Revista de Ciencias Tecnológicas (RECIT). Volumen 3 (1): 10-22
Revista de Ciencias Tecnológicas (RECIT). Universidad Autónoma de Baja California ISSN 2594-1925
Volumen 8 (2): e398. Abril-Junio, 2025. https://doi.org/10.37636/recit.v8n2e398
ISSN 2594-1925
1
Research article
AI in public administration-transformative opportunities
for climate resilience and sustainable development
IA en la administración pública: oportunidades transformadoras
para la resiliencia climática y el desarrollo sostenible
María E. Raygoza-L. , Jesús Heriberto Orduño-Osuna , Gabriel Trujillo-Hernández ,
Fabian N. Murrieta-Rico
Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja
California, México
Corresponding author: María E. Raygoza-L. Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376
Mexicali, Baja California, México. E-mail: maryraygoza963@gmail.com. ORCID: 0009-0000-5969-6602.
Received: January 8, 2025 Accepted: March 27, 2025 Published: April 3, 2025
Abstract.- The accelerated growth in demands for natural resources such as water and energy has generated a potential
energy and water crisis, while the requirements have been hastily driven by the development of emerging technologies that
have spanned the various sectors, so the intersection of these technologies, such as Artificial Intelligence (AI), in sustainability,
governance and public policies, offers transformative opportunities to combat climate change and promote sustainable
development. This study explores the integration of AI in public administration to promote climate resilience, equity and
innovation, highlights the applications of AI in resource management, disaster prediction, renewable energy optimization and
planning. sustainable, highlighting the priority role of public policies, ethical frameworks and public-private collaborations to
ensure the equitable and transparent deployment of AI. Challenges such as data accessibility, resource allocation and adjacent
regulatory balance are analyzed with strategies to overcome them, including capacity development and infrastructure
investment. The innovative findings suggest that AI as a tool for efficiently managed climate action helps to address
environmental challenges, highlighting key elements such as sustainable development through AI that requires collaborative
integration between stakeholders, such as those across sectors, integrating equity and ethical principles into climate action
and resource management policies. This integrated approach positions AI as a fundamental tool for a more sustainable and
equitable future.
Keywords: Artificial intelligence (AI); Climate change sustainable development; Renewable energy; Public policies;
Governance.
Resumen.- El crecimiento acelerado de las demandas de recursos naturales como el agua y la energía ha generado una
potencial crisis energética e hídrica, mientras que los requerimientos han sido impulsados apresuradamente por el desarrollo
de tecnologías emergentes que han abarcado los diversos sectores, por lo que la intersección de estas tecnologías, como la
Inteligencia Artificial (IA), en la sostenibilidad, la gobernanza y las políticas públicas, ofrece oportunidades transformadoras
para combatir el cambio climático y promover el desarrollo sostenible. Este estudio explora la integración de la IA en la
administración pública para promover la resiliencia climática, la equidad y la innovación, destaca las aplicaciones de la IA
en la gestión de recursos, la predicción de desastres, la optimización de las energías renovables y la planificación sostenible,
destacando el papel prioritario de las políticas públicas, los marcos éticos y las colaboraciones público-privadas para
asegurar el despliegue equitativo y transparente de la IA. Se analizan desafíos como la accesibilidad de los datos, la asignación
de recursos y el equilibrio regulatorio adyacente con estrategias para superarlos, incluido el desarrollo de capacidades y la
inversión en infraestructura. Los hallazgos innovadores sugieren que la IA como herramienta para la acción climática
gestionada de manera eficiente ayuda a abordar los desafíos ambientales, destacando elementos clave como el desarrollo
sostenible a través de la IA que requiere la integración colaborativa entre las partes interesadas, como las de todos los
sectores, integrando la equidad y los principios éticos en la acción climática y las políticas de gestión de recursos. Este enfoque
integrado posiciona a la IA como una herramienta fundamental para un futuro más sostenible y equitativo.
Palabras clave: Inteligencia artificial (IA); Cambio climático; Desarrollo sostenible; Energías renovables; Políticas públicas;
Gobernanza.
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1. Introduction
One of the most pressing global challenges of our
time is the climate crisis. Over the past century,
human activities, particularly fossil fuel
combustion, industrialization, and unchecked
economic growth have led to unprecedented
levels of greenhouse gas emissions, resulting in a
rapid rise in global temperatures. According to
the Intergovernmental Panel on Climate Change
(IPCC), the global temperature has increased by
1.1°C above pre-industrial levels, already
exacerbating extreme weather events,
accelerating biodiversity loss, and driving sea-
level rise at an alarming rate. The IPCC further
warns that, without immediate and significant
intervention, temperatures could rise by an
additional 1.5°C by 2050, leading to irreversible
impacts on ecosystems, economies, and
communities worldwide [1].
This multifaceted crisis demands urgent and
coordinated global solutions, alongside localized
action, to address the various symptoms of
climate change and mitigate its ongoing impact.
However, current commitments fall far short of
the necessary actions outlined in scientific and
policy frameworks. For example, the Paris
Agreement has set ambitious goals to limit global
warming to well below 2°C, yet the progress in
meeting these targets remains insufficient. To
accelerate this process, the integration of cutting-
edge technologies, particularly Artificial
Intelligence (AI), presents a unique opportunity
to expedite efforts in climate action and foster the
development of resilient system action across
syndromes of impact urgently [2]. AI has
emerged as a transformative tool in the fight
against climate change, offering innovative
solutions across multiple domains, including
renewable energy management, environmental
monitoring, disaster prediction, and urban
planning. The ability of AI to process vast
amounts of data, identify complex patterns,
optimize resource allocation, and predict future
climate trends presents an extraordinary
opportunity for enhancing climate resilience. As
AI continues to advance, it holds the potential to
revolutionize our approach to combat climate
change, enabling more efficient, data-driven
strategies for adaptation and mitigation [3].
