Abstract
Maritime Spatial Planning (MSP) has received increasing attention from policy-makers around the world as an ecosystem-based approach to the waters under the jurisdiction of coastal states, with the aim of enhancing socio-economic development while promoting environmental protection and conservation. However, this planning process requires abundant and diverse types of data and information that are not easily operationalised in a spatially efficient manner for MSP. Aiming to overcome this barrier, the present study proposes a suitability zoning methodology based on an ad hoc developed decision support system (i.e. INDIMAR) capable of integrating the required spatial data collected and structured around a proposed suitability framework organised around five key components: environmental sensitivity, marine conservation, natural oceanographic potential, land-sea interactions, and operational maritime uses and activities. This suitability zoning framework and decision support system was tested for individual maritime activities in different Atlantic outermost regions, configuring different use cases: aquaculture in the Canary Islands, offshore wind farms in the Madeira archipelago and aggregate extraction in the Azores. The proposed methodology has resulted in a flexible model that identifies the most suitable sites for the sustainable development of maritime activities, taking into account the natural potential and compatibility with nature conservation, while mitigating potential environmental impacts and minimising conflicts with other coastal and maritime activities. However, it's important to note that the results of this study are strongly influenced by the availability and quality of data, identifying the main gaps in each region that are recommended to be filled in view of the formal processes of MSP. In essence, this study underlines the broad applicability of the proposed methodology and framework, which can be adapted and implemented in other regions after due consideration of several aspects such as: data availability, contextual differences, legal and governance frameworks, institutional capacity and spatial interactions. By taking these aspects into account, the resulting decision support system has the potential to provide valuable insights, thereby increasing the effectiveness of MSP efforts.