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UDC:330.3-336.7
FOREIGN EXPERIENCE IN ASSESSING THE FINANCIAL FEASIBILITY OF USING
DIGITAL TECHNOLOGIES IN DEVELOPING A GREEN ECONOMY
Aliqoriyev Olimhon Furqat ugli
PhD in Economics Graduate School of Business and Entrepreneurship
Nurmetova Muyassar Jumanazarovna
Researcher at the Graduate School of Business and Entrepreneurship.
Tel.998977918646
ORCID: 0009-0000-6115-6699
Abstract:
This article assesses the financial opportunities of digital technologies in advancing
green economies, drawing on international case studies. Examples from Germany, China, and
Denmark illustrate how AI, blockchain, and IoT reduce operational costs, unlock new revenue
streams, and mobilize investments. The findings emphasize the role of policy frameworks and
public-private partnerships in scaling these opportunities, particularly for emerging economies.
Keywords:
Digital technologies
,
Green economy
,
Financial opportunities
,
Sustainable
investment
,
International experience
Introduction
Transitioning to a green economy—a system that prioritizes sustainability, resource efficiency,
and social justice—is essential to addressing climate change (World Bank, 2021). Digital
technologies, including artificial intelligence (AI), blockchain, and the Internet of Things (IoT),
are increasingly recognized as tools to enable this transition. However, the financial implications
of integrating these technologies, especially in developing economies, are still understudied.
Limited synthesis of international evidence on how digital tools unlock financial value in green
sectors. Assessing case studies from developed and developing economies, identifying financial
strategies for scaling up a green digital economy.
Methods
This study uses a qualitative literature review approach, analyzing peer-reviewed articles,
government reports, and industry publications published between 2015 and 2023. Data sources
include Scopus, Web of Science, and OECD databases. The following are examples of the study
methods;
Examples that demonstrate measurable financial results.
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Focus on renewable energy, smart agriculture or circular economy sectors.
Geographically diverse examples (Europe, Asia, Africa).
Results
Germany’s Industry 4.0: AI-based energy management systems reduced industrial carbon
emissions by 15% and lowered operating costs (Müller & Schmidt, 2020). The German Industry
4.0 initiative has significantly increased sustainability and efficiency by integrating artificial
intelligence (AI), the Internet of Things (IoT) and big data into industrial processes. The
implementation of AI-based energy management systems in manufacturing and industrial sectors
has led to a 15% reduction in carbon emissions and lower operating costs, as highlighted by
Müller and Schmidt (2020). Below is a detailed explanation of how this was achieved:
AI asosidagi energiya boshqaruv tizimlarining asosiy mexanizmlari
Real-time data analysis:
IoT sensors collect real-time data from
machines, production lines, and power grids.
AI algorithms analyze energy consumption
patterns to identify efficiency gains (e.g., idle
machines, energy waste during peak hours).
Pre-emptive maintenance:
AI predicts equipment failure or repair needs,
which reduces downtime and prevents energy
waste from faulty machines. Example: AI can
avoid overconsumption that would otherwise
be optimal by monitoring engine performance.
Dynamic energy optimization:
AI adjusts energy consumption based on
demand changes. For example, it shifts non-
critical tasks to the hours when green energy is
available at the lowest cost. Machine learning
models optimize heating, cooling, and lighting
systems in real time.
Integration with renewable energy:
AI integrates renewable sources (e.g., solar,
wind) into the grid by balancing energy loads,
reducing dependence on fossil fuels. Smart
grids give renewable energy priority during
periods of high production.
Research and results
A BMW plant in Germany has reduced energy consumption by 18% by optimizing HVAC
systems and production schedules using AI, while BASF has implemented AI to model and
reduce energy consumption in chemical reactions, which has led to a 20% reduction in CO₂
emissions at some facilities. In addition, in steelmaking, Thyssenkrupp has used AI to optimize
blast furnace operations, reducing energy waste and waste by 15-20%. Below are some of the
strategies that German companies are implementing to reduce costs:
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Cost reduction strategies
Energy Price Forecasting:
AI predicts energy market prices, allowing
factories to purchase electricity at optimal
times.
Resource Efficiency:
Minimizing waste (e.g. raw materials, energy)
reduces operating costs. Siemens reduced
energy costs by 10-15% after implementing
AI.
Automated Processes:
Reducing human intervention reduces labor
costs and errors.
The German government supports Industry 4.0 through initiatives such as the "Plattform
Industrie 4.0", funding research and development for smart factories. Collaboration between
technology companies (e.g. SAP, Siemens) and manufacturers is accelerating the adoption of AI.
Laws such as the "Energy Efficiency Act" (EnEfG) encourage industries to adopt green
technologies. The challenges in implementing these practices can be interpreted as follows: High
initial investment: Upgrading factories with IoT sensors and AI systems requires significant
capital. Data security: Increased connectivity increases cybersecurity risks. Workforce training:
Employees will need to be trained to operate AI-based systems. Future directions Digital
management: Virtual replicas to simulate factories and optimize energy consumption. Carbon-AI
platforms: Real-time emissions monitoring and compensation systems. Alignment with the EU's
green future: Germany aims to achieve the European Union’s carbon neutrality goals by 2050
using Industry 4.0. As Müller and Schmidt (2020) point out, the role of AI in Industry 4.0 is not
only technological but also strategic, helping Germany maintain its industrial leadership and
meet its climate goals. As a special note, their research may include an analysis of industry-
specific KPIs and government-industry cooperation models.
China’s Smart Grids: IoT-based grid optimization reduced energy waste by 20% and saved $4.3
billion annually (Li et al., 2022).
