A low-energy development pattern refers to the reduction of energy consumption in the process of economic development by means of, for example, the adoption of energy-saving technologies and the optimization of the energy structure. Thus, the aim is to reduce pollution of the environment and protect natural resources. The low-energy development pattern is an important part of sustainable development and an important way to address the challenges of climate change and energy security. The low-energy development pattern can reduce energy consumption and carbon emissions, reduce the risk of environmental pollution and climate change, while promoting economic development and social progress.
Electricity intensity refers to the amount of electricity consumed per unit of gross domestic product. It is an important indicator of the efficiency of energy use in a country or region. The main characteristic of a low-energy development pattern is to maintain economic growth while minimizing energy consumption and environmental pollution. Therefore, the convergence analysis of electricity intensity can reflect the current trend of the low-energy development pattern.
The convergence analysis of electricity intensity makes it possible to compare trends in the variation of electricity intensity between different regions over time. If the variation in electricity intensity between regions tends to decrease, this indicates that overall regional energy efficiency is gradually increasing, demonstrating a trend toward low-energy development in the region. People are becoming more efficient in the way they use energy, which is relatively less consumed, and electricity intensity is gradually decreasing. This trend indicates that the low-energy development pattern is gradually becoming the mainstream trend in social development. Conversely, if the electricity intensity shows a fluctuating or increasing trend between different regions, this indicates that the regional energy use efficiency still needs to be improved, and more energy-saving efforts are needed.
1.1. Overview of China’s Industrial Structure
China has a strong industrial foundation and a complete industrial and supply chain, despite the uncertain external environment [
1]. In the current era of high-quality development, China’s industrial structure is gradually optimizing, and the modernization of its industrial chain continues to improve, with the manufacturing and modern service industries serving as the twin engines of growth. Under the ongoing COVID-19 pandemic, China must strive to maintain its stable position in the global industrial and supply chain, attract high-quality investment, strengthen industrial clusters, and adopt sustainable development policies for the industrial structure [
2,
3,
4]. The path of China’s industrialization will have a profound impact on its own economic development and that of the world as a whole, given the upstream and downstream linkages of its industries.
Over the past decade, China has witnessed rapid growth in its tertiary industry, which has served as the largest driving force for the country’s economic development for six successive years. Specifically, between 2013 and 2021, the added value of the tertiary industry averaged an annual growth rate of 7.4%, 0.8 percentage points higher than that of the gross domestic product (GDP). Correspondingly, the average annual contribution of the tertiary sector to economic growth reached 55.6%, surpassing that of the secondary sector by 16.4 percentage points. Notably, in 2021, the added value of the tertiary industry accounted for 53.3% of the GDP, representing an increase of 7.8 percentage points compared to 2012, indicating a steady growth trajectory. Furthermore, in 2021, the investment in the tertiary industry reached 5624.7 billion USD, a 2.1% increase from the previous year, and the three industrial structures witnessed an adjustment from 9.1:45.4:45.5 in 2012 to 7.3:39.4:53.3 in 2021.
Both industrial rationalization and upgrading can lead to reductions in total electricity consumption, but it is worth exploring further whether there is regional heterogeneity in this effect and whether it will widen the gap in electricity intensity between Chinese provinces and industries.
1.2. Electricity and High-Quality Development
Since China’s reform and opening up in 1978, the country’s electrification level has steadily increased in tandem with its economic growth. The electric power industry has experienced tremendous development, transitioning from lagging behind to surpassing global benchmarks within a 40-year period. Proving its exceptional performance, China has been the world’s largest producer and consumer of electrical power since 2011. Since 1978, the country’s total power generation has soared from 257 billion kWh to 7327 billion kWh in 2019, marking a 27.5-fold increase. Moreover, per capita power generation has ascended from 268 kWh to 5233 kWh, representing an 18.5-fold increase [
5]. Over the past two decades, China has experienced a significant rise in electricity consumption due to the improvement of industrialization, rapid economic growth, and enhanced living standards, resulting in a 9.8% annual growth rate in total electricity consumption between 1999 and 2018 [
6].
China is the world’s largest emitter of carbon dioxide, and electricity generation is based to a large extent on fossil fuels [
7]. Electrification, meanwhile, refers to supporting the construction of new energy systems and building new power systems. As China strives to achieve its carbon peak, the development of electrification has been a crucial factor in successfully attaining sustainability targets [
8]. Notably, China’s electrification development level continues to rise while the proportion of electricity in terminal energy consumption consistently increases. In 2021, China’s total social electricity consumption reached 8312.8 billion kWh, translating to a year-on-year growth rate of 10.3%, indicating a 14.7% hike when compared to figures from 2019. Regarding industrial power consumption, the primary industry consumed 102.3 billion kWh, marking a 16.4% increase from the preceding year. Concurrently, the secondary industry consumed 5613.1 billion kWh, recording a 9.1% increase. The tertiary industry consumed 1423.1 billion kWh, signifying a 17.8% rise, while the urban and rural residents’ domestic electricity consumption amounted to 1174.3 billion kWh, indicating a 7.3% increase.
