近年來各國為了降低核能依賴、開發替代能源、減少能源進口與溫室氣體減量，推出了不少能源政策，如何減緩家戶能源消費成為本研究的核心問題。許多研究文獻指出，家戶能源消費主要會受到都市土地使用型態與家戶生活型態的影響；過去受限於資料取得困難，國內關於都市能源探討之文獻多半以縣市尺度進行實證分析，少以節能社區的尺度做為範疇，然而本研究認為不同尺度之下的家戶能源消費與都市空間結構特性的關聯性亦會有所不同。
因此本研究以多元迴歸模型建構相關變數與家戶電力消費之影響關係，以原台南市231個里作為研究範圍，相關變數包含外部環境因子、都市密度、住宅型態、人口及社經等面向，並依其估算結果以近似函數描繪各變數的邊際效果變化情形和彈性分析。
實證結果顯示，平均樓層高每增加1%，平均每年家戶電力消費的變動量也會增加0.26%。住宅平均面積每增加1%，平均每年家戶電力消費的變動量也會增加0.25%。市中心控制開放空地比率，較郊區來的有效果，郊區控制平均樓層規模與平均住宅面積則較市中心來的有效果。但整體而言，控制住宅屬性變數，對於家戶節能的效果相對都市密度、外部環境因子、社會經濟屬性等變數來的有效果。

In recent years, governments launched a lot of energy policy in order to reduce the reliance on nuclear energy, searching alternative energy sources, reducing energy imports and greenhouse gas. How to slow down household energy consumption has become the core issue of this study. Many studies mentions that household energy consumption will be influenced by urban land use and household lifestyle. According to the past reference, limited by the obtaining data, the literature on domestic energy mostly had been researched in counties scale, rather than community scale. However, we suggests that under different scales, the association between household energy consumption and urban spatial structure characteristics will be different.
Therefore, we run regressive analysis and find significant factors of household electricity consumption in 231 communities of the Former Provincial Tainan City, and we establish indicators of the impact of household energy consumption, such as external environmental factors, urban density, housing type, demographic and socioeconomic indicators. Finally, according to curve estimates result, we analyze the changes in marginal effects and elasticity of each variable.
The empirical results show that if the average floor high increase 1%, the average annual household electricity consumption will increase 0.26%. If residential average area increase 1%, the average annual household electricity consumption will increase 0.25%. Controlling the open space ratio in city region is more effective in energy-saving than suburbs. Controlling the average floor high and residential average area in suburbs is more effective in energy-saving than city. To sum up, housing type variables are more elastic for household energy-saving than urban density, external environmental factors and socio-economic indicators.

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