1. Introduction
With the mushroom growth of the economy, China’s energy consumption has grown rapidly in the past 40 years since the reforms and opening up of the country. The proportion of China’s primary energy consumption in the world increased from 6.10% in 1980 to 23.6% in 2018, ranking first in global energy growth for 18 consecutive years [
1]. With the growth of energy demand, China’s external dependence on energy continues to rise [
2]. In 2018, China’s external dependence on oil has reached 71%, and that on natural gas has reached 43% [
3]. The problem of energy shortage has been increasingly serious in China [
4], which has captured the attention of energy security officials. In addition, excessive energy consumption, especially the large share of fossil fuels, is the main driver of climate change [
5,
6]. According to the estimates, carbon dioxide emissions from fossil fuel combustion and industrial processes contributed about 78% of the total increase in greenhouse gas emissions between 1970 and 2010 [
7]. Moreover, energy consumption, especially fossil energy consumption, is also the main cause of air pollution [
8,
9]. Specifically, the problem of haze pollution has become more and more serious recently, which has captured the attention of wider society. Studies have confirmed that energy consumption has a significant positive effect on haze, and measures such as controlling total energy consumption and optimizing energy structure will effectively improve air quality [
10,
11,
12]. Due to the significant impacts of energy consumption on energy security, climate change, and atmospheric pollution, it is imperative to control and manage energy consumption in China.
Recently, numerous studies have been conducted focusing on energy consumption. The relationship between energy consumption, economic growth, and CO
2 emissions [
13,
14,
15,
16,
17], the spatiotemporal patterns and driving forces of energy consumption carbon emissions [
18,
19,
20,
21], and the characteristics of a specific item energy consumption [
22,
23,
24,
25] was frequently researched. For example, Zhang and Cheng [
13] investigated the existence and direction of Granger causality between economic growth, energy consumption, and carbon emissions in China over the period of 1960–2007 and found that neither carbon emissions nor energy consumption leads economic growth. Lv et al., [
21] explored the spatiotemporal changes in energy consumption CO
2 emissions in China during 1995–2016 and provided policy suggestions for China to reduce CO
2 emissions based on their results. Zou and Luo [
24] analyzed the characteristics of rural household energy consumption and estimated the determinants of rural households using the data from the Chinese General Social Survey of 2015. However, there are few studies that focus on exploring the spatial and temporal characteristics of the total energy consumption.
Different from the traditional statistical data sources, which only provide the numeric records of the entire unit (such as nation, province, and municipality, etc.) without showing the internal spatial pattern, remote sensing imagery can provide valuable data sources on a finer scale [
26]. Numerous studies have demonstrated that nighttime light remote sensing imagery performed well in estimating socioeconomic activities, such as urban extent and urbanization process [
27,
28,
29,
30], population distribution [
31,
32,
33], economic development [
34,
35], and energy consumption CO
2 emissions [
26,
36,
37,
38], etc. In 1997, Elvedge et al. [
39] found that there was a linear relationship between the area lit and power energy consumption. Similarly, Amaral et al. [
40] observed that electrical power consumption was linearly correlated with the nighttime light pixels of DMSP-OLS imagery. Consequently, a blossoming number of studies have successfully managed to apply nighttime light data to estimate electrical power consumption on multiple scales [
41,
42,
43,
44]. However, it should be noted that the nighttime light data detected by DMSP-OLS is not only from the light generated by electrical power consumption, but also from other energy consumption, such as automobile light generated by the consumption of oil [
45]. Therefore, some scholars explored the relationship between the overall energy consumption and nighttime light data and tried to apply these remote sensing data to the estimation of energy consumption. For example, Wu et al. [
46] reconstructed the temporal and spatial changes in energy consumption on the prefecture level in 30 provinces of China from 1995 to 2009 following the linear relationship between total nighttime light data and provincial energy statistics. However, only using nighttime light imagery from DMSP-OLS over the period of 1992–2013 to estimate energy consumption has a time limit, NPP-VIIRS nighttime light data from 2012 up to now should be combined to improve the accuracy of estimation.
There is no single means to detect the drivers behind energy consumption. The decomposition method is often used to investigate the impact element of energy consumption, which can be mainly divided into three categories, namely, index decomposition analysis (IDA) [
47,
48], structural decomposition analysis (SDA) [
49,
50], and production-theoretical decomposition analysis (PDA) [
51]. Note that all of the above methods ignore the existence of spatial effects. In fact, neglecting the effect of spatial correlation will result in partial or even biased estimation [
52] and a lack of convincing explanatory power [
53]. Spatial econometric models considering spatial autocorrelation and heterogeneity have been widely used to solve these problems. For instance, considering the regional differences and spatial effects of China’s energy intensity, Yu [
54] used spatial panel data models to explore the regional imbalance and spatial correlation of China’s provincial energy intensity. Zhao and Lu [
55] studied the impacts of industrial transfer on China’s regional energy intensity based on the spatial Durbin panel model. Cheng et al. [
19] explored the driving factors and their spatial effect on China’s carbon intensity from energy consumption by utilizing the spatial panel econometric model. Compared with traditional models, the spatial econometric models used in all these studies have gained more explanatory power by increasing spatial effects. Therefore, this article investigated the dominating factors of China’s energy consumption over the period of 1995-2016 based on the spatial econometric model.
