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LEVERAGING ARTIFICIAL INTELLIGENCE FOR ECONOMIC DEVELOPMENT:
INNOVATIONS IN CIRCULAR ECONOMY AND WASTE REDUCTION STRATEGIES
Salayeva Quvonchoy
Tashkent State University of Economics
ORCID: 0009-0005-7287-5919
quvonchoysalayeva2004@gmail.com
Abstract.
The integration of Artificial Intelligence (AI) into the circular economy offers
transformative solutions to the global challenge of waste management. This article explores the
role of AI-driven innovations in promoting waste reduction, resource optimization, and
sustainability. Through case studies such as AMP Robotics in the U.S. and ZenRobotics in Finland,
the study analyzes how AI enhances waste sorting, recycling efficiency, and product lifecycle
management. The findings underscore that AI not only contributes to environmental
sustainability but also promotes economic opportunities by optimizing resource loops and
minimizing waste generation.
Keywords:
artificial intelligence, circular economy, waste reduction, recycling,
sustainability, smart sorting.
SUN
’
IY INTELLEKTNI IQTISODIY RIVOJLANISH UCHUN QO
‘
LLASH: AYLANMA
IQTISODIYOT VA CHIQINDILARNI KAMAYTIRISH STRATEGIYALARIDAGI
INNOVATSIYALAR
Salayeva Quvonchoy
Toshkent davlat iqtisodiyot universiteti
Annotatsiya.
Sun’iy intellekt (SI)
texnologiyalarining aylanma iqtisodiyotga integratsiyasi
global miqyosdagi chiqindilarni boshqarish muammosiga inqilobiy yechimlar taklif etadi. Ushbu
maqolada chiqindilarni kamaytirish, resurslardan samarali foydalanish va barqarorlikni
ta’minlashda SI asosidagi innovatsiyalarning o‘rni tahlil qilinadi. AQShdagi AMP Robotics va
Finlyandiyadagi ZenRobotics kabi amaliy misollar orqali SI texnologiyalari chiqindilarni saralash,
qayta ishlash samaradorligini oshirish va mahsulotning hayotiy siklini boshqarishdagi ahamiyati
ko‘rsatilgan. Tadqiqot natijalari shuni ko‘rsatadiki, sun’iy intellekt atrof
-muhit barqarorligiga
hissa qo‘shish bilan birga iqtisodiy imkoniyatlarni ham yaratadi, resurs aylanishini
optimallashtiradi va chiqindilar hosil bo‘lishini kamaytira
di.
Kalit so‘zlar:
sun’iy intellekt
, aylanma iqtisodiyot, chiqindilarni kamaytirish, qayta ishlash,
barqarorlik, aqlli saralash.
UOʻK:
330.34.011
205-212
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ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ЭКОНОМИЧЕСКОГО
РАЗВИТИЯ: ИННОВАЦИИ В ЦИРКУЛЯРНОЙ ЭКОНОМИКЕ И СТРАТЕГИЯХ
СНИЖЕНИЯ ОТХОДОВ
Салаева Кувончой
Ташкентский государственный экономический университет
Аннотация
.
Интеграция искусственного интеллекта (ИИ) в модель циркулярной
экономики предлагает прорывные решения для глобальной проблемы управления
отходами. В статье рассматриваются ИИ
-
инновации, способствующие сокращению
отходов, оптимизации ресурсов и устойчивому развитию. На примере
AMP Robotics
(США)
и
ZenRobotics
(Финляндия) анализируется, как ИИ повышает эффективность
сортировки отходов, переработки и управления жизненным циклом продукции.
Полученные результаты подчеркивают, что ИИ не только способствует экологической
устойчивости, но и открывает экономические возможности за счёт оптимизации
циклов ресурсов и сокращения объёмов отходов.
Ключевые слова:
искусственный интеллект, циркулярная экономика, сокращение
отходов, переработка, устойчивость, умная сортировка
.
Introduction.
The global economy has long
operated on a linear model of “take, make, dispose,” leading
to enormous waste and environmental degradation. In response, the circular economy concept
has emerged, aiming to close the loop by keeping products and materials in use for as long as
possible. Artificial Intelligence (AI) is now playing a pivotal role in accelerating this transition.
