1. Introduction
Cities are spatial carriers where capital, labor, and infrastructure are highly aggregated, and they are the main battlegrounds for economic activities such as the allocation of resources and trade transactions in human societies [
1]. The development and construction of cities is an important task for many countries, and the urbanization process of each country is advancing by leaps and bounds [
2,
3]. According to the
World Cities Report 2022 published by UN-Habitat, the world’s urbanization rate has reached 56% in 2021. By 2050, this figure will rise to 68%, with an increase of about 2.2 billion urban dwellers, and a significant increase in urbanization levels is expected in all regions of the world [
4]. Similarly, China’s urban development has been dramatic, with the country’s urbanization level growing at an unprecedented rate, from 17.92% in 1978 to 64.7% in 2022 [
5]. Promoting urbanization is a powerful engine and an important strategy for China’s socio-economic development [
6]. Urbanization is regarded as a necessary path to modernization, and the 20th National Congress of the Communist Party of China further rationalized the idea of a new urbanization strategy. Rational urbanization promotes the modernization of industrial structures and also helps improve energy efficiency and environmental quality [
7,
8].
However, in reality, disorderly expansion of urban space and reckless development of land resources have become common problems in the urbanization process of countries around the world [
9]. Land element-based urbanization promotes land development and utilization, but land expansion that is detached from the actual needs of the population and economic development may lead to the phenomenon of a mismatch in land development in China [
10], which is called urban sprawl [
11]. The impacts of urban sprawl on ecological quality persist, and the impacts have considerable variability and complexity [
12,
13,
14]. At the same time, the problem of climate change caused by large amounts of carbon emissions is extremely serious [
15], and energy consumption is the main “culprit” affecting carbon emissions. The consumption of basic resources such as energy is exacerbated during rapid urbanization [
16], and cities have become an important carbon source for carbon emissions [
17]. The Chinese government pays more attention to the dynamic change in total energy consumption and makes a solemn commitment to energy conservation and emission reduction, and reducing energy consumption is a must for sustainable development [
18]. Electricity is an important energy source to promote the sustainable development of China’s economy, and it has a fundamental role in the national economy [
19]. However, as China’s electricity consumption continues to grow, there has not been an effective shift in the structure of electricity production, which is dominated by “thermal power” [
20]. This type of power generation, which is based on the consumption of coal, a disposable energy source, generates large amounts of carbon emissions during the production process [
21]. China faces energy constraints from urbanization [
22] and carbon reduction targets [
23], and there is an urgent need to clarify the ways in which the complex urbanization system affects electricity consumption [
24]. Meanwhile, China is the world’s second-largest economy and the world’s largest CO
2 emitter [
25], and rationally promoting urbanization and reducing energy consumption are important elements of the visionary blueprint for socialist modernization.
The issue of urban land use has also attracted widespread academic attention. However, most of the existing studies have focused on examining the impact of urban sprawl on economic performance and haze pollution, while less consideration has been given to its impact on energy consumption. How to promote urban green and low-carbon construction remains a key component of China’s sustained high-quality development. In view of this, in the context of rapid urbanization, we try to elaborate on the impact and mechanism of urban sprawl on electricity consumption. Firstly, the urban sprawl index is measured with 283 prefecture-level and above cities in China from 2008 to 2019. Meanwhile, the mechanism of urban sprawl affecting electricity consumption is explained at the theoretical level. Secondly, a series of robustness tests, such as instrumental variables, are used to identify the impact of urban sprawl on electricity consumption as accurately as possible and unfold the heterogeneity. Finally, the spatial econometric model is utilized to expand the analysis of the spatial spillover effects of urban sprawl and electricity consumption. And, in order to provide intellectual support for urban spatial layout as well as green and low-carbon development.
The marginal contribution of this paper may be reflected in three aspects. Firstly, in terms of research perspective, it combines urban sprawl with electricity consumption. It also examines the intrinsic mechanism of urban sprawl affecting electricity consumption from land resources, private transportation, and green technology innovation. It expands the study of the environmental effects of urban sprawl in the context of urbanization and construction in China. Secondly, the impact of urban sprawl on electricity consumption is examined in detail. Based on the spatial structure of cities, the strength of environmental regulation, the level of technological innovation, and the geographic location, this paper further analyzes the differentiated impacts of urban sprawl on electricity consumption so as to put forward more targeted recommendations. This paper analyzes the spillover effects of urban sprawl and electricity consumption from the perspective of spatial correlation. It makes up for the lack of spatial effects in the current research perspective and further enriches the research in the field of urban sprawl.
