Improving the Global Atmosphere and Maintaining Atmospheric

Balance by Empowering Power Stations with AI

Xinxin Wu

School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China

Keywords: AI, Global Climate, Propose, Energy Smart Coordination.

Abstract: Global climate change constitutes a considerable contemporary challenge confronting humanity. The

atmospheric composition is a key factor influencing global climate, and the equilibrium of atmospheric

composition is critical to the stability of the Earth's ecosystems. In view of this, this paper innovatively

proposes the construction of a Global Atmosphere- Energy Smart Coordination System (GAESCS), which

aims to comprehensively cover and monitor the global atmospheric composition in real time, and maintain

the atmospheric balance to improve the climate. At present, the central position of electricity in production

and life is unshakeable, but there is a contradiction between various types of power generation in terms of

efficiency and pollution. Thermal power generation has a high conversion rate but is highly polluting, while

wind and hydropower are clean but inefficient and unstable in supply. This system designed for research can

accurately calculate the average household power consumption and air composition concentration in each

region of the world. This system through intelligently schedules the work of each power station to maximize

the advantages of each power plant and minimize atmospheric pollution. GAESCS can also provide new ideas

to crack the dilemma of energy production and environmental protection, and help global climate governance

and sustainable development.

1 INTRODUCTION

Global climate change has become a serious

challenge to human society, and it has also triggered

a series of ecological problems, such as the

greenhouse effect, sea-level rise, and frequent

extreme weather events. Its core mechanism is

closely related to the dynamics of atmospheric

composition. The atmospheric composition, as the

pivotal element of the Earth's climate system, plays

an important role in global climate regulation. Its

small changes can have a significant impact on the

global climate. Maintaining the stability and balance

of atmospheric composition is therefore crucial to

mitigating global climate change and protecting the

Earth's ecosystem.

Among the many challenges in addressing climate

change, the energy utilization patterns are

undoubtedly one of the key factors. Electric power is

the main form of energy for production and life in

modern societies. It has a direct impact on the quality

of the atmospheric environment due to the quality of

https://orcid.org/0009-0006-0465-2016

its production methods. At present, thermal power

generation, hydroelectric power generation, wind

power generation, nuclear power generation, and

other methods of power production coexist. However,

each method of power generation has its own

limitations. Thermal power generation has a high

conversion rate and is widely used globally, but its

high energy loss and pollution levels make it one of

the main sources of greenhouse gas emissions. While

wind and hydroelectric power generation. Although

they are clean and non-polluting, have low

conversion efficiencies and are highly restricted by

natural conditions, making it difficult to meet global

electricity demand on their own. In this contradictory

energy pattern, how to rationally allocate the work of

each power plant and fully leverage the advantages of

each power generation method while minimizing

atmospheric pollution has become an urgent issue.

This study proposes an innovative concept based

on a paradigm shift in technology governance: the

construction of a Global Atmospheric-Energy Smart

Coordination System (GAESCS). The system aims to

Wu, X.

Improving the Global Atmosphere and Maintaining Atmospheric Balance by Empowering Power Stations with AI.

DOI: 10.5220/0014324400004718

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2025), pages 175-181

ISBN: 978-989-758-792-4

175

integrate high-precision atmospheric detection

technology, a real-time pollutant monitoring network

and artificial intelligence algorithms. For the

intermittent problem of clean energy, it constructs a

power prediction model based on meteorological

prediction, together with the inter-regional power

dispatch mechanism, to ensure the stability of the

energy supply and the environmental friendliness of

the dual objectives. The system will eventually form

a closed-loop optimization model of ‘ pollution

emission-energy production-load demand’, which

will maximize the comprehensive benefits of meeting

the global power demand with minimal

environmental costs.

This study try to break through the dilemma of

limitation and lag in traditional environmental

governance, and explores a systemic climate solution

based on artificial intelligence. Through considering

the global energy system and the atmospheric

environment as an organic whole, the GAESCS

system is expected to provide a new technological

paradigm for addressing climate change and promote

the transformation of human society into an

environmentally intelligent and synergistic

sustainable development model.

