Human-Wildlife Conflict in Gabon is a reality occurring in almost all protected areas in the country. These conflicts create real threats both for the survival of wildlife species and of human beings. This study was c...Human-Wildlife Conflict in Gabon is a reality occurring in almost all protected areas in the country. These conflicts create real threats both for the survival of wildlife species and of human beings. This study was carried out at the periphery of Loango National Park in Gabon. This area is particular of seeing elephants wandering around villages. Respondents for the study were drawn from a wide range of stakeholders (State administrators, farmers and NGOs). Data was collected through administration of structured questionnaires and interview guide on the;socio-economic activities. Crops produced/destroyed. Animals are involved and economic loss is incurred due to conflicts. Data was analysed using SPSS version 16 and Kobo tool box. For qualitative data chi-square, descriptive statistic and linear regression model were also used. The results of the study showed that the elephants account for (60.1%) of crop destroyed followed by Ungulates (30.4%) and lastly by rodents (0.9%). The economic damage caused by the wildlife to crops valued at 72,084 USD in the zone in 2022. An urgent solution to this conflict is needed because the consequences are visible as well as the illegal repression by communities that have led to poisoning and killing of wildlife in the study area.展开更多
Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. Th...Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance.The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.展开更多
In May 2012 a seismic sequence occurred in Northern Italy that was characterized by two main shocks with a magnitude range between 5.5 and 6. These shocks represent a good case study by which to quantify the monetary ...In May 2012 a seismic sequence occurred in Northern Italy that was characterized by two main shocks with a magnitude range between 5.5 and 6. These shocks represent a good case study by which to quantify the monetary losses caused by a moderate earthquake in a densely populated and economically well-developed area.The loss estimation accounts for damage to residential buildings, and considers the full effect of all the seismic aftershock events that lasted for nearly a month. The building damage estimation is based on the European Macroseismic Scale(EMS-98) definitions, which depict the effects of an earthquake on built-up areas in terms of observed intensities. Input data sources are the residential building census provided by Istituto Nazionale di Statistica—the Italian National Institute of Statistics(ISTAT)—and the official market value of real estate assets, obtained from the Osservatorio del Mercato Immobiliare—the Real Estate Market Observatory(OMI). These data make it possible to quantify the economic losses due to earthquakes, an economic indicator updated yearly. The proposed multidisciplinary method takes advantage of seismic,engineering, and economic data sets, and is able to provide a reasonable after the event losses scenario. Data are not gathered for each single building and the intensity values are not a simple hazard indicator, but, notwithstanding its coarseness, this method ensures both robust and reproducible results. As the local property value is availablethroughout the Italian territory, the present loss assessment can be effortlessly repeated for any area, and may be quickly reproduced in case of future events, or used for predictive economic estimations.展开更多
文摘Human-Wildlife Conflict in Gabon is a reality occurring in almost all protected areas in the country. These conflicts create real threats both for the survival of wildlife species and of human beings. This study was carried out at the periphery of Loango National Park in Gabon. This area is particular of seeing elephants wandering around villages. Respondents for the study were drawn from a wide range of stakeholders (State administrators, farmers and NGOs). Data was collected through administration of structured questionnaires and interview guide on the;socio-economic activities. Crops produced/destroyed. Animals are involved and economic loss is incurred due to conflicts. Data was analysed using SPSS version 16 and Kobo tool box. For qualitative data chi-square, descriptive statistic and linear regression model were also used. The results of the study showed that the elephants account for (60.1%) of crop destroyed followed by Ungulates (30.4%) and lastly by rodents (0.9%). The economic damage caused by the wildlife to crops valued at 72,084 USD in the zone in 2022. An urgent solution to this conflict is needed because the consequences are visible as well as the illegal repression by communities that have led to poisoning and killing of wildlife in the study area.
基金funded by the National Key R&D Program of China(Grant No.2022YFC3004404)the Key Research and Development Project of Science and Technology Department of Hebei Province(No.21375410D and No.22375421D).
文摘Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance.The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.
基金the project ‘‘The Economic Assessment of Natural Disasters in Italy’’ (La valutazione economica dei disastri naturali in Italia, in Italian) funded by Fondazione Generali from 2013 to 2017
文摘In May 2012 a seismic sequence occurred in Northern Italy that was characterized by two main shocks with a magnitude range between 5.5 and 6. These shocks represent a good case study by which to quantify the monetary losses caused by a moderate earthquake in a densely populated and economically well-developed area.The loss estimation accounts for damage to residential buildings, and considers the full effect of all the seismic aftershock events that lasted for nearly a month. The building damage estimation is based on the European Macroseismic Scale(EMS-98) definitions, which depict the effects of an earthquake on built-up areas in terms of observed intensities. Input data sources are the residential building census provided by Istituto Nazionale di Statistica—the Italian National Institute of Statistics(ISTAT)—and the official market value of real estate assets, obtained from the Osservatorio del Mercato Immobiliare—the Real Estate Market Observatory(OMI). These data make it possible to quantify the economic losses due to earthquakes, an economic indicator updated yearly. The proposed multidisciplinary method takes advantage of seismic,engineering, and economic data sets, and is able to provide a reasonable after the event losses scenario. Data are not gathered for each single building and the intensity values are not a simple hazard indicator, but, notwithstanding its coarseness, this method ensures both robust and reproducible results. As the local property value is availablethroughout the Italian territory, the present loss assessment can be effortlessly repeated for any area, and may be quickly reproduced in case of future events, or used for predictive economic estimations.
