Spatial disparities altered the determinants of biomass supply intention for clean energy: policy implications

Understanding the spatial variation of factors driving interest in bioenergy is important for designing effective energy policies. Although previous authors have broadly explored the determinants of biomass supply intentions in the field of bioenergy, the potential spatial variation of these factors has received limited consideration from bioenergy research. We performed a structural equation modeling based on the theory of planned behavior to investigate the impact of spatial disparities on factors determining people's intentions to supply forestry residues for clean energy. Our data were generated through a survey of 247 participants randomly sampled from two contrasting geographic regions (north and south) of the Republic of Benin. We found that spatial disparities altered the effects of attitude and knowledge of renewable energy while income and perceived behavioral control exhibited consistent positive effects across geographical regions. Our findings imply that ignoring spatial disparities when analyzing bioenergy acceptance can lead to distorted inferences. These findings can be used as a baseline information by policymakers to develop region-specific actions for achieving local transition toward clean energy in the Republic of Benin.

Automated summary

Understanding the spatial variation of factors driving interest in bioenergy is crucial for effective energy policy making. However, previous research on the determinants of biomass supply intentions in bioenergy has overlooked the potential spatial variation of these factors. Using structural equation modeling, we found that spatial disparities alter the impact of attitude and knowledge on people's intentions to supply forestry residues for clean energy. Income and perceived behavioral control, on the other hand, consistently exhibit positive effects across geographic regions. These findings highlight the importance of considering spatial disparities in analyzing bioenergy acceptance and can inform region-specific actions for achieving clean energy transition in the Republic of Benin.