2 TRADITIONAL POWER

GENERATION METHODS - AN

EXAMPLE OF THE IMPACT OF

COAL-BASED POWER

GENERATION

In the global electricity production pattern, coal

power generation occupies an extremely important

position, accounting for as much as 41% of power

generation.

Coal power generation has significant advantages

in terms of energy efficiency, but its impact on the

atmospheric environment should not be

underestimated. Between 2014 and 2017, China's

thermal power industry consumed between 41.66%

and 46.49% of the country's total coal, and the

consequent emissions of SO ₂ , NOx, and smog

accounted for between 14.72% and 36.93% of the

country's anthropogenic emissions, between 20.44%

and 37.69%, and between 7.26% and 13.55% of the

country's anthropogenic emissions, respectively.

37.69% and 7.26% to 13.55% respectively. Although

thermal power still occupies a dominant position in

China's energy structure, the air pollution problem

behind its high efficiency needs to be solved

(Cui,2021).Not only China, but also other countries.

India is the world's fifth-largest power-generating

country. It has a generating capacity of about 210

GW, and the scale of power generation will continue

to expand in the future. Currently, coal power

generation accounts for as much as 66% of India's

power generation, and coal power generation also

occupies a major part of the country's new power

generation capacity planning. According to relevant

information, during the 12th Five-Year Plan period

from 2012 to 2017, India planned to add 76 GW of

coal power generation capacity, while in the 13th

Five-Year Plan period from 2017 to 2022, it planned

to add 93 GW of coal power generation capacity. In

the 13th Five-Year Plan to be implemented between

2017 and 2022, an additional 93 GW of coal-fired

power generation capacity is planned. The various

types of emissions generated during the operation of

coal-fired power plants have serious and wide-

ranging impacts on human health, mainly in terms of

triggering higher mortality and morbidity rates.

The impacts of coal-fired power plants on human

health are severe and widespread. Back in December

2011, India had 111 coal-fired power plants in

operation, with a total capacity of 121 GW. These

coal-fired power plants consume a huge amount of

coal - about 503 million ton - every year. This process

generates a large number of pollutants, including

about 580 ton per annum of particulate matter with a

diameter of less than 2.5 μm, 2,100 ton per annum

of sulfur dioxide (SO2), 2,000 ton per annum of

nitrogen oxides (NOx), 1,100 ton per annum of

carbon monoxide (CO), 100 ton per annum of volatile

organic compounds (VOCs) and 665 million ton of

carbon dioxide (CO2 ) per annum. emissions. The

emission of these pollutants places a heavy burden on

public health. It is estimated that between 2011 and

2012, there were between 80,000 and 115,000

premature deaths and over 20 million cases of asthma

caused by exposure to total PM10 emissions from

coal-fired power plants. The economic cost to the

public and the government of such a serious health

hazard is in the range of Rs. 16,000 to 23,000 crore,

which translates to US$ 3.2 to 4.6 billion. The

greatest health impacts from emissions from coal-

fired power plants are concentrated in Delhi,

Haryana, Maharashtra, Madhya Pradesh,

Chhattisgarh, the Gangetic plains of India, and most

of central and eastern India (Kone, 2017). This shows

that traditional thermal coal power generation is quite

serious in terms of atmospheric pollution, and as a

result, human life and health are seriously

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

176

endangered. Human beings have also been exploring

and developing green power generation methods.

