In recent decades, the intricate relationship among trade policies, climate, land use and environmental pollution has become a focal point of scholarly inquiry, driven by the urgent need to comprehend the sustainability challenges faced by regions undergoing economic development. This project delves into this complex interplay by conducting a comprehensive study on the impact of trade policies on land use and environmental pollution in the Eastern Mediterranean. Leveraging advanced technologies such as remotely sensed data and machine learning-based algorithms, the research aims to provide nuanced insights into the intricate dynamics shaping the landscape in this critical geopolitical region. The Eastern Mediterranean, a nexus of diverse cultures and economies, has witnessed significant economic growth, urbanization, and trade activities in recent years. As globalization continues to shape the region's economic trajectory, the environmental consequences of intensified trade activities demand rigorous investigation. This study employs cutting-edge remote sensing technologies to collect high-resolution spatial and temporal data, allowing for a granular examination of environmental indicators such as air and water quality, land use changes, and vegetation dynamics.

Machine learning algorithms serve as the analytical backbone of this research, facilitating the extraction of patterns and correlations within the vast datasets. By applying supervised and unsupervised learning techniques, the study endeavors to identify specific links between trade policies, land use and environmental pollution. Understanding the negative effects of climate change and changes to land use/cover on natural hazards is an important feature, as these aspects are a key point in landslide susceptibility assessment. Moreover, predictive modeling will be employed to forecast potential future scenarios, providing stakeholders with valuable information for informed decision-making. The integration of machine learning into environmental research represents a paradigm shift, allowing for the extraction of meaningful insights from vast and complex datasets that traditional analytical methods struggle to handle. The innovative combination of remotely sensed data and machine learning algorithms enhances the accuracy and granularity of the analysis, providing a robust foundation for evidence-based decision-making.

The research methodology involves the integration of data from multiple sources, including images collected with high-resolution thermal, hyperspectral, and optical sensors mounted on an aerial platform, satellite images, terrestrial sensor networks and trade statistics. These diverse datasets enable a holistic understanding of the environmental impact of trade policies, considering both direct and indirect consequences. The temporal aspect is crucial, as it allows for the identification of trends and fluctuations in environmental variables over time, aiding in the establishment of causal relationships. Inferred interconnection among the different environmental factors will contribute to formulate adaptation strategies against future calamities.

One key aspect of the analysis is the consideration of different trade policies and their varying impacts on environmental parameters. The study evaluates the influence of tariff policies, trade agreements, and regulatory frameworks on pollution levels, emphasizing the need for nuanced policy interventions that balance economic development with environmental sustainability. Through the utilization of machine learning algorithms, the research aims to discern not only the overall impact of trade policies but also the specific sectors and activities contributing most significantly to environmental degradation. The findings of this study hold implications for policymakers, environmental agencies, and industries operating in the Eastern Mediterranean. The identification of hotspots and vulnerable areas allows for targeted interventions, enabling the formulation of region-specific policies to mitigate environmental and natural risks associated with trade activities. Furthermore, the predictive modeling component aids in scenario planning, enabling stakeholders to anticipate and proactively address potential future challenges.

The objective of this project is to contribute to the ongoing debate on the link between trade policies, climate, land use and environmental pollution by offering a comprehensive analysis of the Eastern Mediterranean region. Leveraging cutting-edge technologies and methodologies, the study aims to reveal the intricate relationships between economic activities and environmental health. The findings of this research will be useful in informing sustainable development strategies, promoting environmental management and guiding the formulation of policies that reconcile economic growth with ecological preservation and social safety in the Eastern Mediterranean and beyond.

 Culture numérique

 Europe créative

 Horizon Europe

 INTERREG