The International Energy Agency expresses the
need to adapt to emerging technologies,
especially in the field of renewable energy, AI
has been identified as a fundamental tool to
optimize the efficiency of the energy network
and improve the integration of renewable
sources. Machine learning (ML) algorithms, for
example, can analyze data in real time to improve
energy distribution, predict fluctuations in
demand and facilitate the efficient use of
renewable resources. These capabilities are
necessary in the global transition towards cleaner
and more sustainable energy systems [4]. In
addition, AI is being applied in the monitoring of
environmental pollutants, where it helps track
changes in air and water quality and provides
early warnings of dangerous conditions, thus
preventing environmental degradation and
protecting public health. This information is a
platform for government decision-making and a
platform for smart public policies.
The experience and background to address the
potential of AI in relation to climate change is
immense, and its successful implementation
requires strong public sector engagement. The
policies, regulations and funding mechanisms
needed for the effective implementation of AI
technologies in sustainability initiatives.
Governments and public administrators have a
responsibility to create an enabling environment
that supports the large-scale adoption of AI
solutions for climate action, ensuring that AI is
developed and used in an ethical and sustainable
manner [5].
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Public administrations have access to vast data
sets, such as demographic information, health
records, and infrastructure data, which are often
unavailable or costly to obtain for private
entities. These large data sets provide AI models
with the basic knowledge needed to address
climate change on a global scale. By leveraging
these data sets, AI can help inform policy
decisions, predict climate impacts, and optimize
interventions in national and global contexts.
This data-driven approach can improve the
effectiveness of climate action, making it more
targeted, equitable, and efficient. AI has recently
been identified as a viable solution in numerous
domains of climate action, including renewable
energy management, environmental monitoring,
disaster prediction, and urban planning [6]. AI
could use public health data to model the effects
that climate change will have on disease
transmission, and urban planning data to plan
more resilient cities. These resources can spur
innovation, enabling more inclusive and data-
driven climate action when governments make
them available. Government leadership is also
critical to guiding AI deployment toward
equitable outcomes. Climate change affects all
people, and generally the impact can be greater
on the socially vulnerable population, who are
also those who have the least impact on the
environment in terms of Greenhouse Gas
(GHG) emissions , as they are communities in
developing countries [7].
2. Methodology
This research employs a multidisciplinary
approach and a qualitative and quantitative
research design to explore the implementation of
AI in climate change mitigation and adaptation,
with a particular emphasis on the role of public
administration. The study seeks to understand
how governments can use AI to promote
sustainable solutions, enhance climate resilience,
and ensure equitable outcomes for the most
vulnerable sectors. The phases of the
methodological process are described below.
Literature Review: To theoretically underpin
the research, a systematic literature review on AI
applied to climate change and sustainability will
be conducted. Peer-reviewed scientific articles,
reports from international bodies (such as the
IPCC and the UN), and public policy documents
will be included. The review will focus on three
main areas.
Applications for AI in climate change:
Identification of key areas in which AI has been
successfully applied, such as renewable energy
management, natural disaster prediction, and
urban infrastructure optimization.
Role of public administration in
sustainability: Analysis of the role of
governments and public bodies in the
implementation of sustainable technologies, with
an emphasis on public policies that facilitate the
development and adoption of AI.
Ethical and social challenges: Review of
current debates on the ethics of AI, equity in
access to its benefits and the risks associated with
algorithmic bias and the protection of personal
data.
The selection of studies will be carried out
following criteria such as the impact of the
research, its relevance to the practical application
of AI in sustainability and its contribution to
understanding the interaction between AI and
public policies.
A government-driven approach guarantees
Extending the Scope of AI-Powered Solutions
for Climate Action and Equity
Artificial Intelligence holds transformative
potential in addressing climate change while
ensuring equity in its application. For
populations often marginalized by systemic
inequalities, AI-powered solutions can help
bridge disparities instead of exacerbating them.
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For instance, AI can significantly improve early
warning systems for natural disasters, which are
traditionally less accessible to vulnerable and
underrepresented communities. By integrating
AI-driven predictive analytics, these systems can
provide timely alerts and more precise forecasts,
enabling at-risk populations to take necessary
precautions and reducing the overall impact of
such events [2].
Innovation and development for climate change
mitigation by Innovation and development for
climate change mitigation by applying AI tools,
integration into public policies and quality
governance, is closely aligned with all the United
Nations Sustainable Development Goals, in
particular SDG 10 (reduced inequalities), SDG 7
(affordable and clean energy) and SDG 13
(climate action). The ethical and equitable
implementation of AI can help these
technologies reduce the gap between emerging
and developing economies, as well as empower
vulnerable areas, addressing inequalities in
addition to being tools that help public health and
can save lives with access to life-saving
innovations and reduce economic losses [8].
Ensuring inclusion in AI applications is essential
to foster global sustainability and avoid a
technological divide.