3 ADVANTAGES OF NEW

GREEN POWER GENERATION

- SOLAR, WIND AND WATER

AS EXAMPLES

Compared with traditional thermal power generation,

solar power generation does not need to rely on

traditional fossil fuels such as coal and oil, and

produces almost no carbon dioxide during the power

generation process. These power generation methods

greatly reduce the emission of air pollutants, greatly

maintain the atmospheric balance, and reduce the

incidence of illnesses caused by air pollution. As a

source of energy, solar energy has significant

advantages: first, solar energy resources are

extremely abundant and sustainable, theoretically not

facing the problem of depletion; second, the

development of solar power generation can reduce the

large-scale exploitation of the Earth's limited

petroleum resources, and help to achieve the

sustainable use of energy and environmental

protection (Wu,2012).

Despite the significant benefits of green

development, there are several limitations and

shortcomings. Some green energy projects require

large amounts of land resources. For example, large-

scale solar power plants and wind farms need to

install equipment over a wide area. It may have

certain impacts on the local ecosystem, such as

destroying wildlife habitats and affecting land use. In

particular, in the field of hydropower development.

Hydroelectricity is more specific in terms of siting

requirements. Because of its dependence on the

conversion of potential and kinetic energy of water

flow, hydropower plants usually need to be built

adjacent to important water systems. When planning

a hydropower project, the stability of the runoff from

the water system must be accurately assessed, and the

carrying capacity of the surrounding ecosystem must

be fully considered to ensure the long-term stable

operation of the power station. The construction of

large reservoir projects may bring many disturbances

to the surrounding ecosystem. For example, changes

in hydrological conditions can affect the habitat of

aquatic organisms in the basin, and land inundation

can destroy terrestrial vegetation communities and

animal habitats. In addition, if the reservoir project

fails to ensure adequate safety performance during the

design and construction phases, in the event of force

majeure natural disasters (e.g., powerful earthquakes,

massive floods, etc.), it may face serious risks such as

dam failure, which can be devastating to the safety of

downstream residents' lives and property, as well as

to the ecological integrity of the region. In addition,

the production process of green energy devices may

also generate a certain amount of environmental

pollution, such as the manufacturing process of solar

photovoltaic panels, which involves heavy metal

pollution and other environmental issues.

Furthermore, due to technological limitations, green

power generation, such as solar and wind power, is

also highly demanding of the natural environment,

and the supply of these energy sources is dependent

on weather conditions, which does not ensure a stable

power output. Some green power generation has high

initial investment costs. For example, the

construction of large-scale solar power plants or wind

farms requires significant capital investment for

equipment procurement, installation and

infrastructure development. According to relevant

studies, the conversion efficiency of solar power is

only 15-25%, and that of wind power is only 30-45%,

while conventional power generation can maintain a

stable 35-45%.

Therefore, the new green power generation cannot

be widely promoted yet and cannot completely

replace traditional power generation. This paper will

also focus on the innovative idea of building a Global

Atmosphere-Energy Smart Cooperative System, or

GAESCS for short.

4 GAESCS DESIGN AND

FUNCTIONS

GAESCS uses a multi-tier architecture design,

including data acquisition layer, data processing

layer, intelligent decision-making layer, and

application service layer, as shown in Figure 1

(He,2025).

Improving the Global Atmosphere and Maintaining Atmospheric Balance by Empowering Power Stations with AI

177

Figure 1: System structure block diagram.

The system will integrate data from multiple

sources, including meteorological satellite data,

ground sensor monitoring data and real-time

monitoring of key parameters such as atmospheric

composition (e.g., carbon dioxide, methane, water

vapour, etc.), temperature, barometric pressure, wind

speed and so on to build a global atmospheric

information database. At the same time, the system

will access the global power production database to

obtain information on the type of power generation,

installed capacity, operational status, emission data,

etc., of each power station, and will be integrated with

the global energy consumption data, including the

consumption of electricity in various regions and

industries. Specifically, the system will integrate low-

orbit multiple satellites to ensure tight and seamless

coverage across countries around the globe (Bai,

2024), ground-based sensor arrays and high-altitude

detection platforms to form a three-dimensional

observation network.

