Moreover, CE substantially endeavors for
effective resource utilization in the supply chains. The potential of CE is to redevelop stagnant industries and reduce greenhouse emissions. This subsection shows
the potential of CE and its contribution to the sustainability of agriculture.
Artificial Intelligence and Digital Twin for Circular Economy
Progressions and Sustainable Development Goals
The circular economy continuously demands efficient, intelligent, and innovative
technologies to boost quality, quantity, and sustainability with diminishing costs,
inputs, and waste. This section evaluates the concepts of artificial intelligence and
digital twins. Academics and industries have conducted many experiments for
applying these technologies to accelerate the CE transition. The significant roles of
AI and DT are discussed as fundamental enablers of CE that achieve SDGs. However, many industries face challenges in adopting AI-related technologies and DT.
These challenges are also evaluated in this section.
Conception of Artificial Intelligence
John McCarthy, the father of AI, introduced the concept of AI as the science and
engineering that develops intelligent machines or computer programs (Noman et al.,
2022). His concept enables intelligence, algorithms, assistance, augmentation,
and automation systems for executing tasks beyond the capabilities of humans. AI
primarily learns from numbers, images, audio, and videos then reduces the error
intending to generate accurate models. Research by Oruganti et al. (2023) evaluates
machine learning (ML), data mining, and deep learning (DL) as the widely applied
subsets of AI. An explorative study by Ali et al. (2023) discusses robots, drones,
smart devices and sensors, virtual agents (chatbots), natural language processing
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(NLP), etc. as other evolving AI techniques. Figure 1 displays these AI technologies,
and a brief description of these technologies is given below.
ML is an AI branch and sub-field of computer science that enables systems to
learn and modify automatically with experience. ML algorithms can access, analyze,
classify, and predict data (Chen, 2022). On the other hand, DL models are based on
neural networks (NN) that copy the neural circuits of the brain for data processing
and pattern creation while making decisions. These models are applied for image or
video recognition and classification as well as speech recognition. Another AI-based
technology is robots and drones that perform various tasks and enable automation
in corporate processes. These complement human skills and even replace humans
in executing complex tasks (Fraga-Lamas et al., 2021). Similarly, smart devices
and sensors allow real-time monitoring and interaction with other devices and users
via different wireless connections. The other advantageous AI-driven technology is
computer-based intelligence called virtual agents that provide online assistance for
customers. As an example, a chatbot optimizes the customer services of organizations. Chatbots converse with customers and impressively respond to their queries
(Zota et al., 2023). Whereas, NLP concentrates on the communications between
computers and humans. It analyzes the structure, interpretation, and intentions of
human sentences. It drives crucial information during decision-making and unstructured data during automatic assistance. The role of these technologies in accelerating the CE transition is discussed in the preceding subsection.
Artificial Intelligence‑Powered Circular Economy
Since the last decade, the computational powerful machines have expanded the coverage of AI. Now, it serves in various fields such as agriculture, education, science,
medicine, finance, marketing, business, stock market, and economies (Barros et al.,
2020). Figure 2 illustrates various AI technologies integrated into CE lead to many
opportunities for smart and sustainable agriculture. A growing trend is observed
in fusing AI technologies with CE aspects. In this context, we have analyzed and
summarized abundant studies that signify the usage of AI technologies for CE
transformations.
Rooting for waste sorting, recovery, and reduction, AI plays a significant role.
Tu et al. (2022) incorporate AI to design a more intelligent, functional, and efficient
Fig. 1 Various kinds of artificial
intelligence technologies
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Fig. 2 Artificial intelligencedriven circular economy for
smart and sustainable agriculture
garbage bin. The speech recognition module receives the keywords and identifies
the garbage category. Then, it opens the lid of that category garbage bin for accurate garbage sorting. Additionally, it sends signals to the garbage management to
alert them to clean and replace the bins on time. Similarly, the predictive qualities of ML support optimization, management, and recovery of several beneficial
resources from organic waste. Soo et al. (2023) analyze advancements in ML for
macronutrient recovery in organic waste for constructing CE in smart cities. Findings reveal that ML modifies the scalability, productivity, transparency, and accuracy of nutrients in terms of recovery and reuse technologies and practices, logistics,
and dissemination.
Other contributions of ML for CE are that it strengthens reverse logistics for
remanufacturing processes and assesses the environmental impacts. Schlüter et al.
(2021) employ ML to advance identification and inspection by improving the sorting processes and working environment. This use case displays that digitalization
and intelligent algorithms simplify complicated tasks in reverse logistics and the
management of recycling activities. This suggested concept addresses the reverse
logistics challenges as well as economic uncertainties for companies. Moreover, ML
can also be used for assessing the environmental impacts of agricultural processes.
In this regard, Prioux et al. (2023) hybridize ML with environmental assessment for
improving lifecycle analysis (LCA). This study details the pre-treatment procedures
for rice straw and corn stover to transform them into energy. Results highlight that
the suggested processes are sustainable.
The cooperation of experts belonging to different fields is essential for supporting
CE practices on a wider scale. Decision-making models based on NLP are comprised of multiple features adopted to remove or reduce conventional disorganization and incapabilities of systems. Likewise, a hybrid model of NLP techniques and
experts is proposed by Mboli et al. (2021) to define and validate the ontology of
the CE business model. This hybrid model identifies gaps and yields satisfactory
outcomes for the participating experts. Zota et al. (2023) present five chatbot solutions for the CE domain. In this study, these chatbots are analyzed for assistance in
designing the procedures for collecting data, training, developing, and testing a chatbot through deep processing and NLP techniques. Another powerful NLP tool is the
Chat Generative Pre-Trained Transformer (ChatGPT), which enables a more circular and regenerative economy, especially in the domains of education and research.
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Verma (2023) explores the potentials of ChatGPT, which increases science, technology, engineering, and mathematics (STEM) education, assists research, and provides opportunities for innovation and a sustainable future.
