The 21st century climate crisis has been compounded by the COVID-19 health crisis and the Russian war. What at first appeared to be an opportunity to move towards sustainable growth and development has turned into the opposite. In this context, it seems necessary to pause and analyze what countries are doing and where they are heading in order to ensure that their environmental efforts are not in vain. This article analyzes the environmental policies of the seven countries emitting the most GHGs from 1990 to the present day and compares them with the reality of their emissions. These behaviors are extrapolated into the future and, finally, conclusions are drawn as to which countries are not fully living up to their commitments, which have implemented the most effective measures, and where particular attention needs to be directed for maximum efficiency in decarbonizing the energy mix.

Climate change is impacting atmospheric patterns and therefore wave conditions, with ports being among the most affected infrastructures, making it crucial to ensure their operability under changing climatic conditions. Most scientific studies on climate change focus on coastal erosion and flooding, whereas research on its impact on port operability remains relatively scarce. This challenge could be tackled with the emergence of Artificial Intelligence (AI), where alternative modeling approaches can be developed. Thus, a novel AI-based model specifically designed for studying port agitation is introduced herein. By integrating a hybrid deep learning approach, combining Feedforward Neural Networks (FFNNs) to model wave climate and Convolutional Neural Networks (CNNs) for port image analysis, port agitation has been successfully predicted compared to linear wave propagation models. This marks the first instance of utilizing image processing tools to analyze port agitation, resulting in a model with a remarkably low error rate, while offering a significant reduction in computational time compared to traditional wave propagation models, reducing computational time by a factor of four to ten. The accuracy of the proposed model has been investigated and validated for the Port of Valencia, located in the Spanish section of the Mediterranean Sea.

The application of artificial intelligence (AI) models in maritime and coastal engineering has gained increasing relevance, demonstrating performance comparable to traditional approaches in wave climate analysis and propagation. However, their use in climate change impact and adaptation studies remains limited, particularly for the design and upgrading of coastal protection structures. To address this gap, this study focuses on the development of an AI-based framework to support the adaptation of breakwaters to future climate conditions. A hybrid approach combining artificial neural networks (ANNs) and genetic algorithms (GAs) was implemented, with two feedforward neural networks-based models developed and applied to different sections of the north breakwater of the Port of Valencia, specifically a vertical section and a compound breakwater. The results indicate that, under future climate scenarios (2050), increases of up to 1.2 m in crest elevation, together with reinforcement of the armor layer, are required to ensure adequate structural performance. The analysis also highlights the critical role of extreme events, as approximately 60% of the model errors were concentrated in the upper 90th percentile of wave conditions. Overall, the proposed hybrid ANN-GA framework demonstrated very strong performance, achieving computational efficiencies 30 to 40 times greater than ANN-only models in terms of computational time. These findings underscore the necessity of adapting coastal structures to climate change and confirm the potential of AI-based models as effective tools for climate-resilient coastal engineering, while emphasizing the importance of accurately representing extreme wave conditions.