DOI: 10.3303/CET24113011 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 21 April 2024; Revised: 2 August 2024; Accepted: 21 October 2024 
Please cite this article as: Shen N., Zhang L., 2024, How Does Industrial Agglomeration Affect Total Factor Energy Efficiency in China? 
Perspectives on the Collision of Digital and Green, Chemical Engineering Transactions, 113, 61-66  DOI:10.3303/CET24113011 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 113, 2024 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Jeng Shiun Lim, Bohong Wang, Guo Ren Mong, Petar S. Varbanov 

Copyright © 2024, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-14-4; ISSN 2283-9216 

How Does Industrial Agglomeration Affect Total Factor 

Energy Efficiency in China? Perspectives on the Collision of 

Digital and Green 

Neng Shen, Lin Zhang* 

School of Economics and Management, Fuzhou University, Fuzhou, 350108, PR China 

zhanglin_daniel@163.com 

Industrial agglomeration is an important way to affect total factor energy efficiency (TFEE), and digitalization 

level (DL) and green technology innovation (GTI) can help improve the effect of industrial agglomeration. This 

research examines the impact of specialized industrial agglomeration (SIA) and diversified industrial 

agglomeration (DIA), the two main forms of industrial agglomeration, on TFEE under the mediating role of GTI 

and the moderating role of DL. Three main results were obtained based on the China's provincial data from 

2010 to 2021. (1) Both SIA and DIA can significantly promote TFEE, but their effects are different. (2) GTI plays 

a mediating role in the positive promotion effect of DIA and SIA on TFEE. In addition, GTI not only has obvious 

inertial effects in time, but also has significant spillover effects in space. (3) DL not only strengthens the impact 

of SIA or DIA on TFEE, but also enhances the impact of SIA or DIA on GTI. The results show that DL and GTI 

are effective ways for industrial agglomeration to build a sustainable economy. The article finally puts forward 

policy recommendations, emphasizing accelerating digital transformation, strengthening industrial 

agglomeration planning, promoting green technology innovation, and adopting a multi-path development model. 

1. Introduction 

China is one of the largest coal consumers and carbon dioxide emitters, it is very important to ensure the 

sustainable growth of economy. In the past few decades, China has relied on resource consumption to promote 

economic growth, especially traditional energy such as coal. Therefore, the key to solving the above problems 

lies in improving energy efficiency.  Industrial agglomeration areas have become an important engine for China's 

rapid economic growth by relying on the concentrated production of enterprises in geographical space. At the 

same time, they have inevitably become a source of environmental pollution due to the concentrated emission 

of greenhouse gases such as carbon (Lu and Jiao, 2018). Industrial agglomeration can provide the geographic 

basis for technology diffusion and knowledge spillovers among firms in the agglomeration area, thereby 

improving overall energy use efficiency. On the contrary, industrial agglomeration can also trigger environmental 

problems such as production expansion and pollution concentration, which will accelerate energy consumption. 

At present, industrial agglomeration is mainly divided into SIA and DIA, whose different modes will also bring 

different results. However, existing studies have ignored the heterogeneous ways of industrial agglomeration. 

This research trying to responses the following questions. What would be the same trends and different 

characteristics of SIA or DIA impacts on TFEE? Especially from the perspective of spatial spillover, what 

characteristics will these impacts present? At the same time, in response to climate challenges, countries have 

encouraged the development of GTI to promote the green and low-carbon development of local industries. 

Therefore, does GTI act as a mediating variable in the impact of SIA or DIA on TFEE? 

In addition, as an important driving force for social change and an important carrier of technological change, the 

digital economy have an important impact on industrial agglomeration areas (Yan et al., 2023). The digitalization 

will improve the production methods and resource acquisition channels of enterprises in the cluster area. 

Therefore, this article further explores the following two questions: Can increased digitization play a moderating 

role in the mechanism of industrial agglomeration's impact on TFEE? At the same time, can the digitalization 

help firms in the agglomeration area to better apply green technological innovations and thus increase TFEE? 