AI technologies can analyze data at unprecedented speeds and make intelligent decisions,
enabling more efficient resource use and waste minimization. The world’s ec
onomy operates
largely upon linear economic principles (Ellen MacArthur Foundation, 2020). A traditional
linear economy follows the “take
-make-
waste” approach where the natural raw materials are
extracted and then manufactured into products. These products are used for a certain period
of time and eventually discarded as waste (Akter, Pranto, & other, 2022). This economic model
encourages over- consumption of material resources, creates unsustainable waste
management practices, and creates serious health, biodiversity, and climate problems
(Ogunmakinde, Sher, & other, 2021; Elghaish & other, 2022). It is estimated that by 2060, the
amount of material resources consumed worldwide will almost double from 90 gigatonnes in
2020 to 167 gigatonnes while the number of consumers will increase by three billion by 2030
(Roberts, & other, 2022). This over-consumption leads to higher demands for resource
extraction which in turn increases the levels of greenhouse gas emissions from mining and
extraction, worsening air quality and accelerating habitat destruction (Da Silva, Sehnem,
2022).
A solution to overcome the many negative aspects of the linear economy is called the
circular economy (CE). CE is an economic system operating on the principles of restoration and
aims at reduced material consumption and waste elimination while promising economic
development (Stahel, MacArthur, 2019; Schneider, 2019). The European Union (EU) defines CE
as “an economy in which the value of products, materials and resources is preserved for as long
as feasible, by designing durable products that can be reused
, repaired, and recycled”. This sees
the replacement of the ‘end
-of-
life’ approach
(Bartekov
á
, B
ö
rkey, 2022). Furthermore, in CE,
the waste from one product is seen as a resource for another. This greatly reduces the value of
consuming finite material resources (Ghoreishi, Happonen, 2020). CE is restorative and is
regenerative by its design. It reduces natural resource depletion, negates waste production, and
enables green and sustainable economic development (Macarthur, Cowes, 2019). The
objectives of CE are similar to those of the Sustainable Development Goals (SDGs), such as
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SDG 12, 13, 14
, in the promotion of economic growth without depleting the earth’s resources
by 2030 (Kholikova, 2024).
Literature review.
The integration of artificial intelligence (AI) into economic development strategies,
particularly within the framework of circular economy and waste reduction, has garnered
significant attention in recent literature. According to Geissdoerfer et al. (2018), the circular
economy aims to minimize waste and make the most of resources, and AI technologies can
enhance these efforts by optimizing resource management and improving efficiency in
production processes. In a study by Wastl et al. (2020), the authors highlighted how AI-driven
analytics can facilitate better decision-making in waste management systems, leading to
reduced landfill use and increased recycling rates.
In the context of Uzbekistan, research by Abdullayev et al. (2021) emphasizes the
potential of AI in transforming traditional economic practices into more sustainable models
that align with circular economy principles. They argue that local industries can leverage AI for
predictive maintenance, which not only reduces waste but also extends the lifespan of
machinery. Similarly, the work of Rakhimov et al. (2022) focuses on the application of AI in
agricultural sectors, where precision farming techniques can minimize resource use and
enhance productivity while reducing environmental impacts.
Furthermore, a review by Bocken et al. (2016) discusses how AI can support business
model innovation within the circular economy, enabling companies to create value from waste
materials. The authors contend that AI applications such as machine learning can identify new
opportunities for recycling and resource recovery. In addition, research by Zhang et al. (2021)
explores the role of AI in developing smart waste management systems that utilize IoT and big
data analytics to optimize collection routes and improve recycling processes.
Moreover, studies by Kjaer et al. (2020) illustrate how AI can facilitate consumer
engagement in waste reduction initiatives by providing personalized recommendations and
feedback on sustainable practices. This aligns with the findings of Lee et al. (2022), who
emphasize the importance of public awareness and participation in achieving circular economy
goals. Overall, the literature indicates that leveraging AI for economic development through
innovations in circular economy practices and waste reduction strategies holds considerable
promise for enhancing sustainability and resource efficiency across various sectors.
Methods.