DL models can be applied for predicting the waste quantity and even business
survival. Akanbi et al. (2020) developed DL models to forecast the amount of waste
and salvage materials of buildings that are about to be demolished. The results display that the applied models can predict the amount of recoverable materials after
demolition with high accuracy simply by providing fundamental features of that
building. Similarly, Uribe-Toril et al. (2022) employ a DL technique to determine
the linkage between the resistance of some commercial premises and their classification in the CE plane. Findings show the effectiveness of the applied DL technique
for prediction and evaluation that businesses with CE practices survive more over
extended periods. While designing and testing different technologies for CE principles, the applications of robots are replacing the workforce. These automate various
processes for reverse logistics and manage the supply chain with real-time data analysis. Beloev et al. (2021) designed a robot to perform small-scale agricultural tasks.
It uses AI algorithms for decision-making and carries out specifications according to
the surrounding situation and environment.
AI-based drones enable remote monitoring, automation, and competent data
analysis for the efficiency and sustainability of various sectors. For example, Yaqot
et al. (2021) analyze the capabilities of agricultural drones for sub-processes such as
monitoring, irrigation, spraying, and others for corn production. This research concludes that drones refine the sub-processes within CE principles in an environmentally friendly manner. AI monitors climate and carbon footprints through satellites
and sensors and helps researchers to predict forecasts. Moreover, it generates and
optimizes innovative eco-friendly ideas such as renewable energy production like
biofuel, solar, wind, hydro, and geothermal. Bachs-Herrera et al. (2023) refer valorization of biomass waste as an alternative to carbon mining. AI, ML, and multiscale
modeling are discussed as potential techniques in this study, boosting the development of materials and process engineering and producing renewable energy. The
analysis of these studies indicates that the cognitive abilities of AI are creating profound impressions on business, society, and the planet.
Contributions of AI for Sustainable Development Goals
AI renders exceptional services to resolve vital issues concerning CE, such as waste
management, sustainable manufacturing systems, energy resource optimization,
reverse logistics, and supply chain management. Similarly, its significant contributions are making grounds to achieve SDGs (Danish et al., 2023). A review is carried
out by Onyeaka et al. (2023) to explore the role of AI in enhancing CE and tackling
food waste. Findings reveal that employing ML, NLP, AI-based image recognition,
reinforcement learning, drones, and satellite mapping with data analysis allows realtime resource allocation and agricultural productivity growth that eradicates poverty
and hunger. Various intelligent devices are capable of modifying the work environment and industrial infrastructure, increasing productivity and providing exponential
innovation. Baduge et al. (2022) propose AI technologies for the complete building
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lifecycle and analyze that vision and sensor-based data gathering facilitate DL and
ML applications. Besides these, AI assesses the impacts of generated waste and
sorts them for recycling and waste management, leading to sustainable development
with a reduction in costs and carbon footprint.
AI achieves sustainability goals for society upgrading related to education, health,
gender equality, and others. It is revolutionizing education by providing virtual and
productive learning. ML extends medical facilities and care to remote regions.
Moreover, it empowers women for new opportunities and growth to promote gender equality. AI-based sensors generate predicting patterns for clean water consumption and sanitation (Mehmood et al., 2020). AI addresses environmental sustainability goals, for example, pattern recognition tracks life on land and below water to
enhance sustainable ecosystems and counter illegal poaching and fishing. Other than
this, fuzzy logic and time series can make predictions for climate and disaster. Goh
and Vinuesa (2021) investigate AI applications for accelerating the achievement of
SDGs. This study has a few considerations that can slow the efforts as well.
Among all the digital technologies, AI is a paramount one that is capable of
achieving all the 17 SDGs. According to (ICS, 2023), all the developed as well as
developing countries are striving hard to utilize AI to boost their gross domestic
product (GDP). China is developing commercial AI to drive a 26% rise in its GDP
by 2030. Similarly, North America is predicted to achieve a 14.5% rise in its GDP
with the interventions of AI.
Insights of Digital Twin
Michael Grieves developed the concept of a digital twin as a mirror space model
(Broo & Schooling, 2023). Later, researchers evaluated DT as a virtual version of
physical entities that aims at achieving modified simulation outcomes for information monitoring, problem prediction, and decision-making improvements. This
virtual version is the development and simulation of a physical system, process, or
entity in the information platform. A digital simulation is developed in this information platform using physical feedback data with the assistance of related technologies. Then, this simulation utilizes the feedback and makes corresponding changes
according to the variation in the physical system, process, or entity (Preut et al.,
2021).
The fundamental components of DT architecture are the physical space, the
virtual space, and a connection between these spaces through the flow of data and
information (Semeraro et al., 2023). The physical space is comprised of physical
entities, sensors, and actuators, whereas the virtual space is comprised of multiscale,
multiphysics, and probabilistic models. With time, researchers expanded the components of the DT framework. A study by Tao et al. (2021) included the data fusion
and the service system components of the DT framework. Another study proposed
other enabling components such as sensors, actuators, data and analytics, and processes that act, create, communicate, aggregate, analyze, and perceive the physical
and virtual spaces. On the other hand, the integration is responsible for connecting
virtual and physical worlds.
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DT possesses the capabilities to learn itself under the multi-feedback source data
and portray the real-world situation of a physical system, process, or entity in the
digital world. These capabilities are facilitated by digital simulation, VR presentation, advanced sensing acquisition, high-performance computing, intelligent data
analysis, and others (Peladarinos et al., 2023). Moreover, integrating AI for creating
DT results in developments and improvements in various domains. Advanced ML
approaches are extending the usage of DT.
Digital Twin‑Driven Circular Economy
Coupling AI, BDA, cloud computing, and other digital technologies with DT
enhances the potential value of DT for applications in various fields. Agriculture,
smart cities, medicine and health, construction, manufacturing, and aerospace are
the most served domains (Maheshwari et al., 2023). Furthermore, DT requires an
appropriate command of supporting methods for proper functioning. These supporting methods are industrial resource management, natural resource management,
technology lifecycles, etc. For this reason, the applications of DT empower CE transitions. An extensive literature is examined and summarized below that validates the
employment of DT is advantageous for CE.
