Abstract
This chapter presents an analysis of the research published in the Scopus database regarding artificial intelligence and environmental sustainability. From bibliometric indicators, various aspects are analyzed complementing the study with maps visualization that allow identifying relevant trends by analyzing co-citations, co-occurrence, and bibliographic coupling. The analysis includes academic publications, citations, keywords, authors, journals, organizations, and countries in which the publications were developed. The results show that the publication trend in this field is increasing, particularly in the last 4 years. Results identify a growing interest from different areas of science, as well as authors, organizations, sponsors, and countries to develop research in this field.
TopIntroduction
Despite dramatic improvements in survival, nutrition, and education over the past few decades, the world's population, particularly children, faces an uncertain future. Climate change, ecological degradation, migrant populations, conflicts, widespread inequalities, and predatory business practices threaten the health and future of future generations worldwide (Clark et al., 2020). Environmental degradation and the climate crisis are extremely complex phenomena that require advanced and innovative solutions (Nishant et al., 2020).
Industry 4.0 technologies offer significant prospects for future innovation, business growth, and environmental sustainability. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, Machine Learning (ML), and other emerging advanced technologies help create a sustainable environment in manufacturing and other industries (Javaid et al., 2022). AI is one of the most disruptive technologies of our time (Yigitcanlar et al., 2020) and has the potential to transform business practices and industries, as well as contribute to important social issues, including sustainability (Nishant et al., 2020).
Various fields of science are making efforts to use AI to contribute to environmental sustainability, including computer science (Nishant et al., 2020; Zampbelli et al., 2012; Javaid et al., 2022), environmental sciences (Herva & Roca, 2013; Feroz et al., 2021; Wang & Huang, 2021), engineering (Bildirici & Ersin, 2023; Tseng et al., 2021; Maynard, 2015; Ferreira et al., 2023), social sciences (Ogbeibu et al., 2021; Balsalobre_Lorente et al., 2023; Sharma et al., 2020), management and business (Garg et al., 2015; Balaman et al., 2016; Aziz et al., 2021; Frank, 2021; Walk et al., 2023), agriculture and biological sciences (Clifford, 2023; Gonzalez-De-Santos et al., 2020; Neethirajan & Kemp, 2021), mathematics, and finance (Martire et al., 2015; Dauvergne, 2022; Mohammad et al., 2021), among many others.
In recent decades, scientific information regarding the potential applications of AI to contribute to a more sustainable environment has significantly increased. In the Scopus database, 88.29% of total publications on this topic were generated from 2015 to 2023 (Scopus, 2023), and in the Web of Science (WoS) database, 82.88% of publications related to AI and environmental sustainability were generated between 2020 and 2023 (WoS, 2023).
In this sense, bibliometric analysis evaluates the impact and influence of scientific publications in terms of quality and reach through statistical indicators (Pinto-López & Montaudon-Tomas, 2021). Bibliometrics has been widely used to analyze scientific activity in artificial intelligence on one hand (Waltman & Van, 2012; Di Vaio et al., 2020; Zhao et al., 2020; Tseng et al., 2021; Kumar et al., 2023) and environmental sustainability on the other (Fahimnia et al., 2015; Maditati et al., 2018; Alsmadi et al., 2023). However, there is no reference in WoS or Scopus to any bibliometric analysis that considers both artificial intelligence and environmental sustainability variables.
Key Terms in this Chapter
Intelligence: Faculty of the mind that enables learning, understanding, reasoning, making decisions, and forming a specific idea of reality.
Sustainability: That can be sustained without depleting its resources.
Climate Change: Anticipated change in Earth’s climate caused by human activity leading to the greenhouse effect and global warming.
Sustainable Development: Utilization and enjoyment of natural resources that promote economic and social development of human populations, ensuring the maintenance and preservation of those resources for future generations.
Artificial Intelligence: Scientific discipline that deals with creating computer programs that perform operations comparable to those carried out by the human mind, such as learning or logical reasoning.
Environmental Impact: Set of potential adverse effects on the environment resulting from modifications to the natural environment, because of construction or other activities.
Intersection: Set of elements common to two or more sets.
Machine Learning: The discipline that studies how to endow computer systems with autonomous learning capability.