Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 15, No. 2, 2024 

 

124 

Research on the Coupling Mechanism between Digital 
Economy Development and Household Energy 
Transition in China 

-- An Empirical Analysis Based on Provincial Panel Data 

Yi Zhang 1, *, Jiayue Chai 2, Haozhe Jin 1, Deyu Li 3 

1 School of Finance, Anhui University of Finance and Economics, Bengbu, Anhui 233030, China  

2School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui 233030, China 

3School of Economics, Anhui University of Finance and Economics, Bengbu, Anhui 233030, China 

* Corresponding author: Yi Zhang (Email: aufezhangyi@163.com) 

 

Abstract: In order to explore the relationship between the digital economy and China's household green energy structure and 
utilization efficiency, an empirical test is conducted based on the data of 31 Chinese provinces from 2015 to 2022 using a two-

way panel fixed-effects model as well as a coupling model. The results show that the digital economy has a significant positive 

effect on household green energy structure and utilization efficiency; secondly, this paper focuses on the bi-directional 

relationship between the two, and finds that the overall coupling situation is good, and the digitalization level and household 

green energy utilization efficiency in the regions under the "inverted triangle" pattern are highly coupled. The study finds that 

the overall coupling is good, and the digitalization level and household green energy utilization efficiency in the "inverted 

triangle" pattern are highly coupled. This study provides a reference for accelerating the national energy transition, boosting 

China's digital economy in the reverse direction, and providing green and digital kinetic energy for realizing the overall high-

quality development of the economy. 

Keywords: Digital economy, Green energy efficiency, Coupling mechanism. 

 

1. Introduction 

Against the background of the current global green 

economy, China is committed to promoting the construction 

of an ecological civilization and sustainable economic 

development. In view of the increasingly serious problem of 

global warming and the rising call for emission reduction, 

low-carbon development has become the common pursuit of 

the international community. As one of the important sources 

of carbon emissions, the energy consumption of households 

is gradually attracting the deep attention of the international 

community and the scientific community. 

According to statistics, China's total energy production in 

2020 will be 4.08 billion tons of standard coal, an increase of 

nearly 2.77% year-on-year, while consumption exceeds 

production, reaching 4.98 billion tons of standard coal, an 

increase of 2.16% year-on-year. For a long time, coal has been 

the main source of China's energy consumption, and the 

proportion of coal consumption in 2020 will be as high as 

56.8%. Although China has achieved the goal of prioritizing 

the development of electric power during the 13th Five-Year 

Plan period, it is still facing the challenges of irrational 

structure of energy production and consumption, weak 

foundation of new energy technology innovation, and low 

efficiency of energy utilization. 

The report of the twentieth CPC National Congress clearly 

pointed out that in-depth promotion of the energy revolution, 

accelerate the planning and construction of a new type of 

energy system for the new journey to promote the 

development of high quality energy has pointed out the 

direction. The "14th Five-Year Plan" of modern energy 

system clearly points out that by 2035, China's energy quality 

development will make decisive progress. Transformation of 

the energy sector is not only the key pivot point for realizing 

China's high-quality economic development, but also the core 

battlefield for reaching the goal of carbon peak and carbon 

neutrality. Therefore, accelerating the clean and low-carbon 

transformation of the energy sector and enhancing the energy 

efficiency of all green elements are crucial to solving the 

contradiction between energy supply and demand in China. 

Therefore, it is of great significance to study the 

development trend of digital economy, energy efficiency and 

structural optimization, and to explore the intrinsic 

connection between the level of development of digital 

economy and the green energy structure and utilization 

efficiency of Chinese households in order to improve energy 

efficiency, promote the transformation of Chinese 

households' energy to low carbon, and realize the high quality 

development of the society and economy as well as the goal 

of "dual-carbon". It is of great practical significance. 

2. Literature Review 

Tapscott, an American scholar, is the original proponent of 

the concept of digital economy [1], and Carlsson believes that 

the digital economy is a new type of economic form formed 

after the emergence of the Internet technology, emphasizing 

the in-depth application of the Internet [2]. With the evolution 

of the times, the connotation of digital economy has been 

continuously adjusted and enriched, although a unified 

standard definition has not yet been formed, but the 

connotation of digital economy proposed by the Hangzhou 

G20 Summit has been widely recognized in the academic 



 

125 

community. This paper adopts the definition that digital 

economy refers to economic activities relying on digital 

knowledge and information as the core production factors, 

relying on modern information networks as the key carriers, 

and relying on the effective use of information and 

communication technologies to promote the efficiency 

improvement and optimization of economic structure [3]. 

