Predictive Scenarios of LULC Changes Supporting Public Policies: The Case of Chapecó River Ecological Corridor, Santa Catarina/Brazil

Juliana Mio de Souza (), Paulo Morgado, Eduarda Marques da Costa and Luiz Fernando de Novaes Vianna
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Juliana Mio de Souza: Agricultural Research and Extension Service Institution of the State of Santa Catarina, Rua Admar Gonzaga, 1347, Itacorubi, Florianópolis 88034-901, Brazil
Paulo Morgado: Centre of Geographical Studies, Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Eduarda Marques da Costa: Centre of Geographical Studies, Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Luiz Fernando de Novaes Vianna: Agricultural Research and Extension Service Institution of the State of Santa Catarina, Rua Admar Gonzaga, 1347, Itacorubi, Florianópolis 88034-901, Brazil

Land, 2023, vol. 12, issue 1, 1-24

Abstract: The studies of spatial-temporal land use and land cover (LULC) change patterns, supported by future scenarios and simulation methods based on the assumption of natural socio-economic and territorial driving forces, allow us to go beyond an accurate diagnosis of the dynamics that have occurred so far, providing a picture of possible alternative futures, and are fundamental in assisting with the planning and policy-making in the territory. In this paper, we use LULC maps and explanatory variables aggregated in five dimensions (physical/natural, economic, sociocultural, technological, and demographic) to identify which are the main drinving forces in the evolution process and the simulation of LULC dynamics for 2036, using as a case study the Chapecó River ecological corridor (Chapecó EC) area. The Chapecó EC was created by the state government in 2010 with the goal of combining nature conservation with local and regional development. In this region, in the last two decades, the loss of areas of natural grassland and forest was on average five times higher than the average recorded in the state. Based on scenario-building methods using artificial neural networks, six predictive scenarios were elaborated, based on three socioeconomic scenarios (current conditions, growth, and socioeconomic recession) and two territorial intervention options (actions). This includes an action based on maintaining the current LULC, and another action of a conservationist nature with the recovery of forest and natural grassland areas to the proportions of areas found in 1990. The results indicate that if the current LULC is maintained, forest, pasture and agriculture areas tend to increase, while silviculture and natural grassland areas decrease, driven by economic and physical/natural driving forces. If there is a conservationist action, natural grassland and pasture areas tend to increase and silviculture and agriculture tend to lose area due to economic, technological, and physical/natural driving forces. These trends have revealed that the natural grassland preservation/restoration, the encouragement of conservationist agricultural practices combined with economic strategies, and the technological development of the rural sector seem to form the basis of economic development combined with biodiversity conservation.

Keywords: spatial modelling; predictive scenarios; artificial neural networks; good farming practices; agricultural technological development; spatial planning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:1:p:181-:d:1026420

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