The study employs a qualitative case study method, analyzing secondary data from
industry reports, company websites, peer-reviewed journals, and government publications. A
comparative approach is used to evaluate the impact of AI applications across different sectors
of the circular economy, particularly in waste management and recycling.
Results.
Digitalisation has been widely recognised as one of the most important ways to unlock
the benefits of a more inclusive and sustainable economy (Zhou, 2021). Digitalisation is the use
of digital technologies to enhance business processes by leveraging digital technologies and
digitized data (Prioux, & other, 2023). Effective utilisation of digital technologies such as Big
Data, Blockchain, the IoT, Cloud Computing, and Online Digital Platforms sometimes referred
as I4.0 tech- nologies enable circularity in a number of ways. These technologies can create
knowledge about the material composition of products, their origin and properties, their
location, condition, and availability, as well as their respective manufacturing processes and
condi- tions for maintenance, dismantling, and recycling (Chlingaryan, & other, 2018;
Shumway, & other, 2000). Digitalisation is disrupting the parameters of the current economic
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system by transforming business processes, facilitating data-driven decisions, affecting
consumer behaviour, and mitigating some environmental effects.
Figure 1.
Artificial intelligence-driven circular economy for smart and sustainable
agriculture
https://www.researchgate.net/figure/Artificial-intelligence-driven-circular-economy-for-smart-
and-sustainable-agriculture_fig2_380788817
Circular economy puts a strong focus on innovative design to maintain the utility and
value of products, components, and materials at all times (Makov, & other, 2020). Such designs
can empower increased cycles of reuse, repair, and recycling of many products and their
constituent materials. This is a difficult task. However, AI can be a helpful tool in enabling
product designers to manage this complexity through iterative assisted design processes. These
processes allow for rapid prototyping and testing, leading to better design outcomes in a
shorter period of time (
Kholikova, 2025)
. In this way, new products can be formed through
circular design and these products can then be safely maintained and preserved in the economy
for a longer period of time. As a result, the amount of resource extraction and waste production
associated with excessive product development can be reduced substantially. AI can also help
in predicting how materials change over time, such as their overall durability and potential
toxicities (ZenRobotics, 2023). This type of information can help in advancing the reverse
logistics and maintenance of products.
Machine learning (ML) is a branch of AI that provides computers with the ability to learn
from data, analyse and draw inferences from complex data patterns, and make predictions with
minimal human intervention (
Kholikova,
2024). ML algorithms are provided with data and then
through the use of statistical formulas, the algorithms are trained to derive results. This training
process can be repeated and configured to improve the quality of derived results. ML
algorithms can detect significant depen- dencies between the data features of real-time datasets
and this ability can identify opportunities for circular solutions (Makov, & other, 2020). For
example, ML approaches can be used to forecast the demand for a product as per consumer
purchasing behaviour. Within agricultural settings, ML can be used to predict the right time to
sow crops by using data related to the quality of the soil, weather, and possible future market
conditions for the crop output.
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Figure 2. Circular economy model
Timeseries Analysis is an AI technique capable of working with variables evolving over
time. This technique is very efficient in identifying specific trends in historical data in order to
predict future events (Zhou, 2021). Methods include lines of Best Fit, Auto Regression, Moving
Average, and more advanced Deep Learning (DL) models such as Long Short-Term Memory
(LSTM). Applications can be found in predicting food demand based on consumption patterns
to minimize food waste, predictive maintenance of equipment for reduced maintenance costs,
and increasing the overall lifespan of equipment.