The current literature on the development of the digital 

economy on the current energy transition of Chinese 

households generally concludes that there are positive 

spillover effects of the digital economy on the current green 

energy structure and utilization efficiency of Chinese 

households [4]. Zhang Sanfeng et al. (2019) showed that the 

development of technology has enabled digitalization to bring 

positive effects on the high efficiency of green energy [5]. As 

an innovative production factor, the digital economy plays an 

important role in the manufacturing process mainly due to the 

use of technology and its integration. According to the theory 

proposed by Schumpeter, innovation means establishing a 

new production function. Wang Dongfang et al. (2019) 

applied this theory to the impact of digital economy on green 

energy efficiency, and argued that digital economy, as a kind 

of technological innovation and a production element with 

network characteristics, produces a multiplier effect in all 

aspects of production, transaction and consumption [6]. Zhou 

et al. (2016) further elaborated that the digital economy, with 

data as the main production element, can significantly reduce 

the excessive consumption of physical resources and energy 

in the traditional manufacturing process, accelerate the 

adjustment of the elemental structure, and enhance the 

efficiency of elemental use [7]. Along with the continuous 

progress of digital economy, the barriers to the flow of 

resources in different regions will be significantly lowered, 

which will promote the implementation of green 

technological innovation in each place to achieve the goal of 

low energy consumption and high quality development. In 

addition, the digital economy can also stimulate the passion 

of innovation in each region and promote the transformation 

of industrial structure towards high-end and rationalization. 

According to Han Pioneer et al. (2014), the development of 

production technology is the basic driving force for the 

improvement of manufacturing structure [8]. Digitalization 

has the characteristics of technological progress, and its 

application in production operation will lead to the 

reconfiguration of production resources and elements, 

optimize the production system and organizational structure, 

and improve the efficiency of resource allocation. At the same 

time, the popularization of digital economy will promote the 

optimization and upgrading of industrial structure in an all-

round way, and match the regional development level. In such 

an environment, digitalization will change the degree of 

economic dependence on energy resources and the efficiency 

of their use, improve the efficiency of resource allocation, and 

continue to enhance green energy efficiency [9]. 

The literature has analyzed the digital economy and energy 

structure in one direction, and lacks an in-depth examination 

of the relationship between the two. This project aims to 

redefine the digital economy under the OECD framework by 

constructing a coupling model and referring to the 2022 U.S. 

"Updated Digital Economy Measurement" report, which will 

provide a theoretical basis and policy recommendations for 

geographic areas with poor coupling, and at the same time 

make some contributions to the smooth development of the 

two-way transformation of China's digital economy and 

energy sector. 

3. Theoretical Mechanisms and 
Research Hypotheses 

3.1. The Single Mechanism of Action of the 
Digital Economy on Household Energy in 

China 

The digital economy has reshaped the structure of 

household energy use. In the past, household energy 

consumption relied mainly on traditional fossil energy 

sources, such as coal and oil. However, with the 

popularization and application of digital technology, more 

and more households are using renewable energy sources 

such as solar and wind power. This shift not only helps to 

reduce carbon emissions and alleviate environmental 

pollution, but also lowers the cost of household energy 

consumption. For example, many households have installed 

solar power systems, which convert light energy into 

electricity through solar panels, not only to meet the daily 

electricity needs of the household, but also to sell the excess 

electricity to the grid company, realizing a two-way flow of 

energy. Accordingly, the following assumptions are made: 

H1: There is a positive contribution of digitization to the 

optimization of the energy use structure of Chinese 

households. 

H2: There is a positive contribution of digitalization to the 

improvement of green energy use efficiency in Chinese 

households. 

3.2. Mechanisms of Coupling the Digital 
Economy and the Energy Structure of 

Chinese Households 

In the current context, the relationship between the digital 

economy and the energy mix has clearly demonstrated a non-

unidirectional correlation. With the optimization of household 

energy structure and the popularization of clean energy, it also 

promotes the development of digital economy. On the one 

hand, the widespread application of clean energy provides 

more stable and environmentally friendly energy support for 

the digital economy. On the other hand, the change in 

household energy structure has given rise to new business 

models and services. 

This two-way coupling and coordination mechanism plays 

an important role in promoting the high-quality development 

of the regional economy. On the one hand, the development 

of digital economy promotes the optimization of household 

energy structure, improves the efficiency of energy use and 

reduces environmental pollution, providing strong support for 

the sustainable development of the regional economy. On the 

other hand, the change of household energy structure also 

promotes the innovation and development of digital economy, 

and injects new vitality into the regional economy. 

Accordingly, the following assumptions are made: 

H3: There is a coupling effect between the digital economy 

and the efficiency and structure of green household energy 

use in China. 



 

126 

4. Indicator Construction and 
Research Methodology 

4.1. Indicator Construction and Data Sources 

4.1.1. Indicator Construction 
This paper draws on the research of Chen Siyi (2021) to 

analyze the energy consumption structure of the household 

sector by standardizing the calculation of the conversion 

factor, unifying it into the standard coal unit, and de-

quantifying it [10]. Using the data from the National Bureau 

of Statistics (NBS) and combining the Super-SBM and SDM 

methods, we accurately calculate the energy efficiency HGEE 

of each province in China.2 Finally, we choose the green 

energy efficiency (HGEE) of Chinese households as an 

evaluating index, and assess the level of its level in terms of 

the optimization of the structure of energy use (HGEE1) and 

the efficiency of energy use (HGEE2). 

The measurement of digitalization in this paper refers to the 

study of Yanru Li et al. (2023), which uses the improved 

entropy method to measure the scale of digital economy [11]. 

This paper focuses on digitization (DE) as the core 

explanatory variable, subdividing the indicators into three 

categories: industrial digitization, digital industrialization and 

digital infrastructure, and selecting 10 sub-indicators. 

The indicator data are standardized by using the method of 

polar deviation (Tian Jiali, 2022) [12], in order to eliminate 

the existence of the influence of the scale between each 

indicator. At the same time, in order to ensure the objectivity 

and referability of the results of the calculation of indicator 

weights, this paper uses the entropy weighting method to 

solve the weights (Cai Qiang, 2022; Sun Miaoying, 2022) [13, 

14], and the specific results are shown in Table 1.

 

Table 1. Digital Economy Measurement Indicator System 

Composite 

indicators 

Indicator 

category 
Indicator name unit of measure 

Indicator 

properties 

Level of 

development 

of the digital 

economy 

Industrial 

Digitizatio

n 

Provincial e-commerce turnover trillion dollars + 

Websites per 100 businesses 

classifier for individual things 

or people, general, catch-all 

classifier 

+ 

Number of enterprises with e-commerce trading 

activities 
ten + 

digital 

industriali

zation 

Revenue from software operations ten thousand dollars + 

Electronic information manufacturing growth rate % + 

Employment in the information and 

communications industry 
all the people + 

Proportion of fixed investment in ICT industry to 

total investment in society as a whole 
% + 

digital 

infrastruct

ure 

Provincial Internet penetration rate % + 

Number of IPV4 addresses ten dollars + 

Fiber optic line length kilometer + 

The improved entropy method was used for the 

determination of indicator weights: 

𝑊𝑖 =
𝑔𝑗

∑ 𝑔𝑗
𝑛
𝑗=1

                  (1) 

Among them:  

𝑝𝑖𝑗 =
𝑥𝑖𝑗

∑ 𝑥𝑖𝑗
𝑚
𝑖=1

                  (2) 

 

𝑒𝑗 =
1

ln 𝑚 ∑ (𝑝𝑖𝑗 ln 𝑝𝑖𝑗)
𝑚
𝑖=1

           (3) 

 

𝑔𝑗 = 1 − 𝑒𝑗                   (4) 

The final comprehensive assessment is measured: 

𝑠𝑖 = ∑ 𝑤𝑗𝑎𝑖𝑗
𝑛
𝑖=1                 (5) 

Among them, if the 𝑠𝑖 is larger, it indicates that the sample 
effect is better, the higher the digitization level of the province. 

In addition, the raw data of each index𝑎𝑗𝑗 In addition, the 

weights of the indicators were determined after their 

dimensionless processing by the extreme value method before 

measurement, and the dimensionless processing formula was 

as follows: 

𝑥𝑖𝑗 =
𝑎𝑖𝑗−𝑚𝑖𝑛(𝑎𝑖𝑗)

𝑚𝑎𝑥(𝑎𝑖𝑗)−𝑚𝑖𝑛(𝑎𝑖𝑗)
             (6) 

The weights of the three types of indicators obtained using 

the entropy method are as follows Table 2. The weights of the 

three types of indicators using entropy value method are 

shown in Table 2, and then weighted comprehensively to get 

the comprehensive score of provincial digitization level. 

 

 

 

 

 

 

 

 

 

 

 



 

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Table 2. Weights of the various indicators 

Indicator category Indicator name 
Indicator 

properties 

entropy 

(physics) 

coefficient of 

variation 
weights 

Industrial 

Digitization 

Provincial e-commerce turnover + 0.835 0.165 0.127 

Websites per 100 businesses + 0.933 0.167 0.051 

Number of enterprises with e-commerce 

trading activities 
+ 0.857 0.143 0.110 

digital 

industrialization 

Revenue from software operations + 0.876 0.124 0.095 

Electronic information manufacturing 

growth rate 
+ 0.854 0.146 0.112 

Employment in the information and 

communications industry 
+ 0.878 0.112 0.094 

Proportion of fixed investment in ICT 

industry to total investment in society as 

a whole 

+ 0.855 0.145 0.111 

digital 

infrastructure 

Provincial Internet penetration rate + 0.897 0.103 0.079 

Number of IPV4 addresses + 0.877 0.123 0.094 

Fiber optic line length + 0.835 0.165 0.079 

Considering the complexity of the factors affecting energy 

efficiency, this paper refers to Xia Li's study [15], and chooses 

the economic development level X1  urbanization level X2 
energy consumption structureX3  the degree of government 

interventionX4 and the degree of opening up to the outside 
worldX5 as control variables.

