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The current intense food production-consumption is one of the main sources of environmental pollution and contributes to anthropogenic greenhouse gas emissions. Organic farming is a potential way to reduce environmental impacts by excluding synthetic pesticides and fertilizers from the process. Despite ecological benefits, it is unlikely that conversion to organic can be financially viable for farmers, without additional support and incentives from consumers. This study models the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain. Findings demonstrate the feasibility and superiority of the proposed model over the traditional sustainable supply chain models in incorporating the feedback between consumers and producers and analyzing management scenarios that can urge farmers to expand organic agriculture. Results further indicate that demand-side participation in transition pathways towards sustainable agriculture can become a time-consuming effort if not accompanied by the middle actors between consumers and farmers. In practice, our proposed model may serve as a decision-support tool to guide evidence-based policymaking in the food and agriculture sector.

Fires are an important aspect of environmental ecology; however, they are also one of the most widespread destructive forces impacting natural ecosystems as well as property, human health, water and other resources. Urban sprawl is driving the construction of new homes and facilities into fire-vulnerable areas. This growth, combined with a warmer climate, is likely to make the consequences of wildfires more severe. To reduce wildfires and associated risks, a variety of hazard reduction practices are implemented, such as prescribed burning (PB) and mechanical fuel load reduction (MFLR). PB can reduce forest fuel load; however, it has adverse effects on air quality and human health, and should not be applied close to residential areas due to risks of fire escape. On the other hand, MFLR releases less greenhouse gasses and does not impose risks to residential areas. However, it is more expensive to implement. We suggest that environmental, economic and social costs of various mitigation tools should be taken into account when choosing the most appropriate fire mitigation approach and propose a conceptual framework, which can do it. We show that applying GIS methods and life cycle assessment we can produce a more reasonable comparison that can, for example, include the benefits that can be generated by using collected biomass for bioenergy or in timber industries. This framework can assist decision makers to find the optimal combinations of hazard reduction practices for various specific conditions and locations.

In the efforts to ensure the health of the Australian population during the COVID pandemic, social, economic, and environmental aspects of people’s life were impacted. In addressing the pandemic risks, a number of governments prioritized people’s health and well-being over GDP growth. The Genuine Progress Indicator (GPI) is used to account for factors that influence well-being. We used the GPI to assess the pandemic’s impact on well-being and we examined our results in relation to the GDP. We estimated the GPI for the first 6 months of 2019 and the same period in 2020, during which the first stages of the COVID pandemic and the first nationwide lockdown in Australia took place. We examined two scenarios, in the first we found that in Q1 the GDP growth (1.4%) was accompanied by a significant GPI growth (5.3%), showing a positive relation to the GDP; but in Q2 the significant drop (-6.3%) in the GDP was not followed by the GPI, instead the GPI growth remained almost steady with even a relatively small increase (0.33%), indicating a negative relation to the GDP growth. Whereas in the second scenario, the GPI growths (7.12%) in Q1 and (-2.60%) Q2 were positively related to the GDP growths (4.6%) in Q1 and (−0.25%) Q2.We discuss the reasons for the divergence between the two indicators and one of the limitations of the GPI as a measure of well-being. Lastly, we discuss the behavioural and policy lessons of the lockdown and their relevance to what is proposed by degrowth economists.