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• We provide a typology of communities based on their access to common-pool resources. • The influence of capitals and climatic shocks on livelihoods is spatially variable. • The type of community modifies livelihood opportunities and coping strategies. • Climatic shocks drive precarious livelihoods, except in best-connected communities. Spatial factors, such as environmental conditions, distance to natural resources and access to services can influence the impacts of climate change on rural household livelihood activities. But neither the determinants of precarious livelihoods nor their spatial context has been well understood. This paper investigates the drivers of livelihood precariousness using a place-based approach. We identify five community types in rural regions of the Mahanadi Delta, India; exurban, agro-industrial, rainfed agriculture, irrigated agriculture and resource periphery by clustering three types of community capitals (natural, social and physical). Based on this typology, we characterise the associations between precarious livelihood activities (unemployment or engagement in agricultural labour) with agricultural shocks and household capitals. Results demonstrate that, the type of community influences the impact of agricultural shocks on livelihoods as four of the five community types had increased likelihoods of precarious livelihoods being pursued when agricultural shocks increased. Our research demonstrates that the bundle of locally available community capitals influences households' coping strategies and livelihood opportunities. For example, higher levels of physical capital were associated with a lower likelihood of precarious livelihoods in agro-industrial communities but had no significant impact in the other four. Results also indicate that agricultural shocks drive livelihood precariousness (odds ratios between 1.03 and 1.07) for all but the best-connected communities, while access to household capitals tends to reduce it. Our results suggest that poverty alleviation programmes should include community typologies in their approach to provide place-specific interventions that would strengthen context-specific household capitals, thus reducing livelihood precariousness. [ABSTRACT FROM AUTHOR]

Abstract Despite the increasing interest of the Sustainable Livelihood Framework in the field of international development and in academia and the recent call for the use of mixed-methods approach, there has been little analysis that brings together qualitative and quantitative methods over a large geographical extent. Based on findings from participatory rural appraisals during which participants identified the key assets needed to achieve their livelihoods, this paper argues that common-pool resources (community capitals) should be differentiated from private goods (household capitals) as they operate under different dynamics of decision-making and management. We then create quantitative indicators that can be mapped across a large geographical extent by using data derived from national census and satellite sensors. Spatial patterns and differentials in access to livelihood capitals across the case study are examined and the associations that exist between household capitals, between community capitals, and between both are quantified. The results demonstrate that household physical capital is positively associated with household financial and social capitals but negatively associated with household natural capital, supporting the hypothesis that households trade their natural assets to cope with shocks. It is also shown that proximity to main axes of communication increases access to village amenities but decreases access to natural resources, while remoteness increases household human capital but decreases household physical and financial capitals. Such a cross-scale study adds to the understanding of the question of scale regarding rural livelihoods and community development, which could act as a bridge between the implementation of policy programmes (often targeted at the community level) and their expected outcomes (often targeted at the household level). Highlights • We advocate for the separation of community capitals from household livelihood capitals to characterise rural livelihoods. • We observed spatial variability in access to common-pool resources and private assets affecting livelihoods in rural India. • Workforce availability and access to community natural resources increase with remoteness. • Access to community natural capital is negatively associated with access to community infrastructures. • Households make trade-offs between natural and financial assets to meet their needs and mediate vulnerabilities. [ABSTRACT FROM AUTHOR]

Relationships 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. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies, and trade-offs between individual goals across multiple temporal and spatial scales. This paper proposes a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyze such SDG interactions. We focused on the Sundarban Biosphere Reserve (SBR), India, within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty, and a strong dependence on rural livelihoods. It is adjacent to the growing megacity of Kolkata. The area also includes the Indian portion of the world's largest mangrove forest––the Sundarbans––hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across scales. The IAM framework is designed to investigate socio-environmental change under a range of explorative and/or normative scenarios and explore associated policy impacts, considering a broad range of subthematic SDG indicators. The following elements were explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans' mangroves under sea-level rise and different management strategies. The novel, high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis. [ABSTRACT FROM AUTHOR]

Climate-related hazards accounted for over 90% of disasters over the past two decades and cause massive losses every year worldwide. In the face of the climate crisis, we are confronted with unprecedented challenges that require transformational change. The Sustainable Development Goals, the Paris Agreement and the Sendai Framework for Disaster Risk Reduction set ambitious global goals and targets. Monitoring and reporting are fundamental towards their achievement. We are, thus, faced with an urgency to step up accountability efforts. India is one of the top ten countries by cumulative disaster losses, with the most intense recorded event being the 1999 Odisha super cyclone. Twenty years later, there is still no comprehensive documentation of the losses caused by the cyclone at the micro-level, nor an understanding of long-term post-disaster recovery patterns. To fill this gap, a systematic review has been conducted to gather evidence of recorded losses by type and their spatial distribution. Results show that satellite remote sensing has contributed to a finer and more localised estimation of losses compared to official records from 1999; that coastal and riverine districts are proven to be the worst impacted; and that we now have an understanding, albeit partial, of the non-physical impacts associated with the 1999 cyclone. This review provides the most comprehensive catalogue of documented losses induced by the 1999 super cyclone and is the best estimate of a baseline of impacts which can serve to investigate long-term recovery trends. [ABSTRACT FROM AUTHOR]

Abstract: Social 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. [Copyright &y& Elsevier]

Summary This article presents results from an investigation of the relationships between welfare and geographic metrics from over 14,000 villages in Assam, India. Geographic metrics accounted for 61% of the variation in the lowest welfare quintile and 57% in the highest welfare quintile. Travel time to market towns, percentage of a village covered with woodland, and percentage of a village covered with winter crop were significantly related to welfare. These results support findings in the literature across a range of different developing countries. Model accuracy is unprecedented considering that the majority of geographic metrics were derived from remotely sensed data. [ABSTRACT FROM AUTHOR]