1. Introduction
Worldwide, energy consumption and its associated greenhouse gas (GHG) emissions has increased over the past few decades [
1] and is expected to grow by 28% between 2015 and 2040 [
2]. The U.S. households alone are responsible for about 20% of annual global GHG emissions, yet they represent only 4.3% of the global population [
3]. High levels of energy consumption take place at the HH level because of electrical appliances, heating and cooling equipment, vehicles, and various goods and services [
4]. Electricity, heat, and transportation that are largely dependent on burning fossil fuels are the major sources of greenhouse gas emissions from human activities in the United States [
5]. If this trend continues, the Earth’s temperature will rise between 2 and 4.9 degrees Celsius by the end of century [
6] resulting in disruptive consequences such as sea level rise [
7]. The potential economic cost for the United States is roughly 1.2% of gross domestic product (GDP) for each degree Celsius rise in global temperature with greatest effect in the southeastern part of the country [
8]. A better understanding of HH energy expenditure and its key drivers are essential to move forward with more sustainable consumption and reduction of greenhouse gas emissions [
9,
10].
The influence of various factors such as urban form, socioeconomic, demographic, climate, and housing on energy consumption have been widely studied [
11,
12,
13,
14,
15,
16], yet the empirical evidence is inconclusive [
9,
17]. The disagreements may exist due to the limitation of datasets, different methodologies, variations in countries, the scale being used, and lack of locational contexts analysis being conducted [
18,
19]. Most of past energy consumption studies are aspatial in nature neglecting the locational information associated with study areas in the analysis. The importance of understanding the spatial dimension of energy consumption is undeniable [
20,
21] since local contexts and practices are inherently linked to the energy consumption and sustainability. More differentiated local knowledge (e.g., land-use regulations, policies, and practices) that plays particular role in energy consumption is essential for community-scale urban design [
22]. Research addressing spatial aspects thus far has been conducted mostly at the macro scales such as country, region, state or city [
1,
23,
24]. Microscale (e.g., census tract) energy consumption analysis remains under explored [
17]. Analytical results drawn from geographically aggregated data are sensitive to the scaler issues such as size of zones (metropolitan statistical area (MSA) versus census tract), which affect the validity and reliability of findings [
25]. To minimize this issue, individual level data is recommended. In the absence of such data, however, the smallest geographically aggregated available data is highly preferable [
26] especially for policy makers [
27].
Conventional ordinary least square (OLS) method utilized in prior studies is incapable of capturing spatial variation of energy consumption in different areas of cities and the location-specific impacts of the driving factors [
28]. Tobler’s first law of geography “everything is related to everything else, but near things are more related than distant things” [
29] (p. 236) indicates the possible spatial non-stationarity in the geographical data [
30]. Therefore, energy consumption in different communities of cities should not be considered as being independent of each other due to spatial autocorrelation [
28]. Additionally, the impact of diving factors on energy consumption may vary spatially [
28]. Geographically weighted regression (GWR) has been proved to be highly effective in capturing such spatial dimensions, however its potential application in energy consumption has not been utilized [
31,
32].
This study, thus, seeks to add new understanding to the growing body of literature in identifying the key driving factors for HH energy consumption utilizing geographically weighted regression (GWR) method at the census tracts of 14 metropolitan statistical areas (MSAs) in North Carolina (NC). Our study is different from past studies in at least three ways: First, we use census tract data, the smallest aggregated level data, to examine the relationship between HH energy consumption and various explanatory factors including socioeconomics and demographics, housing characteristics, urban form, and temperature. The utility (electricity and gas) and transportation expenditures are considered as measures of HH energy consumption for our analysis. To our knowledge, no prior study has examined these interactions at finer scale geographic data. Second, we applied geographically weighted regression (GWR) method to capture the spatial variation in HH energy expenditures as well as to identify the spatial factors in energy consumption of different areas of cities. Third, the study area, NC utilized in this analysis has never been examined for energy expenditure context, but warrants attention [
33]. The results of this study will provide new understanding about the role of human and spatial context in energy use for planners and policy makers at community level. The paper is organized as follows.
