Renewable technologies such as solar present some of the possibilities for indoor and outdoor lighting in the remotest rural Africa where grid connections may take ages.This paper examined the key determinants that dr...Renewable technologies such as solar present some of the possibilities for indoor and outdoor lighting in the remotest rural Africa where grid connections may take ages.This paper examined the key determinants that drive household lighting fuel using a nationally representative sample(14,415 households)across Rwanda.Results from a multinomial probit regression show that rural location,house ownership,household wealth,and nonfarm work are some of significant factors that influence lighting fuel choices in Rwanda.Robustness of the results indicates that household wealth levels and other regional differences are likely to influence choice probability for using clean energy sources such as solar confirming the need to priotise wealth generation.The study’s findings suggest the need for joint efforts by government and non-state actors to priotise household wealth generation,promotion of non-farm activities and improvement of infrastructures to reduce rural-urban bias and differences across the regions,assuming that wealth will motivate rural households to switch to clean energy sources.展开更多
The work considers modification of the Best–Worst Scaling(BWS)to the so-called System 1(S1)approach.S1 was described by D.Kahneman as a spontaneous and automatic reaction by an unconsciousway in which human decision-...The work considers modification of the Best–Worst Scaling(BWS)to the so-called System 1(S1)approach.S1 was described by D.Kahneman as a spontaneous and automatic reaction by an unconsciousway in which human decision-makers choose among multiple alternatives.Application of S1 can be seen as a simplified BWS for data eliciting and express analysis of prioritization between many compared items.In S1,a respondent picks the items with which she feels“happy”,and those can be one,several,all,or none items in a task.Estimation of utilities is performed by multinomial-logit modeling with different optimization criteria which produce parameters of the models and choice probabilities of the items.Numerical examples by marketing research data are encouraging and demonstrating that spontaneous choice decisions can make S1 approach very fast,efficient,and convenient for express analysis of items prioritization,especially for big data.展开更多
文摘Renewable technologies such as solar present some of the possibilities for indoor and outdoor lighting in the remotest rural Africa where grid connections may take ages.This paper examined the key determinants that drive household lighting fuel using a nationally representative sample(14,415 households)across Rwanda.Results from a multinomial probit regression show that rural location,house ownership,household wealth,and nonfarm work are some of significant factors that influence lighting fuel choices in Rwanda.Robustness of the results indicates that household wealth levels and other regional differences are likely to influence choice probability for using clean energy sources such as solar confirming the need to priotise wealth generation.The study’s findings suggest the need for joint efforts by government and non-state actors to priotise household wealth generation,promotion of non-farm activities and improvement of infrastructures to reduce rural-urban bias and differences across the regions,assuming that wealth will motivate rural households to switch to clean energy sources.
文摘The work considers modification of the Best–Worst Scaling(BWS)to the so-called System 1(S1)approach.S1 was described by D.Kahneman as a spontaneous and automatic reaction by an unconsciousway in which human decision-makers choose among multiple alternatives.Application of S1 can be seen as a simplified BWS for data eliciting and express analysis of prioritization between many compared items.In S1,a respondent picks the items with which she feels“happy”,and those can be one,several,all,or none items in a task.Estimation of utilities is performed by multinomial-logit modeling with different optimization criteria which produce parameters of the models and choice probabilities of the items.Numerical examples by marketing research data are encouraging and demonstrating that spontaneous choice decisions can make S1 approach very fast,efficient,and convenient for express analysis of items prioritization,especially for big data.