The rest of the study is structured as follows:
Section 2 is a literature review.
Section 3 attempts to elucidate the mechanism of action of urban sprawl in affecting electricity consumption.
Section 4 is the model setting and variable descriptions.
Section 5 is the empirical results, including the benchmark regression, robustness test, endogeneity problem test, mechanism of action, heterogeneity test, and extended analytical analysis.
Section 6 is the discussion and analysis.
Section 7 is the conclusions of research and policy recommendations.
5. Results
5.1. Trends in Spatial-Temporal Evolution of Urban Sprawl and Electricity Consumption
In order to visualize the spatial and temporal evolution trend of urban sprawl and electricity consumption, this paper uses ArcGIS to map the spatial distribution of urban sprawl and electricity consumption in 2009 and 2019, respectively. As shown in
Figure 2 and
Figure 3.
In terms of trends in temporal evolution, the level of urban sprawl increases significantly over time during the period 2009–2019. A small number of cities were in the not-yet-spreading stage, 63.60% of the cities had a sprawl index greater than 1, and the growth rate of the built-up area of cities was smaller than the growth rate of the urban population in 2009. Up until 2019, the majority of Chinese cities showed the urban sprawl phenomenon and were dominated by low sprawl types, 89.40% of cities had a sprawl index greater than 1, and the sprawl indexes were in the range of 1.00–2.00. A small number of cities have medium sprawl status. No city reached a high state of sprawl, i.e., an urban sprawl index of 3.00 and above. The proportion of cities with different types of urban sprawl is not balanced, and the spatial distribution is relatively decentralized. The overall situation is characterized by “sporadic high-spread”. On a smaller administrative scale, a significant increase in the degree of sprawl could be found in some cities such as Erdos, Ya’an, and Anshun during the study period. Overall, urban sprawl is increasing, but it is not out of control in China. This may be influenced by the price of land urban construction gradually migrating from the central area to the periphery. Moreover, investment in land development is an important source of funding for renovation and new town development. A combination of factors has led to an increase in overall urban sprawl. At the same time, however, urban sprawl is not yet uncontrollable, due to China’s two-way control of the usage and quantity of land for urban construction.
In terms of general evolutionary trends, during the study period, the total electricity consumption of cities has been increasing year by year. The electricity consumption of most cities was less than 20 billion Kw·h in 2009, and urban electricity consumption increased significantly in 2019. China’s average urban electricity consumption grew from 9.89 billion Kw·h to 14.17 billion Kw·h from 2009 to 2019, with an overall growth rate of 43.30% and an average annual growth rate of 4.33%. Cities with high electricity consumption levels are mostly concentrated in the eastern region, such as Beijing and Shanghai, and are clearly and progressively consuming more than 80 billion Kw·h of electricity. And individual cities in the western region are also increasing their electricity consumption, but overall urban electricity consumption is less than 60 billion Kw·h. The reason for this phenomenon may be that electricity is also an essential production factor in industrial production and is vulnerable to economic conditions and other factors. And industrial electricity consumption occupies a large proportion of the city’s total electricity consumption. The eastern coastal cities are economically ahead of the inland cities, which makes the eastern coastal cities become the main force of electricity consumption.
5.2. Baseline Regression Results
Based on the econometric model constructed above, this paper examines the study of the effect of urban sprawl on electricity consumption, and the results are presented in
Table 2. Column (1) of
Table 2 shows the estimation results with only time and individual fixed effects and without the inclusion of control variables, and columns (2) to (7) gradually include city-level control variables. The regression results show that the estimated coefficient of the effect of urban sprawl on electricity consumption without the inclusion of control variables is 0.0232 and is significant at the 1% level. This result remains largely stable with the gradual addition of city-level control variables. The result of column (7), which includes the full set of control variables, shows that the sign of the regression coefficient for the core explanatory variable, urban sprawl, is significantly positive at the 1% level. The results of the baseline model estimation tentatively indicate that urban sprawl significantly increases urban electricity consumption, validating Hypothesis 1.