The major objectives of our study are (1) estimating China’s energy consumption based on different models utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets, (2) exploring the spatiotemporal distribution characteristics of China’s energy consumption on the basis of the best estimated results, and (3) investigating the influential factors of China’s energy consumption for 1995-2016 on the provincial level. The main steps to achieve the above-mentioned goals are as follows. We first integrate the two nighttime light datasets, namely DMSP-OLS and NPP-VIIRS, to extend the time series of nighttime light data for improving the estimation accuracy. Second, we estimate China’s energy consumption based on the relationship between nighttime light data and statistical energy consumption using the best model (among the four common estimation models) and panel data model, respectively. Third, on the basis of the best estimation results, we investigate the temporal variation and spatial distribution characteristics of China’s energy consumption using the Natural Break method and exploratory spatial data analysis. Finally, we examine the influencing factors of China’s provincial energy intensity during the observation period using the spatial econometric model.
5. Discussion
The data on energy consumption research are generally from statistical yearbooks, such as the China Energy Statistics Yearbook and the Statistical yearbook of provinces. However, the statistical scale of energy consumption data in the statistical yearbook is relatively coarse, due to the lack of continuous years of energy consumption statistics on the prefecture level and county level. In this article, nighttime light data were employed to estimate China’s energy consumption at a 1-km resolution on the basis of the correlation between the existing statistical energy consumption data and nighttime light data. To be sure, there exist errors in the energy consumption estimation based on nighttime light data, for the nighttime light can only represent a portion of energy consumption while a large portion of energy consumption occurs in daytime by industrial and other activities. However, in the absence of statistical energy consumption data on the county level or pixel level, this method can yet be regarded as an approach to estimate energy consumption. Moreover, the accuracy assessment results show that the energy consumption statistics on the prefectural level were positively correlated with the estimated energy consumption, with an average R2 of 0.734, an average RMSE of 974, and an average ARE of 10.71%.
This study has certain limitations that are worth mentioning. First, the provincial statistical data of energy consumption, which was used to simulate the spatial and temporal characteristics of China’s energy consumption, may be distorted due to the inconsistent statistical caliber and artificial error. So, to some extent, it has an impact on the results of estimation and analysis. Second, although the method based on nighttime light data proved to be an effective way to estimate energy consumption, there indeed exist errors with nighttime light data as the only index of the estimation model. To further improve the estimation accuracy, other indicators (such as economic development, population size, land area, etc.) should also be taken into account when building an energy consumption estimation model. Third, as for the influencing factors of energy consumption, some socio-economic factors were selected in this paper to explore the driving mechanism of energy intensity in China. But there is no doubt that energy consumption is affected by many factors. So, climatic factors (such as temperature) and other socio-economic factors (technology level, energy structure, and energy price, etc.) will be added to explore the mechanism of energy consumption in China, comprehensively. Fourth, although the influential mechanism of China’s energy consumption was explored in this paper on the provincial level, due to the scale effect and the uniqueness of socioeconomic development, it is not appropriate to apply our findings to other scales. Subsequently, we should explore and compare the influential mechanism of independent variables on energy consumption from different levels (such as provincial scale and prefectural scale) so as to provide more theoretical support for the formulation of relevant policies.
6. Conclusions and Policy Implications
Based on the integrated two nighttime light satellite imagery datasets, this study estimated the energy consumption of China’s thirty provinces from 1995 to 2016 at a 1-km resolution, investigated the spatiotemporal variations in energy consumption, and explored the influential mechanism of energy consumption on the provincial level. Several conclusions were formulated:
The panel data model has proved to be feasible to estimate energy consumption at a high resolution using nighttime light data and provincial energy consumption statistics. According to the goodness of fit of the correlation (linear, quadratic polynomial, logarithmic, and exponential) between the statistical energy consumption and total nighttime light, the quadratic polynomial with the highest goodness of fit was selected to simulate energy consumption. Then, two different models, namely the quadratic polynomial model and the panel data model, were structured for energy consumption estimation, and the estimation results of these two models were assessed respectively based on the prefectural statistical data. According to the accuracy results, the panel data model performed better than the quadratic polynomial model. Additionally, from the accuracy evaluation results, our estimation results based on the panel data model are more credible compared with the existing related research.
From 1995 to 2016, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals, such as Chongqing, Chengdu, Wuhan, Changsha, Zhengzhou, and Shenyang. Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clustering, and its spatial agglomeration was strengthened year by year during the research period.
The SDM model with the spatial fixed effect has proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure is the most important influencing factor of provincial energy intensity in China. Moreover, the changes in industrial structure and FDI can not only leave a deep influence on the local energy intensity but also affect the energy intensity of surrounding provinces.
Aiming at cutting down the national energy intensity for realizing a harmonious development of both economy and environment, three policy proposals on the basis of the spatiotemporal distribution characteristics and the influential mechanism of China’s energy consumption were put forward:
When formulating the development policy of a regional economy, we should take into account the mutual influence of economy and industries between the adjacent regions. Considering the significant spatial spillover effect of provincial energy intensity, the construction of the economy demonstration area with the low-energy consumption industry can be conducive to reducing the energy intensity of the surrounding provinces.
Considering the great differences of energy intensity among regions, the Chinese government should adopt differentiated strategies for different regions. For example, for the eastern and central regions with higher levels of economic development, the reduction strategies of energy intensity should focus on adjusting and optimizing the industrial structure. That is to say, reduce the proportion of the second industry and increase the proportion of the third industry in the national economic structure.
Governments should continue to improve the business environment and attract green foreign direct investment. Due to the advanced technology and scientific management experience, foreign-funded enterprises can play a demonstrating and promoting role in improving the production technology of the domestic enterprises and increasing the production efficiency of the whole society.
The methodology of the spatiotemporal variations in energy consumption and their influencing factors in China based on nighttime light data developed in this article can be extensively used to energy consumption spatiotemporal estimation and influencing factors analysis on the global, national, and regional levels. It can provide a quick and accurate supplement to the monitoring on continuous variations in energy consumption, with the update of NPP-VIIRS nighttime light data. This methodology can also be used as a reference for similar studies involving power consumption, CO2 emissions, population distribution, and economic development, etc.