1,721,097 research outputs found
The Climate Crisis is a Child Rights Crisis: Introducing the Children's Climate Risk Index
No full textThe climate crisis is a child rights crisis presents the Children’s Climate Risk Index (CCRI), which uses data to generate new global evidence on how many children are currently exposed to climate and environmental hazards, shocks and stresses. A composite index, the CCRI brings together geographical data by analyzing 1.) exposure to climate and environmental hazards, shocks and stresses; and 2.) child vulnerability. The CCRI helps to understand and measure the likelihood of climate and environmental shocks or stresses leading to the erosion of development progress, the deepening of deprivation and/or humanitarian situations affecting children or vulnerable households and groups
Challenges of climate change in tropical basins: vulnerability of eco-agrosystems and human populations
No full textClimate change impacts are already happening through the world, and it is now clear that there is the need for an adaptive response from global institutions down to the local level. Reducing vulnerability to cope with climate variability might be more challenging in tropical countries than in North America or Europe. The ten papers of this special issue were presented during the Adaptclim conference that was held by the Sinergia Project, the CLARIS LPB project, and the GeoData Institute in Asunción, Paraguay, in 2010. All papers, except one regarding the Brahmaputra Basin in South Asia, present studies from South America. These studies are first contextualized geographically and then are related one to another by a simplified vulnerability concept linking climate stress to sensitivity and adaptive capacity of natural and human systems. One half of the papers focus on actual or future climate change and the present-day causes of the vulnerability of natural and agrosystems. Droughts are and will be the main source of stress for agriculture in South America. Increasing fragmentation of forest of the center of this continent is aggravating their vulnerability to dry spells. Another half of the studies of this special issue deal with the adaptive capacity human populations to system perturbations produced or enhanced by climate change. The studies point out inclusion of traditional knowledge and involvement of local actors in their own vulnerability assessment to increase adaptive capacity. These elements of climate justice, giving voice to those less responsible for carbon emissions but bearing their most severe consequences, allow the particular needs of a community to be considered and can direct adaptation policy toward preserving or rebuilding their specific capabilities under threat from climate change. The special issue also made clear that a basin analysis of the climate change problem could provide information, results, and methods more readily of use for the local population and decision makers
Where and whom you collect weightings from matters…” Capturing wellbeing priorities within a vulnerable context: a case study of Volta Delta, Ghana
No full textWellbeing is a crucial policy outcome within sustainable development, yet it can be measured and conceptualised in various ways. Methodological decisions, such as how different components are weighted, can influence wellbeing classification. Many studies utilise equal weighting, assuming each component is equally important; however, does this reflect communities’ lived experiences? This study outlines a multidimensional basic needs deprivation measure constructed from the Deltas, Vulnerability and Climate Change: Migration and Adaptation (DECCMA) survey dataset in Volta Delta, Ghana. Participatory focus groups, interviews and weighting exercises with communities and District Planning Officers (DPOs) explore different subgroups’ wellbeing priorities. Comparative analysis examines the weights provided across genders, decision-making levels and livelihoods; including farming, fishing and peri-urban groups. Objective survey data is also combined with various subjective weights to explore the sensitivity of the overall deprivation rate and its spatial distribution. Significant weight differences are found between livelihoods, with farming and fishing communities weighting “employment”, “bank access”, and “cooperative membership” higher, whereas peri-urban communities apply a greater weight to “healthcare access”. Differences between decision-making levels are also noted. Community members weight “employment” higher, while DPOs assign a larger score to “cooperative membership”. In contrast, consistent weights emerge across genders. Furthermore, applying community livelihood weights produces lower deprivation rates across most communities compared to DPO or equal nested weights. Overall, significant differences between subgroups’ weights and the sensitivity of wellbeing measurement to weighting selection illustrate the importance of not only collecting local weights, but also where and whom you collect weightings from matters
“Happy Farmers” in Volta Delta, Ghana? Exploring the relationship between environmental conditions and happiness