As far as the control variables are concerned, the level of economic development significantly increases the consumption of electricity. As the main energy substance in production and life, electricity is highly relevant to modern economic and social development. In the secondary industry-dominated economic model to promote economic development, the industry is still the most important sector of electricity consumption. Therefore, economic development is the most important factor to increase electricity consumption, which is similar to the finding of Song et al. [
101]. The government is the main body of “emission reduction and energy saving”, and the government’s policies and behaviors will have an important impact on electricity consumption. At this stage, with the promotion of ecological civilization construction, local governments begin to pay attention to environmental protection, promulgate policies to guide the development of energy-saving and emission-reduction technologies, and force enterprises to green transformation and technological innovation, which has a negative impact on electricity consumption. An advanced industrial structure significantly reduces urban electricity consumption. Compared with the traditional manufacturing industry and other secondary industries, the tertiary industry’s power demand is smaller. Moreover, the upgrading of the industrial structure is essentially the transfer of resource elements from inefficient to efficient sectors, which is conducive to the improvement of urban energy efficiency and thus reduces electricity consumption, and the result is basically consistent with the finding of Guang et al. [
102]. Openness to the outside world significantly increases electricity consumption. This may be due to the fact that the increase in the level of openness to the outside world is accompanied by a large inflow of FDI, which leads to the agglomeration of pollution-intensive industries and promotes electricity consumption. The level of urbanization has a significant positive relationship with electricity consumption, which means that the higher the level of regional urbanization, the higher the regional electricity consumption. Rural residents consume less electricity for living and travelling than urban residents. Therefore, the increase in the urbanization rate leads to an increase in electricity consumption.
5.3. Robustness and Endogeneity Tests
5.3.1. Replacing the Measurement of the Independent Variable
Urban sprawl is typically characterized by rapid land expansion leading to a decentralized distribution of population, which is reflected in low density and decentralization. Higher urban population densities may indicate a greater likelihood of agglomeration of economic activities and a lower tendency to urban sprawl. In contrast, in low population density areas, the spatial structure tends to be decentralized and the tendency of urban sprawl is higher [
103]. This means that urban population density and the level of urban sprawl are roughly inversely quantitatively related [
104]. Therefore, the logarithmic value of urban population density (lnDens) is used to replace the urban sprawl index in the benchmark regression as the independent variable. The results of the model estimation are shown in column (1) of
Table 3, where the coefficient of urban population density is significantly negative. This means that higher population densities can reduce electricity consumption. And, it indicates that urban sprawl promotes electricity consumption.
5.3.2. Replacing the Measurement of the Dependent Variable
Considering the possible bias of electricity consumption measured from lighting data, the regression is further conducted using the whole society’s electricity consumption data published in the statistical yearbook as the dependent variable [
105]. The regression results, as shown in column (2) of
Table 3, show that the coefficient of urban sprawl is significantly positive at the 1% level, again indicating that the conclusion of the baseline regression is robust.
5.3.3. Removing Interference from Relevant Policies
Another challenge to the regression results is that in verifying the impact of urban sprawl on electricity consumption, it may be interfered with by policies such as New Energy Demonstration Cities, Low-Carbon Pilot Cities, and Broadband China. In order to exclude the interference of the above policies and ensure the accuracy of the benchmark regression, the samples of policy implementation are excluded from the regression, and the results are shown in columns (3)–(5) of
Table 3, respectively. Compared to the baseline results, the significance level of the urban sprawl coefficient does not change after taking into account the relevant policy disturbances. This also indicates that the conclusion that urban sprawl increases electricity consumption remains robust.
5.3.4. Excluding Macro-Systemic Differences
(i) Incorporating multidimensional interaction fixed effects. Urban sprawl has different development trends in different regions. Therefore, this paper controls for the unobservable effects at the provincial level over time by controlling for the joint “province-year” fixed effects. As shown in column (1) of
Table 4, the Sprawl coefficient is still positive at the 1% significance level. It remains consistent with the baseline regression results. (ii) Replacing the standard error clustering hierarchy. The baseline regression refers to the general practice of standard error clustering at the same level (city level) of the study object. However, this clustering approach ignores the fact that there are often strong correlations (e.g., infrastructure, environmental regulation intensity, etc.) among cities in the same province. We therefore re-clustered the standard errors to the province level and report the estimates as a robustness test in column (2) of
Table 4. As can be seen from the results, there is no obvious change in the size or significance of the coefficients.
