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Climate change has become one of the most important development challenges worldwide. It affects various sectors, with agriculture the most vulnerable. In Ethiopia, climate change impacts are exacerbated due to the economy’s heavy dependence on agriculture. The Ethiopian Government has started to implement its climate-resilient green economy (CRGE) strategy and reduce CO₂ emissions. Therefore, the purpose of this study is to examine the impact of CO₂ emission on agricultural productivity and household welfare.

This study aims to fill these significant research and knowledge gaps using a recursive dynamic computable general equilibrium model to investigate CO₂ emissions’ impact on agricultural performance and household welfare.

The results indicate that CO₂ emissions negatively affect agricultural productivity and household welfare. Compared to the baseline, real agricultural gross domestic product is projected to be 4.5% lower in the 2020s under a no-CRGE scenario. Specifically, CO₂ emissions lead to a decrease in the production of traded and non-traded crops, but not livestock. Emissions also worsen the welfare of all segments of households, where the most vulnerable groups are the rural-poor households.

The debate in the area is not derived from a rigorous analysis and holistic economy-wide approach. Therefore, the paper fills this gap and is original by value and examines these issues methodically.

Inaccurate product information on retail websites lead to dissatisfied customers and profit losses. Yet, the effects of product information failures (PIFs) remain under-explored, with the mobile commerce channel commonly overlooked. This paper aims (1) to investigate the negative effects of PIFs on shoppers' attitudes and behaviours in a mobile context. The authors further (2) evaluate the impacts of the cause and duration of a PIF, changes of expectations towards the retailer after a PIF occurred and how previous mobile shopping experience in general decreases the effects of PIFs.

The authors conducted a scenario-based experiment with a one-factorial between-subjects design. The six most common PIFs of an international leading online fashion retailer are operationalized and tested against a control group. The final sample consists out of 758 mobile shoppers from the UK.

The results demonstrate that the perceived severity of PIFs based on showing customers incorrect information is higher when key information is lacking. Further, when the cause of a PIF is attributed to the retailer, it results in higher recovery expectations towards them. The results also reveal that respondents who have shopped mobile before perceive PIFs as less severe than inexperienced ones.

This research expands the online service failure literature by examining PIFs and its effects in the specific context of mobile commerce. The authors also provide recommendations for a better management of PIFs like the incorporation of PIFs information into reporting packs.