DT improves information management in CE and develops new and innovative
business strategies and models by fulfilling the requirements. Jäger-Roschko and
Petersen (2022) consider DT and blockchain as two emerging information-sharing
technologies and identify that better information-sharing advances in CE activities.
DT monitors the real counterpart with an exchange of information throughout different processes and the entire life cycle. Therefore, it enables the product conditions
and processes to grow more transparent and traceable. Vilas-Boas et al. (2023) conducted a review on DT, AI, IoT and distributed ledger technology for convergence in
fresh food logistics. This analysis shows that the proposed technologies monitor and
track products efficiently, analyze data, and support decision-making in the fresh
food supply chain, which reduces waste, costs, and environmental impacts.
Opportunities of DT that are achieved from higher transparency and traceability
are modifications in manufacturing and product design, reductions in resource waste
and segregated decision-making for seamless material management, and better execution of various R-principles. Modifications in manufacturing and product design
are possible from the data collected during a product life cycle. This data helps in
concluding the requirements of designs, especially for manufacturing new products.
It also speeds up the automation of various processes. Ghandar et al. (2021) performed a case study for planning production from urban farming called aquaponics
using DT and ML. It elaborates on empirical results, highlighting applied methods
as a decision support system for coordinating the activities of urban farming.
The information collected by DT reduces waste by ensuring usability to the
maximum extent. It assists the users in gaining proper knowledge for handling the
products appropriately. Peladarinos et al. (2023) carry out a comprehensive review
for implementations of DT that enhance smart agriculture. This review discusses
that farmers can use DT to capture and implement real-time data for simulating
their farms with significant aspects covering soil composition, crop cultivation,
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and weather conditions. This integrated data allows them to enhance their farming
methods, production, and sustainability. Furthermore, DT facilitates the decision
regarding the purchase of used products using sorting optimization and skipping or
reduction of the inspection processes. Thus, it improves reverse logistics and better
manages the material flow by tracking and managing the returns. Moreover, it promotes and allows better execution of recycling, reusing, or refurbishing by detailing
the exact condition of products as well as predicting the repair and maintenance services. A study by Ma et al. (2022) proposes DT and big data for smart and sustainable manufacturing for energy-intensive industries. The product lifecycle perspective
is adopted with data cleansing and integration strategies and their effectiveness is
shown for Chinese companies. The review of this literature reveals that DT strengthens CE.
Contributions of Digital Twin for Sustainable Development Goals
DT explores strategic options and opportunities while carrying out trials on expensive and complex real entities such as smart farming systems or manufacturing
processes. This reduces costs and mitigates potential liabilities. Besides costs, DT
reduces expected risks while conducting experiments rapidly and providing best
practices for direct applications. Verdouw et al. (2021) propose a conceptual framework to design and apply DTs for farming management. This study validates the
proposed framework for dairy farming, arable farming, organic farming, livestock
farming, and greenhouse horticulture. Results highlight that DTs advance farming
productivity and sustainability to new levels.
DTs provide innovative solutions with real-time monitoring and future predictions with what-if scenarios that restrict the unnecessary usage of resources and
trace environmental emissions. Thapa and Horanont (2022) review DTs in farming
and applied with other agricultural technologies in a solar-energy-supplied farm.
Findings reveal that the DT paradigm allows food sustainability as well as innovative energy that contributes to the SDGs of zero hunger and affordable and clean
energy. Besides securing human life, it can also be employed to save marine life.
Fotre et al. (2023) showcase the work-in-progress of a marine digital twinning platform. This platform is a collaborative work, whereas its outputs meet industry, innovation, and infrastructure goals, responsible consumption and production goals, and
life below water goals.
The potentials of DT are extensively explored in manufacturing, construction,
and agriculture. The significance of DT-driven CE in all three dimensions of sustainability is shown in Fig. 3. However, its opportunities are still undiscovered in
various areas. Hassani et al. (2022) review the current trajectory of DT implementations in diverse domains. It identifies relevant SDGs in these domains, such as manufacturing meets SDG 1, SDG 8, SDG 9, and SDG 12, agriculture achieves SDG 2,
SDG 13, SDG 14, and SDG 15, education attains SDG 4, and healthcare achieves
SDG 3 and 10. Therefore, DT has become an integral part of management strategies for organizations that contribute to occupational productivity, safety levels, and
achieving sustainability.
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Fig. 3 Significance of digital
twin-driven circular economy
Challenges of AI and Digital Twin Implementations
The significance of AI and DT in promoting work for meeting sustainability is evident from the above-mentioned sections. Besides excellent performance and effective applications of AI and DT technologies, their adoption is restricted due to some
challenges. Such as envisioning the system and the interconnected models and technologies, authentication of the operations, maintenance, safety, and results. Additionally, the integration complexity and scale of the application give rise to some
challenges. Limitations that restrict the usage of AI and DT for industry adoption
are classified into the following four major categories.
Data Issues
Acquisition, quality, flow, pre-processing, privacy, and ownership are the identified
sub-criteria of data issues (Purcell & Neubauer, 2023). Data must be kept personal
and protected from cyber-attacks. Data is more susceptible to being influenced by
the system. Another major limitation is to incorporate bi-directional information
flow and real-time data into the DT model. For updating DT with real-time information, the data connectivity and processing pose another challenge of complexity.
The DT and AI models generate and analyze large amounts of data, which requires
powerful allies for satisfying large-scale and long-term data processing and analysis
requirements. Moreover, modeling behaviors that cannot be expressed by numbers is
another issue.
Challenges Related to the System
The sub-criteria challenges are technological infrastructure, interdisciplinary collaboration and compatibility of models and technologies, and faster communication
(Büyüközkan & Uztürk, 2022). Essential support is required by technologies such
as IoT and BDA for creating infrastructure. Moreover, seamless information from
different indicator systems, models, and technologies requires compatibility and collaboration. This also influences challenges while formulating common standards and
policies. Efficient and faster communication networks, for example, fifth generation
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(5G), are essential to enable real-time data connectivity with operational efficiency.