Using structural equation modeling, the study now shows that electricity consumption is an important driver of economic growth and that increased ecological awareness resulting from economic growth further strengthens the demand for clean energy generation. Electricity consumption helps promote clean energy substitution and carbon reduction, especially as China is currently undergoing industrialization and urbanization, where demand for electricity will remain high [
5]. Applying joint cointegration tests and the autoregressive distributed lag (ARDL) method, the empirical analysis shows a long-term positive correlation between China’s hydropower consumption and economic growth, with a bi-directional positive impact between hydropower consumption growth and economic growth based on the vector error correction model (VECM) Granger causality method [
9]. The paper also shows that there is a long-run cointegration relationship between real GDP and social electricity consumption in China, and there is a one-way Granger causality between electricity consumption and real GDP [
10]. The author highlights that China’s economic development has depended on power input to a certain extent since the 1980s, presenting the possibility of strong future dependence [
11]. Green development of the power sector plays a vital role in ensuring a reliable power supply, adjusting the energy structure, increasing energy efficiency, and reducing pollution [
12]. Addressing the influence of the power substitution policy, this study concludes that the electricity sector can lead to economic output stimulation; promoting renewable energy consumption in the electricity sector contributes to sustainable economic growth, while the power substitution policy can contribute to China’s green economic development [
13].
In September 2020, China pledged to the global community its commitment to achieving a carbon peak by 2030 and carbon neutrality by 2060. According to the “Opinions of the Central Committee of the Communist Party of China and the State Council on Fully Implementing the New Development Concept and Doing a Good Job in Carbon Peak and Carbon Neutrality”, the government aims to replace its energy consumption with electric energy, making electricity consumption the primary source of energy consumption. By 2060, the consumption of electric energy will replace traditional energy sources in industries, transportation, construction, and other sectors. Presumably, the proportion of electrification in China’s energy consumption will notably increase. In summary, to realize sustained economic growth and achieve China’s “double-carbon” goal, promoting the proportion of electricity in energy consumption remains a priority for the country [
14].
China is facing the challenge of uneven regional economic development, but the gap has been narrowing year by year. The issue of unbalanced regional economic development is not unique to China, as it is a common problem in many countries around the world [
15]. The development pattern of the Chinese economy reflects this unevenness, with the southeast coast being the key area for trade and economic growth. The western region faces environmental, development, and investment limitations, while the central region has also experienced slower economic development compared to the southeast, which has more favorable conditions [
16]. The study uses the entropy method to calculate the regional economic development imbalance index and finds that the imbalance has been decreasing from 2008 to 2017 [
17]. The authors of the study focus on eight typical urban agglomerations in different regions of China and note that, while there are economic disparities among them, they are within a reasonable range. The authors suggest that a coordinated regional development strategy can help alleviate the imbalance [
18].
Over the past decade, China has solidly promoted its regional development strategy, resulting in a new regional pattern and a narrowing of the economic gap. From 2013 to 2021, the gross domestic product of the eastern, central, western, and northeastern regions is expected to grow at average annual rates of 7%, 7.5%, 7.7%, and 4.7%, respectively. The central and western regions are developing faster than the eastern regions. In 2021, the per capita disposable income of residents in the eastern, central, western, and northeastern regions will be USD 6972, USD 4595.8, USD 4308.8, and USD 4730.4, respectively. The income ratio between the highest eastern region and the lowest western region has shrunk from 1.70:1 in 2013 to 1.62:1. The positive regional interaction has led to a gradual reduction in the relative gap.
China’s energy consumption similarly shows regional imbalances. The level of urbanization affects energy consumption. Globally, urban energy consumption accounts for about 70% of total energy consumption since 2010 [
19]. Household energy consumption in China differs among the three regions of eastern, central, and western. Moreover, the most important influencing factors of household energy consumption are different in all three regions [
20,
21]. China’s energy consumption has increased by 3.5% compared to 2016. Furthermore, energy consumption in the eastern region is significantly higher than in other regions, and the gap is tending to widen [
22]. The eastern provinces have high energy consumption, such as Shandong and Guangdong. There are significant differences in energy consumption between regions [
23]. The distribution of residential energy consumption in China shows an “East-Middle-West” gradient pattern [
24]. The growth rate of energy consumption in the eastern part of China is gradually decreasing. However, the western region is growing faster, with growth rates even exceeding those of the eastern region [
25]. Using the Theil index and functional data analysis methods, the authors explored the differences in regional energy consumption in China. They found that the eastern region contributed the most to the overall difference in energy consumption. Additionally, the population factor is one of the most important influencing factors of energy consumption [
23].
Reducing electricity intensity, minimizing electricity losses, and exploring a low-energy consumption development model for China to optimize its ability to serve the economy and society are among China’s current priorities [
26]. Therefore, it is worth investigating whether regional differences in energy intensity will narrow over time, leading to convergence. Analyzing the convergence process across regions is crucial because the finding of convergence may imply a diffusion of energy-related technologies across regions, and the differences may be a reason to alert the government to take further measures, especially in regions with low levels of energy efficiency.