The Role of Public Administration in AI
Deployment: Although the potential of AI is
undeniable, its meaningful application requires
robust public administration to guide its
development and use. Governments and
policymakers play a critical role in creating a
supportive ecosystem for AI applications tailored
to specific climate challenges. This involves a
regulatory framework that encourages
innovation while ensuring ethical data use and
equitable outcomes. Policies must also address
concerns about bias in AI algorithms and
prioritize transparency to build trust and promote
widespread adoption [9]. Public administration
must further support AI initiatives by providing
funding mechanisms and fostering public-private
partnerships that incentivize research and
development of climate-focused AI solutions.
For example, initiatives like the European
Union’s Green Deal have demonstrated how
strategic government-led investments can drive
innovation in AI applications for energy
efficiency and carbon reduction [10].
Public Administration and Sustainable
Innovation: The emergence of AI and other
advances in technology as essential tools for
sustainable development is creating a new
responsibility across public administration to
play an active role in their ethical deployment.
Governments in many countries have started to
see the power of AI for solving environmental
issues from mitigation measures such as carbon
emissions reduction process to adaptation and
biodiversity conservation and are also likely to
play an essential role in ensuring that these
solutions develop and scale through public
administration [11].
Public administration can take a variety of
approaches to promote sustainable AI, ranging
from incentive program implementation to
regulatory frameworks that ensure responsible
adoption of AI. These roles range from
guidelines and policies that promote innovation,
standards of data privacy and transparency as
well as incentives for the private sector to employ
sustainable practices. With consistent regulations
and policies, public institutions can reduce the
possible risks of AI while increasing their
potential for sustainable innovation. For instance,
public administration can be a driver of
sustainability in AI via providing financial
incentives/grants for research and development
(R&D) on green technologies [12].
AI projects in the field of environmental
sustainability and modelling for climate change
mitigation, waste reduction, renewable energy
optimization and other fields related to reducing
GHG emissions in developing countries maintain
funds and economic resources for program
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development. One program that takes advantage
of this situation is the European Union's Horizon
2020, which has allocated billions of euros to
research projects, many of them related to the use
of AI to address climate and energy challenges
[10]. Funding for research institutes, universities
and private industries promotes the creation of AI
models designed for use in issues related to our
environmental problems [13]. Public
administration is also responsible for creating the
ethical code and regulatory frameworks that
allow them to be accountable for the responsible
and transparent use of AI in relation to climate
objectives. Governance policies that align the
needs of climate change mitigation strategies,
climate solutions, and AI tools are critical, as
many of these AI-powered climate solutions rely
on the integration and alignment of all
stakeholders.
Strategic Policy Approaches and
Recommendations
To maximize the benefits of AI in climate action,
governments must adopt a comprehensive policy
framework that addresses the key drivers of AI
innovation while ensuring ethical, equitable, and
sustainable outcomes. By implementing strategic
approaches, public administrations can
effectively facilitate the deployment of AI in
climate action and sustainable development.
Governments play a critical role in shaping the
policy landscape, ensuring the responsible
development and use of AI technologies, and
fostering the equitable distribution of their
benefits. Public administrations can also act as
key enablers, addressing challenges such as
regulatory complexities, infrastructure gaps, and
capacity shortages. The following strategies and
policy recommendations outline a roadmap for
integrating AI into climate action:
Regulatory Support: Governments should
establish clear and comprehensive regulatory
frameworks to guide the ethical development and
deployment of AI technologies in climate action.
These frameworks should include guidelines to
ensure transparency, accountability and fairness
in AI systems, addressing critical concerns such
as data privacy, algorithmic biases and
unintended consequences of automation in
environmental decision-making. The
Organization for Economic Co-operation and
Development (OECD) recommends offering
support to provide regulatory approaches that
balance innovation with accountability. By
promoting transparent and accountable AI
systems, governments can mitigate risks and
maximize the potential of AI to advance climate
action [14].
Infrastructure Investment: A foundational
component of AI innovation is the availability of
infrastructure that enables access to large
datasets and powerful computational resources.
Governments should invest in creating shared
platforms for climate data, making these
accessible to the public and open for AI-driven
research and innovation. Such investments can
also promote collaborations among academia,
industry, and government, leading to the
development of AI tools that address pressing
environmental challenges. For example,
Microsoft’s AI for Earth initiative provides
access to advanced AI models to support
environmental protection [15].
Capacity development: To ensure the effective
and equitable implementation of AI solutions,
governments should prioritize capacity
development initiatives, particularly in
developing countries and marginalized
communities. Education and training programs
in AI and sustainability are essential to create a
skilled workforce capable of addressing the
complexities of climate change with AI tools.
Governments should support the integration of
AI curricula in universities, research institutions,
and platforms that offer valuable opportunities
for capacity development in AI applications for
sustainable development [16].
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Fostering Local Innovation and
Entrepreneurship: Governments should
encourage local entrepreneurship in AI-driven
sustainability solutions. Supporting the creation
of startups focused on leveraging AI to focus
environmental challenges will foster innovative,
context-specific solutions. Facilitating access to
resources such as funding, technical expertise,
and support networks is crucial for enabling
entrepreneurs to scale their AI solutions, thereby
contributing significantly to climate mitigation
efforts.
Monitoring and Managing Natural Resources
with Artificial Intelligence: has the potential to
transform the way natural resources are
managed, including everything from water to
vegetation cover. Governments should
encourage the adoption of AI technologies that
enable more efficient monitoring and
management, to increase climate resilience.
These technologies not only increase resource
use efficiency, but also contribute significantly to
climate resilience, helping communities better
adapt to the impacts of climate change.
Integrating AI into Climate Policies: AI can play
a critical role in developing policies to predict
and mitigate the impacts of climate change.