Such communication networks can connect more devices and sensors with improved
reliability, high-speed connectivity, and much lower power consumption.
Challenges Faced by Stakeholders
Stakeholders face certain challenges while integrating AI and DT like less knowledge regarding the concepts, frameworks, and regulations, high implementation
costs, less availability of computational resources, and collaboration of the supply
chain (Perno et al., 2022). Various stakeholders are less familiar with the advances
and frameworks of these evolving technologies. Additionally, the lack of standards
and regulations also restricts their adoption. Furthermore, the increased prices of
computational resources and sensors decrease the applications of DT and AI. These
computational resources are less accessible in developing countries, limiting the
accessibility of AI and DT.
Effects of Unfavorable Factors
Factors involved in various tasks, such as complex production, face the implementation effects of these techniques. These unfavorable factors are restricted cultural and
scientific exchanges, reduced global economy, and lack of infrastructure, knowledge,
talent, policies, and regulations (Nie et al., 2022). Therefore, one crucial challenge is
to balance the effects of these factors.
Initiatives Taken in China Promoting Circular Economy in Agriculture
Academic research is significantly focused on evaluating and tracking the CE sustainability in China that advances CE development. Zhu et al. (2019) carried out
a field study and research at a farm in Pingxiang City, which is a pig breeding
enterprise. Multiple stakeholders are interviewed, and field research is conducted
at the local markets. Findings reveal economic viability is necessary for successful circular operation, whereas, for the sustainability of these operations, sufficient production pathways are essential. Furthermore, entrepreneurship is a vital
requirement for constructing and advancing a circular business. The development
level of CE is different in all regions of China. Hao et al. (2020) conducted a
survey in Chongqing City and in western Sichuan Province to analyze the factors
that influence the willingness of people to participate in CE practices. Results
indicate four major influencing factors subjective norm, positive anticipated emotion, willingness to sacrifice for the environment, and the perceived economic
benefit. Green purchase intention is a relationship mediator, whereas perceived
behavior control is a relationship moderator among the identified factors. Li et al.
(2021) apply an emergy-based method to examine the sustainability of Rizhao
City, a coastal CE eco-city (CEE). This study identifies that socioeconomic
development is highly dependent on the consumption of non-renewable resources
that influence the environment by producing more pollution. A decreasing trend
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is observed in the previous data, while in contrast, an increasing trend is observed
in the recent data due to the rise in recycle ratios of resources. Some recommendations are also given for policy that will improve the coordinated development
and overall sustainability in Rizhao City. Wang (2022) examines the decisionmaking trial and evaluation laboratory (DEMATEL) method for quantitative analysis of agro-industrial parks in China. He identifies that economic criteria play
a driving role in developing the sustainability of the agro-industrial park (AIP),
whereas no considerable causal link exists between the criteria criterion and further criteria. This study also demonstrates that the sustainable development level
of AIP is more elevated in the developed areas rather than the national average.
Agricultural waste streams must be used as raw materials for sustainable CE.
Yrjälä et al. (2022) investigate biomass intermediate pyrolysis poly-generation
(BIPP) for treating agriculture waste in China. In this study, biochar is produced
using the given approach that addresses climate concerns and environmental
problems. Song et al. (2021) conducted a literature review on biomass utilization methods and developed a new recycling model to promote the sustainability
of agriculture and energy in China. This model uses biomass waste to produce
biochar and biogas, which is further transformed into biochar. Additionally, this
model merges all the processes to excel the biomass usage, enhances its conversion efficiency, and strengthens its agricultural applications. A study by Feng and
Lam (2021) elaborates on the CE practices in China and reviews the enabling
drivers for CE transition. The recycling rate is analyzed in China, which shows
its progress in recycling waste from industries and renewable resources. Meng
et al. (2020) constructed the specific index system and an improved fuzzy minmax neural network coupled with fuzzy lattice inclusion measure (FL-IFMM) to
divide the Heilongjiang province into four agricultural CE regions. The selected
CE modes are the recycling use mode of by-products considering biogas, edible
fungi, and product deep processing, complex recycling mode, and eco-agricultural tourism mode. FL-IFMM exhibits high reliability in the clustering results
and embodies economic status, resources, and land as the factors that influence
the production as well as the economic development of agriculture in all the
regions.
Nevertheless, the use of CE for the development of agriculture in China witnessed some barriers. Xia and Ruan (2020) identify key hindrances from stakeholders, enterprises, farmers, and the government. According to this study, these barriers
include high production costs, weak technology innovation, traditional organizational mechanisms, less advanced processes of design and production, low industrialization of small-scale agriculture, and no equilibrium between demand and supply.
Farmers have limited skills, knowledge, and environmental awareness and cannot
use green financing policies properly. Barriers from the government are imperfect
policies, regulations, and administrative mechanisms, lack of financial subsidies,
weak infrastructure, and slow promotion of green technology. This study employs
the Grey-decision-making trial and evaluation laboratory (Grey-DEMATEL)
method to express the correlation between these barriers. Along with a feasible
framework, various proposals are recommended to the government for supporting
sustainable CE.
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Case Studies
Conventional agriculture and farming are based on previous data records, human
observations, and farmer experience., digital technologies especially AI and DTs
possess the power to transform the conventional model into smart, intelligent,
and futuristic agriculture that can significantly fuel better business strategies and
decisions, improve the health and wellbeing of animals, and enhance agricultural
resources and productivity. Various farming, animal husbandry, and agricultural
product manufacturing companies in China are adopting innovation and digitalization to achieve their goals. Moreover, various technology provider companies are
also implementing evolving technologies in agriculture. This section presents certain case studies highlighting how Chinese companies and technology providers
incorporate AI and DT to advance CE transitions. A precise overview and services
of these companies and technology providers are detailed, followed by the applications of frontier technologies and R-principles they have adopted to promote CE in
China and accomplish SDGs. Table 1 summarizes the opportunities of applied AI
technologies and DT and implemented R-principles in all the considered cases.