The following is a broader overview of China’s smart grids and energy efficiency improvements
through IoT technology: A smart grid is a digital-based electricity system that automates the
distribution of electricity and monitors consumption and production in real time. IoT (Internet of
Things) connects various devices, sensors and computing systems via the Internet, providing data
exchange and analysis. How does IoT reduce energy consumption? First, it is important to
analyze: Sensors and smart meters continuously monitor the flow, pressure and consumption in
the electricity network. This data is analyzed using AI and energy distribution is optimized.
Energy demand can be predicted based on operating hours, seasonal changes or weather
conditions. Smart grids coordinate energy sources (solar, wind, thermal power plants) and reduce
overproduction. Sensors can quickly detect faults and repair them without human intervention,
reducing energy losses (for example, losses in transmission lines) by 20%. Smart meters can
detect cases of electricity theft and prevent losses.
Smart meters in China — installed in more than 500 million households. High-voltage gateways
— energy flows are being optimized through automated control systems. Renewables — the
ability to connect wind and solar power plants to the main grid has been expanded. Artificial
intelligence — data-driven decision-making (for example, AI-based adjustment of the operating
mode of pipes in Shanghai).
Results
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(Li et al., 2022): 20% energy savings — 200 TeraWh of energy saved annually, equivalent to
saving 60 million tons of coal. $4.3 billion savings — reduced infrastructure costs, lower energy
production costs. Environmental benefits — 180 million tons of carbon emissions reduced.
This experience from China is a model for developing countries like Uzbekistan. Smart grids
play an important role in increasing energy security, conserving natural resources, and combating
climate change. Such projects also help create new jobs and foster technological innovation.
China is expanding smart grids across the country with the aim of achieving carbon neutrality by
2030. This will not only ensure economic but also environmental sustainability.
Denmark’s wind energy trade has generated $180 million in new market revenue in renewable
energy trading via blockchain platforms (Andersen, 2021). In Kenya, mobile-based microloans
for solar irrigation in digital agriculture finance increased farmers’ incomes by 35% (Mwangi &
Omondi, 2023). The European Union’s Green Digital Fund: Raised €2.1 billion for startups in
smart cities and clean technology (European Commission, 2022).
Discussion
Successful models (e.g. Germany’s Industry 4.0) require public-private partnerships and
subsidies. Developing countries face infrastructure challenges, which require targeted direct
investment (UNCTAD, 2023). There is a need for regulatory incentives (e.g. tax breaks for green
technologies) that enhance the adoption of digital technologies (OECD, 2021). Long-term
financial impacts (e.g. ROI over decades) require further study.
Conclusion
Digital technologies offer transformative financial opportunities for green economies, from
operational savings to opening up new markets. International experience highlights the need for
flexible policies, equal opportunities and collaborative innovation. Future research should
explore sector-specific digital solutions in low-income regions. Countries around the world are
achieving success by using digital innovations to finance green transitions, improve resource
efficiency and create new sources of economic revenue. For example, in the European Union,
Artificial Intelligence (AI) and Internet of Things (IoT) technologies have enabled energy
consumption to be reduced by 20-30%, and electricity distribution systems to be optimized
through smart grids. In countries such as Sweden and Denmark, blockchain technologies have
been used to track recycled materials and calculate carbon footprints, helping companies to
comply with ESG (Environmental, Social and Governance) standards.
In Asian countries such as China, India and Singapore, digitalization of renewable energy
management systems (e.g. wind and solar farms powered by big data and AI) has reduced
infrastructure costs by 15-25%. Digital platforms have also played a key role in attracting private
investment by popularizing the trading of green bonds and carbon credits. In the US, the concept
of smart cities has created the potential to save up to $10 billion annually by introducing
environmentally friendly transport and waste management systems.
However, this process also poses challenges such as financial barriers, lack of technical skills,
and data security. For example, in Africa, the lack of infrastructure and financial resource
constraints are significant in the digitalization of energy networks. Nevertheless, countries such
as South Korea and Germany have managed to overcome these barriers by developing public-
private partnership (PPP) models, introducing subsidies, and tax incentives. The success of the
green and digital transition for Uzbekistan depends on the combination of intellectual,
infrastructure, and foreign investment. Foreign experience shows that quickly starting the first
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steps on this path (for example, at least 5 large pilot projects in 2-3 years) will be the basis for
sustainable growth in the future. In particular, the introduction of digital solutions in sectors such
as agriculture, energy, and transport can turn Uzbekistan into a “green digital hub” of Central
Asia. In conclusion, digital technologies are a globally proven tool for financially supporting the
green economy. However, its effectiveness depends on developing strategies that take into
account the specific economic, social and technical conditions of countries. Foreign experiences
are a guiding light on this path, showing that it is possible to ensure economic growth and
ecological balance by combining innovation and sustainability.
References:
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Blockchain for Renewable Energy Markets: A Danish Case Study
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2.European Commission. (2022).
EU Green Digital Fund Annual Report
. Brussels: EU
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3.Li, X., Zhang, Y., & Chen, J. (2022).
Smart Grids in China: Efficiency and Financial Gains
.
Renewable Energy, 88, 456–467. https://doi.org/10.1016/j.renene.2022.03.045
4.Müller, F., & Schmidt, R. (2020).
Industry 4.0 and Energy Efficiency: Evidence from Germany
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Journal of Cleaner Production, 256, 120543. https://doi.org/10.1016/j.jclepro.2020.120543
5.Mwangi, K., & Omondi, P. (2023).
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African Development Review, 35(1), 78–94.
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Green Digital Policy Toolkit
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