Governments should incorporate AI tools into
their national climate adaptation plans, allowing
them to predict risks and design proactive and
effective solutions, such as early warning
systems such as those implemented by the World
Bank for floods and tropical cyclones have
proven to be essential to save lives and reduce
economic losses [17]. Climate impact modeling,
and proactive mitigation policies that have been
developed by countries such as Germany, AI
systems have been implemented to predict the
impact of urban expansion on ecosystems and
develop more robust environmental regulations
.The integration of AI into climate policies
allows governments to act more quickly and
accurately in the face of environmental
challenges. Furthermore, by combining these
technologies with participatory strategies,
inclusive solutions can be generated that consider
the needs of the most vulnerable communities
[18].
Promoting Research and Development (R&D) in
AI for Climate Action: To establish AI as a
cornerstone in combating climate change,
governments must invest in R&D for new
technological applications. Public funding
should support scientific research on AI for
sustainability, encouraging collaborations among
universities, research institutes, and industry.
Facilitating public-private partnerships can
maximize resources allocated for R&D and
foster knowledge exchange across sectors,
accelerating the development of impactful AI
solutions. By implementing these strategic
approaches, governments can harness the
transformative potential of AI to address climate
challenges, fostering a sustainable and resilient
future [19].
Design finances Design of Financial Incentives
for Artificial Intelligence Startups: The design of
specific financial incentives can be a relevant
factor to boost the growth of startups that address
sustainability-related challenges through the
application of AI. Measures such as tax breaks,
subsidies and low-interest loans have proven to
be effective tools to accelerate the development
of startups focused on high-impact solutions to
environmental, social and economic problems.
Some examples can be the Acceleration
Programs in Germany where financial support
has been incentivized for startups that develop AI
technologies applied to sustainability. These
include tax incentives and access to low-cost
capital, which has allowed the creation and
scaling of innovative solutions in areas such as
renewable energy and efficient water resource
management. In addition, subsidies in the
Netherlands in the Dutch government offers
subsidies to companies that integrate AI in
sustainability projects, such as agriculture and
sustainable urban mobility [14].
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In resource management and the formulation of
sustainable policies, elements such as the
following should be considered:
Data Collection – Sensors, satellites, and
databases gather real-time climate, energy, and
environmental information.
AI Analysis – Machine learning and deep
learning algorithms process the data to detect
patterns and generate predictions.
Resource Optimization – Smart grids, water
efficiency, and sustainable urban planning are
enhanced through automation.
Government Decision-Making – Control
panels with key metrics enable leaders to design
data-driven strategies.
Policy Implementation – Sustainability
regulations and strategies are approved and
executed based on predictive models.
Sustainable Impact – Reduced CO₂ emissions,
green urban development, and resilient
communities reflect the benefits of technological
integration.
As mentioned, environmental and social
challenges require increasingly strategic
responses. From the points mentioned above, it
can be understood that the integration of AI,
Research and Development (R&D) in AI projects
that promote sustainability, integrated through
adequate public administration management, can
strengthen the fight against climate change, the
reduction of emissions and sustainability. The
following diagram (Figure 1) through
connections and visual elements illustrates how
all these elements are key components to achieve
sustainable development.
Figure 1. Methodology for the Role of Public Administration in Implementing Sustainable Solutions. Source: Own
development
Challenges in public administration and the
implementation of public policies
Although progress has been made in using AI to
promote sustainable development, public
administration faces several challenges that
hinder the effective implementation of AI-based
public policies. These challenges include:
Resource Allocation: Implementing AI solutions
for sustainability requires significant financial
resources and a reorganization of human capital.
However, governments often face budget
constraints and competing political priorities,
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making it difficult to secure the necessary
investment [20].
Access to Quality Data: AI models rely on large
volumes of accurate and up-to-date data. In many
regions, especially in developing countries,
accessing high-quality environmental data is a
challenge. It is crucial for governments to
establish frameworks for data sharing and ensure
data quality.
Data Availability and Quality: AI systems need
complete and consistent datasets to function
effectively. In many areas, data is incomplete or
inconsistent, limiting the effectiveness of AI
solutions [21].
Balancing Innovation and Regulation: Striking a
balance between fostering innovation and
establishing regulations is complex. Overly strict
regulations can hinder progress, while overly
lenient ones may lead to ethical or environmental
risks. Governments must continuously adopt
regulatory frameworks to keep pace with rapid
technological advancements.
Training AI Models: Training AI models,
especially in areas like energy consumption,
require significant computational power, which
in turn generates carbon emissions. Developing
energy-efficient AI algorithms is essential to
mitigate this impact [22].
Bias and Equity: AI systems can perpetuate
existing inequalities if trained on biased datasets.
Ensuring equitable access to AI technologies and
addressing algorithmic biases is crucial for
promoting inclusive climate action [23].
Regulatory and Ethical Frameworks: The rapid
advancement of AI demands robust regulatory
frameworks to ensure ethical implementation.
Concerns such as data privacy, transparency, and
accountability must be addressed to build trust in
AI systems [24].
The International Energy Agency has begun to
use AI as a tool to address the scale and
complexity of climate change, offering
opportunities to improve renewable energy
management, monitor environmental pollutants,
optimize transportation systems, and even
improve urban planning. For example, machine
learning models have been deployed to optimize
power grid operations, allowing for better
integration of renewable energy sources and
reducing overall energy waste [4]. Though,
challenges remain in ensuring that these
technologies are accessible and scalable.
Resource constraints, particularly in low-income
regions, can hamper the deployment of AI
solutions where they are most needed.
