FES Funded Projects Outputs Show only Author
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Just don’t call it climate change: Climate-skeptic farmer adoption of climate mitigative practices Despite low levels of agreement that climate change is caused primarily by humans, respondents to a survey of climate change beliefs and adoption of climate-mitigative practices among beef and grain producers in Alberta, Canada, indicate a high level of adoption of several agricultural practices with climate-mitigative benefits. Respondents’ motivations for adoption of climate-mitigative practices rarely include the belief that climate change is caused by humans, but rather expectations for economic benefits, improvements in soil quality, and biodiversity, among other things. The strongest predictor of mitigative practice adoption is a learning orientation, defined as valuing improvement, research, learning, and innovation, followed by a conservation orientation that values land stewardship. Predictors are not consistent across practices; however, in some but not all cases adoption is predicted by climate change norms, or assumption of personal responsibility to address climate change, and other predictors vary by practice as well.T05-P04 University of Alberta Publication 2019-03-15 " Debra Davidson
" ,
" Curtis Rollins
" , Lianne Michelle Lefsrud,
Anders, S. ,
" Andreas Hamann
" T05-P04 Enabling transdisciplinary education for energy systems transitions T06-P03 University of Alberta Publication 2020-01-01 T06-P03 Electric Vehicle Load Forecasting in Rural Areas: A Systematic Review The growing adoption of electric vehicles, combined with increasing interdependence between urban and rural areas, raises concerns about the resilience of electrical networks, particularly in rural regions where infrastructure is less robust and more limited in complexity. Accurate load forecasting is therefore essential to support effective planning and mitigate potential stress on the grid. This study aims to evaluate and synthesize methodologies for predicting electrical loads generated by electric vehicles in rural areas, with the objective of identifying current practices, data characteristics, and methodological gaps. Following a systematic review approach, the work compiles and analyzes recent literature to provide a structured reference framework for researchers and practitioners. The findings reveal a growing research interest in this field, particularly in Europe and North America, with both model-based and data-driven approaches used in comparable proportions, and short-term forecasting emerging as the most common horizon. However, a lack of standardization in the documentation of network characteristics remains a significant limitation across studies. The review contributes by clarifying the state of research, highlighting critical gaps, and offering guidance for future work. These results underscore the importance of developing standardized criteria for documenting network properties and integrating diverse data sources to enhance the accuracy and applicability of load forecasting in rural distribution networks.T06-A04 University of Alberta Publication 2025-10-13 T06-A04
