Cold Houses and Warm Climates Revisited: On Keeping Warm in Chicago, or Paradox Lost

Dewees, Donald N.; Wilson, Thomas A.

doi:10.1086/261699

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…On the one hand, some authors have suggested that colder regions choose higher temperatures regardless of the associated higher costs (Friedman, 1987). On the other hand, other papers found the opposite (Dewees and Wilson, 1990;Gillingham et al, 2012). Our results coincide with the latter, indicating that the climatic zone variable has positive effects on indoor temperatures: households in warmer regions of the Continental and Mediterranean zones set higher heating temperatures as compared to households in the Atlantic area.…”

Section: --supporting

confidence: 83%

Pro-environmental Households and Energy Efficiency in Spain

Ramos

Labandeira

Löschel

2015

Environ Resource Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu Terms of use: Documents in Pro-Environmental Households and Energy Efficiency in SpainAna Ramos*, Xavier Labandeira and Andreas Löschel AbstractThe residential building sector is a major driver of current and future energy consumption and associated emissions, which can be potentially mitigated through significant energy-efficiency (EE) improvements in both emerging and developed countries. Yet, there are several persistent barriers that hinder the attainment of EE improvements in this area. Using data from a 2008 national representative survey of Spanish households, this paper is interested in the determinants of EE-related decisions. In particular, a discrete-choice model empirically analyzes whether proenvironmental households are more likely to invest in EE and to adopt daily energysaving habits. We show that households with eco-friendly behaviors are more likely to investment in well-differentiated EE measures as well as to steer daily habits towards energy savings. However, no effects are found for households with environmental attitudes based on stated willingness to pay to protect the environment. In addition to this, households belonging to higher income groups and education levels are more likely to invest in EE but not to adopt energy-saving habits; while households with older members are less likely to invest in EE and show fewer eco-friendly habits.

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Section: --supporting

confidence: 83%

Pro-environmental Households and Energy Efficiency in Spain

Ramos

Labandeira

Löschel

2015

Environ Resource Econ

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“…A large body of research has focused on energy demand and the drivers of demand, and a number of excellent surveys exist exploring relations between various factors; e.g., [3,5,12,19,20,22,29,30].…”

Section: Factor Descriptionmentioning

confidence: 99%

“…The relative importance of environmental factors depends on the local climate [19], individual perception of weather [5,12], and on the building's physical and operating characteristics [18,31]. In Louisiana, humidity is an important factor in energy consumption [26].…”

Section: Humiditymentioning

confidence: 99%

“…The climate at the monitoring station is assumed to be representative of the climate at the household which may lead to location bias, since the environmental variables that drive demand are not measured at the location where demand occurs, but rather, at the nearest monitoring station that has a reasonably complete data series. 5 Further, since the climate record may not be available for some periods of time, it may be necessary to use data from neighboring stations to infer missing data. Missing data does not occur frequently, but it can and does occur, especially for remote stations.…”

Section: Data Collectionmentioning

confidence: 99%

“…The analyst should nonetheless be prepared to accept that for some households, because of unobservable factors and/or conditions beyond the ability of the model, no factor set will yield statistically significant results. 5 Fortunately, this is believed to be a reasonable assumption, since environmental data is rarely available at the household level. Locational bias is considered part of the background ''noise" which in most cases is not expected to be a significant factor.…”

Section: Model Constructionmentioning

confidence: 99%

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