Furthermore, the energy-intensive nature of AI
itself raises concerns about its carbon footprint,
necessitating advances in energy-efficient
computing and greener AI development practices
[25].
AI has emerged as a transformative technology
with the potential to significantly address
challenges related to climate change and
environmental sustainability. Its unique
capabilities, including advanced data analysis,
pattern recognition, predictive modeling, and
process optimization, align seamlessly with the
complex demands of environmental science and
resource management [26].
AI enables the analysis of vast datasets from
diverse industries, providing actionable insights
and fostering the development of innovative
strategies that were previously unattainable with
conventional methods. However, its
implementation is not without obstacles and
risks. These challenges span across technical,
ethical, and environmental dimensions,
highlighting the need for a balanced and
responsible approach to integrating AI into
climate solutions. AI presents transformative
opportunities in areas such as:
Climate Modeling: AI enhances the accuracy of
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climate models by analyzing historical data and
simulating future scenarios with greater
precision. Machine learning algorithms can
uncover subtle patterns in weather and climate
systems, helping scientists predict long-term
changes and assess potential impacts.
AI for sustainability and climate change:
Climate change is a global phenomenon that
requires international cooperation. This requires
governments to also participate in global regimes
and enter into bilateral agreements and global
alliances that promote the transfer of AI tools and
technologies relevant to climate action [27], that
promote collaboration between countries to use
AI in sustainable development goals, with the
aim that these associations facilitate the transfer
of technology and R&D projects around AI and
environmental sustainability [28].
Resource Management: AI applications in
resource management range from optimizing
energy grids to improving water usage
efficiency. By leveraging real-time data, AI
systems can identify inefficiencies, minimize
waste, and support the transition to renewable
energy sources.
Disaster Prediction and Mitigation: AI-driven
tools have proven effective in predicting natural
disasters such as hurricanes, floods, and
wildfires. Early warning systems powered by AI
can help mitigate risks, protect vulnerable
populations, and reduce economic losses [29].
Sustainable Urban Planning: Through AI,
urban planners can model sustainable city
layouts, optimize transportation systems, and
design buildings that minimize carbon footprints.
This ensures that urban growth is aligned with
environmental goals [29].
Methodology for Implementing Sustainable
Solutions
This framework emphasizes the collaborative
efforts required between governments,
industries, and academia to foster AI-driven
innovations while addressing ethical and
environmental concerns.
Data Integration: Governments should
facilitate the aggregation of diverse datasets,
ensuring accessibility for AI-based climate
research and applications.
Capacity Building: Training initiatives aimed at
equipping stakeholders with AI literacy are
pivotal for informed decision-making.
Policy Formulation: Developing inclusive
policies that guide ethical AI deployment in
climate-related applications forms the backbone
of sustainable AI adoption.
Public-Private Partnerships: Collaborative
ventures between public and private sectors can
accelerate the scaling of AI solutions to address
climate challenges effectively.
Figure 2 illustrates a representative example of
the methodology for the role of public
administration in implementing sustainable AI
solutions, incorporating the need for a regulatory
framework and infrastructure investment. By
integrating these strategic components, public
administrations can improve the effectiveness of
AI applications in climate action and help ensure
that the benefits of the technology are distributed
equitably and sustainably across all sectors of
society.
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Figure 2. Methodology for the role of public administration in implementing sustainable solutions. Source: Own
development.
Climate Modeling and AI One of the most
fundamental areas where we can apply AI to
climate action is in climate modeling. Climate
models, however, are incredibly useful for
scientists and policymakers because they can
simulate how the global temperature will respond
to things like emissions, deforestation or ocean
temperatures. Since traditional climate models
are computationally expensive and need
terabytes of data and days to process the last
simulations for even tiny geographical areas,
Climate models driven by AI can speed these
processes up to an exponentially higher range of
accuracy and resolution, while incorporating a
wider array of variables and scenarios [30].
Studies indicate that increasing or decreasing the
precision of climate projections helps researchers
explore a mitigation path with high resolution
that was previously almost impossible.
Currently, tools such as neural networks and
machine learning algorithms predict the future
with greater precision with relevant information
regarding climate change such as energy
potential, deforestation, water stress, etc. [31].
AI for Climate Action: Benefits, Challenges
and Risks
Climate scenarios that use historical weather data
to discern patterns that capture carbon emissions
and trends in natural resource data, such as
modeling weather predictions that allow for more
accurate short-term forecasts, which is especially
important during rapid response and long-term
planning. By facilitating a proactive
environmental decision-making system, such
predictive capability can help policymakers
prioritize conservation areas, plan climate-
resilient infrastructure, and allocate mitigation
resources more effectively [32]. While the
benefits of AI are promising, its potential use in
climate change does present several ethical and
environmental challenges.
Resource intensive: Governments play a critical
role in data that requires a lot of computational
power. A single deep learning training is
comparable to the lifetime energy consumption
of an average car. The data centers within the AI
model training process generated CO2 emissions
when powered by non-renewable energy sources.
Researchers found data showing that training a
large AI model can produce as much CO2 as five
cars over their lifetime. Data centers need to use
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renewable energy to mitigate the environmental
impact of AI [33].
Data privacy and security: Many AI applications
for climate monitoring require significant data
sets to process, aggregated data that includes
geolocation data, weather, and population health
records. These data sets are necessary for
accurate climate models, but they also pose
significant privacy risks that can hinder their
responsible use. Especially if data subjects
receive personal information, formulating strong
data governance policies is very important to
ensure that there will be no breaches [10].
