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Modeling Drivers of Deforestation in Uganda Using Regression Analysis: Efforts Towards Zero Deforestation by 2030

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F22%3A43921434" target="_blank" >RIV/62156489:43110/22:43921434 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/62156489:43410/22:43921434

  • Výsledek na webu

    <a href="https://doi.org/10.11118/978-80-7509-831-3-0204" target="_blank" >https://doi.org/10.11118/978-80-7509-831-3-0204</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.11118/978-80-7509-831-3-0204" target="_blank" >10.11118/978-80-7509-831-3-0204</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Modeling Drivers of Deforestation in Uganda Using Regression Analysis: Efforts Towards Zero Deforestation by 2030

  • Popis výsledku v původním jazyce

    Uganda, located in the Tropical region of Africa, is blessed with natural forests that serve enormous environmental ecosystems and biodiversity. Moreover, the country is known for its tropical rain forests and various hardwood, birds, and animal species. Over the years, the Trend in the natural forest land has declined at an alarming rate; hence need to investigate the possible drivers. The loss of such biodiversity and ecosystems risks desertification and extreme climatic condition. As the world moves towards Zero Deforestation 2030, understanding the determinants of deforestation and forest degradation is paramount. Therefore, the main objective of this study is to understand the impact and relationships between net forest conversion, energy emission, agriculture, and forest production of Roundwood. We used data from FAO for the period 2004-2016. Using the ADF and KPSS test, we checked for the unit root presence in the variables. Also, the study used two different regression models; ordinary multiple linear and dynamic linear regressions. The results showed that there were unit roots in the selected regressors. To analyze the determinants of deforestation, we used net forest conversion in Uganda. There was 94 % variation in the dependent variable (Net Forest conversion). The outcome of the dynamic linear regression showed that agriculture and energy emission positively impact net forest conversion, whereas forest production of Roundwood has a negative effect. Based on our findings, this study recommended the modernization of agriculture by the government of Uganda to stop cutting down the forests on a big scale. Also, the study suggested that, as Roundwood production has a negative impact on net forest conversion, there is a need for the government to strictly legislate to ensure effective and efficient management and production of Roundwood products towards total forest conservation by 2030.

  • Název v anglickém jazyce

    Modeling Drivers of Deforestation in Uganda Using Regression Analysis: Efforts Towards Zero Deforestation by 2030

  • Popis výsledku anglicky

    Uganda, located in the Tropical region of Africa, is blessed with natural forests that serve enormous environmental ecosystems and biodiversity. Moreover, the country is known for its tropical rain forests and various hardwood, birds, and animal species. Over the years, the Trend in the natural forest land has declined at an alarming rate; hence need to investigate the possible drivers. The loss of such biodiversity and ecosystems risks desertification and extreme climatic condition. As the world moves towards Zero Deforestation 2030, understanding the determinants of deforestation and forest degradation is paramount. Therefore, the main objective of this study is to understand the impact and relationships between net forest conversion, energy emission, agriculture, and forest production of Roundwood. We used data from FAO for the period 2004-2016. Using the ADF and KPSS test, we checked for the unit root presence in the variables. Also, the study used two different regression models; ordinary multiple linear and dynamic linear regressions. The results showed that there were unit roots in the selected regressors. To analyze the determinants of deforestation, we used net forest conversion in Uganda. There was 94 % variation in the dependent variable (Net Forest conversion). The outcome of the dynamic linear regression showed that agriculture and energy emission positively impact net forest conversion, whereas forest production of Roundwood has a negative effect. Based on our findings, this study recommended the modernization of agriculture by the government of Uganda to stop cutting down the forests on a big scale. Also, the study suggested that, as Roundwood production has a negative impact on net forest conversion, there is a need for the government to strictly legislate to ensure effective and efficient management and production of Roundwood products towards total forest conservation by 2030.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    40102 - Forestry

Návaznosti výsledku

  • Projekt

  • Návaznosti

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2022

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    Public recreation and landscape protection – with environment hand in hand…: Conference proceedings

  • ISBN

    978-80-7509-830-6

  • ISSN

    2336-6311

  • e-ISSN

    2336-632X

  • Počet stran výsledku

    6

  • Strana od-do

    204-209

  • Název nakladatele

    Mendelova univerzita v Brně

  • Místo vydání

    Brno

  • Místo konání akce

    Křtiny

  • Datum konání akce

    9. 5. 2022

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku