You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Energies
Download PDF
  • Article
  • Open Access

9 June 2024

A Study on the Distribution Dynamics, Regional Disparities, and Convergence of China’s Energy Transition

,
and
1
School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
2
School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing 102617, China
3
Development Research Centre of Beijing New Modern Industrial Area, Beijing 102617, China
4
School of Economics, Beijing Institute of Technology, Beijing 100081, China
Energies2024, 17(12), 2842;https://doi.org/10.3390/en17122842
This article belongs to the Section C: Energy Economics and Policy
Version Notes
Order Reprints

Abstract

Energy transition, as a crucial aspect of the country’s high-value-added economic development, involves the construction of an energy transition index system and empirical analysis using methods such as the entropy weighting method, kernel density estimation, Markov chain, Dagum Gini coefficient, σ-convergence, and β-convergence. This study measures the level of energy transition in 280 Chinese cities from 2010 to 2019 and analyzes their evolutionary trends, regional disparities, structural differences, and convergence. The findings reveal that China’s energy transition generally exhibits characteristics of “improvement in development levels and reduction in absolute disparities”. The disparities in energy transition primarily stem from developmental differences among the three major regions, displaying typical σ-convergence and β-convergence characteristics. This analysis contributes to understanding the real level and distribution features of China’s energy transition, providing a basis for identifying focal points for enhancing energy transition in the current and future stages.

1. Introduction

As global energy security issues and environmental protection concerns grow increasingly severe, energy transition has attracted significant attention from governments, businesses, and scholars worldwide []. As the largest developing country, China faces particularly acute energy challenges, reflected in the need for the optimization of the supply–demand structure of its energy industry, the still prominent issue of excess capacity, and the daunting tasks associated with the “dual carbon” goals. Especially under the trend of high-quality development, the stable, orderly, and sustainable upgrade and transformation of China’s energy sector have become crucial for tasks such as the energy production and consumption revolution []. Although the objectives of China’s energy transition are clear, the process still confronts numerous losses and uncertainties. These include issues such as ultra-low emission transformation in traditional fossil energy industries, insufficient utilization hours of new energy equipment, high average costs of renewable energy, severe rationing problems like “curtailed wind, solar, and hydro”, excessive transformer capacity ratios in power grids, and the contradictions between “electricity substitution” and “multi-energy complementarity”, all of which constrain the efficiency of the transition and weaken its effectiveness []. Table 1 presents the relevant policies for energy transition over the past three years. A major achievement of China’s energy transition has been its implementation of these policies. For instance, as of the first quarter of 2024, newly installed energy-efficient power generation capacity made up 90.81% of brand new power generation installed nationwide, an increase of 31.46% over the same period last year. Clean energy power generation accounted for approximately 28.35% of national power generation, an increase of 10.12% over the previous year. However, coal consumption has not significantly decreased, with coal accounting for 55.9%, 56.2%, and 55.3% of total energy consumption in 2021, 2022, and 2023, respectively. Data on energy transitions over the past three years can be viewed in Table 2 and Table 3.
Table 1. Energy transition policies in China over the past three years.
Table 2. Data related to energy transformation in the past three years.
Table 3. Comparison of energy transition data from 2019 to 2021.
Table 3 shows that there are two key areas in China’s transition: firstly, the adjustment of the energy structure on the supply side, particularly the increasing proportion of non-fossil energy in primary energy, which is consistent with the conclusions of research studies [,]. For example, in 2021, China promoted the structural de-capacity of coal, closing down over 450 outdated coal mines. Secondly, the reduction of energy intensity on the consumption side involves not only improving energy utilization efficiency but also adjusting the industrial structure. A significant portion of the energy consumed in 2019 came from clean energy sources, such as natural gas, hydropower, nuclear power, and wind power. The proportion of clean energy consumption increased by one percentage point between 2014 and 2021. Natural gas, hydropower, nuclear power, wind power, and solar power are the most popular clean energy sources. In this sense, the structure of consumption of energy is shifting in a cleaner and more sustainable direction. This is also consistent with the conclusions in the literature [,]. The aforementioned achievements in energy transition are attributed to the policy-driven transformation practices over the past decade.
It is noteworthy that while China’s energy transition has certain institutional advantages, it is similar to that of other major industrialized countries: the transformation process is still at the stage of “quantitative change”, and a sustainable “qualitative change” turning point is not yet in sight. Therefore, ensuring the scientific nature, practicality, and efficiency of the choices made in energy transition paths has become urgent to achieve high-quality development and the goals of a modernized powerful nation, addressing major contradictions in China’s energy industry such as “unbalanced and inadequate development” []. It is important to accurately comprehend China’s developmental essence, measure the energy transition scientifically and rationally, and depict its spatial and temporal distribution dynamics, regional differences, and convergence characteristics to effectively promote China’s energy transition. Understanding these aspects helps to objectively recognize the real conditions and distribution features of China’s energy transition development, providing reference ideas for promoting energy transition in various regions, which has great theoretical and practical significance.
Addressing the deficiencies in the existing literature, this paper proposes to expand related research in the following areas: firstly, by constructing a scientifically rational energy transition evaluation index system supplemented by rigorous indicator processing methods to measure the energy transition levels of various regions; secondly, by comprehensively grasping the dynamic evolution trends of China’s energy transition using kernel density estimation and spatial Markov chain models; and thirdly, by identifying the spatial and structural differences and sources of China’s energy transition based on the Dagum Gini coefficient. Additionally, the paper will use σ-convergence and β-convergence to explore China’s energy transition convergence characteristics in detail.
This paper aims to make marginal contributions in the following areas: first, the creation of a multi-dimensional index system for evaluating energy transitions, comprising five secondary indices and twelve tertiary parameters; and second, a comprehensive presentation of the structural characteristics of energy transitions in terms of distribution dynamics, regional differences, structural differences, and convergence.

2. Literature Review

2.1. The Connotation of Energy Transition

Originally defined in 1982 in the book Energy Transition as the transition from oil and uranium to renewable energy sources, energy transition is the process of switching from one energy source to another. Later, energy saving and emission reduction initiatives and green low-carbon initiatives have been added to the concept of energy transition. Canadian scholar Smil [] states: “Energy transition refers to the process of shifting from one specific form of energy to another”. Podobnik [] suggests that energy transition involves a process in which a novel form of energy is extensively utilized for human consumption, facilitated by the discovery and acquisition of novel technologies. The “Fostering Effective Energy Transition 2019” report further elaborates that energy transition includes various energy types and energy-consuming sectors (including industry, buildings, transportation, and households) and involves various stakeholders. Specifically, it encompasses aspects such as energy structure, energy investments, energy regulations and political commitments, infrastructure and innovation environment, human capital and consumer participation, institutions, and governance (https://www.weforum.org/).
Synthesizing previous studies, this paper encompasses two meanings of energy transition: firstly, it refers to changes in the scale and composition of energy consumption based on environmental sustainability and economic growth, including both primary and secondary energy sources (the composition of power generation); and secondly, it discusses corresponding changes in energy investments, economic development, and GDP losses.

2.2. Research on Energy Transition

The existing literature has explored various aspects of energy transition, including its connotation, the construction of indicator systems, and distribution characteristics. Research on energy transition can generally be divided into two categories: firstly, studies on the factors influencing energy transition. For example, Wu et al. [] estimated the impact of energy price distortions on energy transition using China’s heavy industry as a sample from 2003 to 2019. Milan Babic evaluated green finance’s influence on the transition to a low-carbon economy []. Using the construction of demonstration cities for new energy in China as a quasi-natural experiment, Hou et al. conducted a comparison of different mechanisms of urban energy transition []. Energy transition and financial development have been studied by Regean and colleagues using two-stage least squares models that incorporate fixed effects and instrumental variables []. Sami Ullah et al. [] examined whether digital inclusion plays a role in the energy transition of the five top green economies over the period 2001–2021, controlling for environmental taxes. Q. Wang et al. analyzed the influence of artificial intelligence on the transition to a clean energy economy and emissions of greenhouse gases, utilizing data from 69 countries spanning 1993 to 2019 []. They employed the STIRPAT method as well as mediation effect techniques and panel threshold models to analyze the correlation between artificial intelligence and energy transition. As reported by Ma et al. [], there is a heterogeneous relationship between innovation in renewable energy and the transformation of the energy sector in the G7 nations; this has been analyzed through aggregated and decomposed analyses. In their study of the effect of media attention on Chinese energy enterprises and the energy transition, Liang et al. [] utilized publicly listed data from publicly traded Chinese energy companies from 2012 to 2021. In an analysis carried out by Anna et al. [], the authors used data from Eurostat for 2022 and 2013 to develop a composite index that measures the progression of the energy transition by using the Energy Transition Progress Index (ETPI) as a measure of the process. Tan et al. [] investigated the possibilities of facilitating China’s energy transition by creating virtual power plants. As a result of examining the relationship between green tax policies and the energy transition from both the standpoint of consumption and production, Fang et al. [] found a positive correlation between the two. Wan et al. [] used data from 30 provinces between 2004 and 2020 and systematically analyzed the spatiotemporal evolution characteristics of green financing and energy structure transition across provinces. They used fixed effects models, GMM models, mediation effect models, and spatial Durbin models. The second category includes studies on the role and urgency of energy transition, such as Moussa et al. [], who highlighted the urgency of energy transition in Africa and provided an overview of factors influencing energy transition. X. Wang et al. [] investigated how the transition to renewable energy could promote environmental sustainability in the ten countries with the greatest emissions of carbon dioxide. As a result of the global shift towards a low-carbon future, Qusay et al. [] examined how hydrogen plays a critical role. A comparison of hydrogen’s advantages and disadvantages with renewable energy sources was presented in the article, including an analysis of its production, storage, distribution, and use as an energy carrier. Lamiae et al. [] examined how monetary complexity modifies the relationship between economic growth and energy transition using robust weighted least squares estimation techniques from 124 countries from 2000 to 2020, and found that energy transition negatively impacts economic growth in general. An initial assessment of solar photovoltaic project sites was performed using satellite imagery by Nagendra et al. []. Solar energy integration with battery storage was studied by Li et al. []. An innovative intelligent building energy management technology was developed by Yu et al. [] to facilitate energy conservation in buildings. Using temporal dependences as the basis for prediction and forecasting energy demand and consumption, Ishaq et al. [] proposed a deep learning approach optimized for prediction and forecasting.

3. Measurement Results of China’s Energy Transition

3.1. Indicator System

Adhering to the principles of comprehensiveness, representativeness, and contemporaneity in the construction of indicators, this paper constructs an energy transition indicator system with 12 fundamental indicators, based on the interpretation of the connotation of energy transition and borrowing from existing research. These indicators are divided into internal and external categories related to energy transition, as shown in Table 4.
Table 4. China’s energy transition indicator system.
From 2010 to 2019, 280 prefecture-level cities in China were surveyed regarding their energy transition development. The primary source of energy data for this paper was the China Energy Statistical Yearbook. PM2.5 data were sourced from the global grid data provided by the University of Washington, St. Louis campus, and other control variable data were from the China City Statistical Yearbook.

3.2. Overall Trends

This study aims to measure the development level of energy transition in 280 prefecture-level cities in China from 2010 to 2019. On one hand, to maximize the representation of the horizontal differences in energy transition among these cities, and on the other, to ensure the dynamic comparability of evaluation results, this paper employs the entropy weighting method to assign specific weights to various indicators. To more vividly display the energy transition status nationwide and in the three major regions, Figure 1 and Figure 2 illustrate the trend of changes in the national energy development index from 2010 to 2019. As shown in Figure 1, overall, the municipal-level energy transition index in China showed an upward trend from 2010 to 2019, with the national average energy transition index increasing from 0.0638 in 2010 to 0.0971 in 2019, indicating significant progress.
Figure 1. Overall trends in energy transition.
Figure 2. China energy transition index.
From a regional perspective, the eastern region experienced substantial increases in indexes related to the energy transition, followed by the western and central regions. This variance may be related to the initial states of energy transition in each area. China’s overall energy distribution is constrained by factors such as resource endowments, with major power supplies centered in the northwest. Due to this, there has long been a pattern of energy flow: “west-to-east electricity transmission, west-to-east gas transmission, and north-to-south coal transportation”. The initial levels of energy transition in the western and central regions are relatively low, and these areas are comparatively weaker in terms of economic operation, growth momentum, ecological environment, and social welfare. Although there is a “catch-up effect” for regions with higher energy transition indices, and rapid growth has been fostered under the exemplary demonstration and support from the eastern regions, they still lag behind the national average. In contrast, the eastern region, which started from a much higher initial level of energy transition and possesses more advanced technological innovations, leads above the national average.

3.3. Spatiotemporal Characteristics

As observed from Figure 2, the national trend in 2010 shows that high-value areas of municipal energy transition in China were quite dispersed, with only regional “peaks” and no clustering of high values. This was closely related to the challenges of urban energy transition, where a single city’s “outbreak” did not have enough “energy” to drive the development of surrounding cities. By 2019, the level of urban energy transition had significantly improved, with a dominant number of cities achieving higher index levels and exhibiting a clustered distribution pattern, primarily reliant on cities with high levels of transition. In contrast, the number of cities at low index levels further decreased, with a wide spatial distribution. Overall, the level of urban energy transition continuously evolved and improved, transitioning from a “dispersed” sporadic distribution dominated by low-level cities to a “clustered” grouping centered around higher level cities.

4. Dynamics of China’s Energy Transition

4.1. Kernel Density Estimation

As shown in Figure 3, this paper analyzes the distribution location, shape, extensibility, and polarization phenomenon of China’s energy transitions nationwide and in three major regions based on kernel density estimation in order to examine the dynamic characteristics of such transitions in China.
Figure 3. Dynamics of the energy transition index distribution nationwide and in the three major regions. China energy transition index: (a) national overall, (b) eastern region, (c) western region, (d) central region.
Figure 3a presents the dynamic evolution trend of national energy transitions during the sample period. Regarding the distribution location, both the center and variation range of the distribution curve are relatively stable, with no significant variation in the movement rate. In terms of the distribution shape, the main peak of the distribution curve generally shows a rising trend, but the development process displays a quasi-N-shaped evolution characterized by “rising–falling–rising–stable” phases; simultaneously, the main peak width has narrowed, indicating a centralizing trend in the national energy index and a continual reduction in regional development differences. In terms of extensibility, the left tail-dragging phenomenon of the curve has weakened, but a slight right tail-dragging tendency still exists, indicating that cities with lower levels of energy transition are gradually converging towards the average, while provinces with higher levels of energy transition still maintain a “leading” advantage. As for polarization, the curve’s distribution appears as a single or dual peak, indicating minor absolute differences in the energy transition of various cities.
These figures illustrate the dynamic evolution trends of energy transitions in the eastern, western, and central regions during the sampling period, as shown in Figure 3b–d. Firstly, in terms of distribution location, the evolution trends of the three major regions are consistent with the overall national distribution, generally showing an upward trend. Secondly, regarding the distribution shape, the main peak height of the eastern region’s distribution curve underwent a “rise–fall–stable” change process; the main peak height of the western region significantly rose amidst fluctuations, and the central region’s main peak height experienced alternating fluctuations of “sharp rise–slight fall”. Thirdly, regarding extensibility, the distribution curve of the central region shows no significant tail-dragging phenomenon, indicating no extremely high or low levels of energy transition within the region. The eastern and western regions exhibit a slight right tail-dragging phenomenon, suggesting that despite the generally higher levels of energy transition, individual areas such as Beijing and Shanghai in the eastern region maintain absolute leadership positions. Finally, regarding polarization, the eastern, western, and central regions maintained an unimodal state during the observation period, with no evidence of multipolar or bipolar differentiation.
From a nationwide perspective, or considering the four major regions, the following conclusions can be drawn: the general trend of energy transition shows characteristics of “level improvement and reduction in absolute differences”, although the development process has its ups and downs. It is noteworthy that after 2017, the eastern region showed signs of stagnation and decline in energy transition, indicating the need to further grasp the core of development, deepen reforms, break the existing development pattern, and quickly overcome the bottleneck period in subsequent development processes.

4.2. Markov Chain Analysis

To further reflect the internal flow direction and position transition characteristics of the energy transition, this paper employs the Markov transition probability matrix for analysis, as shown in Table 5. The diagonal elements are always greater than the off-diagonal elements, indicating that cities at low, lower middle, upper middle, and high levels maintain their original levels after one year with probabilities of 80.53%, 69.43%, 71.90%, and 91.16%, respectively. This stability between different levels of the energy transition index suggests the presence of “club convergence”. Additionally, both ends of the diagonal possess higher elements than those in the middle. This suggests that cities at low and high levels have a higher likelihood of maintaining their original levels, demonstrating the phenomenon of club convergence at both low and high levels. Furthermore, grade transitions occur only between adjacent types, indicating that energy transition is a progressive process and “leapfrog” transitions are rarely achieved. Simultaneously, the probabilities of cities at low, lower middle, and upper middle levels moving up by one level after one year are 18.87%, 21.18%, and 12.98%, respectively, demonstrating that the energy transition process is dynamic and fluctuating, with different challenges at different levels. The probabilities of moving down by one level from lower middle, upper middle, and high levels are 8.84%, 14.55%, and 8.13%, respectively, indicating certain risks of degradation in energy transition. As the energy levels increase, the probability of moving downward also tends to increase. However, once the highest level is reached, the probability of moving downward decreases. Therefore, cities must be vigilant about the risks of moving down a level, prevent any regression in energy transition achievements, maintain the stability of current developmental gains, and strive for upward-level transitions.
Table 5. Markov transition probability matrix of China’s energy transition index, 2010–2019.

4.3. Spatial Markov Chain Analysis

Existing research indicates a significant spatial correlation in energy transition. Consequently, this study calculated the global Moran’s Index of China’s energy transition index based on the geographical location of the economy’s weight matrix for further verification. As shown in Table 6, the global Moran’s Index of economic high-quality development is significantly positive from 2010 to 2019, indicating a positive spatial correlation between provinces’ economic high-quality development.
Table 6. Global Moran’s Index of China’s energy transition index, 2010–2019.
Table 7 illustrates specific results highlighting the need to consider spatial factors and establish a matrix of probabilities based on spatial factors. Initially, the four transition probability matrices under different types of spatial lags are all distinct, suggesting that the probability of transition in a city’s energy transformation is affected differently by the energy transition disparities in neighboring cities. A significant proportion of diagonal elements are larger than off-diagonal elements in all spatial lag types, indicating that there is a high likelihood of “level lock-in” when spillover effects occur. Further, the presence of non-zero elements in the triangle indicates instability during energy transitions; in addition to the possibility of upward transitions, there is also the possibility of downward transitions, which only occur between adjacent levels, making it difficult to jump across levels. It should be noted that different types of lag have different impacts on the same level. For example, the probability of moving from a low to a lower middle level under upper middle lag conditions is 17.11%, which is distinctly lower than the 20.67% transition probability under lower middle lag conditions. Finally, the same lag type has different impacts on different levels; under upper middle level lag conditions, the probabilities for low, lower middle, and upper middle levels to move up one level are 17.11%, 21.77%, and 13.81%, respectively, showing a fluctuating downward trend. This suggests that the type of lag, as well as the initial point of a transition in energy, influences the likelihood of such a transition.
Table 7. Spatial Markov transition probability matrix of China’s energy transition, 2010–2019.

5. Analysis of Differences and Convergence in China’s Energy Transition

5.1. Regional Differences

According to Table 8, this paper examines regional differences in China’s energy transition and the sources of these differences using the Gini coefficient and its decomposition methods. Furthermore, Figure 4 depicts the evolution trends of regional differences during the sample period to provide a better understanding of China’s energy transition overall and geographically. A comprehensive interpretation of Table 8 and Figure 4 provides the following information.
Table 8. Regional differences in China’s energy transition and decomposition results.
Figure 4. Evolution of regional differences in China’s energy transition. (a) Evolution of the overall Gini coefficient and its decomposition. (b) Contribution rates of the overall Gini coefficient decomposition. (c) Evolution of inter-group differences among the three major regions. (d) Evolution of intra-group differences within the three major regions.
(1) There is an increasing disparity between Chinese energy transition levels overall. Over the course of the sample observation period, China’s energy transition index increased by 29.2%, from 0.250 in 2011 to 0.323 in 2019. This indicates that the differences among various energy transition indices are continuously expanding, displaying a clear dispersion trend. As seen in Figure 4a, the trends in inter-group differences (Gb) and intra-group differences (Gw) are relatively consistent with the changes in the overall Gini coefficient (G), suggesting that the overall disparity may largely depend on both inter-group and intra-group differences. Meanwhile, the overlapping density (Go) tends to decline amid minor fluctuations of “slight rise followed by gentle decline”, indicating that cross-overlapping issues do not considerably influence the overall disparity among the regions. Figure 4b confirms this, showing that during the sample period, the contribution rates of the three are not significantly different, with intra-group differences consistently contributing around 31% to the overall disparity, while inter-group differences and overlapping density show alternating trends in their contributions. This suggests that the spatial disparities in China’s energy transition primarily stem from differences between the three major regions, and narrowing these regional development disparities is a key direction for future efforts.
(2) A fluctuating upward trend is observed concerning the inter-group differences among the three major regions. An illustration of the evolution of differences between the three major regions can be found in Figure 4c. In general, there is either a rising trend or an intermittent upward trend in the differences between regions. The largest increase in differences was observed between the eastern and central and between the eastern and western regions. There are indications that the eastern region, which has been leading in terms of energy transition, may have a driving influence on the central and western regions, gradually reducing the disparities between various regions in terms of energy transition and moving toward a more balanced development pattern. A distinct difference in the transition of energy occurred between the eastern and central and between the eastern and western regions throughout the observation period, which was significantly broader than the difference between the western and central regions. According to the conclusions earlier in the text, the eastern region has higher levels of energy transition development than the western and central regions, indicating that the east is at an advantage in the energy transition development process.
(3) Regional heterogeneity can be demonstrated by the decline in intra-group differences within each of the three major regions. Figure 4d depicts the evolution of intra-group differences within the three major regions over the course of the study. Intra-group differences show a fluctuating upward trend. There has been a substantial increase in intra-group differences in the eastern region, followed by the central region, while the western region has experienced a smaller increase. Among the three regions, the eastern region has consistently recorded the largest intra-group differences, indicating an imbalance in energy transition within the eastern region. For example, Beijing in 2019 scored 0.7152 on the energy transition index, while Chaoyang scored only 0.0464 on the same index, demonstrating a significant gap in development between Beijing and Chaoyang. Furthermore, intra-group differences are more pronounced in the central region than in the western region. This suggests that cities in the western region are experiencing an increased concentration of energy transitions. In contrast, the central region includes both benchmark cities, such as Wuhan and Zhengzhou, and “mediocre” energy provinces, such as Wuhai and Yichun, resulting in substantial regional differences.

5.2. Convergence Analysis

(1) σ-Convergence Result Analysis
This study employs coefficients of variation to test for σ-convergence and has calculated the coefficient of variation for China’s energy transition index from 2010 to 2019, with the convergence trend depicted in Figure 5. The national energy transition index’s σ-coefficient shows a stable annual increasing trend, indicating typical σ-convergence at the regional level. Specifically, the eastern region shows the largest increase in the σ-coefficient, and its convergence trend is closest to the overall national trend. In contrast, the central and western regions demonstrate the next highest increases in their σ-coefficients, with slightly weaker convergence. Based on the Dagum Gini coefficient analysis presented earlier, this result is generally consistent with the findings presented here.
Figure 5. Evolution trend of the σ coefficient of the energy transition index nationwide and in the three major regions, 2010–2019.
(2) β-Convergence Result Analysis
This study conducted a convergence test on China’s energy transition index to gain a deeper understanding of energy transition convergence characteristics. Table 9 summarizes the test results for absolute convergence degrees. The estimated β coefficients are always negatively skewed, demonstrating a high degree of convergence in the economic well-being development index across the nation and all three major regions. The convergence results indicate that convergence speed varies by region. The central and western regions exhibit a higher convergence speed than the national average, while the eastern region shows a relatively lower convergence speed. This may be due to large regional differences in the energy transition index towards the east. Additionally, factors such as geographical location and resource endowment result in lower convergence speeds compared to other regions.
Table 9. β-convergence test results for China’s energy transition index.
In this paper, the spatial Durbin model is introduced to further analyze dimensional convergence, as illustrated in Table 10. Previous analyses have found significant spatial correlations in energy transitions, so the multidimensional Durbin model is introduced to explain these correlations. Table 10 reports the results for absolute spatial β-convergence across the national and three major regional levels. The estimated β coefficients are consistently significantly negative, indicating that China’s energy transition index exhibits both spatial absolute and spatial conditional convergence. Among the three regions, the western area exhibits the highest spatial absolute convergence speed, followed by the central region and the eastern region. Comparing these results with convergence speeds that do not consider spatial factors reveals that the introduction of spatial elements significantly alters the relative characteristics of regional convergence, underscoring the non-negligible spatial spillover effects of the energy transition. From a national perspective, both the spatial absolute convergence speed and geographical conditional convergence speed are significantly higher than the convergence speeds without geographical considerations. Additionally, the spatial autoregressive coefficient ρ is significantly positive, indicating that under the economic geographical weight matrix, the energy transition has significant spatial spillover effects that can accelerate the convergence speed and shorten the convergence period of the energy transition.
Table 10. Spatial β-convergence test results for the energy transition index.

6. Conclusions

This article constructs an energy transition indicator system based on the internal and external driving forces of energy transition, measures the energy transition of 280 cities in China, and employs methods such as kernel density estimation, Markov chain analysis, Dagum Gini coefficient, variance decomposition, σ-convergence, and β-convergence to examine and test the internal dynamics of distribution, differences between regions, variations in structure, and convergence of the energy transition across China’s three major regions.
According to the results of this study, the following conclusions can be drawn. First, from the measurement results, the energy transition index in China steadily increased during the sample period, exhibiting a distribution characteristic that decreases sequentially from east to west. Second, in terms of distribution characteristics, the nation overall and the three major regions all demonstrate “improvement in development levels and reduction in absolute disparities”, though the development process has its ups and downs and varies across regions. Additionally, there is a “club convergence” phenomenon in energy transition, with level transitions occurring only between adjacent types, making “leapfrog” transitions difficult. Considering spatial factors, the initial level of energy transition can also influence transition probabilities. Third, the overall inequality in energy transition shows a declining trend from a geographical perspective, primarily due to regional differences; however, there is also a decline in intra-regional differences within the three major regions. Fourth, in terms of convergence, the nation overall and the three major regions exhibit typical σ-convergence and β-convergence characteristics, but the convergence speeds and periods vary among the regions. Moreover, the introduction of spatial factors can accelerate the convergence speed of energy transition and shorten the convergence period, and the spatial spillover effects of energy transition also significantly alter the relative characteristics of regional convergence.
In light of the conclusions stated above, this article provides several insights. First, China should address the weaknesses in energy transition, especially the slight decline observed since 2018. The country should properly handle the balance between “quality” and “quantity” during the energy transition process, recognizing that these are not mutually exclusive, and aim to significantly enhance quality while maintaining continuous quantity growth. Second, China should pay attention to the issue of regional differences during the energy transition process, especially the development disparities between the three major regions. The country should strengthen inter-regional communication and cooperation, allocate resources rationally, and fully utilize and capitalize on the spatial spillover effects of energy transition. Third, China should adopt region-specific development strategies to drive upward transitions in energy transition. The eastern region has always had a leading advantage in energy transition levels, which means that the eastern region needs to undertake more cutting-edge technological tasks and pilot error-tolerance costs. Therefore, to achieve a new leap in national energy transition, it is crucial to leverage the leading role of developed provinces in the east while striving for the rise of less developed regions. Fourth, by upgrading and innovating technology, we can reduce emission reduction costs and promote the scaling up of key technologies such as new energy, energy storage, carbon storage, and carbon removal. At the same time, under the negotiation framework of the Paris Agreement, we should promote the introduction of advanced technologies from developed countries. Additionally, promoting technology export can dilute the costs of technological innovation. China’s achievements in new energy utilization technology have been significant, and transferring these technologies to emerging markets can reduce the emission reduction demands on China.

Author Contributions

Conceptualization, P.T.; Methodology, P.T.; Software, Z.G. and Y.H.; Validation, Z.G.; Formal analysis, Z.G.; Investigation, Z.G. and Y.H.; Resources, P.T. and Y.H.; Writing—original draft, P.T. and Z.G.; Writing—review & editing, P.T.; Visualization, Z.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zadeh, O.R.; Romagnoli, S. Financing sustainable energy transition with algorithmic energy tokens. Energy Econ. 2024, 132, 107420. [Google Scholar] [CrossRef]
  2. O’Connell, A. The financing dynamics of niche innovation in energy transitions: A South African perspective. Energy Res. Soc. Sci. 2024, 111, 103478. [Google Scholar] [CrossRef]
  3. Zhou, Z.; Sharif, A.; Inglesi-Lotz, R.; Bashir, M.F. Analysing the interplay between energy transition, resource consumption, deforestation, and environmental factors on agricultural productivity: Insights from APEC countries. J. Clean. Prod. 2024, 446, 141408. [Google Scholar] [CrossRef]
  4. Fan, M.; Zhou, Y.; Lu, Z.; Gao, S. Fintech’s impact on green productivity in China: Role of fossil fuel energy structure, environmental regulations, government expenditure, and R&D investment. Resour. Policy 2024, 91, 104857. [Google Scholar]
  5. Wang, Y.; Yang, R.; Zhang, J.; Sun, L.; Xiao, W.; Saxena, A. A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production. Energy 2024, 291, 130368. [Google Scholar] [CrossRef]
  6. Hongjun, G.; Liye, D.; Aiwu, Z. Energy structure dividend, factor allocation efficiency and regional productivity growth--An empirical examination of energy restructuring in China. Energy Policy 2023, 172, 113307. [Google Scholar] [CrossRef]
  7. Zhao, J.; Sinha, A.; Inuwa, N.; Wang, Y.; Murshed, M.; Abbasi, K.R. Does structural transformation in economy impact inequality in renewable energy productivity? Implications for sustainable development. Renew. Energy 2022, 189, 853–864. [Google Scholar] [CrossRef]
  8. Genc, T.S. Energy Transition and the role of new natural gas turbines for power production: The case of GT11N2 M generators. Energy Econ. 2024, 131, 107412. [Google Scholar] [CrossRef]
  9. Smil, V. Energy Transitions: History, Requirements, Prospects; Praeger: Santa Barbara, CA, USA, 2010. [Google Scholar]
  10. Podobnik, B. Global Energy Shifts: Fostering Sustainability in a Turbulent Age; Temple University Press: Philadelphia, PA, USA, 2006; p. 320. [Google Scholar]
  11. Wu, L.; Xu, C.; Zhu, Q.; Zhou, D. Multiple energy price distortions and improvement of potential energy consumption structure in the energy transition. Appl. Energy 2024, 362, 122992. [Google Scholar] [CrossRef]
  12. Babic, M. Green finance in the global energy transition: Actors, instruments, and politics. Energy Res. Soc. Sci. 2024, 111, 103482. [Google Scholar] [CrossRef]
  13. Hou, Y.; Yang, M.; Ma, Y.; Zhang, H. Study on city’s energy transition: Evidence from the establishment of the new energy demonstration cities in China. Energy 2024, 292, 130549. [Google Scholar] [CrossRef]
  14. Mugume, R.; Bulime, E.W. Delivering double wins: How can Africa’s finance deliver economic growth and renewable energy transition? Renew. Energy 2024, 224, 120165. [Google Scholar] [CrossRef]
  15. Ullah, S.; Niu, B.; Meo, M.S. Digital inclusion and environmental taxes: A dynamic duo for energy transition in green economies. Appl. Energy 2024, 361, 122911. [Google Scholar] [CrossRef]
  16. Wang, Q.; Zhang, F.; Li, R.; Sun, J. Does artificial intelligence facilitate the energy transition and curb carbon emissions? The role of trade openness. J. Clean. Prod. 2024, 447, 141298. [Google Scholar] [CrossRef]
  17. Ma, X.; Khan, M.N.; Awosusi, A.A.; Uzun, B.; Shamansurova, Z. Heterogeneous impact of green energy innovation on energy transition in the G7 nations: An aggregated and disintegrated analysis through advanced quantile approach. Int. J. Sustain. Dev. World Ecol. 2024, 31, 264–278. [Google Scholar] [CrossRef]
  18. Liang, C.; Zhu, L.; Zeng, J. Analysis on the impact of media attention on energy enterprises and energy transformation in China. Energy Rep. 2024, 11, 1820–1833. [Google Scholar] [CrossRef]
  19. Manowska, A.; Bluszcz, A.; Chomiak-Orsa, I.; Wowra, R. Towards Energy Transformation: A Case Study of EU Countries. Energies 2024, 17, 1778. [Google Scholar] [CrossRef]
  20. Tan, C.; Tan, Q.; Geng, S.; Tan, Z.; Xiao, J. Can virtual power plants promote energy transformation—Empirical test based on pilot provinces. Energy Rep. 2023, 9, 6135–6148. [Google Scholar] [CrossRef]
  21. Fang, G.; Chen, G.; Yang, K.; Yin, W.; Tian, L. Can green tax policy promote China’s energy transformation? A nonlinear analysis from production and consumption perspectives. Energy 2023, 269, 126818. [Google Scholar] [CrossRef]
  22. Wan, Y.; Sheng, N.; Wei, X.; Su, H. Study on the spatial spillover effect and path mechanism of green finance development on China’s energy structure transformation. J. Clean. Prod. 2023, 415, 137820. [Google Scholar] [CrossRef]
  23. Blimpo, M.P.; Dato, P.; Mukhaya, B.; Odarno, L. Climate change and economic development in Africa: A systematic review of energy transition modeling research. Energy Policy 2024, 187, 114044. [Google Scholar] [CrossRef]
  24. Wang, X.; Sun, X.; Ahmad, M.; Chen, J. Energy transition, ecological governance, globalization, and environmental sustainability: Insights from the top ten emitting countries. Energy 2024, 292, 130551. [Google Scholar] [CrossRef]
  25. Hassan, Q.; Algburi, S.; Jaszczur, M.; Al-Jiboory, A.K.; Al Musawi, T.J.; Ali, B.M.; Viktor, P.; Fodor, M.; Ahsan, M.; Salman, H.M. Hydrogen role in energy transition: A comparative review. Process Saf. Environ. Prot. 2024, 48, 100545. [Google Scholar] [CrossRef]
  26. Sarsar, L.; Echaoui, A. Empirical analysis of the economic complexity boost on the impact of energy transition on economic growth: A panel data study of 124 countries. Energy 2024, 294, 130712. [Google Scholar] [CrossRef]
  27. Nagendra, N.P.; Narayanamurthy, G.; Moser, R.; Singh, A. Open innovation using satellite imagery for initial site assessment of solar photovoltaic projects. IEEE Trans. Eng. Manag. 2020, 69, 338–350. [Google Scholar] [CrossRef]
  28. Li, Y.; Wu, J. Optimum integration of solar energy with battery energy storage systems. IEEE Trans. Eng. Manag. 2020, 69, 697–707. [Google Scholar] [CrossRef]
  29. Yu, L.; Qin, S.; Zhang, M.; Shen, C.; Jiang, T.; Guan, X. A review of deep reinforcement learning for smart building energy management. IEEE Internet Things 2021, 8, 12046–12063. [Google Scholar] [CrossRef]
  30. Ishaq, M.; Kwon, S. Short-term energy forecasting framework using an ensemble deep learning approach. IEEE Access 2021, 9, 94262–94271. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland
Previous
A Dynamic Tanks-in-Series Model for a High-Temperature PEM Fuel Cell
Next