Mengniu Dairy Company Limited
Overview and Services
Mengniu Dairy, a China Oil and Foodstuffs Corporation (COFCO) brand, is a fullyintelligent dairy enterprise. It was founded in 1999 in the Inner Mongolia Autonomous Region with its headquarters in Hohhot. It constantly strives for creative
solutions and self-improvement to accomplish its mission of flourishing every individual life through every nutritional drop. Being a livelihood-representative industry, it covers agriculture, manufacturing, animal husbandry, and service industries.
Focusing on healthy, nutritious, and delicious dairy products, its rich brand portfolio
includes Milk Land, Just Yoghurt, Milk Deluxe, Real Fruits, Shiny Meadow Deluxe
Ice Cream, Bellamy’s Organic, Champion, and Yoyi-C.
AI and DT Applications
It utilizes the Data Mid-End (DME) methodology provided by Alibaba Cloud
for constructing a smart milk production and supply chain platform (Alibaba
Cloud, 2023). This methodology improves their arrangement, production, sales,
and logistics efficiency. It helps the company improve data resources and connect businesses and associate organizations while reducing manual operations
and maintenance costs. According to Gartner, Mengniu applied its solutions and
implemented DT to achieve intelligent manufacturing, covering intelligent management of formulas, projects, processes, and packaging (Gartner, 2023). With
the acquisition and corporation of different technology providers, it provides
Artificial Intelligence of Things (AIOT) solutions for agriculture and animal
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Technologies
JD.com
First Tractor Company
• Delivers high-quality nutritional services
• Allows close loop analysis, supply chain analysis, and production scheduling optimization
AI nutritionist based-GPT
AI-algorithms
• Allows real-time monitoring
• Conducts reviews of automatic inventory
• Improves pig-breeding and supply chain
• Automates the biological assets verification
• Reduces management costs of posts-loan
• Accumulates huge, high-quality, and interactive data
• Provides automated consultation and services
• Generates smart marketing content
• Enhances marketing production efficiency
• Enables automatic sorting and warehousing
DT and smart cameras
AI algorithms
Pig face recognition algorithm
WeChat mini program
ChatRhino or Yanxi and
ChatJD
AI-driven robots
AI lab
• Performs various activities from crop planting to harvesting
3D object detection and MVS fusion algorithm • Allows eco-friendly autonomous driving vehicles
• Enables supervision and expansion of operations
Biological assets digital platform
Intelligent unmanned cluster technology
DT
• Reduce
• Reuse
• Recycle
• Recovery
• Redesign
• Renew
• Regenerate
R-principles
• Reduce
• Recycle and reuse
• Renew
• Regenerate
• Reduce
• Improves and predicts the operation quality and maintenance • Recycle
• Reuse
control processes
• Recover
• Improves the operational reliability
• Reduces errors
• Provides higher quality services
• Creates content without human interventions
AIGC platform
• Allows intelligent quality inspection
• Provides intelligence, insights, and predictions in all operations
AI-driven DT engine
5G and AI
• Tracks new and previous information
• Achieves intelligent manufacturing and management
Opportunities
AIOT
Mengniu Dairy Company Limited DT
Examples
Table 1 Opportunities of deploying AI technologies and DT and implemented R-principles in case studies
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• Enables a detailed spectrum of control variables
• Model a large number of planting processes
• Optimize the planting strategy
• Achieve fully controlled climate
• Processes satellite images
• Predicts the weather and environmental impacts on crops
• Provides information for management
• Performs classification, quality inspection, and packaging
• Optimizes logistics and distribution
• Ensures fresh product delivery in the shortest time
• Enables intelligent decision-making, greenhouse control,
greenhouse production, and carbon assessment
• Allows efficient management, energy conservation, and
carbon emissions reductions
AI simulator and iGrow greenhouse simulator
ML
Computer vision-guided robotic arms
AI algorithms
Energy DT
Tencent
Opportunities
Technologies
Examples
Table 1 (continued)
• Reduce
• Recycle
• Reuse
• Renew
• Regenerate
R-principles
Journal of the Knowledge Economy
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husbandry. It uses these solutions for raw milk delivery, raw materials procurement, production process, and sales. It can track forward and backward information with these technologies. Furthermore, its digital intelligence 3.0 strategy
uses an AI-driven DT engine to lead the dairy industry toward a new start of
intelligence (Business Wire, 2023). The artificial intelligence generated content
(AIGC) platform uses AI assistants to transform employees into super employees.
For instance, AI nutritionist-based-GPT delivers high-quality nutritional health
services in real time. It uses AI algorithms for close-loop analysis and production
scheduling optimization that enables online monitoring, equipment control, and
decision-making (McKinsey & Company, 2023). This predictive maintenance
ensures stable operations. Moreover, it uses AI for supply chain analysis with
maximum efficiency.
Circular Economy and Sustainability
According to its sustainability report, it reduces food, industrial, and packaging
waste besides water and energy resource consumption (China Mengniu Dairy
Company Limited, 2022). It minimizes plastic and ink consumption by designing
semi-transparent white bottles and fossil fuel usage by shifting towards renewable
energy sources. It reduces the risk of sludge disposal by refining the sewage treatment process, biodiversity threats, and deforestation risks. Employing intelligent
technologies and processes reduces feed, energy, operating, and production costs
along with energy losses and heat stress impacts on products. It reuses maximum
resources on pasture, reclaimed water, and waste and recovers condensate, cooling, and wastewater. It also recovers biogas green energy from waste. It recycles
waste into recycled resources, for instance, paper for packaging, plastic bottles
for silk scarf manufacturing, and manure for biogas power generation. Further, it
recycles surplus energy, waste condensed water, milk-concentrated water, natural resources, and heat energy waste. It is focused on renewable energy through
biomass green energy and photovoltaic energy. Moreover, following the redesigning principle, it adopts a recyclability design, socially acceptable and recycled
materials.
