Encouraging pro-environmental behaviour: Energy use and recycling at Rhodes University, South Africa

2016 ◽  
Vol 53 ◽  
pp. 142-150 ◽  
Author(s):  
Paidamoyo Mtutu ◽  
Gladman Thondhlana
2018 ◽  
Vol 19 (4) ◽  
pp. 773-789 ◽  
Author(s):  
Angel Ancha Lindelwa Bulunga ◽  
Gladman Thondhlana

Purpose In response to increasing energy demand and financial constraints to invest in green infrastructure, behaviour change energy-saving interventions are increasingly being considered as a tool for encouraging pro-environmental behaviour in campus residences. This paper aims to report on a pilot programme aimed at reducing energy consumption via behaviour change interventions, variably applied in residences at Rhodes University, South Africa. Design/methodology/approach Data were collected via structured questionnaires, energy consumption records and post-intervention programme focus group discussions. Findings Participant residences that received a mix of different interventions in the forms of pamphlets, face-to-face discussions, incentives and feedback recorded more energy reductions of up to 9 per cent than residences that received a single or no intervention. In post-experiment discussions, students cited personal, institutional and structural barriers to pro-environmental energy-use behaviour. Practical implications Overall, the results of this study suggest that information provision of energy-saving tips combined with regular feedback and incentives can result in energy-use reductions in university residences, which may yield environmental and economic benefits for universities, but addressing barriers to pro-environmental behaviour might maximise the results. Originality/value Given the lack of literature on energy conservation in the global South universities, this study provides the basis for discussing the potential for using behavioural interventions in universities for stirring pathways towards sustainability.


2014 ◽  
Vol 18 ◽  
pp. 1-8 ◽  
Author(s):  
David Kimemia ◽  
Claire Vermaak ◽  
Shonali Pachauri ◽  
Bruce Rhodes

Author(s):  
Evans Zhandire

Solar radiation under clear-sky conditions provides information about the maximum possible magnitude of the solar resource available at a location of interest. This information is useful for determining the limits of solar energy use in applications such as thermal and electrical energy generation. Measurements of solar irradiance to provide this information are limited by the associated cost. It is therefore of great interest and importance to develop models that generate these data in lieu of measurements. This study focused on four such models: Ineichen-Perez (I-P), European Solar Radiation Atlas model (ESRA), multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) models. These models were calibrated and tested using solar irradiance data measured at eight different locations in South Africa. The I-P model showed the best performance, recording relative root mean square errors of less than 2% across all hours, months and locations. The performances of the MLPNN and RBFNN were poor when averaged over all stations, but tended to show performance similar to that of the I-P model for some of the stations. The ESRA model showed performance that was in between that of the Artificial Neural Networks and that of the I-P model.


Scilight ◽  
2019 ◽  
Vol 2019 (28) ◽  
pp. 280004
Author(s):  
Savannah Mandel

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