Equity and access: Creating climate change
solutions using AI will require a lot of funding,
access to data and resources, and expertise in
ML. Consequently, the immediate beneficiaries
of these technologies are wealthy nations and
corporations. This inequity can worsen
inequalities that already exist, depriving
communities that need it the most (those most
affected by climate change) of these
technologies. Public administration plays an
important role in reducing the digital divide and
ensuring that AI technology is available to all
regions and demographic groups, as indicated by
the International Development Research Centre
(IDRC), and thus reducing the gap in the most
vulnerable groups [34]. AI models must work to
produce unbiased results, to obtain less biased
results, and avoid making incorrect predictions
for certain areas, which could affect disaster
response or resource distribution [35].
Resource Management and Optimization: The
potential of AI even extends to the way we
manage our natural resources, allowing us to use
them more efficiently and sustainably with a
lower environmental impact. AI in energy
management is used to forecast demand,
optimize distribution, and combine renewable
sources with current grids. AI reduces emissions
and allows energy providers to respond to
changes in supply and demand by improving grid
efficiency and ensuring that the cleanest forms of
energy are used as much as possible. World-class
companies such as “Google” have demonstrated
the impact of this potential with the “DeepMind
AI” initiative, which improved energy efficiency
in its data centers by 40%, demonstrating the
power of this technology to reduce carbon
consumption in high-energy consumption sectors
[36].
Applications in agriculture: In agriculture, AI
helps sustainable practices by assessing soil
health, observing crop health, and managing
water consumption. Using the data available
from satellite images and IoT devices in the
fields, their machine learning models process
them to make decisions about irrigation, early
detection of pests and minimize the use of
chemicals [37] . They can also improve the
impact of water stress by using applications for
optimizing water based on needs, which can help
reduce environmental pollution in the
agricultural sector, which is one of the economic
activities that generates large GHG emissions.
Waste management: which is an integral part of
the circular economy [38]. While computer
vision algorithms manage the automatic sorting
of recyclable materials, several predictive
models collect and optimize collection routes for
waste management to minimize fuel
consumption and emissions during them.
Previous analysis and experience indicate that
many AI tools can be applications that speed up
the process of sorting recyclable materials,
leading to a reduction in the amount of waste
thrown into landfills. These and many more
advances in AI tools for environmental
improvement show the ability of AI to enable
sustainability in all economic sectors, just as
these applications can be extended to the
domestic sector.
Anticipation and response to natural disasters:
This important use of AI in climate action is to
predict and respond to natural disasters. Global
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warming is making natural disasters such as
hurricanes, floods, wildfires, and droughts more
frequent and intense. These predictive models are
powered by AI, so they not only predict, but they
also do so with significantly greater accuracy and
in less time, providing timely alerts to allow
authorities and communities to prepare.
Foresight allows for better resource gathering,
evacuation measures, and the prevention of
deaths and property damage when appropriate.
Based on these AI models, hurricane trajectories
and wildfire risks can be predicted, and timely
alerts can be provided that save lives as well as
economic losses [39]. Another essential
application of satellite imagery and machine
learning algorithms is disaster monitoring. In the
aftermath of a disaster, AI models rapidly
process satellite imagery to assess how much
damage has occurred, speeding up the response
and delivering resources where they are most
needed. In partnership with private tech
companies, the United Nations used AI to
monitor areas at risk of flooding and pre-allocate
resources to safeguard lives and assets [40].
Success stories in countries with governmental
initiatives applying AI tools
In the current era, Artificial Intelligence (AI) has
become a key tool for addressing some of the
most pressing challenges related to sustainability
and climate change. Governments around the
world are implementing innovative initiatives
that leverage the power of AI to optimize
resources, reduce emissions, and improve
efficiency across various sectors. These projects
not only demonstrate the potential of AI to
transform environmental and energy
management but also serve as inspiring models
for other countries seeking sustainable solutions.
Some of the benefits shown in projects when
applying AI to sustainability can be the
following:
Climate Prediction: AI can analyze historical
climate data to identify patterns and predict
extreme events, such as hurricanes or floods,
enabling more effective planning and rapid
response.
Natural Resource Management:
Sustainable Agriculture
Table 1 below shows success stories in countries
with government initiatives that apply AI tools.
These examples illustrate how technology is
being used to predict natural disasters, manage
waste, optimize the use of renewable energy, and
improve energy efficiency, among others. Each
project highlights the specific application of AI,
its impact, and the technological tools used.
Table 1. Success stories in countries with governmental initiatives applying AI tools. Source: Prepared by the authors with
information from OECD and EPA.
Country
Project
Impact
AI Tool
Japan
Earthquake Prediction
System
Reduction in response times and
optimization of emergency resources.
Real-time seismic data
analysis
Denmark
AI4PublicPolicy
Greater efficiency in resource
allocation and improved data-driven
policy formulation.
Data analysis for
public policies
Singapore
Smart Nation Initiative
- Waste Management
Reduction in operational costs and
lower carbon footprint in public
services.
Sensors and route
optimization
Kenya
AI for Renewable
Energy Management
Contribution to the transition toward
a low-carbon economy.
Renewable energy
optimization
United
States
Grid Modernization
Initiative
Reduction in energy losses and
greater integration of renewable
energy sources.
AI for electrical grid
management
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Country
Project
Impact
AI Tool
Mexico
Energy Efficiency
Program for Buildings
Reduction in energy consumption and
savings in operational costs.
