RESUMEN
The global COVID-19 pandemic is a health crisis, an economic crisis, and a justice crisis. It also brings to light multiple ongoing, underlying social crises. The COVID-19 crisis is actively revealing crises of energy sovereignty in at least four ways. First, there are many whose access to basic health services is compromised because of the lack of energy services necessary to provide these services. Second, some people are more vulnerable to COVID-19 because of exposure to environmental pollution associated with energy production. Third, energy services are vital to human wellbeing, yet access to energy services is largely organized as a consumer good. The loss of stable income precipitated by COVID-19 may therefore mean that many lose reliable access to essential energy services. Fourth, the COVID-19 crisis has created a window of opportunity for corporate interests to engage in aggressive pursuit of energy agendas that perpetuate carbon intensive and corporate controlled energy systems, which illuminates the ongoing procedural injustices of energy decision making. These four related crises demonstrate why energy sovereignty is essential for a just energy future. Energy sovereignty is defined as the right for communities, rather than corporate interests, to control access to and decision making regarding the sources, scales, and forms of ownership characterizing access to energy services. Energy sovereignty is a critical component in the design of a post-COVID-19 energy system that is capable of being resilient to future shocks without exacerbating injustices that are killing the most vulnerable among us.
RESUMEN
Perennial crops offer the opportunity to harvest from the same plant many times over several years while reducing labor and seed costs, reducing emissions and increasing biomass input into the soil. We use system dynamics modeling to combine data from field experiments, crop modeling and choice experiments to explore the potential for adoption and diffusion of a sustainable agriculture technology in a risky environment with high variability in annual rainfall: the perennial management of pigeonpea in maize-based systems of Malawi. Production estimates from a crop model for the annual intercrop system and data from field experiments on ratooning for the perennial system provided the information to create a stochastic production model. Data from choice experiments posed by a farmer survey conducted in three Malawi districts provide the information for parameters on farmers' preferences for the attributes of the perennial system. The perennial pigeonpea technology appeared clearly superior in scenarios where average values for maize yield and pigeonpea biomass production were held constant. Adoption was fastest in scenarios where relatively dry growing seasons showcased the benefits of the perennial system, suggesting that perennial management may be appropriate in marginal locations. The potential for adoption was reduced greatly when stochasticity in yields and seasons combine with significant social pressure to conform. The mechanism for this is that low yields suppress adoption and increase disadoption due to the dynamics of trust in the technology. This finding is not unique to perennial pigeonpea, but suggests that a critical factor in explaining low adoption rates of any new agricultural technology is the stochasticity in a technology's performance. Understanding how that stochasticity interacts with the social dynamics of learning skills and communicating trust is a critical feature for the successful deployment of sustainable agricultural technologies, and a novel finding of our study.
RESUMEN
Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.