دانلود کتاب هوش مصنوعی مولد برای اقتصاد بدون کربن -مدیریت تغییرات اقلیمی و نوآوری در کسب و کار در عصر دیجیتال

The climate crisis is the defining issue of our time. Growing global temperatures, melting ice caps, and an uptick in extreme weather events have forced humanity to respond to an existential imperative: to radically curtail greenhouse gas emissions while adapting to climate change’s irreversible effects on our environment. Classic forms of climate mitigation—such as policy reform, renewable energy adoption, and behavioral change—remain essential but are inadequate. Here comes artificial intelligence (AI), a technology with the transformative power to accelerate and improve our response to this crisis (Borgia et al., 2024). This chapter, “The Carbon Code: Deciphering the AI Code of Climate Mitigation,” also explores the opportuni ties presented by AI technologies that are already redefining climate action, both as a theoretical framework and as an applied guide to how machine learning, neural networks, and data-driven strategies could be used to combat planetary warming (Das, 2020). Climate change is a hypercomplex problem involving interlocking, atmospheric, oceanic, ecological, and human systems, making for a terrible modeling challenge. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the need to rapidly decarbonize all parts of our economy to limit warming to 1.5 °C, an endpoint requiring extraordinary innovation levels. Here, AI comes forth not as a cure-all but a magnifier of human ingenuity. Its ability to process massive datasets, detect hidden patterns, and refine choices in real time makes it uniquely suited to meet the scale and urgency of the climate challenge. AI at its core operates as a decoder of complexity (Das, 2023). The satellites, sensors, and simulations driving climate systems churn out exabytes of data, straining traditional analytical techniques (Das et al., 2024a). However, this flood of information is a boon for machine learning (ML) algorithms, which harness it to distill insights that drive predictive models and strategic interventions (Das et al., 2024b). For instance, neural networks trained on past climate data can better predict future warming scenarios, while reinforcement learning agents increasingly optimize energy grids based on renewables (Das et al., 2023). The “carbon code” is a term that conveys the delicate balance of human activity, emissions, and overall planetary health and this synergy of computational power and environmental science underlies it—an advance on the model not just of human activity but amounts of knowledge.