Sensors and data
analysis
The success stories presented in this table
demonstrate how AI can support the fight against
climate change and the promotion of
sustainability. However, it is crucial to address
associated challenges, such as resource intensity
and privacy concerns, to ensure these
technologies are used responsibly and equitably.
3. Results and Discussions
Policy Recommendations for Enabling AI
Applications through New Sustainable
Solutions
There are several measures that the government,
together with different stakeholders such as
academia, public and private institutions, as well
as civil society, can adopt to facilitate the
development and adoption of AI technologies
that promote sustainability in sectors such as
energy, agriculture and waste management. Key
policy recommendations to support the
implementation of AI at the intersection of these
areas: Investment in research and development
(R&D): Governments should increase funding
for research projects, as well as generate new
grants that integrate AI with sustainability issues.
This involves working with universities, research
institutes and private companies that create AI-
based renewable energy optimization solutions,
carbon capture technologies, biodiversity and
ecosystem monitoring tools and climate
prediction model databases. By investing in
green technologies and providing tax incentives
for companies to take steps to reduce carbon
emissions, the shift from conventional energy to
clean energy can be combined with the
development and stimulation of innovation and
reduce costs by driving the adoption of AI for
environmental protection. By creating AI
innovation hubs, governments can establish
collaboration between private and public sector
stakeholders to develop and implement
sustainable AI-based solutions. These hubs could
help facilitate knowledge transfer, test new
technologies, and effectively scale up the
application of AI. Innovation hubs would
facilitate the creation and mobilization of AI for
climate action through visionary collaborative
ecosystems [41]. Table 2 shows countries with
successful cases of sustainability policies
integrating government or public administration.
Table 2. Successful AI and Sustainability Policies As case studies within the realm of public administration.
Country
Policy/Initiative
Key Features
Outcomes
Singapore
Smart Nation
Initiative
Integration of AI and IoT technologies for
sustainable urban development. Real-time
monitoring of energy, waste, and air quality.
Efficient resource allocation,
pollution reduction, and enhanced
urban management.
Finland
National AI
Strategy
Collaboration with local businesses to optimize
waste management, reduce landfill, and promote
recycling.
Enhanced waste management
systems, improved recycling rates,
and public-private synergy.
Netherlands
(Amsterdam)
AI for Good and
SDGs
Policies leveraging AI for urban mobility, carbon
mitigation.
Improved public transport systems,
reduced carbon emissions.
United
States
AI for
Environmental
Monitoring
Use of AI for wildlife protection, air quality
analysis, and water resource management.
Advanced monitoring systems to
prevent biodiversity loss and
improve environmental.
Germany
Sustainability
Accelerator
Program for AI
Financial incentives such as subsidies and tax
breaks for startups addressing sustainability
challenges with AI innovations.
Boosted innovation and market
entry for green AI startups,
promoting long-term sustainability.
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Startups
Canada
AI for Climate
Adaptation
Program
AI applications for early disaster detection, urban
resilience planning, and sustainable agriculture.
Reduced vulnerability to climate-
related risks and enhanced
sustainable agricultural practices.
Source: Own development.
Public-private collaboration is essential for
innovation to contribute to sustainable
development. Its integration into the guidelines
of public policies that drive the government are
platforms that can generate radical changes in the
transition to environmental sustainability. The
support of public and private initiatives is
especially important for emerging and complex
technologies such as AI, which require
significant financial investment, technical
capabilities and access to data, as well as trained
personnel with extensive experience. Public
administration, in association with the private
sector, can generate a link that can take full
advantage of all the resources and innovation
capacity obtained from industry [42]. This means
that public-private partnerships, which allow
governments to take advantage of knowledge and
resources, could help build policies that drive
regulations that ensure the long-term public
interest without being sensitive to political
changes, thus aligning themselves with the
objectives presented by climate change.
Integrating AI into climate change mitigation and
adaptation strategies offers opportunities across
sectors, as illustrated in the study. The findings
reveal that AI’s ability to process large data sets,
predict trends and optimize resource allocation
has transformative implications for combating
climate change.
Key findings include:
Establishing clear frameworks: AI governance
frameworks should set boundaries on the things
that society considers ethical when AI is being
deployed and thus governments must come up
with such stuff. These frameworks need to ensure
that AI solutions used in the interest of our
environment do not threaten the livelihoods of
vulnerable communities or violate privacy rights
further exacerbating disparities between “rich
and poor”. The EU, through its AI, is rolling out
a whole new set of guidelines covering high risk.
Advocating for Transparency and
Accountability: To earn public trust in AI
technologies, governments must regulate
transparency of AI algorithms and processes
involved with decision-making. Transparency
initiatives can involve companies having to
disclose how their AI models were trained, what
data was used and what steps are taken to relate
outcomes. For instance, the U.S. has initiated AI
transparency measures in government
contracting that could be adapted to the same
sector for assessing fairness and accountability in
climate change mitigation.
Introducing Guidelines and Certifications for
Ethical AI: Governments need to come up with
some form of guidelines or certification process
on ethically designed Ai systems. Such standards
should be developed in association with
international agencies, laying down points on
fairness and accountability, along with non-
discrimination principles governing AI systems.
Applications Across Sectors: AI technologies
have successfully contributed to renewable
energy optimization, disaster prediction,
sustainable urban planning, and waste [43].
Public Administration's Role: Public
administration has a pivotal role in facilitating
the adoption of AI technologies by establishing
regulatory frameworks, promoting public-private
partnerships, and fostering local
entrepreneurship. Case studies from countries.