 

Table 3. Selection of partially relevant variables 

serial 

number 
variant descriptive 

control 

variable 

Level of economic development (X1 ) Annual real per capita GDP in provinces 

Level of urbanization (X2 ) Total urban population as a percentage of total population 

Energy consumption structure (X3 ) 
Renewable energy production as a percentage of total 

primary energy production 

Level of government intervention (X4 ) General fiscal expenditure as a percentage of GDP 

Degree of openness to the outside world (X5 ) Total exports and imports as a percentage of GDP 

4.1.2. Sample Selection and Data Sources 
This paper considers the feasibility of the study and the 

availability and timeliness of the data, and chooses 31 

provinces and autonomous regions in China as the research 

object. The year 2015 is chosen as the starting year because 

the popularization of smartphones and other mobile terminals 

has laid the foundation for the digital economy since 2015. 

Therefore, taking 2015 as the starting point, the impact of the 

digitalization process on the economy and society can be 

more comprehensively and accurately analyzed. 

In this paper, the green energy efficiency panel data of 

Chinese provinces in 2015-2022 is used as a research sample, 

and the data is obtained from the Gross national product by 

province and household data by province from the China 

Statistical Yearbook (2015-2022). Total energy inputs by 

province from China Energy Statistics Yearbook (2015-2022). 

Labor input and capital stock by province are from China 

Urban Statistical Yearbook (2015-2022) and provincial 

statistical yearbooks. Data on provincial emissions of 

wastewater, SO2 and other undesired outputs are from the 

China Environmental Statistics Yearbook and the China 

Energy Statistics Yearbook (2015-2022). 

4.2. Research methodology 

4.2.1. Baseline Regression Model 
In order to examine the impact of digitalization on green 

energy efficiency of Chinese households, the following 

benchmark regression model is constructed: 

𝐻𝐺𝐸𝐸𝑖𝑡 = 𝛼1 + 𝛼2𝐷𝐸𝑖𝑡 + 𝛼3𝐶𝑜𝑛𝑡𝑖𝑡 + 𝜇𝑖 + 𝜎𝑡 + 𝜀𝑖𝑡  (7) 

Where: i, t represent province and year subscripts, 

respectively. The explanatory variables are the green energy 

efficiency of households in each province ( 𝐹𝐺𝐸𝐸𝑖𝑡 ). The 
digitization level of each province ( 𝐷𝐸𝑖𝑡 ) is the core 
explanatory variable. cont is the control variable: containing 

the level of economic development 𝑥1 urbanization level 𝑥2 
energy consumption structure 𝑥3  government intervention 
level 𝑥4 the degree of government intervention, the degree of 
openness to the outside world, and the level of economic 

development, urbanization, energy consumption structure 𝑥5 
Cont is the control variable. 𝜇𝑖 is individual fixed effects. 𝜎𝑡 
represents year fixed effect. 𝜀𝑖𝑡 represents the random error 
term. 

4.2.2. Coupled Models 
Since the data outlines and units of the indicators 

measuring the level of development of digital economy and 

green energy efficiency of Chinese households are different, 

in order to increase the scientificity of the calculation, this 

paper programmatizes the data, determines the weights of 

each indicator by using principal component analysis, 

calculates the index of the energy consumption situation and 

the level of digital development, and analyzes the coupled 

degree of coordination between Chinese households' energy 

consumption and the degree of coordination of the high-

quality development of the digital economy. 

The coupling coordination evaluation index system is 

established, the positive and negative indicators are 

standardized by using the extreme difference method, and the 

entropy weighting method is used to objectively assign 

weights to each indicator. After calculating the weights of 

each index, linear weighting is carried out to calculate the 

comprehensive index of the corresponding system. 



 

128 

In order to study the coupling relationship between green 

energy efficiency of Chinese households and the degree of 

coordination of high-quality development of digital economy, 

the following coupling degree model is established: 

C = 2 ∗ √(U1 ∗ U2)(U1 ∗ U2)
2          (8) 

 

T = a ∗ U1 + b ∗ U2               (9) 

The evaluation criteria of coupling degree C are as follows: 

C=0 no coupling, 0<C≤0.3 for low level coupling, 0.3<C≤0.5 

for antagonistic coupling, 0.5<C≤0.8 for friction coupling, 

0.8<C≤1 for high level coupling, C=1 for complete coupling. 

Where, U1, U2 is the calculated system composite index 

indicating the green energy efficiency of Chinese households 

and digital development, C represents the coupling degree of 

the degree of coordination between the green energy 

efficiency of Chinese households and the high-quality 

development of the digital economy, C ∈ [0, 1], the larger 
the value of C, the stronger the degree of interconnection 

between energy consumption and the development of the 

digital economy. The degree of coupling between green 

energy efficiency of Chinese households and high-quality 

development of digital economy in China's provincial areas is 

calculated by the coupling degree model. 

T reflects the coupling contribution of each subsystem to 

the composite system. a, b reflect the weights of the first-level 

indicators corresponding to each subsystem, a+b=1, in this 

paper, we approximate that the two systems are equally 

important, so a, b are taken as 0.5. 

5. Empirical Analysis 

5.1. Impact of Digitization on Provincial 
Household Energy Efficiency and 

Consumption Structure 

Using OLS regression and two-way fixed effect model to 

study the impact of digitization on provincial household 

energy consumption structure and energy use efficiency, the 

significance and goodness of fit of the two-way fixed effect 

model is better than OLS regression, so the two-way fixed 

effect model is used to analyze the impact of industrial 

digitization on provincial household energy structure and 

energy use efficiency, and the results of the study are as 

follows table 4. Model (1) shows that at the 5% significance 

level, digitalization has a significant positive effect on the 

improvement of provincial household energy use efficiency, 

and every 1% increase in the degree of digitization will 

increase energy use efficiency by 0.605%. Digitization 

effectively reduces the cost, providing a digital basis and 

guarantee for the growth of green energy use scale, 

assumption H1 is valid. Model (3) shows that, at the 1% 

significance level, digitalization has a significant positive 

effect on the optimization of the provincial household energy 

structure, digitalization of every 1% increase, the degree of 

optimization will be up 1.063%. Digitization through the 

digital transformation and upgrading of the energy-related 

industrial chain, effectively improve the efficiency of energy 

use and the degree of utilization of scientific and 

technological resources, and promote the overall efficiency of 

energy use, assumption H2 holds. 

 

Table 4. Impact of digitization on the structure and efficiency of household energy consumption in provincial areas 

mould (1) (2) (3) (4) 

explanatory variable HGEE1 HGEE2 HGEE1 HGEE2 

DE 1.043*** 2.764** 0.601** 0.401* 

lnX1 2.828** 2.929*** 0.101** 0.104** 

X2 1.210** 1.567** 0.093** 0.026** 

X3 3.584** 2.603** 0.260*** 0.002 

lnX4 3.273** 1.2138* 0.605* 0.403* 

Individual fixation be clogged be clogged 

Year fixed be clogged be clogged 

𝑹𝟐 0.913 0.823 0.921 0.801 

5.2. Research on the Degree of Harmonization 
between Green Energy Efficiency of 

Chinese Households and High-Quality 

Development of the Digital Economy 

Based On the Coupling Approach 

In order to explore the spatial distribution characteristics of 

the coupling degree and coupling between household green 

energy efficiency and the high-quality development of digital 

economy at the provincial scale, the coupling index scores of 

each province in China (excluding Hong Kong, Macao and 

Taiwan) were calculated, and the results are shown in Figure 

1: 

 



 

129 

 
Figure 1. Visualization of the coupling degree and its evolutionary trend 

 

(1) In 2015, the highly coupled region showed a clear 

distribution of the "inverted triangle" structure of "Coastal + 

Yangtze River Delta Economic Belt", indicating that the 

degree of regional digitization of the southern coastal 

provinces and the energy consumption structure of the higher 

degree of interaction between. 

(2) In 2017, the "inverted triangle" pattern is still clear, and 

the digitalization level and energy consumption structure of 

the regions under the "inverted triangle" pattern are highly 

coupled. Other regions, except Shanxi, Ningxia and Qinghai, 

have eliminated the uncoupled state, and the overall coupling 

degree has been improved. 

(3) 2017-2019 highly coupled areas from the "inverted 

triangle" to the development of centralized and continuous 

layout, coupling degree distribution differences between the 

east and west began to highlight the pattern. The coupling 

situation in the eastern region is significantly better than that 

in the west, but there are depressions in Shanxi and Jiangxi. 

(4) In 2019, there is a new change in the highly coupled 

region: the coupling phenomenon, which was originally 

mainly concentrated in the eastern region, has begun to spread 

to the western region. Both in the east and in the west, the 

interaction between innovation capacity and energy 

consumption structure is deepening. 

The above spatial pattern of change indicates that: the 

coupling of regional digital economy level and energy use 

structure has gradually deepened with the improvement of 

digitalization level in recent years, and the breadth along the 

"inverted triangle" structure of Yangtze River Delta 

Economic Belt has radiated from highly coupled areas to 

uncoupled areas. The areas along the Yangtze River Delta 

Economic Belt are highly coupled and concentrated in 

regional innovation capacity and energy utilization efficiency. 

In addition, the positive supporting effect of the improvement 

of the energy utilization level of the whole society on the 

further improvement of the digitization level is also 

strengthened over time, and hypothesis H3 is verified. 

5.3. Grey forecasting Model to Predict the 
Future of Energy Digitization 

In this paper, the collected data on household energy use 

efficiency and the level of development of digital economy in 

China's provinces are used to make gray model predictions, 

and the predictions of energy use efficiency are shown in 

Table 5 below: 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5



 

130 

Table 5. Projections of Household Energy Utilization Efficiency in China's Provincial Areas 

area 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 

Beijing 1.014 1.062 1.112 1.165 1.22 1.278 1.338 1.401 

Tianjin 1.078 1.058 1.039 1.021 1.002 0.984 0.967 0.949 

Hebei 0.948 1.036 1.128 1.225 1.326 1.431 1.541 1.657 

Shanxi 0.338 0.343 0.348 0.353 0.358 0.363 0.368 0.374 

Nei Monggol 0.393 0.388 0.383 0.379 0.374 0.37 0.365 0.361 

Liaoning 0.542 0.533 0.524 0.516 0.508 0.499 0.491 0.484 

Jilin 0.575 0.57 0.565 0.561 0.556 0.552 0.547 0.542 

Heilongjiang 0.799 0.81 0.822 0.834 0.846 0.859 0.871 0.884 

Shanghai 1.175 1.18 1.186 1.192 1.198 1.204 1.21 1.216 

Jiangsu 1.072 1.064 1.057 1.049 1.042 1.034 1.027 1.02 

Zhejiang 0.673 0.66 0.647 0.634 0.621 0.609 0.597 0.585 

Anhui 0.633 0.602 0.573 0.545 0.519 0.493 0.469 0.446 

Fujian 0.964 0.955 0.946 0.937 0.929 0.92 0.911 0.903 

Jiangxi 0.6 0.605 0.61 0.615 0.621 0.626 0.631 0.636 

Shandong 0.553 0.511 0.473 0.437 0.404 0.374 0.346 0.32 

Henan 0.517 0.522 0.527 0.531 0.536 0.541 0.546 0.551 

Hubei 0.742 0.745 0.749 0.753 0.756 0.76 0.764 0.768 

Hunan 0.607 0.614 0.621 0.628 0.635 0.642 0.65 0.657 

Guangdong 1.01 1.011 1.012 1.012 1.013 1.014 1.015 1.016 

Guangxi 0.426 0.422 0.418 0.414 0.41 0.407 0.403 0.399 

Hainan 1.475 1.448 1.421 1.395 1.37 1.345 1.321 1.297 

Chongqing 0.579 0.578 0.577 0.575 0.574 0.573 0.572 0.571 

Sichuan 0.578 0.58 0.582 0.583 0.585 0.587 0.588 0.59 

Guizhou 0.333 0.329 0.324 0.32 0.316 0.311 0.307 0.303 

Yunnan 0.342 0.335 0.328 0.322 0.315 0.309 0.303 0.297 

Xizang 1.429 1.459 1.49 1.521 1.553 1.586 1.619 1.653 

Shaanxi  0.363 0.357 0.351 0.345 0.34 0.334 0.328 0.323 

Gansu 0.523 0.525 0.527 0.528 0.53 0.532 0.534 0.535 

Qinghai 1.399 1.422 1.445 1.468 1.492 1.516 1.541 1.566 

Ningxia 0.472 0.45 0.428 0.408 0.388 0.37 0.352 0.335 

Xinjiang 0.287 0.274 0.262 0.251 0.24 0.23 0.22 0.21 

Based on the data, projections show that Hainan's energy 

efficiency will decline in the future. Comparatively speaking, 

the green energy efficiency of Shanxi and Xinjiang is lower, 

but the energy utilization efficiency of Shanxi is gradually 

increasing, while Xinjiang is on a downward trend. Analyzing 

the magnitude of changes in energy efficiency, this paper 

finds that Beijing, Hebei, Anhui, Shandong and Hainan all 

show large fluctuations. 

