The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition.

This chapter explores the potential and challenges of implementing generative artificial intelligence (AI) in engineering education in Mexico. It highlights the relevance of developing broader competences like critical thinking, collaboration, and cognitive processing to prepare students for the Fourth Industrial Revolution. Integrating generative AI and STEM education can foster collaboration, communication, critical thinking, and creativity. Challenges include the gap between demand and supply of skilled engineers and the need to enhance education quality. The chapter proposes personalized learning experiences, enhanced student engagement, workload reduction for teachers, and the use of large language models and simulated environments as key applications of generative AI. Leveraging learning analytics and generative AI can tailor content to students' needs. Ethical considerations and human oversight are crucial for successful integration.

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.

The current era of globalization has exacerbated the problem of climate change, affecting all ecosystems, The anthropogenic footprint has been the leading cause of the current environmental impact on the planet, and socioeconomically developed countries have implemented various strategies to mitigate the environmental impact of anthropogenic damage on marine and terrestrial ecosystems. The two main challenges are environmental protection and education. It is necessary to develop a linkage between education, government, society, and industry to address climate change as the principal measure for creating programs with international reach. Governmental and educational institutions must develop quality educational policies. The implementation of a triad between public institutions, private institutions, and education, which collaborate with society and the government through programs that integrate institutional internal plans and programs with economic support for the incorporation of public educational policies, the promotion of R&D projects.

This chapter is focused on the interest that the industry has placed in recent years regarding the environmental repercussions derived from the various industrial processes in general, which over time have triggered multiple negative impacts on the environment, such as greenhouse gas (GHG) emissions into the atmosphere and direct discharges to effluents, which probably contribute to global warming, causing a series of negative externalities such as chronic serious diseases such as asthma, allergies, bronchitis, and chronic obstructive pulmonary disease (EPOC), among many others, leading the industry to take intelligent measures and actions based on sustainable consumption and production, which contribute to the fulfillment of the sustainable development objective (SDG) 12 of the 2030 agenda. For this reason, the authors will focus on the study and analysis of supply chain management, offering significant guidance in the implementation of practices for sustainable management.

The foot is considered a wonder of biological engineering due to its structure, formed by bones, ligaments, and tendons that collaborate to ensure stability and mobility. A key area often examined by medical professionals in patients with diabetic feet is the plantar surface, due to the risk of ulcer development. If left untreated, these ulcers can lead to severe complications, including amputation of the toe, foot, or even the limb. Interpolation methods are used to find areas with overloads in a system of sensor maps that are based on capacitive, load cells, or force-sensitive resistors (FSRs). This manuscript presents the assessment of linear, nearest neighbors, and bicubic methods in comparison with ground truth to calculate the root mean square error (RMSE) in two assessments using a dataset of eight healthy subjects, four men and four women, with an average age of 25 years, height of 1.63 m, and weight of 72 kg with shoe sizes from 7.3 USA using FSR map with 48 sensors. Additionally, this paper describes the conditioning circuit development to implement a plantar surface system that enables interpolating loads on the plantar surface. The proposed system’s results show that the first assessment indicates an RMSE of 0.089, 0.126, and 0.089 for linear, nearest neighbor, and bicubic methods, while the second assessment shows a mean RMSE for linear, nearest neighbor, and bicubic methods of 0.114, 0.159, and 0.112.