Section 2 presents the influencing factors on energy consumption of households and theoretical framework. The modeling methodology is described in
Section 3. The results are given in
Section 4 followed by a discussion in
Section 5. We present conclusions and suggestions for future research in
Section 6.
5. Discussion
Various factors have been examined in the literature to understand energy consumption using different regression analyses [
44,
91]. We used geographically weighted regression (GWR) method to study spatial heterogeneity in energy expenditures of households in NC. In global models, non-complete dataset with missing information might result in spatial heterogeneity [
92]. Since complete datasets are difficult to obtain in many studies, applying local models to include spatial information can significantly improve the prediction results [
90]. However, the problem of multicollinearity should be taken into consideration when interpreting the results. Although the explanatory variables are not collinear in this study, it should be noted that the lack of this problem in the global model does not guarantee coefficient independency in the GWR model [
93].
Socioeconomic and demographic characteristics of households, urban form, housing characteristics, and local temperature are used to understand the complexity of energy consumption. As expected, OLS models show that higher income households spend more on both transportation and utilities, as richer households are interested to purchase larger houses, more goods and services, luxurious vehicles, comfortable indoor environments and recreational activities, which all result in higher energy consumption [
69]. Higher education is associated with higher transportation expenditures that might be due to higher income, higher car ownership, and longer drive for commuting purpose [
36,
41]. Sprawl and distance from the primary city center are significant factors influencing transportation expenditures in the OLS model. This is in line with the literature suggesting the positive impact of compact development on reducing transportation consumption through transportation choice (public vs. private), automobile dependency, and vehicle miles travel (VMT) [
48,
52,
94,
95,
96]. Contrary to expectations, the urban form variables were not significant predictors of HH utility expenditures in NC. Although none of the urban form factors are significant, the negative sign of sprawl indicates the lower utility expenditures spent by households living in compact areas. Housing age is also not a statistically significant factor related to utility expenditures. However, the positive correlation between housing age and energy expenditures, showing that new houses consume more energy, is in contrast with previous findings [
34,
97]. These results might be due to the larger size of new houses that are usually located in the less compact census tracts outside the city center.
Furthermore, the percent of detached housing has a positive impact on HH utility expenditures as it has been found in the past studies [
9,
53,
54,
55]. In addition to loosing or gaining heat in a detached home for more exposed walls, higher income households are also more likely to live in detached houses with large spaces and more rooms, all of which may suggest their higher utility consumption [
9]. Higher percentage of detached housings in our study area are located outside the city center and in the areas that are representative of less compact urban forms. However, the measurement of urban form, in our case sprawl index, has remained a challenging issue for studying utility consumption. The attributes such as accessibility to green spaces, shading effects from the trees, and water bodies that may affect utility consumption [
14,
17] are not considered in this index. Additionally, the benefits of density on utility or transportation expenditures can be superseded by other factors such as lifestyle choices of individuals [
50,
95,
98].
The GWR models in this research have a slightly better performance than the OLS models and provide local variations in relationships between our explanatory variables and HH energy expenditures. HH median income, householder median age, sprawl index, and distance from the primary city center explain spatial variability of HH transportation expenditures. HH median income, householder median age, and percent of housing with one unit detached are the main factors in the GWR model of utility expenditures. While the OLS results show a global trend of the impact of explanatory variables on energy expenditures, the GWR model indicates the spatial variation of the influence. For example, the OLS model shows that a census tract would have higher transportation expenditures if it is either far away from the city center or in more sprawl areas. The GWR results show where the influence of sprawl and distance from city center are higher in specific MSAs and census tracts. Identifying these local variations can be the most effective way of suggesting to urban planners where to allocate future land uses and changes in urban form to minimize HH transportation expenditures. Urban planners and policy makers can also target housing stock to reduce residential energy consumption with a transition from detached housing to attached or multifamily housing [
16]. The reduction in energy consumption would be higher in the areas where GWR shows a greater relationship between housing type and HH utility expenditures illustrating by larger local coefficients. The GWR model can also offer an opportunity to see if the insignificant global parameters will show locally significant influence [
84] though this was not the case for our analysis.
Both OLS and GWR modeling for HH transportation expenditures have a better fit compared to the utility model suggesting additional data requirements for the future analysis. The influence of non-economic factors such as information, attention, individual attitudes, social norms, and lifestyle on HH energy expenditures has been identified in different countries [
10,
99,
100,
101]. These variables are not included in our models because these types of data are not publicly available and are needed to be collected. The homogenous nature of NC might be the other reason that the GWR models did not provide strong results. A big advantage of the semiparametric GWR model is that it does not assume the spatial non-stationarity for all variables and allows for both global and local variables to be included in the model. However, GWR results cannot easily be transferred to other places. This is a disadvantage of GWR compared to OLS model that can be applied in similar physical settings [
82]. Our aim here, however, was to capture the spatial variation of energy consumption in NC and location-specific impacts of contributing factors. The GWR model, therefore, can be a useful tool for urban planners and decision makers to develop local plans and policies to reduce and manage HH energy consumption. Socioeconomic–demographic, housing and urban form characteristics do not generally change in a short period of time and are easily available through different data sources in the U.S., thus providing adequate information for local decision makers to implement the GWR approach for other areas. Though the results may vary in different locations, the GWR modeling has been proved to be an effective method in past studies and our research for prioritizing the suitable strategies. The GWR model can be improved and extended into an optimization-modeling framework [
84] that would help to solve spatial–social problems related to energy consumption. The outputs of the GWR model could serve as inputs to the optimization model [
84]. For instance, an optimization model could be developed to allocate future land-use and buildings in a way to minimize energy consumption.
From a policy perspective, the general assumptions that certain policies are applicable in all parts of a country or a state need to be revisited. The results of this study show that the effects of socioeconomics and demographics, urban form, and housing characteristics on household energy consumption are different across the region. This suggests the importance of developing location-specific guidelines for decision makers in order to evaluate the consumption patterns in local areas and prioritize the suitable strategies for different areas. In addition, much focus in the U.S. has been targeted on the housing stock for improving energy efficiency of buildings through technologies. The results of this study, however, show the significant impact of socioeconomic and demographic factors and housing type on electricity and gas consumption that need to be addressed in the future policies. Local policies also need to address more compact developments in their approach to reduce transport-related energy consumption.
6. Conclusions
A spatial analytical approach was developed to study HH energy consumption in 14 MSAs of NC using geographically weighted regression (GWR) modeling at census tract geography. The estimations of the OLS and GWR models were presented to investigate the global and local effects of various explanatory factors on HH transportation and utility expenditures. The findings reveal the spatial variation of the relationship between energy expenditures and the influencing factors. To the best of our knowledge, this research is the first to have applied the GWR modeling in HH energy expenditures at census tract level in the U.S. Although there are no changes in the signs of the regression coefficients in both the OLS and the semiparametric GWR models, the explanatory power of the GWR had a slight increase characterized by higher adjusted R2, reduction of AICc, and smaller residual values.
The main contribution of this research is evaluating the influence of a range of factors in HH energy expenditures at census tract level using a spatial approach. For designing intervention aimed at changing the future land use plans for the cities across the world, the micro scale analysis is crucial. The global energy demand has raised many concerns across the world particularly for the U.S. that has the highest demand among countries [
1]. A complete assessment of households’ energy consumption and its relationships to spatial urban structure need to be provided to help engineers and planners in their approach toward sustainability. The major limitation in this research is difficulty in collecting individual data that might be one of the reasons our models did not have a strong explanatory power particularly for the utility expenditures. Similarly, sprawl index does not have additional attributes (e.g., green area and tree shading) of urban form. Our housing characteristics data also have limitations such as missing information on housing shape, materials being used and ratio of window–wall. There is no complete set of data that represent such housing characteristics in census tract geography. Impact of individuals’ attitude and life style choice on energy consumption should be evaluated through detailed HH surveys. Although obtaining such data for a state such as NC is practically impossible, it should be noted that technology or planning alone might not encourage people to decrease their consumptions. Another limitation is the homogenous nature of NC. Applying our GWR model to other states might provide stronger and more reliable results. Developing policies to address these issues, improving the goodness of fit of GWR model, and extending the model to optimization frameworks remain some areas for future research.