Drivers of European landscape change: stakeholders’ perspectives through Fuzzy Cognitive Mapping

Abstract

Understanding complex processes of landscape change is crucial to guide the development of future landscapes and land resources. Through Fuzzy Cognitive Mapping, we studied the processes of landscape change of six different environmental zones in Europe. Results show that landscapes are complex systems, with many interactions. Except for one, all regions show a strong decline in landscape quality. Dominant drivers are EU policy and the global economy, sometimes in conjunction with environmental drivers or the governance system. The process of change differs for all cases, through urbanisation or land abandonment in some cases, and agricultural intensification in others. The (un)intended effects of policies are difficult to predict. Although some EU Policies directly improve landscape quality, their indirect effects as well as other EU policies outweigh this positive influence and jointly result in a decrease of landscape quality. To counter these negative side effects, targeted landscape policies are urgently needed.

Evaluating poverty alleviation strategies in a developing country

“…We conducted the study with the aid of the FCM-based approach introduced by Kosko in 1986 [58]. The process of data capture in the FCM approach is considered quasi-quantitative because the quantification of concepts and links may be interpreted in relative terms [59] allowing participants to debate the cause-effect relations between the qualitative concepts while generating quantitative data based on their experiences, knowledge, and perceptions of inter-relationships between concepts [60][61][62][63][64][65][66][67][68]. The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing selfloop and feedback within complex systems [62,63,69].…”

“…After constructing the FCMs each participant made a presentation, which was video-recorded, explaining their map to the researchers. The researchers, based on causal relationships between the concepts, assigned positive and negative polarities to the weights of the links [59,60,[62][63][64][66][67][68]72].…”

Fuzzy Techniques for Artificial Snow Cover Optimization in the Ski Areas. Case Study: Obârșia Lotrului (Southern Carpathians, Romania)

“…Only 11 ISI articles were published by Romanian authors. These articles refer to issues related to GIS assessment of a winter sports resort location [2], landslide susceptibility assessment in urban development [3], the resolution of land-use conflicts [4], snow avalanche risk assessment [5,6], and mountain tourism [6]. The risk assessment can be done by identifying and monitoring the (key risk indicators) KRIs [7,8].…”

“…The Fuzzy analysis is successfully used in several geographic fields, particularly in the ones with impact on anthropic elements, but scientific papers using the Fuzzy analysis for establishing the sustainability of the ski area have not been identified in specialized literature so far. Significant analyses in the fields of urban-rural [1,2], concerning the environmental planning [3,4], The Fuzzy Cognitive Map related to the functional transformation of the urban space, the transformations of the European landscape [5,6], the ecological benefits [7], and the assessment of regional functions [8] were identified instead.…”

The Water–Energy–Food Nexus: A Fuzzy-Cognitive Mapping Approach to Support Nexus-Compliant Policies in Andalusia (Spain)

“…FCM has proven to be very useful in a variety of domains, as emphasized by Papageorgiou and Salmeron [29] in a review of recent applications of this approach. In the environmental field, FCMs have been used to support watershed ecosystems management [30][31][32][33][34], landscape modelling [35,36], forest management [37], farming systems analysis [38], fisheries management [39], bioethanol production from biowaste [40], and intrusion of mining into landscape [41]. FCMs have been also applied to assess climate change impacts and effects of adaptation measures [42,43].…”