In the future, up to 2030, projections show that Hebei, Tibet 

and Qinghai are expected to outperform other provinces in 

terms of energy utilization efficiency, demonstrating the 

potential for energy transition development in these regions. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 

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Table 6. Forecast of China's provincial digital economy development level 

area 2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E 

Beijing 0.617 0.691 0.775 0.869 0.975 1.093 1.226 1.374 

Tianjin 0.102 0.105 0.109 0.113 0.117 0.121 0.125 0.13 

Hebei 0.095 0.096 0.096 0.096 0.097 0.097 0.097 0.098 

Shanxi 0.08 0.086 0.091 0.097 0.104 0.111 0.118 0.126 

Nei Monggol 0.047 0.048 0.049 0.05 0.05 0.051 0.052 0.053 

Liaoning 0.103 0.103 0.102 0.102 0.102 0.102 0.102 0.102 

Jilin 0.032 0.03 0.028 0.026 0.025 0.023 0.022 0.02 

Heilongjiang 0.034 0.033 0.031 0.029 0.028 0.026 0.025 0.024 

Shanghai 0.508 0.533 0.558 0.585 0.613 0.643 0.674 0.706 

Jiangsu 0.502 0.528 0.556 0.584 0.615 0.647 0.68 0.716 

Zhejiang 0.426 0.461 0.5 0.542 0.587 0.636 0.69 0.747 

Anhui 0.253 0.251 0.25 0.248 0.246 0.245 0.243 0.241 

Fujian 0.232 0.253 0.276 0.301 0.328 0.357 0.39 0.425 

Jiangxi 0.201 0.219 0.238 0.257 0.276 0.296 0.315 0.336 

Shandong 0.279 0.281 0.283 0.284 0.286 0.288 0.289 0.291 

Henan 0.172 0.178 0.184 0.189 0.196 0.202 0.208 0.215 

Hubei 0.203 0.216 0.229 0.243 0.258 0.274 0.291 0.309 

Hunan 0.157 0.163 0.17 0.177 0.184 0.191 0.199 0.207 

Guangdong 1.139 1.287 1.454 1.642 1.855 2.096 2.368 2.675 

Guangxi 0.104 0.112 0.122 0.132 0.144 0.156 0.169 0.184 

Hainan 0.024 0.024 0.024 0.025 0.025 0.025 0.025 0.025 

Chongqing 0.135 0.142 0.149 0.157 0.165 0.173 0.182 0.191 

Sichuan 0.253 0.273 0.295 0.319 0.345 0.373 0.403 0.436 

Guizhou 0.08 0.086 0.091 0.097 0.104 0.111 0.118 0.126 

Yunnan 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.062 

Xizang 1.433 1.464 1.496 1.528 1.561 1.595 1.629 1.664 

Shaanxi  0.156 0.172 0.19 0.209 0.23 0.253 0.279 0.307 

Gansu 0.034 0.034 0.034 0.034 0.034 0.035 0.035 0.035 

Qinghai 0.009 0.008 0.008 0.007 0.006 0.006 0.005 0.005 

Ningxia 0.02 0.021 0.021 0.022 0.023 0.023 0.024 0.025 

Xinjiang 0.091 0.097 0.104 0.111 0.117 0.124 0.131 0.138 

After analyzing Table 6, it can be clearly seen that China's 

provinces generally show positive growth in the process of 

digital economy development. Among them, the digital 

economy of Guangdong Province is particularly eye-catching, 

not only showing a significant upward trend, but also growing 

at an extremely rapid rate. This indicates that Guangdong 

Province is expected to be one of the fastest growing 

provinces in terms of digitalization in the future, and may 

even surpass other provinces to become a leader in the 

digitalization field. Meanwhile, Beijing is also showing 

impressive speed in its digitization development. 

5.4. Robustness Tests 

In order to ensure the accuracy of the research results, this 

paper, in view of the research of other scholars to take the 

following methods for the robustness test[16]: first, the 

interpreted variables for the extreme value processing, 

eliminating the minimum and maximum values, and then use 

the same empirical method to re-test; second, replace the 

model, taking into account the household energy structure and 

the impact of the efficiency of the use of the level of coupling 

and coordination degree of the digital economy with a certain 

degree of lag, and therefore take the data of a lagged period 

substituted for the re-test. Secondly, replace the model, 

considering that the impact of household energy structure and 

utilization efficiency and the coupling coordination degree of 

digital economy level has a certain lag, so take the data of the 

lagged period and substitute it into the re-examination. 

 
Figure 2. ADF test 

The unit root test (ADF test) was used to test the 

smoothness reversibility of the digital economy and the green 

energy utilization efficiency of Chinese householdsin 31 

provinces and cities in China from 2015 to 2022, and the 

conclusions obtained were consistent with the results of the 

baseline regression and had smoothness. The results are 

shown in Figure 2. 



