Dataset for drivers of forest area development

Determining the drivers of natural and planted forest area development The table contains metadata on global forest area development for natural forests and planted forests including plantations, as well as different socio-economic drivers from the FAO[1], FRA[2], World Bank[3], Bonannella et al. (2023)[4], and the Freedom House databank[5]. The data are stored as a complete dataset in an Excel table containing five data sheets. There is i.) an overview over all data for dependent and independent variables (drivers), ii.) metadata of all dependent and independent variables (drivers), iii.) complete dataset of all variables and their corresponding values, iv.) dataset used to estimate development models for planted forest area including the dependent variable and 33 respective independent variables (drivers), and v.) dataset used to estimate development models for natural forest area containing the dependent variable and 26 respective independent variables (drivers) and vi.) correlation matrix to highlight potential intercorrelations within all drivers (before starting the regularization methods). These data are used in a holistic econometric assessment on the relevance and difference of multiple drivers for the development of natural forests as well as planted forests, including plantations. The raw data had been cleaned prior to modelling to create complete datasets for the two endogenous variables ((i) planted forest, including plantations, and (ii) natural forest), without using imputation techniques. Out of the initial 95 independent variables (drivers), only 33 independent variables, represented by 800 sets of complete data, remain as input for modelling planted forest area development while only 26 drivers, represented by 950 sets complete data, remain for natural forests. These data are then used in different machine learning approaches, which first identify the relevant independent variables (drivers) for the respective dependent variable, and subsequently assess significance, impact and direction of selected drivers. A comprehensive methodological description as well as results and discussion can be found in an article (currently under review; source DOI). [1] [dataset] FAO (2023): Forestry Production and Trade. Available online at https://www.fao.org/faostat/en/#data/FO, checked on 5/10/2023. [2] [dataset] FAO (2020): Global Forest Resources Assessment 2020. Main report |Policy Support and Governance| Food and Agriculture Organization of the United Nations. Available online at https://www.fao.org/policy-support/tools-and-publications/resources-details/en/c/1459931/, updated on 5/10/2023, checked on 5/10/2023. [3] [dataset] World Bank (2023): World Development Indicators. Available online at https://databank.worldbank.org/source/world-development-indicators, checked on 6/26/2023. [4] Bonannella, Carmelo; Hengl, Tomislav; Parente, Leandro; Bruin, Sytze de (2023): Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. In PeerJ 11, e15593. DOI: 10.7717/peerj.15593. [5] [dataset] Freedom House (2023): Freedom House Index. Available online at https://freedomhouse.org/, checked on 5/10/2023.

Tags: deforestation-drivers, forest-area-development, forest-transition-hypothesis, forward-selection, machine-learning
Organization: OpenAgrar