It promotes technological innovations for rural revitalization. It transforms and
develops animal husbandry and agriculture for national poverty alleviation. It integrates sustainable development with its biogas power generation, savage treatment,
energy savings, low carbon transportation, and offices. With its “Green Bank,” it
plants trees and carries out other environmental protection activities. Whereas
with its sterile packaging materials from renewable forests, it reduces air pollution
(Mengniu, 2023). Adhering to the SDGs enhances corporate governance sustainability through risk management, business ethics, and ESG governance. It promotes
social prosperity by enhancing value in employee well-being, charity, and rural revitalization. It refines environmental performance using its dual carbon action, recycling, and green operations. From the ecosystem perspective, it conducts responsible and sustainable procurement to progress sustainable agriculture and biodiversity
conservation.
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First Tractor Company Limited
Overview and Services
The First Tractor Company Limited, a predecessor of the Yituo (YTO) Group
Corporation, was established in 1955 in Luoyang. It is an outstanding contributor
to agriculture mechanization, agricultural machinery industry, and rural revitalization with a hundred production lines. The company follows its mission of harvesting dreams to the land and responsibility of delivering efficient and premium
quality agricultural equipment for advancing modern agriculture while addressing the challenges. Its diversifying product portfolio includes caterpillar tractors,
unmanned tractors, harvesters, and movers, wheeled tractors, combined harvesters, special vehicles, diesel engines, agricultural implements, and generator sets.
AI and DT Applications
It deployed an IoT platform, Dongfanghong Cloud, for modern agriculture machinery. It
uses a quality control solution based on 5G and AI technology provided by Diesel Engine
Company in its intelligent workshops and factories (FTC, 2022a). It employs DT to demonstrate the application of intelligent control technology to produce agricultural equipment
(FTC, 2022b). Following its craftsmanship and collaboration with YTO, Luoyang Luoyang’s advanced manufacturing industry, and Luoyang Research Institute for Intelligent
Agricultural Equipment Co., Ltd. have introduced unmanned tractors. These tractors are
equipped with 5G and intelligent unmanned cluster technology driven by hydrogen energy
(Future Farming, 2023). This tractor is capable of towing agricultural equipment for work
on farms, improving operational reliability, and providing higher-quality services.
Circular Economy and Sustainability
According to its ESG report, it employs effective technologies and follows a dual
carbon strategy to save energy and reduce pollutant discharge and carbon emissions (FTC, 2022a). It further employs robots for spraying paint in the production line. These improvements in manufacturing processes decline material and
energy consumption and manufacturing costs. It recycles solid waste such as
iron fillings for casting and foraging purposes and efficiently disposes of the gas
waste collected during operations. Its wastewater treatment station recycles the
waste heat into tap water, reduces the water supply requirements, and reuses it for
equipment washing, cooling, garden irrigation, and other purposes. Moreover, it
uses water-source heat pump technology to recover low-grade heat energy.
Its harvesters and tractors are equipped with new diesel engines and hydrogen
energy that are more efficient, have long endurance, and save more energy. Its heavyduty dump trucks are battery-electric vehicles that produce no odor, zero emissions,
and no pollution. The annual report provides relevant information on pollution prevention, ecological control, and environmental prevention (FTC, 2022b).
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JD.com
Overview and Services
JD.com, an e-commerce giant in China, was established in 2004 in Beijing. Its mission is to be powered with technology to make the world more productive and sustainable. It offers products covering a wide range, from fresh food to electronics. Its
subsidiary, JD Technology, is engaged in shaping the digital technology future in
sustainable ways. It is engaged in various businesses across the agriculture and husbandry industry, logistics, retail, finance, health, etc. JD technology is making giant
strides in six core areas for rural revitalization and smart agriculture in the Chinese
coastal province.
AI and DT Applications
The main functional areas of its Demonstration Park include a science and innovation training center, IoT demonstration, smart agricultural production, seedling
breeding with high-tech technologies, sightseeing and leisure fruit picking, and
processing logistics and delivery. For instance, it provides a biological assets digital platform leveraging AI, IoT, and blockchain (JD.com, 2023a). It constructs DT
of agricultural products that allows real-time supervision of these assets, whereas
this platform enables farmers and businesses to further expand their operations.
For example, it deployed DT for Xinzhongsheng’s breeding bases along with smart
cameras to support supervision and AI algorithms to conduct reviews of automatic
inventory. Its developed pig face recognition algorithm improves pig breeding and
the supply chain in China. This AI algorithm notifies the breeders about real-time
physiological and immune conditions and growth status. Moreover, an AI-based
WeChat mini program has been launched for automating biological assets verification and reducing management costs of post loans.
Other uses of AI technologies include the launch of ChatRhino or Yanxi, which
provides services to merchants and customers. It has also launched an industrial version of ChatGPT called ChatJD for its retail and finance (JD.com, 2023b). Moreover, its logistics park is the world’s largest intelligent logistics park, equipped with
cutting-edge automatic and intelligent robot-based warehousing and sorting capabilities. It leveraged three-dimensional (3D) object detection and has developed a
Multiview Stereo (MVS) fusion algorithm for autonomous driving of its delivery
vehicles. Its designed drones deliver supplies and services to unreachable areas.
Circular Economy and Sustainability
Its plant factory features hydroponic technology, incorporating solar or artificial
lights that produce premium quality and environmentally friendly plants. Automatic management system reduces water, pesticides, and agrochemicals consumption and increases nutrients in veggies (JD.com, 2019). It employs robots,
algorithms, and other technologies to identify diseases in pigs, treat and process
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their waste, allocate feeding ingredients, and control the environment. According
to its ESG report, it reduces paper waste by issuing electronic invoices and uses
circular insulated boxes, recyclable green boxes, and bags (Jingdong, 2022). It
reduces environmental impacts with a focus on renewable energy by installing
photovoltaic power generation systems on rooftops and using new smart energy
delivery vehicles. It forms a circular industry chain that boosts farmers’ income
and competitiveness and reduces costs. Its Green Trade-In Alliance focuses on
the proper disposal and recycling of used appliances collected by customers.
