AI in public administration-transformative opportunities for climate resilience and sustainable development

María E. Raygoza-L.; Jesús Heriberto Orduño-Osuna; Gabriel Trujillo-Hernández; Fabian N. Murrieta-Rico

doi:10.37636/recit.v8n2e398

Authors

María E. Raygoza-L. Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México https://orcid.org/0009-0000-5969-6602
Jesús Heriberto Orduño-Osuna Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México https://orcid.org/0009-0004-4850-7481
Gabriel Trujillo-Hernández Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México https://orcid.org/0000-0003-1556-387X
Fabian N. Murrieta-Rico Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México https://orcid.org/0000-0001-9829-3013

DOI:

https://doi.org/10.37636/recit.v8n2e398

Keywords:

Artificial intelligence (AI), Climate change sustainable development, Renewable energy, Public policies, Governance

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.

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Author Biographies

María E. Raygoza-L., Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México

María E. Raygoza-L received his PhD in Chemistry and Energy from the Autonomous University of Baja California and a bachelor’s degree in industrial engineering. Her doctoral research focused on Renewable Energies, exploring public policies for energy transition and sustainable development in Mexico. She is a full-time professor and researcher at the Polytechnic University of Baja California and teaches at the Autonomous University of Baja California. With experience in the food and aerospace industries, her interests include renewable energies, energy management systems, and public policies. She is currently pursuing a second bachelor’s degree in psychology, aligning her research with the Sustainable Development Goals (SDGs). Her recent works are Leadership and Coaching Management as a Governance Instrument for Sociocultural Development and Management of public and fiscal policies for the energy transition and sustainable development in Mexico.

Jesús Heriberto Orduño-Osuna, Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México

Jesus Heriberto Orduño-Osuna received his MSc in Computational Sciences and Applied Mathematics from Universidad Internacional de La Rioja, Mexico (UNIR Mexico). He has experience as an engineer in Post-Harvest Automation and manufacturing, focusing on automation processes, vision systems, and industrial robotics. Currently, he lectures at the Universidad Politécnica de Baja California, teaching mechatronics, manufacturing, and energy courses. His published research includes machine learning applied to social innovation and GenAI in engineering education. His research interests are computer science, industrial process control, and digital signal processing.

Gabriel Trujillo-Hernández, Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México

He received a bachelor’s degree in mechatronics engineering from the Universidad
Autonoma de Baja California, México, in 2015. He received a master’s degree in science
from the Universidad Autónoma de Baja California, México, in 2020. He received a Ph.D. at the Universidad Autónoma de Baja California, México, in 2024. He is currently a
research professor at the Universidad Politécnica de Baja California. He has seven
manuscripts published in Elsevier and IEEE. Furthermore, he has four chapters in Springer and several international congresses in IECON and ISIE.

Fabian N. Murrieta-Rico, Universidad Politécnica de Baja California, Av Claridad, Plutarco Elías Calles, 21376 Mexicali, Baja California, México

Fabian N. Murrieta-Rico received his PhD in Materials Physics at Centro de Investigación Científica y Educación Superior de Ensenada (CICESE). Currently he works as a professor at the Universidad Politécnica de Baja California. His research has been published in different journals and presented at international conferences since 2009, in addition, he has served as reviewer for different journals. He is member of the system of national researchers (SNII) in Mexico. His research interests are focused on the field of time and frequency metrology, synthesis and characterization of zeolites, and highly sensitive chemical detectors.

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