One of the most impactful applications of AI in the circular economy is automated waste
sorting. Traditional sorting methods are labor-intensive, slow, and prone to human error. AI-
enabled robots can rapidly identify and separate various waste types with high accuracy. AMP
Robotics uses AI and computer vision to recognize different materials on recycling belts and
sort them automatically. Its system, called Neuron, can distinguish between plastics, metals,
and paper with over 90% accuracy. In Denver, Colora
do, AMP’s robots have increased the
recovery rate of recyclables by up to 80% and reduced labor costs.ZenRobotics has developed
AI-powered robotic arms that can sort construction and demolition waste. Their system uses
machine learning algorithms and multispectral sensors to detect and pick up valuable materials
from mixed waste. This has helped reduce landfill contributions by over 30% in some European
recycling facilities (Akter, et all., 2022). AI is also used to predict waste generation patterns,
allowing municipalities to optimize waste collection routes and schedules. EcoSmart uses AI
algorithms to predict the fill level of waste bins using IoT sensors. This has enabled the city to
reduce fuel consumption and greenhouse gas emissions by optimizing garbage truck routes,
saving both environmental and financial resources (Schneider, 2019). AI is enhancing product
design and manufacturing by suggesting more sustainable materials and predicting failure
rates, extending product life and minimizing waste. Using AI-driven platforms, Dassault
Systèmes allows manufacturers to simulate product lifecycles and environmental impacts,
enabling the creation of longer-lasting, recyclable products. This design-for-sustainability
approach is crucial for reducing waste (ZenRobotics, 2023).
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Discussion.
AI significantly contributes to the circular economy by providing intelligent systems that
reduce human error, increase efficiency, and lower costs. It not only improves the mechanical
processes of waste management but also enhances strategic decision-making through
predictive analytics. However, challenges remain. AI implementation requires significant
investment, technical expertise, and data infrastructure. Moreover, ethical concerns around
surveillance and data privacy need to be addressed. Nonetheless, the benefits of AI for
sustainability outweigh the challenges when deployed responsibly (
Kholikova,
2024).
The efficiency of AI-based systems to be trained and tailored for various CE approaches
offers great potential. The major challenge is that in order to train and build intelligent AI
models for CE, very large amounts of training data is usually required (Makov, Shepon, 2020).
The lack of high-quality training data can be a potential hurdle in the effective utilisation of AI
For CE. Training datasets may be difficult to generate and indeed to do so can be expensive.
Collection and curation of training data can also take a great deal of time. In the absence of
appropriate volumes of training data, one possible solution is to consider the usage of transfer
learning. Transfer learning is a popular approach within deep learning applications. Transfer
learning is a method where an already pre-trained existing AI model working for a particular
task is reused and
transferred
for a new problem. AI-gathered knowledge from a high-quality
existing dataset is then
transferred
to a new target application which is lacking in data, using
the pre-trained existing AI model (Kholikova 2024).
To design efficient AI models for CE, an amount of data from various platforms is required
to train and test the models in order to achieve circularity at a higher level. However, collection
and analysis of such data could also pose various privacy, ethical, and legal risks. In many CE
applications, particularly those related to consumer or customer behaviour, one finds that AI
models are trained on the data generated by humans interacting with systems such as Internet
applications, social media applications, and so on. These are heavily reliant on knowledge of the
user’s location (and associated geospatial data) and other personal characteristics. The use of
these types of data streams introduces privacy considerations that are not easily solved
(Schneider, 2019). For example, geospatial data about people makes it possible to connect or
link those people to other types of user information including work, social, political affiliation,
and other behavioural patterns, all of which represent highly confidential information
(ZenRobotics, 2023). Furthermore, in terms of the analysis of such data, the inferences AI could
make about an individual or group could also raise ethical issues.
Conclusion.
AI is a powerful enabler of the circular economy, offering innovative solutions to waste
reduction and resource efficiency. From robotic sorting systems to predictive analytics and eco-
design platforms, AI helps close the resource loop and drive sustainable economic
development. Continued investment and cross-sector collaboration are essential to scale these
innovations globally. A transformation from a linear to a circular economy is more important
than ever and digitalisation can play a very critical role in this transformation (
Kholikova,
2025)
. Over the past decade, the world has seen a significant industrial development using
digital technologies. Stakeholders are considering incorporating AI into their businesses to
attain economic growth along with substantial environmental benefits. AI can positively
influence the development and the adoption of CEs worldwide.
However, gaps exist in the knowledge around how AI techniques can be practically
applied in order to implement more circular business models and achieve CE ambitions across
organisations. It is now of high importance to raise awareness about how AI can support CE so
that governments, organisations, and sectors benefit from CE opportunities driven by AI. The
range of tools available to develop AI software is expanding and becoming more user-friendly.
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This is an important step towards more widespread consideration and adoption of AI as a
digitalisation tool in practical real-world situations.
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