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ARTIFICIAL INTELLIGENCE AS AN IMPORTANT DIRECTION FOR
THE DEVELOPMENT OF THE GREEN ECONOMY.
Ahmedov Azimjon Olimjon og`li
Bukhara State University teacher.
E-mail: a.o.akhmedov@buxdu.uz,
Xabibov Shamshod Akbar o'g'li
Mirzayev Shohijahon Rustam o'g'li
Bukhara State University students.
E-mails: shamshodxabibov4@gmail.com
mirzayevshohijahon55@gmail.com
https://doi.org/10.5281/zenodo.14799552
The global environmental impact of AI is still largely unknown. This study
uses a balanced panel data set to examine the complex role of AI in increasing
global green productivity between 2010 and 2024[1]. Studies show that AI
indirectly contributes to green productivity by increasing the use of renewable
energy, attracting skilled workers, and reducing stock market activity.
Additional analyses confirm that financial development generally enhances the
positive impact of AI on green productivity. These results provide valuable
insights into the interrelationship between AI and the green economy.
Climate change is one of the most pressing threats facing humanity in the
21st century, with far-reaching implications for socio-economic development.
The scientific community recognizes that human activities, particularly the
burning of fossil fuels and deforestation, are causing unprecedented changes in
the global climate system, contributing to rising global temperatures and
accelerating environmental degradation. As temperatures continue to rise, the
impacts of climate change are becoming increasingly clear and severe, posing
serious threats to ecosystems, biodiversity, food security, water resources and
human well-being.
As the world enters the digital era, AI has become a buzzword and a
strategic investment target for governments around the world to achieve
dominance in the digital economy. Countries such as China, Japan, the United
States and others have put forward national strategic initiatives to strengthen
AI, indicating that AI, with strong financial support, is ready to become a
powerful engineer to drive future socio-economic transformation. The rapid
development of machine learning algorithms, industrial robotics and other
technologies has promoted the deep integration of AI into various socio-
economic sectors. It has improved production efficiency and information
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processing power, and rationalized resource configuration, making significant
contributions to global sustainable development. [2] The application of AI shows
great potential in reducing energy consumption, improving resource utilization
efficiency, and developing green energy and emerging industries. [3] Therefore,
considering the promising features of AI, this study departs from classical
practices and identifies the relationship between AI and green productivity from
a global perspective.
The contribution of this study includes two aspects. First, the novelty of this
study is that it models the intrinsic relationship between AI and green
productivity on a global scale for the first time[4]. In contrast, significant works
have mainly focused on the impact of AI on various topics such as traditional
productivity, income, and labor share. Green productivity has richer economic
meanings because it goes beyond traditional productivity measures to include
environmental protection and sustainability. It is worth noting that green
productivity is consistent with the principles of sustainable development, which
seeks to balance growth with environmental protection and social justice. By
measuring productivity in a way that takes into account environmental
sustainability, green productivity provides insight into whether economic
growth is achieved in an environmentally sustainable manner in the long term.
The driving force behind AI-based devices lies in energy consumption, but the
regenerative effect of AI has been confirmed in the work of Wang et al.[5].
Therefore, it is crucial to comprehensively assess the relationship between
smart and green productivity.
Thus, the second perspective is that the main mechanism analysis in this
study is based on mediation and moderation. It is worth noting that the use of
different verification methods lies in different economic foundations. First,
intelligent algorithms have been widely used in the development of renewable
energy. For example, intelligent algorithms can simplify the operation of energy
storage systems such as batteries and pumped storage, collect excess renewable
resources during low demand, and release them during peak demand or decline
in renewable energy generation. Thus, the relationship between artificial
intelligence and renewable energy cannot be ignored, especially in the current
low-carbon era. Second, the impact of artificial intelligence on labor share and
income gap has been well studied in past works [6]. In contrast to these efforts,
this study finds that artificial intelligence supports green productivity by
attracting high-skilled labor due to the agglomeration effect[2]. Furthermore, it
is a common phenomenon that investors prefer to use AI methods to predict the
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“future” by combining different forecasting models. This means that AI can
influence green productivity by shaping investor sentiment in the stock market.
AI has the potential to revolutionize green productivity by optimizing
resource allocation and increasing energy efficiency in various sectors. For
example, AI can improve supply chain management, leading to more sustainable
production processes and reduced carbon footprints[1]. The expected result is
that firms can reduce costs by integrating intelligent algorithms into their
operations and comply with increasingly stringent environmental regulations,
thus gaining a competitive advantage in the global market.
Traditional economic models often do not take into account the rapid
changes that AI will bring to production and consumption patterns.
Incorporating AI into these models will provide a more complete understanding
of the dynamics between technology and environmental sustainability.
Furthermore, studying this connection can enrich the literature on the diffusion
of innovation, particularly how AI technologies spread across industries and
affect green practices[1]. It also allows us to explore potential feedback loops
through which improvements in green productivity through AI can lead to
further technological progress, creating a virtuous cycle of innovation and
sustainability. This understanding is crucial for developing robust economic
theories that capture the complexity of modern, technology-driven economies.
List of literature used:
1.
ARTIFICIAL
INTELLIGENCE-BASED
MODELING:
AUTOMATION,
INTELLIGENT SYSTEMS, APPLICATIONS, AND RESEARCH.
2.
U Saidov - Science and innovation in the education system, 2024"Gartner,
Inc. 2023 Annual Report (Form 10-K)". U.S. Securities and Exchange
Commission. February 16, 2024.
3.
"What is Machine Learning?". IBM. Archived from the original on 2023-12-
27. Retrieved 2023-06-27.
4.
Ham, Donhee; Park, Hongkun; Hwang, Sungwoo; Kim, Kinam (2021).
"Neuromorphic electronics based on copying and pasting the brain". Nature
Electronics. 4 (9): 635–644.
5.
THEORETICAL AND METHODOLOGICAL FOUNDATIONS FOR STUDYING
THE PRINCIPLES AND MECHANISMS OF MANAGING THE GREEN
DEVELOPMENT
OF
THE
MODERN
ECONOMY
https://cyberleninka.ru/article/n/zamonaviy-iqtisodiyotning-yashil-
rivojlanishini-boshqarish-tamoyillari-va-mexanizmlarini-organishning-nazariy-
va-uslubiy-asoslari
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6.
ANALYSIS OF MODERN PRACTICES AND TECHNOLOGIES OF THE GREEN
ECONOMY
IN
THE
CONTEXT
OF
TEACHING
GREEN
ECONOMICS.https://cyberleninka.ru/article/n/yashil-iqtisodiyot-fanini-oqitish-
kontekstida-yashil-iqtisodiyotning-zamonaviy-amaliyotlari-va-
texnologiyalarini-tahlil-qilish
7.
Akhmedov A.O. DIGITALIZATION OF LOGISTICS: FROM BIG DATA TO THE
INTERNET OF THINGS // Universum: Technical Sciences Issue: 4(121)April
2024 Moscow 2024
8.
https://7universum.com/ru/tech/archive/item/17341
