This study aims to examine the associations between land surface temperature (LST) and annual household energy expenditure (HEE) in urbanized zones of the Netherlands. To do so, satellite images of 96 days at four different overpassing local times (10:30 a.m., 1:30 p.m., 10:30 p.m., 1:30 a.m.) are retrieved, and annual average maximum (LST-Max) and average minimum (LST-Min) LST at each zone are calculated. Employing geographically weighted regression, controlling for 11 control variables, the results indicate that the impact of LST on HEE could not be enhanced unless the interactions between LST and location-specific circumstances are taken into consideration. In this line, four types of socio-spatial characteristics are distinguished: (1) losers, where higher levels of both LST-max and LST-Min are associated with higher HEE, characterised by high population density; (2) peak losers and trough winners, where LST-Max is associated with higher HEE and LST-Min with lower HEE, characterised by large households and elderly citizens; (3) peak winners and trough losers, where LST-Max is associated with lower HEE and LST-Min with higher HEE, characterised by private-rental dwellings and large building surface to volume ratio; (4) winners, where both LST-Max and LST-Min are associated with lower HEE, characterised by old buildings.

这项研究旨在研究荷兰城市化地区的地表温度（LST）与家庭年度能源支出（HEE）之间的关联。为此，检索了四个不同的当地时间（上午10:30，下午1:30，下午10:30，下午1:30）的96天卫星图像，并获得了年平均最大值（LST-Max）和平均值计算每个区域的最小（LST-Min）LST。使用地理加权回归，控制11个控制变量，结果表明，除非考虑LST和特定位置环境之间的相互作用，否则LST对HEE的影响无法增强。在这一方面，区分了四种类型的社会空间特征：（1）失败者，其中较高的LST-max和LST-Min都与较高的HEE相关，高人口密度 （2）峰值输家和低谷赢家，其中LST-Max与较高的HEE相关，而LST-Min与较低的HEE相关，其特征是大家庭和老年人。（3）高峰赢家和低谷输家，其中LST-Max与较低的HEE相关，而LST-Min与较高的HEE相关，其特征是私人租赁住宅和较大的建筑物表面积/体积比；（4）获胜者，其中LST-Max和LST-Min均与较低的HEE有关，其特征是老建筑。特点是私人住宅，建筑物的表面体积比大；（4）获胜者，其中LST-Max和LST-Min均与较低的HEE有关，其特征是老建筑。特点是私人住宅，建筑物的表面体积比大；（4）获胜者，其中LST-Max和LST-Min均与较低的HEE有关，其特征是老建筑。