No full textCommunities’ wellbeing in rural lower-middle-income countries is interlinked with climate and landscape characteristics. Rural inhabitants are often assumed to be “happy farmers”, content with their livelihoods and social connections, despite the financial and material insecurities associated with their fragile environments. However, is this assumption an accurate reflection of reality? This study explores relationships between environmental conditions and subjective wellbeing in Volta Delta, Ghana. Subjective wellbeing is captured through a life domains happiness measure, calculated using the “Deltas, Vulnerability and Climate Change: Migration & Adaptation” survey dataset. A binary logistic model evaluates associations between low happiness, and environmental and control characteristics constructed from survey and remote sensing datasets. The quantitative approach supports the “happy farmer” identity, with lower probabilities of low happiness amongst rural households with a strong attachment to agricultural landscapes. However, the limited availability of permanent employment could offset these subjective benefits. Nevertheless, happiness is not a substitute for objective wellbeing, often defined through monetary wealth; therefore, sustainability policy should not be discouraged from providing tangible support to vulnerable communities. Volta Delta consists of varying landscapes, with model results also illustrating lower happiness within coastal locations, potentially linked to fears of hazards, restricted natural resource governance, and threats to intergenerational land and livelihoods. This study highlights the key role of environmental conditions in potentially influencing subjective wellbeing. Exploring relationships with subjective outcomes ensures sustainability policy captures non-tangible outcomes and feedback effects, which, if incorporated alongside objective targets, can ensure all costs, benefits and challenges are accounted for
Opposing objective and subjective wellbeing outcomes within an environmentally vulnerable delta: a case study of Volta Delta, Ghana
No full textIntroduction: despite a growing interest in the measurement and conceptualization of wellbeing, the integration within sustainability research, and the understanding of how different wellbeing outcomes relate, is limited. Many studies focus on singular, often objectively measured, outcomes, without acknowledging the breadth of available measures. This approach can result in crucial subjective information, which can be explored to understand actors’ behaviors and responses, being omitted from research and policy. This study explores objective and subjective wellbeing outcomes, and how they relate, within an environmentally vulnerable context. Wellbeing and environmental services are intrinsically interlinked, therefore, appropriate policy solutions are required to address human needs and pressures on supporting ecosystems.Methods: this paper uses binary logistic regression modelling, and qualitative participatory rural appraisal methods, to understand the environmental conditions, including climatic hazards and landscape characteristics, associated with households experiencing different objective/subjective wellbeing outcomes within Volta Delta, Ghana.Results: the mixed method approach highlights a differing relationship between inland agricultural areas impacted by drought and erosion, and coastal/riverine, peri-urban landscapes exposed to flooding and salinization. Agricultural areas associate with “poor but happy” outcomes, whereas peri-urban landscapes associate with being “non-poor but unhappy.” Drawing on existing literature, and both quantitative and qualitative results, these varying outcomes are hypothesized to be driven by differences in livelihood vulnerability, relative comparisons to others, responses to climatic hazards, and individualistic/collective wellbeing conceptualizations.Discussion: our study concludes that environmental conditions influence objective and subjective wellbeing through different mechanisms. Sustainable development research should incorporate both objective and subjective measures when implementing and monitoring policy to more comprehensibly capture, and improve, wellbeing in environmentally vulnerable locations
Dataset supporting the doctoral thesis "Assessing the impacts of future climate change on homegarden agroforestry systems: A case study on Mt Kilimanjaro's SE slopes"
No full textThe dataset is a mainly socio-economic quantitative household survey that measures indicators representing subsistence farmers' crop yield and household wellbeing in a homegarden agroforestry system in Tanzania's Moshi Rural District. The dataset was gathered for the purpose of assessing how a change in climate conditions (warmer and drier) could affect the wellbeing of subsistence farmers in the homegardens following a climate analogue analysis study design. This study pertains to Chapter 6 in the doctoral thesis "Assessing the impacts of future climate change on homegarden agroforestry systems: A case study on Mt Kilimanjaro's SE slopes".
The dataset includes:
-Hard_Copy_HH_Survey.pdf
-Coding_Sheet.xlsx
-Moshi_Homegarden_Data.xlsx
-Conversion_Units.pdf
The data will become available after the embargo of 4.12.2024 and can be accessed with CC BY license. </span
Exploring the links between census and environment using remotely sensed satellite sensor imagery
No full textRelationships are often found between socio-economic variables and environmental factors for relatively small study regions. This research forms an exploratory data analysis using logistic regression to explore the (non-causal) relationships between socio-economic variables from a national census (female literacy and involvement in economic alternatives to agricultural work) and environmental metrics extracted from Earth observation (EO) data. The relationships observed often supported those found in the literature and field observations. The research highlighted the limited but potentially valuable use of EO data for monitoring socio-economic conditions which may be used to target development assistance in the future.<br/
Predicting socioeconomic conditions from satellite sensor data in rural developing countries: a case study using female literacy in Assam, India
No full textSocial data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GARerr) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GARerr model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.<br/
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