1.3. Current Literature Review on Convergence
Convergence is an inevitable trend in economic development, and all economies will eventually converge in terms of output per capita [
27]. Convergence analysis is currently used in numerous fields, such as carbon emissions, energy intensity, and energy efficiency. Club convergence is an important part of convergence analysis, interpreted as multiple steady-state paths when an economy reaches its steady-state path. Due to the prominent position of tackling climate change, many scholars have begun to study the convergence of energy intensity, energy productivity, and energy efficiency [
28].
Current research has analyzed convergence at different sample levels. Most studies use quasi-metric and non-parametric methods to examine whether indicators converge across economies, countries, provinces, or sectors. Through log t regression, the paper examines the development trend of total factor carbon productivity in 88 economies and identifies potential convergence clubs. Although the whole sample does not show a convergence trend, there are five convergence clubs with significant differences in carbon productivity growth [
29]. This paper studies the club convergence of total factor energy efficiency in countries along “The Belt and Road”. The results show that the total factor energy efficiency of the countries along “The Belt and Road” appears to have a trend of differentiation and converges into three convergence clubs with different characteristics [
30]. In addition, the convergence of energy efficiency under different sample levels of countries also includes Latin American countries [
31], the European Union [
32], and OECD countries [
33]. The convergence of stochastic power intensity in Chinese provinces is studied. Due to the uneven distribution of energy resources in different regions, the study found that all regions will not form a unique club and will eventually form three clubs and a group. [
34]. Convergence at the economic sector level has been widely discussed. The article notes that there is no club convergence for the emission intensity of the six major industries in all provinces. The convergence level of emission intensity of various sectors in China is determined using the club convergence identification algorithm [
35]. Additionally, club convergence can also be discussed at the municipal sample level. Based on per capita energy consumption data from 243 prefecture-level cities in China, this paper uses a nonlinear time-varying factor model and clustering algorithm to test the club convergence effect of urban per capita energy consumption. The results show four convergence clubs and one divergence club [
36]. Their results support the existence of group convergence, rather than convergence at the full sample level.
In recent years, the concept of club convergence has been used to conduct empirical studies on the influencing factors of energy structure upgrading. The article notes that China’s foreign direct investment in low-carbon industries of countries along “The Belt and Road” is conducive to the convergence of clubs with high energy efficiency and high convergence rates [
30]. The article found four convergence clubs in 193 cities, which showed significant differences in energy intensity. The orderly Probit model is used to conclude that the degree of marketization, population density, foreign direct investment, resource endowment, and industrial structure are driving factors for the formation of convergence clubs [
37]. Empirical results show that there is no uniform convergence of renewable energy in EU countries. However, agricultural added value, foreign direct investment, open trade, land, information and communication technology, population, and institutional quality promote the possibility of forming a final convergence club [
38]. The current aspiration of developed countries is not only to increase the share of renewable energy but also to reduce energy consumption to improve energy efficiency and break the long-term relationship between economic growth and growth in electricity consumption. In this context, it is more important to explore low-energy consumption development models.
Moreover, recent literature has highlighted the policy implications of club convergence. The author clearly points out that understanding the convergence mode of energy intensity and its driving factors is of great significance for local governments to implement targeted energy-saving policies [
37,
39]. The article points out that the development of green finance in China presents a phenomenon of club convergence. At the same time, it reveals the evolution trend and reasons for the green finance development gap. It provides a policy basis for promoting the coordinated development of green finance in China [
40].
There are two problems with the current research on energy convergence. The first is that most of the existing literature has studied energy intensity, ignoring the relationship between China’s electrification process and its coordinated economic development. Therefore, the study of electricity intensity convergence has practical implications for regional differences in China’s electricity development and for exploring low-energy consumption development models. Secondly, the current sample level of research is focused on the national, provincial, and city levels, without further exploring the relationship between industries, ignoring the industrial restructuring that has taken place in China in recent years.
Therefore, considering that electricity intensity reflects the output value brought by kWh consumption, the trend of electricity intensity in each province of China reflects the efficiency of economic development to a certain extent. The key issues that this paper attempts to analyze are (1) to analyze the characteristics of regional industrial development by analyzing the development trend of inter-regional electricity intensity; (2) to explore the characteristics of development efficiency and trends among different industries in China by analyzing from three perspectives of GDP electricity intensity, secondary industry, and tertiary industry electricity intensity; (3) to provide policy recommendations for China’s regional economic development based on these analyses of reference. Accordingly, the innovative points of this study could be concluded in three points. (1) Electricity intensity reflects the output value per unit of electricity, which is an important indicator reflecting the coordination between regional economy and energy consumption as well as high-quality economic development. (2) The club convergence log t-test used in this paper is a data-driven trend analysis method, which can reduce the subjective bias caused by artificial groupings and better reflects the real development and convergence tendency. (3) The results of the study state the regional diversity in electricity intensity, which implies the problems of imbalance during the industrialization of developing countries and provides insight for policymakers.
The paper is organized as follows: The first part is the background and introduction, the second part is the methodology and data, the third part is the empirical results and analysis, and the fourth part is the conclusion and corresponding policy recommendations.