For example, hydroelectric power generation

needs to be constructed over large river falls and also

needs to operate during seasonal periods of high river

flows. In order to be able to capture the right time to

run on time. The author proposes that the water

satellite can be used to collaborate as proposed by Liu

Xinyu and others(Liu, Zhang and Shen et al,2025).

To easy for systematic analysis, assessment, and

prediction of meteorological data and to be able to

make decisions on the optimal solution. The author

thinks that the three-dimensional visualization system

which includes meteorology, satellites and radar for

visualization and analysis by the National Oceanic

and Atmospheric Administration (NOAA) of the

United States (Rao, Zhu and Xu et al, 2023).

For the data processing of ground sensors and

high-altitude detection platforms. GAESCS performs

preliminary analysis on the raw data collected by the

sensors to remove noise, fill in missing values,

eliminate redundancy, and so on. Common

preprocessing methods include data cleaning, data

smoothing, data normalization, etc. Data cleaning is

used to remove erroneous data and outliers; data

smoothing reduces the effect of noise through

filtering algorithms; and data normalization scales the

data to a specific range to facilitate subsequent

processing.

After data processing, the system will be based on

real-time data and forecast results, the system will use

intelligent algorithms i.e. C-IDSS system to optimize

and schedule the work of each power station(Qin and

Kang,1995). According to the power demand and

atmospheric quality requirements of each region, the

tasks of different power generation modes will be

reasonably allocated, priority will be given to the use

of clean energy for power generation, and efficient

transmission and distribution of power will be

realized through smart grid technology.

The application service layer provides users with

various application functions and services. It includes

visualization interfaces for displaying atmospheric

monitoring data and energy production status, and

decision support tools for energy management and

climate governance. Users can access the system

through web browsers or mobile applications for real-

time information and decision support. The system

provides real-time monitoring of global atmospheric

composition and power production processes,

detecting anomalies and alerting relevant personnel in

a timely manner. At the same time, based on the real-

time feedback data, it dynamically adjusts the

scheduling program to ensure that the system is

always in the best operating condition.

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

178

5 MECHANISMS OF THE

SYSTEM'S CONTRIBUTION

TO ATMOSPHERIC BALANCE

AND CLIMATE

IMPROVEMENT

5.1 Optimizing Energy Structure and

Reducing Greenhouse Gas

Emissions

By reasonably allocating the work of each power

station, Priority will be given to the use of renewable

energy, such as wind and water energy for power

generation, and the reliance on high-pollution and

high-emission thermal power generation will be

reduced by reasonably allocating the work of each

power station. The system will intelligently plan the

layout of renewable energy power generation projects

according to the resource endowment and energy

demand of each region, and increase the proportion of

renewable energy in the power supply, thereby

directly reducing greenhouse gas emissions and

mitigating the trend of rising greenhouse gas

concentrations in the atmosphere.

5.2 Realize the Dynamic Balance of

Atmospheric Components

The system is able to monitor changes in atmospheric

composition concentration in real time, and when an

abnormality in atmospheric composition is found in a

certain region (e.g., carbon dioxide concentration is

too high or too low), it will indirectly affect the

emission and absorption of various atmospheric

compositions by intelligently scheduling the power

production process and adjusting the energy

consumption structure. For example, increasing the

proportion of clean energy power generation in high

carbon emission areas reduces carbon emissions; in

low carbon absorption areas, carbon sink capacity is

enhanced through optimizing energy use and

ecological construction

5.3 Promote Synergistic Global

Climate Governance

The system will break national boundaries and

geographical restrictions to achieve the sharing and

collaborative management of global atmospheric and

energy information. Countries can understand the

global climate situation and energy development

trend through the system platform, and formulate

more scientific and reasonable climate policies and

energy development strategies. At the same time, the

system can promote international energy cooperation

and technology exchanges to jointly address the

challenges of climate change.