 

132 

6. Conclusions and Suggestions 

6.1. Conclusions 

In this paper, the entropy weight method is used to 

construct indices for the digital economy and the green energy 

utilization efficiency and structure of Chinese households. 

Firstly, a two-way fixed-effects model is established to 

empirically analyze and judge the nonlinear influence 

characteristics and one-way influence mechanism of the two 

systems; secondly, a quantitative prediction is made on the 

two-way interaction linkage of the two systems through the 

coupling model combined with the grey prediction. The 

following conclusions are drawn: 

(1)The digital economy has a significant positive 

contribution to the optimization of the provincial household 

energy mix. Using a two-way fixed-effects model, it was 

found that digitization could significantly improve energy 

efficiency, and for every 1 per cent increase in the level of the 

digital economy, energy efficiency would increase by 0.605 

per cent. In addition, the digital economy can also 

significantly promote the optimization of the energy structure, 

and every 1% increase in the level of digitization will result 

in a 1.063% increase in the degree of optimization. 

(2) Predicted future energy consumption in some areas of 

serious problems, digital monitoring demand is prominent. 

Household energy efficiency in Hainan province is 

performing well, but is likely to decline in the future as it faces 

stricter regulations on energy reduction. In Shanxi, household 

energy efficiency is improving, while in Xinjiang it is 

declining. Household energy consumption efficiency 

fluctuates widely in Beijing, Hebei, Anhui, Shandong and 

Hainan, with Beijing and Hebei on the rise and Anhui, 

Shandong and Hainan on the decline. 

(3) High coupling accuracy between household energy 

utilization efficiency and high-quality development of digital 

economy in China. Examining the relationship between 

household energy consumption and the digital economy in 

China's provinces (excluding Hong Kong, Macao and 

Taiwan), we find that the coupling between the improvement 

of digitalization level and the use of energy efficiency has 

deepened. The Yangtze River Economic Zone is particularly 

obvious, showing an "inverted triangle" structure, with highly 

coupled areas gradually radiating to uncoupled areas. 

6.2. Suggestions 

After an in-depth study of the digital economy in the field 

of environment and energy development in China, we put 

forward the following suggestions, aiming to give full play to 

the potential of the digital economy and realize a win-win 

situation in terms of the improvement of energy efficiency 

and environmental protection. 

(1) We must deeply understand the double impact of the 

digital economy on the environment and energy development. 

On the one hand, the digital economy promotes the 

improvement of energy efficiency; on the other hand, it also 

brings new energy consumption and environmental pollution 

problems. Therefore, we need to comprehensively analyze the 

mechanism of the digital economy in promoting energy 

efficiency and environmental protection, in order to seek a 

balance between the growth of energy consumption and the 

green transformation of energy. For example, we can enhance 

the application of digital technologies such as smart grids and 

clean energy to improve energy utilization efficiency and 

reduce energy waste and pollution emissions. 

(2) Strengthening the infrastructure of digital economy 

development is an urgent task. China's investment in digital 

equipment still needs to be upgraded, which limits the in-

depth application of the digital economy in the energy sector. 

Therefore, we need to increase investment to improve the 

digital infrastructure, including high-speed networks, big data 

centers, cloud computing platforms, etc., in order to promote 

the comprehensive development of digital economy across 

the country. This will provide new impetus for the 

improvement of China's energy efficiency and help optimize 

the energy structure and green transformation. 

(3) Improve the top-level design of the digital economy and 

promote the balanced development of the digital economy in 

all provinces. The development of digital economy can help 

build a long-term mechanism for energy conservation and 

emission reduction, and promote the green development of 

energy. Local governments should strengthen the policy 

guidance of promoting energy conservation and emission 

reduction through the digital economy, especially for 

provinces with high energy dependence, and formulate more 

targeted policies to promote the transformation of their energy 

efficiency. At the same time, by strengthening the 

popularization of the digital economy in various regions, the 

development dividend of the digital economy can be further 

utilized to enhance energy efficiency and realize green and 

sustainable development. 

In summary, the digital economy, as a new engine for 

China's economic development, is of great significance in 

promoting energy efficiency and environmental protection. 

We need to comprehensively recognize the dual impacts of 

the digital economy, strengthen infrastructure construction, 

improve top-level design, protect intellectual property rights 

and personal information security, and formulate appropriate 

environmental regulations and related policies to promote the 

coordinated development of the digital economy with energy 

and the environment, so as to achieve the goal of green and 

sustainable development. 

Acknowledgments 

This work is funded by the Undergraduate Research 

Innovation Fund Program of Anhui University of Finance and 

Economics (No. XSKY24007ZD). 

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