5.3.5. Removal of Outliers
(i) Excluding extreme value interference. In order to avoid possible outliers in the data from influencing the benchmark regression results, this paper applies a bilateral 1% shrinkage to all continuous variables. The results are shown in column (3) of
Table 4, and the core explanatory variable urban sprawl remains consistent with the benchmark regression results. (ii) Excluding municipalities and provincial capital cities. On the one hand, municipalities directly under the central government and provincial capital cities may be affected by policy favoritism and may have a relatively large advantage in energy-saving technology and high-quality human capital compared with general prefecture-level cities. This makes the energy use efficiency of these cities stronger. On the other hand, the governments of municipalities and provincial capitals generally pay more attention to environmental issues. Tougher environmental constraints and more rational planning of the urban sprawl process may also lead firms to favor green technological innovation. Therefore, failure to exclude these factors may have some impact on the stability of our estimation results. The results in column (4) of
Table 4 show that the results remain robust after excluding municipalities and provincial capitals.
5.3.6. Endogeneity Test
To mitigate the possible endogeneity problems of mutual causality and omission of unobservable variables in the baseline regression, this paper adopts the instrumental variable test to regress. Specifically, the relief degree of land surface is selected as an instrumental variable for urban sprawl [
98]. The greater the relief degree of land surface, the more segregated it presents geographic features and the more dispersed population distribution. This implies that the possibility of population density and industrial agglomeration is lower. And the urban spatial pattern tends to develop in a more disorderly and decentralized spreading pattern. Therefore, the relief degree of land surface theoretically positively affects urban sprawl, and the relief degree of land surface does not directly affect urban power consumption. It meets the requirement of exogeneity. However, since the relief degree of land surface is a non-temporal variable, it is further constructed a cross-multiplier by the relief degree of land surface and lagged period of the urban sprawl index. It is ultimately used as an instrumental variable indicator (Tsrdls) of urban sprawl [
106]. From the regression results in column (1) of
Table 5 in the first stage, it can be seen that there is a significant positive correlation between the instrumental variable (Tsrdls) and urban sprawl (Sprwal). Also, the Cragg–Donald Wald F-statistic and Kleibergen–Paap Wald rk F-statistic, are both greater than the Stock–Yogo critical value of 16.38 under the Stock–Yogo weak ID test critical values (10%), thus ruling out the possibility of a weak instrumental variable. The regression results from the second stage column (2) show that urban sprawl significantly exacerbates urban electricity consumption. The above analysis shows that the baseline regression conclusions of this paper still hold after considering potential endogeneity issues using the instrumental variables approach.
5.4. Mechanism Testing
Based on the theoretical analysis, this paper further identifies and tests the mechanism by which urban sprawl affects urban electricity consumption through land resource allocation, private transportation and green technological innovation. The specific estimation results are shown in
Table 6. From the results in column (2) of
Table 6, it can be seen that the coefficient value of sprawl is significantly positive at the 1% level. It indicates that urban sprawl changes the way of land resource allocation, increases the proportion of industrial land, and indirectly promotes urban electricity consumption, verifying Hypothesis 2. Columns (3)–(4) of
Table 6 show the regression results for private transportation. It can be seen that urban sprawl increases private transportation trips, and the number of private cars increases, which promotes energy consumption [
107], testing Hypothesis 3. From the results in column (6) of
Table 6, it can be seen that the coefficient of Sprwal is significantly negative at the 5% level. This indicates that urban sprawl inhibits urban green technological innovation, which is detrimental to the green transformation of the industry and indirectly promotes urban electricity consumption, validating Hypothesis 4.
5.5. Heterogeneity Analysis
China is a vast country, with different spatial plans for different regions, different environmental goal constraints, and different bases for innovation. The stage of economic development also varies greatly, which may lead to a differentiation of the effects of urban sprawl on electricity consumption. Therefore, this paper focuses on the four dimensions of urban spatial structure, environmental regulation intensity, urban innovation base, and urban geographic location to analyze heterogeneity.