6 VALUE OF THE SYSTEM TO

THE ENERGY SECTOR

6.1 Improve Energy Utilization

Efficiency

Through the in-depth application of intelligent

scheduling and optimization technology, accurate

scheduling models and optimization algorithms are

constructed based on the operating characteristics,

energy consumption indexes, and power production

demand of each power station. Real-time monitoring

and analysis of the power plant operation data,

dynamic adjustment of operating parameters and

power generation strategy, to ensure the power plant

is always in the high-efficiency zone. This can not

only significantly reduce the waste caused by

inefficient operation of equipment and energy

conversion loss, but also optimize the power

production process, improve the utilization efficiency

of power generation equipment, and power

generation quality. Thus realizing the all-around

improvement of power production efficiency,

providing strong support for the sustainable

development of energy and power, which is of great

academic and practical significance (Ren and Huang,

2005).

6.2 Promote Energy Transformation

and Upgrading

Accelerating the development and utilization of

renewable energy is a key path to promote the

transformation of the energy structure in the direction

of clean and low-carbon. Meanwhile, this system

builds a solid data support system and accurate

decision-making basis platform to innovate energy

technology, can effectively stimulate the vitality of

scientific and technological innovation in the field of

energy, promote the continuous innovation of energy

technology. And then enhance the efficiency of the

use of energy and the ability of sustainable

development, and help the global energy transition

and the realization of the goal of sustainable

development, for the construction of a clean, low-

Improving the Global Atmosphere and Maintaining Atmospheric Balance by Empowering Power Stations with AI

179

carbon, safe and efficient modern energy system to

provide a strong impetus (Zhang and Wang, 2025).

6.3 Ensure Energy Security and Stable

Supply

With the efficient operation of a real-time monitoring

and intelligent scheduling system, it can achieve

accurate control of energy supply dynamics. On the

one hand, real-time monitoring can capture the subtle

fluctuations in energy production, transmission and

other links in a timely manner, and accurately locate

potential risk points; on the other hand, intelligent

scheduling is based on big data analysis and complex

algorithms to quickly formulate coping strategies and

rationally deploy energy resources to ensure that in

the face of unexpected events such as extreme

weather and equipment failure, the supply of energy

does not appear to be a large gap or interruption. This

process can not only effectively smooth out short-

term fluctuations in energy supply, but also enhance

the resilience of the energy supply system, provide a

strong guarantee for the sound operation of the global

energy network, enhance the stability and reliability

of the energy system in a complex and volatile

environment, and promote the sustainable and stable

supply of energy.

7 CHALLENGES AND

SHORTCOMINGS

However, there are some challenges and limitations

to the implementation of GAESCS.

7.1 Technical Challenges

The system aims to deeply integrate cutting-edge

technologies such as artificial intelligence, big data

analysis, and satellite remote sensing to achieve the

goal of efficient and accurate application. In the

process of system construction and implementation,

the dynamic characteristics of the current

technological evolution need to be fully considered.

Although the relevant technology has achieved

significant results in theoretical research and practical

application, it must be clear that its development and

optimization process is still advancing, and has not

yet reached the stage of complete maturity and

stereotypes. Therefore, in the implementation of the

system, it is very likely to encounter all kinds of

complex and difficult technical bottlenecks. For

example, there are potential technical challenges in

the optimization of the adaptability of artificial

intelligence algorithms, the improvement of the

accuracy of big data analysis models, and the high-

precision analysis of satellite remote sensing data, etc.

It is necessary for the interdisciplinary team to

collaborate in the research, and gradually overcome

the problems through continuous R&D investment

and innovation practice, in order to ensure the stable

operation of the system and the achievement of the

expected performance.