Challenges Identified: Despite its potential, the
application of AI faces barriers such as high
energy consumption, data quality and
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accessibility issues, and ethical concerns.
Policy Frameworks and Recommendations:
Effective AI adoption requires comprehensive
policies addressing ethical, equitable, and
environmental dimensions. Recommendations
include regulatory support, financial incentives
for startups, investment in AI research, and the
development of guidelines for ethical AI
deployment.
Table 3 highlights practical applications of AI in
the field of sustainability, focusing on
environmental conservation, resource efficiency
and disaster preparedness, and shows AI
solutions to address climate change and promote
sustainable development.
Table 3. Applications for AI in Sustainability There are practical use cases for AI in sustainability and they are growing across
sectors.
Applications for
sustainability
Description
Renewable Energy
AI optimizes energy generation from renewable sources like solar and wind by predicting
generation patterns and improving efficiency and reliability.
Conservation of
Forests and Wildlife
AI-powered drones and satellite imagery analyze forest health, monitor illegal logging, and track
wildlife populations, enabling rapid ecosystem protection.
Sustainable Urban
Planning
AI aids in optimizing public transportation, reducing congestion, and lowering emissions by
analyzing traffic patterns, supporting smart city initiatives.
Waste Management
AI identifies and sorts of waste to improve recycling efficiency and reduce landfill contributions.
Sustainable
Agriculture
AI optimizes water, fertilizer, and pesticide usage through smart sensors and predictive algorithms,
reducing environmental impact and boosting yield.
Climate Resilience
AI predicts natural disasters like hurricanes and wildfires, enabling communities to take
preventative measures.
Energy Storage and
Distribution
AI integrates and optimizes hybrid systems combining renewable sources with battery storage,
reducing energy waste and improving grid reliability.
Source: Own elaboration based on information from OECD (2024), Global Forest Watch (2023), and IEA (2024).
Table 4 and Figure 3 indicate the level of
development of AI applied to climate change
challenges in various countries. These advances
are essential to promote energy transition and
mitigate the effects of climate change.
In this way, the map presented in Figure 2
complements the information in the table in a
visual way.
The analysis shows that countries with emerging
economies have a lower integration of AI tools in
the climate context, compared to developed
nations. This highlights the need to provide
greater support to reduce the technological gap
and promote the adoption of more technological
tools.
In addition, the application of these technologies
has the potential to significantly accelerate
socioeconomic development in countries with
emerging economies, strengthening their
capacities to face environmental and climate
challenges.
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Table 4. Level of application development and description of the application of AI in sustainability in different countries.
Source: Own elaboration based on information from OECD (2024), Global Forest Watch (2023).
Figure 3. Visual Representation of Countries by Level of AI Development in Sustainability and Climate Change
Applications. Source: Own elaboration based on information from World Economic Forum (2024), OECD (2024), IEA
(2024) & European Commission (2024)
4. Conclusions
Artificial intelligence represents an innovative
tool, which is currently being used in all activities
and will begin with this trend towards greater
application in each activity we carry out. It has
the transformative potential to address global
climate challenges and sustainable development,
if they are implemented under principles of
equity, transparency and responsibility. The
analyses and research highlighted the importance
of public administration and public policies
aligned with climate change and sustainability,
through effective governance, comprehensive
public policies and public-private collaboration
to maximize the benefits of AI in sustainability.
Country
Applications
Qualitative
Level
Mexico
Power grid optimization, weather prediction
Low
Germany
Renewable energy planning, urban sustainability
Very High
United States
Disaster management, precision agriculture
High
India
Water resource management, flood mitigation
Low Medium
China
Industrial emissions monitoring, smart grids
High
European Union
Ethical certifications, sustainable transportation
Very High
Japan
Smart city energy planning, carbon tracking
High
Canada
Forest conservation, renewable energy expansion
High
Brazil
Deforestation monitoring, water resources
Low Medium
South Africa
Early warning systems, renewable infrastructure
Low
Level of development of AI applied to climatic change in countries
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Regulatory strategies due to the complexity of
the AI issue must prioritize ethical frameworks,
data quality and equitable access to technology
and mitigation of technological and
socioeconomic biases. Governments have a key
position to lead initiatives that integrate AI into
climate action, relying on robust data
infrastructures and international alliances. The
incorporation and use of these tools in
government objectives and prospects are
essential due to their extensive applications, as
they can also improve resource management,
disaster prediction, and renewable energy
optimization. Regardless of challenges such as
access to quality data and intensive resource
consumption, the integration of AI in climate
solutions can catalyze a transition to a more
sustainable future. Currently, there is great
progress in developed countries reaching
different topics that comprise sustainability,
while countries with emerging economies have
begun a transition to the use of these
technological tools. Undeniably, there continue
to be great opportunities to reduce the gap that
will help improve the economy of these
countries. The key is to promote innovation
while ensuring social justice and long-term
sustainability.
5. Authorship acknowledgements
Maria Eliazar Raygoza Limón:
Conceptualization, Methodology, Formal
analysis, Research, Writing, Resources, Original
draft, Visualization. Jesús Heriberto Orduño
Osuna: Ideas, Review and Editing. Gabriel
Trujillo Hernández: Review and Editing. Fabian
Natanael Murrieta Rico: Review and Editing,
Supervision, Project Administration.
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Derechos de Autor (c) 2025 María E. Raygoza-L, J. Heriberto Murrieta-Rico, Gabriel Trujillo-Hernández, Fabian N.
Murrieta-Rico
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