This chapter delves into the transformative potential of artificial intelligence (AI) in climate change mitigation, examining its applications, ethical implications, and future prospects. Traditional methods, limited by human interpretation and scope, are contrasted with AI's ability to analyze vast datasets and forecast environmental trends. While AI offers unprecedented advantages, it also brings challenges, notably its own environmental footprint and data privacy concerns. Collaborative efforts, involving policymakers, activists, and technologists, are emphasized as crucial for AI's ethical and effective deployment. Ultimately, the research underscores AI's pivotal role in shaping a more resilient and sustainable future.
Top1. Introduction
1.1. The Imperative of Climate Change Mitigation
The ongoing ramifications of climate change are starkly evident in the world we inhabit today. As a culmination of centuries-long anthropogenic activities, the planet confronts a multitude of adverse effects, ranging from prolonged droughts to intensified cyclonic activities (Raju, Safai, Rao, Tiwari, & Devara, 2014). These manifestations are not isolated incidents; they are interconnected threads weaving a tapestry of environmental, social, and economic disruptions. Entire ecosystems stand at the brink, threatened by habitat loss, altered precipitation patterns, and temperature extremities (Allan, Castillo, & Capps, 2021). Such ecological upheavals further exacerbate challenges for human societies, particularly in vulnerable regions, resulting in resource scarcities, agricultural disruptions, and heightened risks of climate-induced migrations (Bamidele & Erameh, 2023).
Addressing climate change is not just about combating its effects but fundamentally altering the trajectories that contribute to its exacerbation (Brodie & Watson, 2023). The emphasis of mitigation strategies pivots on this very principle. It is not merely about developing resilient infrastructures or adaptive mechanisms, though they are vital (Rathnayaka, Siriwardana, Robert, Amaratunga, & Setunge, 2022). The crux of mitigation lies in proactive interventions: transitioning to renewable energy sources, enhancing carbon sequestration efforts, and recalibrating industries to be more sustainable (Olabi et al., 2022). In this context, understanding the importance of education becomes pivotal. Education can enhance an individual’s self-efficacy (Farrukh et al., 2021), equipping them with the knowledge and the motivation to make informed decisions that benefit the environment. As global temperatures continue their upward trend and the window for meaningful intervention narrows, the international community finds itself at a crossroads (Abiodun et al., 2021). The decisions and strategies adopted now will determine the planet's climatic health for centuries to come. Innovations, particularly those harnessing the power of emerging technologies like AI, present a glimmer of hope in this challenging landscape, potentially accelerating.
In recent years, the concept of environmental sustainability has transitioned from a peripheral consideration to a central global priority. Today, with the escalating importance of environmental concerns, environmental sustainability is viewed as a critical aspect, as significant as any economic or social challenge (Aman, Rafiq, Dastane, & Sabir, 2022; Farrukh, Raza, & Rafiq, 2023). Running in parallel to this environmental shift, the rise of Artificial Intelligence (AI) has been nothing short of meteoric. While AI has etched its mark on sectors ranging from healthcare and finance to transportation and entertainment (Rai, 2020), its potent transformative capability in environmental conservation stands out. As Earth wrestles with diverse environmental dilemmas, the massive influx of data from satellites, oceanic sensors, and terrestrial systems tends to exceed the bounds of traditional analytical methods. Here, AI emerges as a game-changer. With its sophisticated pattern recognition and data processing prowess, AI unveils nuanced environmental shifts, anomalies, and patterns that might elude human analysis or require extensive periods for human discernment (Murphy, 2019).