7.2 Data Quality and Availability

In today's complex technological environment, the

accuracy and reliability of the data collected by the

system become the key factors determining the

performance and effectiveness of the system. At the

application level, data quality is susceptible to a

variety of factors that can compromise the integrity

and accuracy of the data. For example, sensor failures

can lead to biased or missing monitoring data, which

cannot truly reflect the actual situation; errors in the

data transmission process, such as signal interference

and network delays, can also impede the accurate

transmission of data, making it deviate from the

original data. In addition, there are significant

differences in the geographical availability of data,

and some regions have limited access to data and a

scarcity of data, which undoubtedly restricts the

global coverage of the system to a certain extent,

making it difficult for it to perform ideally in these

data-weak regions, and affecting the overall

applicability and universality of the system. It is thus

clear that improving data quality is crucial to

guaranteeing the effective operation of the system on

a global scale.

7.3 Collaboration and Coordination

The implementation of GAESCS (the full name of

GAESCS) involves a wide range of actors, including

government agencies, research institutions, and

energy companies. Efficient collaboration and

coordination among these parties will play a key role

in the success of GAESCS. However, it should not be

overlooked that, based on their own functional

positioning and interests, each subject upholds the

goal to different degrees of difference, the interests

and goals of the non-consistency of many obstacles to

the coordination of work, so that the process of

collaboration is faced with a complex dilemma. For

example, the government focuses on macro-planning

and social benefits, research institutes are committed

to technological innovation and knowledge

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

180

development, and energy companies focus on

economic benefits and market competitiveness,

which makes it easy for the three parties to have

disagreements and contradictions in the allocation of

resources, decision-making, and promotion of the

project, thus affecting the overall effectiveness and

quality of the implementation of GAESCS.

8 CONCLUSION

The Global Atmosphere-Energy Smart Coordination

System proposed in this paper provides an innovative

solution to address global climate change and

sustainable energy development. By integrating

global atmospheric monitoring and energy

production scheduling functions, the system is able to

achieve the goals of dynamic equilibrium

maintenance of atmospheric structure and climate

improvement. Although the system is still at the

conceptual design stage, its potential value and

significance should not be overlooked. In the future,

with the continuous development and improvement

of related technologies, this system is expected to

become an important tool for global climate

governance and sustainable energy development,

creating a cleaner, more stable and sustainable home

for mankind.

REFERENCES

Bai, Z. (2024). Research on the performance of multi-

satellite collaborative transmission for low-orbit

satellite constellations (Master's thesis). Beijing

University of Posts and Telecommunications.

Cui, L. (2021). Study on the emission of air pollutants from

26 thermal power enterprises in Yulin City and the

impact on the local atmospheric environment (Master's

thesis). Xi'an University of Architecture and

Technology.

He, J. (2025). Research on mechatronics data acquisition

method based on smart sensors. Technology and

Innovation, (07), 85-88.

Kone, S. K. (2017). Coal oriented thermal power plants in

India - Assessment of atmospheric emissions, pollution

and health impacts. Journal of Scientific Research and

Trends, 2(1), 886-892.

Liu, X. Y., Zhang, Y. J., Shen, X., Zhang, Z., Xu, W. S.,

Wu, Y., & Yang, J. H. (2025). A study on the

configuration of water resources satellite constellation

for watershed monitoring. Journal of Wuhan University

(Engineering Edition), 1-16.

Qin, Z., & Kang, J. C. (1995). Intelligent decision support

system for decision level. Journal of Northwestern

Polytechnical University, (01), 86-91.

Rao, J., Zhu, Z., Xu, P., & Ding, H. P. (2023). Visualisation

and analysis of meteorological data based on NetCDF.

Science and Technology Innovation and Application,

13(17), 18-21.

Ren, Y., & Huang, Q. (2005). Energy efficiency and

demand side management of electricity. Ecological

Economy, (02), 104-107.

Wu, W. (2012). On the economic benefits of green solar

power generation. Science and Enterprise, (08), 120-

121.

Zhang, Y., & Wang, L. (2025). Exploring the application of

intelligent scheduling algorithm in carbon emission

reduction of urban distribution in Shijiazhuang.

National Circulation Economy, (07), 57-60.

Improving the Global Atmosphere and Maintaining Atmospheric Balance by Empowering Power Stations with AI

181