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The purpose of this study is to identify the most vulnerable households and districts in Northwest Ethiopia and help decision-makers in developing and prioritising effective adaptive strategies and actions.

A multi-scale analytical tool and hazard-generic socio-economic indicators were developed to identify and prioritise the most vulnerable households and districts in Northwest Ethiopia. Categorical principal component analysis with 36 indicators was used to develop weights for different indicators and construct a household intrinsic vulnerability index. Data were collected through key information interviews, focus group discussions and a household survey with 1,602 randomly selected households in three districts of Northwest Ethiopia.

Drawing on intrinsic vulnerability index computation, this study highlights that low levels of education, low access to climate information and credit services, long distance travelled to fetch water and frequent food shortages are the dominant factors contributing to high levels of intrinsic vulnerability at district level, while lack of livelihood support and income diversification are the key drivers of vulnerability at household level. The findings of this study further show that the majority of households (78.01%) falls within the very high to moderately high vulnerable category. Disaggregating the data according to agro-climatic zones highlights that the prevalence of high intrinsic vulnerability is most widespread in the lowland agro-climatic zone (82.64%), followed by the highland (81.97%) and midland zones (69.40%).

From a policy intervention vantage point, addressing the drivers of vulnerability provides a reliable approach to reduce the current vulnerability level and manage potential climate change-induced risks of a system. Specifically, reliable information on inherent vulnerability will assist policymakers in developing policies and prioritising actions aimed at reducing vulnerability and assisting in the rational distribution of resources among households at a local level.

This study contributes to the existing vulnerability literature by showing how hazard-generic socio-economic indicators in the vulnerability assessment adopted by the IPCC (2014) are important to identify drives of vulnerability which ultimately may feed into a more fundamental treatment of vulnerability.

The purpose of this paper is to reduce the post-harvest loss occurring through respiration and CO₂ emission produce by the selected produces, during logistics. This paper proposes a supply chain (SC) structure for the Indian traditional agriculture SC planning model to reduce post-harvest loss and mixed closed transportation to reduce CO₂ emission.

The Indian agriculture SC structure is modeled and solved by genetic algorithm using a MATLAB Optimization toolbox. The respiration rate is measured by a static method. These values are applied in an SC planning model and the post-harvest loss and its corresponding CO₂ emission are estimated.

This paper proposes a supply structure for the Indian traditional agriculture SC to reduce the post-harvest loss; the experiments measured the respiration rate to estimate the CO₂ emission. The mixed closed transportation method is found to be suitable for short-purpose domestic transportation.

The optimized supply structure leads to unemployment through eliminating the intermediaries. Therefore, further research encourages the conversion of intermediaries into hub instead of eliminating them.

This paper includes implications for the development of Indian traditional agriculture SC by an optimized supply structure and novel transportation method for the selected agriculture produces based on compatibility.

This paper identified that the agriculture produces respiration can also emit the CO₂. The closed transportation method can reduce the CO₂ emission of produces respiration than traditional open transportation.