5.5.1. Heterogeneity Analysis of Urban Spatial Structure
Columns (2) and (4) of
Table 7 report the heterogeneous effects of urban sprawl on electricity consumption at different levels of sprawl. The paper categorizes the sample cities into high and low sprawl groups according to whether the level of sprawl exceeds the national average for cities. Among them, the regression results for the high sprawl group are significantly positive, while the regression results for the low sprawl group are insignificant. This indicates that the higher the level of urban sprawl, the more significant the marginal incremental effect of urban sprawl on electricity consumption. The higher the level of urban sprawl in the region, the longer the spatial distance between enterprises, colleges and universities, research institutes, and other innovation subjects becomes, and the agglomeration economic effect of innovation factors is weakened. This is not conducive to the positive effect of agglomeration on innovation through risk diversification and knowledge spillover, and it hinders the realization of industrial green transformation and promotes electricity consumption. In addition, the higher the level of urban sprawl, the greater the dependence of residents on private cars, and the more obvious the promotion of electricity consumption.
5.5.2. Heterogeneity Analysis of Urban Environmental Regulation Intensity
This paper further examines whether there are differences in the impact of urban sprawl on electricity consumption across different urban environmental regulatory intensities. The strong environmental regulation group and the weak environmental regulation group are divided according to whether the urban environmental regulation intensity is greater than the national average for cities of environmental regulation intensity. As can be seen from columns (2) and (4) of
Table 8, the regression results for the strong environmental regulation group are not significant, while urban sprawl has a significant contribution to electricity consumption in the urban phase of weaker environmental regulation. This suggests that higher environmental regulation strengthens the emission reduction concept and environmental regulation behavior of local governments, raises the environmental target constraints on local governments, and restricts land grants to high energy-consuming enterprises. In addition, it can also force high-pollution and high energy-consumption industries to accelerate the pace of green transformation. Environmental regulation is used to prompt enterprises to participate in technological innovation, especially in areas involving energy conservation and emission reduction, and to increase the innovation power of industrial green transformation. This has to some extent weakened the role of urban sprawl in promoting electricity consumption. When environmental regulations are weaker, localities face less environmental pressure and less incentive to reduce land concessions to energy-consuming enterprises and urban sprawl has a stronger effect on electricity consumption.
5.5.3. Heterogeneity in the Level of Innovation Base
Differences in the green innovation base of cities may lead to differences in production efficiency, which in turn leads to the spread of cities with heterogeneous electricity consumption effects. In this paper, we use the total amount of green patents granted to measure the city innovation base. And the sample is divided into a high green innovation level group and a low green innovation level group based on the mean value of green patent grants. As shown in columns (2) and (4) of
Table 9, the regression coefficients of urban sprawl are significantly positive at the 1% level for the low green innovation level group and insignificant for the high green innovation level group. It indicates that the effect of urban sprawl on electricity consumption shows very significant heterogeneity in the characteristics of the urban green innovation base. Green technological innovation is a key factor in improving energy efficiency, which helps industrial enterprises to clean up and decarbonize their production, thus reducing electricity consumption. The higher the level of urban innovation, the more it mitigates to some extent the contribution of urban sprawl to electricity consumption.
5.5.4. Heterogeneity of Geographic Location
Cities in different regions have certain differences in economic development and industrial structure. Therefore, the impact of urban sprawl on electricity consumption is also likely to be heterogeneous, and it is necessary to conduct a comparative analysis by region. Additionally, 283 cities at the prefecture level and above are categorized into three regions: east, central, and west. From the regression results in
Table 10, it can be seen that, compared with the east and center, the more obvious the promotion effect of urban sprawl on electricity consumption is in the western region. The reason may be that, since the implementation of the Western Development Strategy in 2000, with the policy advantages and the absorption of a large number of foreign production factors, the size of the cities in the western region has increased rapidly. And, the National New Urbanization Pilot Work Program explicitly requires that new pilot cities should be tilted to the central and western regions. The cities in the central and west regions accounted for 57% of the total number of selected cities in 2014 and were as high as 61% in both 2015 and 2016. The phenomenon of urban sprawl is obvious, leading to a more serious mismatch of land resources and a significant rise in electricity consumption. However, the level of economic development of cities in the east and central regions is leading the country, which has produced a strong “siphon effect” on the resource elements of other regions, with a large number of foreign laborers, high-quality capital, and other agglomerations. Based on that fact, human capital and other characteristics of agglomeration ensure the innovation effect of human capital on the driving role of industrial green transformation. This, to a certain extent, weakens the negative effect of urban sprawl on electricity consumption.