Additionally, it donates these used products to enhance reusing activities. Its
delivery vehicles are eco-friendly. Table 2 portrays the applications of the identified circular principles and the attained SDGs.
Tencent
Overview and Services
Tencent, a technology provider company, was established in 1998 and is headquartered in Shenzhen. Following its mission, it strives to employ social responsibility in its products and services while promoting technology innovation and
assisting industries to upgrade digitally. It offers an extensive portfolio of services, for instance, AI, cloud computing, FinTech, and advertising, and publishes
high-quality digital content and video games.
AI and DT Applications
Considering its services in the agricultural domain, Tencent AI lab utilizes AI
from before planting to crop harvesting (Tencent, 2020). AI detects the soil composition, selects suitable crops, improves crop genes and insect resistance, and
controls diseases. AI simulator enables a detailed spectrum of control variables
like crop management. Its iGrow greenhouse simulator can model plenty of planting processes within seconds, optimizing the planting strategies and achieving
total control of the greenhouse climate. ML accurately processes satellite images,
predicts the weather and environmental impacts on crops, and provides information to the farmers to manage their crops. Computer vision-guided robotic arms
perform classification, quality inspection, and packaging. Contrarily, AI algorithms optimize logistics and distribution and ensure fresh product delivery in
the shortest time. Its customized algorithms enable intelligent decision-making,
greenhouse control, and production besides carbon sequential project assessment
in forestry. Tencent EnerTwin is an energy digital twin that allows companies to
manage and conserve energy and reduce carbon emissions (Tencent, 2022a).
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Circular Economy and Sustainability
According to its ESG report, applications of all these sustainable technologies
reduce resources (food, water, and energy) consumption, costs, and crop and energy
losses, whereas optimizing productivity improves the quality and boosts profits
(Tencent, 2022b). Tencent shrinks food, garbage, hazardous and e-waste with compliant disposal and recycling practices. The evolving technologies reduce packaging
material and paper usage by enabling online meetings and hybrid models. According to its carbon neutrality plan, Tencent promotes best practices to reduce GHG
and carbon emissions while setting targets with climate science. It develops carbon
assets to breed water-saving and drought-resistant crops. It regenerates seagrass
beds and salt marsh ecosystems. It reuses electronic equipment to avoid additional
production and extend its lifespan. Its water reuse facilities conserve water. Additionally, it recycles wastewater and rainwater to irrigate landscapes. It recycles electronic and packaging waste in its Green Recycling Centre. It generates renewable
energy, like electricity, from installed photovoltaic energy facilities and microgrid
and virtual power plants. According to its ESG report [103], it protects the environment by performing green operations, contributing to low-carbon development, and
restoring biodiversity and cultural heritage. In its social sustainability dimension,
Tencent promotes training for agriculture professionals and tenants for rural revitalization, digitalization, and sustainable development. With its blockbuster games, it is
creating awareness about a low-carbon lifestyle.
Discussion and Conclusion
This paper depicts how implementations of AI and DT advance circular economy
and achieve sustainable development goals. It illustrates background research and
current research studies on circular economy, artificial intelligence, and digital
twins. While conceptualizing circular economy, it elaborates on R-principles
and significant contributions of global organizations such as the Ellen MacArthur Foundation, United States Environmental Protection Agency, World Circular Economy Forum, and others. It delineates how a circular economy founded
on R-principles addresses the global challenges of sustainable development of
agriculture. It takes insights into various artificial intelligence technologies, for
instance, machine learning, deep learning, smart devices and sensors, natural language processing, chatbots, and digital twins and supports the argument with a
recent literature review. It portrays the integration of all these AI technologies
and digital twins with circular economy results in real-time, monitoring, optimization, waste sorting, effective decision making, environment impacts assessment, life cycle analysis improvement, and others. These privileges meet the sustainable development goals and transform traditional agriculture into smart and
sustainable agriculture. This study also explores some challenges that restrict the
adoption of these promising technologies and sorts them into four categories data
issues, challenges related to the system, challenges faced by stakeholders, and
effects of unfavorable factors. It elaborates on how academic resources are taking
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First Tractor Company
• Energy based on fossil fuels
• Power like photovoltaic power generation systems and efficient smart energy vehicles
• Packaging and materials e.g. circular insulated and green boxes and bags
• Biodiversity
Renew
Regenerate
Recycle and reuse • Agricultural, food, plastic, and paper waste
• Public system, water, old batteries, and disused electrical appliances
• Disposable packaging and non-degradable consumables, paper waste, and agricultural waste
• Rainwater drainage pressure, resource (water, energy, pesticides, and agrochemicals consumption, carbon emissions, pollution, and deforestation
• Costs and
• Low-grade heat energy
Reduce
• Water
Recover
• Biodiversity
• Waste heat, solid waste, water
• Biomass green energy and photovoltaic energy
Regenerate
Reuse
• Adopts socially beneficial and recycles materials
Renew
Recycle
• Energy and surplus energy, biogas, and water
Redesign
• Pollutant discharge and carbon emissions
• Resource (material, water, and energy) consumption and manufacturing costs
• Natural resources, water, paper, plastic, bottles, manure, and heat energy waste
Recovery
Reduce
• Resources, waste, and reclaimed water
Recycle
• Energy, water, fossil fuels, artificial fertilizer, plastic, and ink consumption
• Industrial, food, and packaging waste
• Deforestation risks, carbon, and GHG emissions
Applications
Reuse
Mengniu Dairy Company Limited Reduce
R-principle
Table 2 Applications of circular principles and related sustainability development goals
Company
• SDG 6
• SDG 7
• SDG 11
• SDG 12
• SDG 13
• SDG 14
• SDG 15
• SDG 6
• SDG 7
• SDG 11
• SDG 12
• SDG 13
• SDG 14
• SDG 15
• SDG 6
• SDG 7
• SDG 11
• SDG 12
• SDG 13
• SDG 14
• SDG 15
Sustainable
development
goals
Journal of the Knowledge Economy
• Resource (food, water, and energy), paper, and packaging material consumption
• Waste and carbon and GHG emissions
• Wastewater and rainwater
• Food, garbage, hazardous, packaging, and e-waste
• Water and electronic equipment
• Resources and Energy
• Biodiversity and cultural heritage sites
Reduce
Recycle
Reuse
Renew
Regenerate
Tencent
Applications
R-principle
Company
Table 2 (continued)
• SDG 6
• SDG 7
• SDG 11
• SDG 12
• SDG 13
• SDG 14
• SDG 15
Sustainable
development
goals
Journal of the Knowledge Economy
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initiatives to promote the circular economy and agriculture transformations in
China.
