Factors Affecting Domestic Water Consumption on the Spanish Mediterranean Coastline
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Citations
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References
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice
Estimation of residential water demand: a state-of-the-art review
Price and income elasticities of residential water demand: A meta-analysis
Price and Income Elasticities of Residential Water Demand: A Meta-Analysis
Related Papers (1)
Frequently Asked Questions (10)
Q2. What are the future works mentioned in the paper "Factors affecting domestic water consumption in the spanish mediterranean coastline" ?
The use of aggregate city-scale data has made it difficult to study climatic variables, which, according to the international literature, are the main variables to explain the differences in domestic consumption between the base use ( winter ) and the seasonal use ( summer ) ( House-Peters and For Peer Review O nly 18 Chang 2011a ; Chang et al. 2017 ). Nonetheless, to carry out this analysis in tourist environments, it will be necessary to calculate the temporary population since this variable affects water consumption more than the climate.
Q3. What is the main purpose of the regression analysis?
In the regression analysis, the logarithmic form of domestic water consumption (LNDWC12) is used to give robustness to the model, to comply with normalcy tests (Kolmogorov–Smirnov and Shapiro–Wilk) and to facilitate the interpretation of the results (March, Perarnau and Saurí 2012).
Q4. Why did it be decided to carry out the analysis for 2012?
Due to the lack of homogeneous data series, it has been decided to carry out the analysis for the year 2012, as are available more records.
Q5. How many municipalities are included in the sample?
It should be noted that although the sample represents 46 percent of the total coastal municipalities, it incorporates 56.2 percent of municipalities with more than 5.000 inhabitants, and concentrates 71 percent of the population registered in 2012 (INE 2013a), so this analysis is being carried out for the most populated municipalities.
Q6. What is the main reason why the GWR results are not in line with other studies?
the results of the GWR indicate that it is necessary to develop further analysis on the local scale since it has been observed that the explanatory power of income level varies spatially.
Q7. What is the main limitation of the study?
Another limitation is the use of aggregate city-scale data on domestic water consumption, because the authors have assumed "lack of variation in spatial patterns and processes" (House-Peters and Chang 2011a:4).
Q8. What is the main reason why the results of the OLS model are improved?
the results of the OLS model are improved since it is possible to differentiate the local spatial variations of the parameters estimated by means of the implementation of a kernel function, that allows to make estimations adjusted to each observation giving greater influence on the closer observations (Brunsdon, Fotheringham and Charlton, 1996).
Q9. What is the effect of the temporary population on the income variable?
In these cases, the temporary population influences the increase of the dependent variable, especially in municipalities with a high percent of second homes, and the underestimation of the income level, since it is calculated from the permanent population.
Q10. What are the limitations of the study?
the authors recognize the limitations of the study, mainly due to the lack of rigorous information on the temporary population and the length of their stay.