5.6. Extensibility Analysis
5.6.1. Spatial Autocorrelation Analysis of Urban Sprawl and Electricity Consumption
OLS does not take into account the effects of spatial interactions between cities. This may lead to biased results and conclusions that inherently lack spatial implications [
108]. Therefore, the spatial relevance of urban sprawl is further considered to investigate whether there is a significant difference in the effect of urban sprawl on electricity consumption on its own and in neighboring cities. Before conducting the spatial econometric analysis, the existence of the spatial effects of urban sprawl and electricity consumption are examined separately. To this end, a spatial autocorrelation test was conducted using the global Moran’s I index method, and the results are shown in
Table 11. The global Moran’s I value for urban sprawl are all positive and pass at least the 5% significance level test overall. There are individual cases of non-significance, but basically it can be assumed that there is spatial correlation of urban sprawl. The global Moran’s I value for electricity consumption are concentrated at 0.109–0.113, and all pass the 1% significance level test, that is, it indicates that electricity consumption has strong spatial autocorrelation. This phenomenon may be attributed to the fact that there is an economic competition effect between cities, which makes the industrial structure tend to be homogeneous. This results in the increase of electricity consumption in the region, which significantly increases the electricity consumption of other cities. And, local governments may have formed vicious competition in land allocation, resulting in a significant positive spatial spillover effect of urban sprawl.
In order to better reflect the characteristics of spatial agglomeration, two representative years, 2009 and 2019, are selected to produce Moran’s I index scatterplots of urban sprawl and electricity consumption. The results are shown in
Figure 4 and
Figure 5. As can be seen in
Figure 4, the local Moran’s I index of urban sprawl is mostly clustered in the third quadrant, showing obvious Low-Low clustering characteristics in 2009. However, after a decade of development, the distribution of urban sprawl scatter points is more decentralized in 2019, and the low aggregation gradually moves to the first quadrant, tending to High-High aggregation. This indicates that the level of urban sprawl is increasing, but there are differences in the strategic orientation, development planning, and economic foundation of each city. This leads to obvious regional differences in the level of urban sprawl in China, and the spatial correlation of urban sprawl is weakened. As can be seen in
Figure 5, the change in the scatter distribution of Moran’s I index of urban electricity consumption between 2009 and 2019 is not obvious. The data points are more evenly scattered in the four quadrants, and the local Moran’s I index is significantly positive at the 1% level. This suggests that urban electricity consumption is strongly influenced by neighboring regions, that is, the coexistence of “High-High”, “High-Low”, “Low-High”, and “Low-Low”. However, there are relatively more data points distributed in the first and third quadrants, and the phenomena of “High-High” and “Low-Low” aggregation are more obvious. This suggests that there is both spatial dependence and heterogeneity in urban electricity consumption. It is the result of a combination of the cities’ industrial structure and ecological emphasis, and the implementation of the concept of synergistic regional governance.
5.6.2. Results of the Spatial Effects Test
Due to the existence of spatial correlations, this paper further analyzes them using spatial econometric models. Firstly, the inverse distance spatial weight matrix is used to estimate the spatial error model (SEM), spatial autoregressive model (SAR), and spatial Durbin model (SDM). The appropriate spatial econometric model was selected using the LM test, etc., and the specific results are shown in
Table 12. The regression results show that the LM statistic and robust LM statistic of the spatial error model passed the 1% significance level test, but the LM statistic and robust LM statistic of the spatial lag model failed the significance test. In addition, based on the results of the test, the spatial error model with double fixation of time–area was chosen as a way to explain the spatial impact of urban sprawl on electricity consumption. From the estimation results of the spatial error model, the spatial coefficient λ of the spatial error term is 3.4318 and passes the 1% significance level test. It indicates that there is a significant spatial agglomeration phenomenon and spatial dependence on urban electricity consumption. That is, the electricity consumption of a city is not only related to its own factors but also affected by the neighboring cities, and the spatial correlation is mainly reflected in the random error term.
