Industrial electricity consumption and economic growth: A spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014

Highlights

• Identify the spatial heterogeneity of energy-economy relationship across China.

• MGWR models are used.

• Results on spatial variations correspond to the knowledge of regional gaps in China.

• Results on temporal effects examine the predictability of energy consumption.

Abstract

The relationship between energy consumption and economic growth has deservedly received much attention in the literature due to its significance to sustainable development, particularly in countries such as China with rapid economic growth. However, few studies have explored the spatial heterogeneous nature of this relationship within countries where there are spatial variations in terms the level and speed of economic development. This study analyzes how industrial GDP and employment are related to industrial electricity consumption at prefecture city level across China using Multiscale Geographically Weighted Regression (MGWR). It also examines the role of foreign direct investment (FDI) and research/development (RD) in mediating the relationship between economic growth and electricity consumption. The results show that, during the study period, industrial electricity consumption became increasingly related to industrial GDP while the influence of employment diminished. However, this relationship varied over space and time with clear differences between the cities in the developed east coast and the rest of the country. Also, the FDI and RD improved efficiency in electricity consumption especially in less developed central and western regions during the study period.

Introduction

The relationship between electricity consumption and economic growth have a significant impact on carbon emissions, the energy efficiency and the policy making on industry development. This is especially important for countries that are undergoing rapid development and industrialization, and have serious differences in regional development, such as China. Globally, however, understanding the nature of this relationship and how it changes over time and across space is also of critical importance for planetary sustainability [1].

Over the past 30 years, China’s industrial sector has modernized rapidly. Still, industrial energy consumption accounts for over 70% of total energy consumption in the country [2]. Because electricity consumption plays such an essential role in industrial development, it has a close relationship with the economic growth. Studies showed the temporal heterogeneity on the electricity consumption and economic growth in China.

Due to the prevalence of inefficient technologies and poor economic planning, China’s energy consumption efficiency was very low before the opening up of the country in early 1980s. One indicator of the electricity efficiency in an economic sector or a country is the electricity consumption elasticity coefficient (EEC), the ratio of electricity consumption growth rate and economic growth rate. The higher the EEC typically the lower electricity efficiency. Indeed, the EEC in China increased from 2.33 to 2.72 between 1950 and 1980 [3]. In contrast, in major industrialized countries such as US and China was, respectively, only −0.13 and 0.54 in 1980 [4]. Since early 1980s, the EEC in China has decreased significantly and was 0.51 in 2014 as a result of the modernization of machinery and transformation of economic structure [5].

Most studies on the relationship between national electricity consumption and economic variables in China used global models with panel or time series data [[6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]], which allow for analyzing the temporal relationship between energy consumption and economic development but cannot account for the potential spatial non-stationarity effect [22]. In China where there is a huge spatial heterogeneity in terms of both economic development level and industrial structure, the relationship between GDP and electricity consumption could vary sharply across different regions. Studies explored these regional variations in energy-economy relationships by dividing the country into several a priori defined regions. Fei et al. [23] found that economic performance was more strongly related to energy consumption in the western provinces of China than in its eastern provinces. Similarly, Hu and Wang [24] reported that the eastern region in China is the most-energy efficient, followed by the central region, and the west region is the least energy efficient. However, another study using industrial GDP instead of total GDP suggested that some provinces in the central region are the most energy efficient in the country [25].

In general, however, there is broad agreement that the economies of the eastern provinces are relatively more developed compared to the central and western regions [26]. For example, the difference between Jiangsu, the province with the highest electricity consumption (4956.62 billion kWh) and Tibet, the province with the lowest electricity consumption, (30.65 billion kWh) was over 100 fold in 2013 [27]. The electricity consumption can vary widely even among cities from the same province. Therefore, for an accurate understanding of the spatio-temporal patterns in the relationship between electricity consumption and economic growth in China, a finer spatial unit of analysis than province-level is warranted. In this respect, Chen and Fang [28] is the only study that used prefecture-level¹ city data to analyze the relationship between electricity consumption and economic growth. They observed the spatial differences by dividing the country into three regions and found direction of the causality to be different between electricity consumption and economic growth in each region. However, the study failed to discuss the spatial heterogeneities on the relationship between the electricity consumption and economic growth as well as the spatial autocorrelation within each region. In addition, the time period of the study, 2003 to 2012, is relatively short to interpret the temporal change.

This paper contributes to the existing literature by examining the spatial heterogeneity of the relationship between industrial output and industrial electricity consumption at the prefecture city level using time series data from 1994 to 2014. To this end, we use a novel local regression approach that allows for a more detailed analysis of spatial heterogeneity of the phenomena in question. Thus, we provide more accurate analysis of the spatio-temporal variation in economic development and its relationship to electricity consumption across China. Our findings serve as a benchmark for the country’s regional economic development strategy, especially in terms of industrial structure upgrading and energy conservation in the central and western regions of China. Furthermore, the approach we present here is also applicable to other developing countries that are in the middle stage of industrialization and have large interregional inequalities in their level of economic development.

Several empirical studies tested the hypothesis of economic growth and electricity consumption and proved its validity. These studies used either time series or panel data or both to test the relationship between economic growth and electricity consumption A variety of statistical methods were employed in these studies [29], [30], [31], [32], [33], [34], [35], [36], [37]. Among this body of research, there are only a few studies that applied GWR to analyze the spatial pattern of the relationship between economic growth and energy consumption. Wu and Li [38] did a provincial-level analysis for the locally relationship between electricity consumption and economic development in China using geographical weighed regression (GWR) model analyses. They found a spatially associated relationship between the electricity consumption and economic growth and the spatial pattern of 30 Chinese provinces. In addition, they found that the electricity prices of most provinces have the unbalanced and negative impact on the regional electricity consumption. Wang and Chen [39] studied the impact of per capita disposable income, industrial structure, household energy consumption structure, urbanization rate, aging rate and educational level on the per capita household energy consumption of 30 provinces in China using GWR. The study found that per capita disposable income, urbanization rate and educational level had a positive effect on per capita household energy consumption. The industrial structure, household energy consumption structure and aging rate had an inhibitory effect on per capita household energy consumption. In addition, a provincial level study on the impact of electricity consumption on economic development in Turkey based on GWR shows the electricity consumption affected the economic development positively, but the level of impact is different across the country [40].

Despite this body of work, there remains gaps in our understanding of the spatio-temporal relationship between electricity consumption and economic activity. First, studies that focus on spatial heterogeneity use provincial level data or regional level data. As mentioned in section 1.1, the electricity consumption and economic development level can vary widely even among cities from the same province in China, the provincial level study cannot capture this heterogeneity and a finer level study is necessary. Second and even more important, the provincial level dataset cannot provide enough data points for a robust implementation of GWR. As pointed out by Wheeler and Tiefelsdorf [41] and Páez et al. [42]; GWR is not recommended to use in situations with small sample sizes because of the high rate of spurious correlations make the results hard to interpret. It is necessary to process a GWR model using a finer level dataset to provide more accurate and detailed conclusion.