Local and national determinants of household energy consumption in the Netherlands

Bardia Mashhoodi*, Dominic Stead, Arjan van Timmeren

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
169 Downloads (Pure)

Abstract

The policies of Third National Energy Efficiency Action Plan for the Netherlands, regarding the reduction of household energy consumption (HEC), were made based on the unwritten presumption that the stimuli of HEC are similar in each and every location of the Netherlands, and that it therefore is possible to formulate an identical set of incentives and regulations that are optimally suitable in all the locations of the country. The objective of this study is to examine the validity of this presumption by formulating two research questions: what are the national determinants of HEC, i.e. the stimuli that trigger the same response across the whole country? What are the local determinants of HEC, i.e. the stimuli which trigger different responses across the country? To identify local and national determinants of HEC, the impact of nine determinants of HEC in 2 462 neighbourhoods of the Netherlands is assessed by employing the geographical variability test. The results show that two of the determinants are national: (1) the number of frost-days, (2) wind speed. The results indicate that seven of the determinants are local: (1) income, (2) household size, (3) building age, (4) surface-to-volume ratio, (5) population density, (6) number of summer days, and (7) land surface temperature. By employing a semi-parametric geographically weighted regression analysis, the impact of the local and global determinants of HEC is estimated and mapped.

Original languageEnglish
Pages (from-to)393-406
Number of pages14
JournalGeoJournal
Volume85 (2020)
DOIs
Publication statusPublished - 2019

Keywords

  • Energy policy
  • Household energy consumption
  • Mixed geographically weighted regression
  • Netherlands
  • Semi-parametric geographically weighted regression

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