Further, this paper demonstrates empirical evidence for the developed theoretical
concepts by carrying out multiple case studies of Chinese agricultural industries and
technology provider companies. Our findings depict that AI is extensively employed
for circularity by all the considered companies; however, the usage of DTs is still in
its infancy and is predicted to evolve quickly within the coming years. AI algorithms
are extensively employed by all companies that enable intelligent decision-making,
emissions control, analysis, and optimization of supply chain and logistics. Most
companies use virtual agents to accumulate data and provide automated services that
regenerate the economy. To some extent, companies are integrating AI with other
technologies like IoT infrastructure and 5G that allow information tracking and quality inspections. They also employ robots for automatic sorting, quality inspection,
and packaging. The technology providers have established AI platforms or labs that
enable supervision and perform various operations intelligently. However, AI technologies, like drones, are not deployed, and simulations are rarely conducted for
operations. It is also observed that each one is deploying DT; however, some are
integrating it with AI and smart cameras for providing insights, intelligence, and
predictions in various operations that result in efficient management and conservation of resources along with emission reductions.
Among all the R-principles, the comprehensively employed ones are reduce,
recycle, and reuse. Reductions in resource consumption, waste, and carbon and
GHG emissions are universally observed. Every company is recycling resources,
like water and energy, waste, plastic, and paper, whereas some are recycling e-waste
also. All of them are reusing the resources to the maximum extent and recycling
waste and water to conserve resources. Most of them are generating renewable
resources, green energy, and photovoltaic power from waste and are keenly focused
on regenerating biodiversity. A few companies are practicing recovery principles for
water, energy, and biogas while the redesign principle is only observed in one case.
All are implementing SDG 12 by practicing sustainable resource consumption.
Through efficient waste management, these companies also reduce land, water, and
air pollution and degradation that protect our ecosystem and address SDG 11, SDG
13, SDG 14, and SDG 15. All are engaged in generating renewable energy from fossil
fuels and waste, conserving water, and treating waste and rainwater to facilitate access
to clean water. Both strategies meet SDG 7 and SDG 6. On the other hand, identifying
the related SDGs, SDG 9 is achieved by every company as they recognize the significance of innovation and technology applications to determine social, economic, and
environmental solutions. Almost all are working in partnerships with other companies
while addressing SDG 17. All are transforming agriculture and animal husbandry to
secure food and meet SDG 2. Eradicating extreme poverty by covering food scarcity,
clean drinking water, and sanitation, all the companies achieve SDG 1. Figure 4 displays all these SDGs achieved by the companies. However, no traces of the remaining
SDGs are found, for example, no company implements AI and DT to improve SDG
5 or SDG 16. We may conclude that implementations of technologies with a circular
economy boost rural revitalization, advance agriculture transformations, and maximize the individual plus economy’s profits with environmental protections.
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Journal of the Knowledge Economy
Fig. 4 Sustainable development goals attained by all the
companies
These promising real-world examples create awareness and encourage industrialists
and academia to explore the opportunities of AI and DT technologies in their businesses.
Future research will be specifically relevant to further exploring the contributions of
digital technologies such as blockchain and big data, as they are promising tools for constructing solutions that address sustainability issues. Further research will be conducted
on identifying the role of digital twins in other domains, energy, construction, and smart
cities. Moreover, we intend to conduct interviews with various stakeholders to obtain
primary data and identify potential barriers to DT application.
Author Contribution Conceptualization: ZAA; methodology: MZ; software: MZ and RZ; validation:
HAS; investigation: BFA; data curation: RZ; writing—original draft preparation: ZAA and RZ; writing—review and editing: ZAA, BFA, and AI; supervision: ZAA; project administration: ZAA
Funding This research was supported by the Researchers Supporting Project no. (RSP2024R244), King
Saud University, Riyadh, Saudi Arabia.
Data Availability This study does not incorporate any data. If any data is documented in this study, it is
linked to references in the below section.
Declarations
Conflict of Interest The authors declare no competing interests.
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Authors and Affiliations
Ali Zain Anwar1,2 · Mahreen Zain3 · Raza Hasan4 · Hussain Al Salman5
Bader Fahad Alkhamees6 · Faisal Abdulaziz Almisned6
·
* Ali Zain Anwar
zainanwar86@hotmail.com
Raza Hasan
raza.hasan@solent.ac.uk
Hussain Al Salman
halsalman@ksu.edu.sa
Bader Fahad Alkhamees
balkhamees@ksu.edu.sa
1
School of Physics and Electronic Engineering, Jiaying University, Meizhou, Guangdong, China
2
Sir Syed University of Engineering & Technology, Electronic Engineering Department,
Karachi, Pakistan
3
College of Economics and Management, Nanjing University of Aeronautics and Astronautics,
29 Jiangsu Avenue, Nanjing, China
4
Department of Science and Engineering, Solent University, Southampton, UK
5
Department of Computer Science, College of Computer and Information Sciences, King Saud
University, Riyadh 11543, Saudi Arabia
6
Department of Information Systems, College of Computer and Information Sciences, King Saud
University, Riyadh 11543, Saudi Arabia
13