7. Conclusions
This paper empirically examines the impact and mechanism of urban sprawl on electricity consumption based on the measurement of the urban sprawl index by processing the panel data of 283 prefectural-level and above cities in China from 2008 to 2019. Secondly, the heterogeneity of urban sprawl on electricity consumption is analyzed from four dimensions: urban spatial structure, environmental regulation intensity, urban innovation base, and urban geographic location. The SEM model is further used to investigate the impact of urban sprawl on electricity consumption, and the main conclusions are as follows: (1) In general, the levels of urban sprawl and urban electricity consumption show an upward trend. There are obvious differences between urban sprawl and electricity consumption in China. And, the spatial distribution of urban sprawl is relatively decentralized, while the cities with high electricity consumption levels are mostly concentrated in the eastern coastal areas. (2) Urban sprawl exacerbates electricity consumption. The conclusion still remains after the robustness test and the consideration of endogeneity issues. Urban sprawl exacerbates electricity consumption by changing the allocation of land resources, increasing the dependence on private transportation and inhibiting green technology innovation. (3) The effect of urban sprawl on electricity consumption is more pronounced in cities with high levels of sprawl, weak environmental regulations, and low levels of green innovation, as well as in west cities. (4) The Moran index shows that urban sprawl and electricity consumption both have a significant positive spatial correlation. The estimation of the SEM model shows that the electricity consumption of cities is not only related to their own regions but also influenced by the neighboring regions, and the spatial correlation is mainly reflected in the random error term.
Based on the above research conclusions, the following suggestions can be obtained. (1) Reasonable planning of urban spatial patterns, focusing on the improvement of energy utilization efficiency. In the process of new urbanization, we should comply with the reasonable demand of population density and economic growth and scientifically plan the urban development boundary. And, weighing the proportion of urban industrial land, residential land, and agricultural land. Correcting the short-sighted behavior of local governments in “seeking development with land” and reduce the degree of mismatch of urban land resources. Activating idle industrial land and optimize the spatial layout of industries; Promoting the advanced industrial structure in a gradual and orderly manner. And, paying attention to the integration of industry and city, the balance of business and residential development, and functional composites in the planning and construction of industrial parks. In addition, we will increase the compactness of urban spatial layout and reduce unnecessary commuting for seeking living services. Emphasis will be placed on the development of high-capacity green public transportation to reduce the proportion of private car trips. At the same time, promoting industrial specialization and diversified agglomeration and facilitates knowledge flow, matching, and sharing. (2) Based on regional development characteristics and advantages, differentiated territorial spatial planning should be introduced. Cities should strengthen the forcing effect of environmental regulation policies and continuously promote the improvement and innovation of environmental regulations. The demand for low-carbon development should be reflected in national spatial planning. With the help of stringent environmental regulation policy tools, the efficiency of land utilization can be improved. At the same time, it promotes intensive spatial layout and clustered industrial development mode and gives full play to the knowledge spillover effect to improve the cultivation of green innovation activities and help the green transformation of the industry. Urban sprawl in the eastern and central regions has not significantly exacerbated power consumption, and the optimal level of agglomeration and optimal city size should be maintained. In the western region, it is necessary to make full use of factor pooling, knowledge sharing, and technological spillover to promote the green development and transformation of cities and maintain a moderate spreading trend. (3) Regional alliances and establishing synergistic mechanisms for low-carbon development should be strengthened. Urban sprawl and electricity consumption have a positive spatial spillover effect. Therefore, sustainable urban development must abandon the “beggar-thy-neighbor” mode of thinking and break down urban administrative boundaries. Regional cooperation and establish a series of cooperative mechanisms should be strengthened, such as information sharing and joint law enforcement. The exchange of experience in land space planning between cities and the matching of measures to improve energy efficiency should be promoted, thus effectively mitigating the boosting effect of land sprawl on electricity consumption. We should deepen the reform of officials’ performance appraisal system and continue to weaken the share of GDP growth rate in the performance appraisal of local officials. We should seek focus points for the emerging competitiveness of cities, reduce energy consumption and change the development model, and rationally promote the construction of new urbanization.