## 4 Conclusions  
The present case study aims to improve the overall comprehension of how Geographic Information System (GIS) capabilities can be utilized to facilitate sustainable land management in mountainous areas that are confronted with the combined challenges of climate change and development. The findings of this study will provide important information for land use planners, environmental managers, and policy makers operating in the Apuseni Mountains (the Somesul Mic watershed) and similar areas. Having knowledge regarding the interaction between climate factors and human activities is essential for predicting future changes in land use and effectively monitoring landscapes in a sustainable manner. The ongoing evolution of climate change and human activities will have a progressively major impact on land usage, creating issues for environmental conservation and resource management. The results reveal significant shifts in land use favorability patterns under future climate scenarios, with certain areas becoming more suitable for agriculture, while others may face increased risks of land degradation or reduced agricultural potential. This GIS-based approach offers valuable insights for regional planning and sustainable land management, helping stakeholders adapt to changing environmental conditions. The findings underscore the need for proactive strategies to mitigate climate change impacts on land use and support the resilience of the Apuseni Mountains' ecosystems and communities. This study incorporates a quantitative Geographic Information System (GIS) model to evaluate the suitability of land for important agricultural purposes, such as grasslands, pastures, and orchards. The assessment is based on environmental criteria, including soil properties, climatic variables, and topographic characteristics. An analysis of SSP climate scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from 2020 to 2099 shows variations in land suitability, with decreases in high favorability for pastures and grasslands and a little increase in favorability for orchards under specific circumstances. The GIS classification of land favorability offers a reliable and data-driven approach to assess current suitability and predict future changes, essential for well-informed land management and agricultural planning. While GIS-based land suitability models offer meaningful insights into agricultural potential in the context of climate change scenarios, they are subject to several limitations. The presence of uncertainty in the climate projections might lead to diverse results, therefore introducing complexity to the dependability of the forecasts. In addition, the reliance on existing spatial data, which may be lacking in completeness or generalization, restricts the accuracy of the model, especially at small scales. The models often failed to consider intricate interactions between natural forces and socio-economic variables that are essential for making land use decisions. Moreover, the level of detail in GIS data may not consistently include slight variations at a small scale, therefore diminishing the success of the models in directing specific agricultural adaptation measures. The potential future research will integrate Geographic Information Systems (GISs) with computer-based machine learning algorithms to improve land suitability evaluations in the context of climate change scenarios. The integration of Geographic Information Systems (GISs) with machine learning facilitates the generation of more advanced and data-oriented insights, therefore enabling the real-time monitoring of land use and climatic effects. The results of this study have substantial consequences for practical implementations. The capacity to forecast the anticipated changes in land suitability under various climatic scenarios enables policy makers, farmers, and local authorities to actively adjust their policies regarding agriculture.