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2. Literature review and research hypothesis  

2.1 The direct path of SIA and DIA to influence TFEE 

Industrial agglomeration will inevitably bring about changes in production methods and pollution emissions, 

thereby leading to changes in energy efficiency. Industrial agglomeration can bring about the agglomeration of 

labor, production linkages between enterprises in the agglomeration area, and knowledge spillovers (Wang and 

Wang, 2019). On the contrary, industrial agglomeration will also lead to the concentration of production within a 

certain geographic area, and the growth of scale within the limited scope of the agglomeration area is prone to 

produce a congestion effect, which will make the industrial agglomeration area become a larger source of 

pollution (Hao et al., 2022). However, it is still important to point out that despite the existing literature on 

industrial agglomeration and TFEE, most of these studies have ignored the fact that differences in the mode of 

industrial agglomeration may also give rise to differences in agglomeration effects. The results based on SIA 

and DIA may be different due to the differences in industries and production behaviors engaged by firms in the 

agglomeration area. Therefore, this article proposes the first hypothesis. 

Hypothesis 1: Both SIA and DIA have a strong connection to TFEE, and both of these connections are positive. 

2.2 The indirect path from GTI 

Due to the significant role of GTI in long-term sustainable growth (Dory, 2023), industrial agglomeration plays a 

crucial role in driving GTI. On the one hand, supporters of SIA believe that SIA can generate homogeneous 

knowledge, promote the absorption and dissemination of homogeneous knowledge by enterprises in the region, 

and thus drive regional industrial innovation (Mariotti et al., 2010). On the other hand, supporters of DIA believe 

that DIA can lead to diversified and heterogeneous knowledge output. Through cross-industry knowledge 

exchange, enterprises in the agglomeration area can use complementarity to improve innovation benefits (Shen 

et al., 2018). Furthermore, Xu and Zhang (2023) found that manufacturing agglomeration has a substantial 

influence on improving GTI’s efficiency. The hypothesis 2 is proposed.  

Hypothesis 2: GTI can play an important mediating role in the influence of SIA and DIA on TFEE, both SIA and 

DIA can improve TFEE through GTI. 

2.3 The moderating role of DL 

As an emerging form of information technology, digital economy can break the limitations of the space of 

agglomeration area, integrate physical resources and information resources (Zhong et al., 2024). In addition, 

Dian et al. (2024) emphasized that the digital economy can change the access to information and the way 

companies manage their production, which can significantly change the perception and acceptance of green 

innovation in the pursuit of green transformation. The advancement of the Internet and related innovative 

applications have had a significant impact on energy conservation and pollution avoidance. Furthermore, 

positive media coverage can significantly promote green innovation, which has become crucial for green 

production in the digital era. Therefore, this article proposes the following hypothesis. 

Hypothesis 3: DL not only strengthen the impact of SIA and DIA on TFEE, also enhance the impact of SIA and 

DIA on GTI. 

3. Methodology and Research Design 

3.1 Basic model estimation of SIA and DIA 

This paper constructed the Eq (1) and (2) following equation to explores the impact of SIA and DIA on TFEE. 

𝑇𝐹𝐸𝐸𝑖𝑡 = 𝛼0 + 𝛼1𝑆𝐼𝐴𝑖𝑡 + 𝛼2𝑋𝑖𝑡 + 𝜇𝑖 + 𝜈𝑡 + 𝜀𝑖𝑡  (1) 

𝑇𝐹𝐸𝐸𝑖𝑡 = 𝛼0 + 𝛼1𝐷𝐼𝐴𝑖𝑡 + 𝛼2𝑋𝑖𝑡 + 𝜇𝑖 + 𝜈𝑡 + 𝜀𝑖𝑡   (2) 

where i represents province, and t represents year. Xit represents the control variables.  

3.2 Measurement of variables 

Explained variable: TFEE. (1) Labor input, expressed by the stock of human capital. Capital input, indicated as 

the fixed capital stock, which is calculated by the perpetual inventory method. Energy input, expressed by 

converting energy consumption in production and life, such as coal, natural gas and petroleum, into standard 

coal (tons of standard coal). (2) Desirable output. Includes economic and environmental benefits. The economic 

benefits employed the nominal GDP of each province, and the percentage of forest cover is used to represent 

the environmental benefits. (3) Undesirable output. In this study, the standardized indices of the four pollutants 

are used to measure the undesirable outputs through the index weight entropy method. This article measures 

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TFEE in China based on a super SBM model with undesirable output (Zhao et al., 2020), and the sample is 

drawn from the data of 30 provinces in China (excluding Taiwan, Hong Kong, Macao, and Tibet due to large 

data gaps) from 2010 to 2021.The spatial and temporal distribution of TFEE of China were shown in the Fig. 1. 

 
Figure 1: The TFEE of China at (a) 2010, (b) 2014, (c) 2018 and (d) 2021 

Core explanatory variable: SIA and DIA. In this study, the Herfindahl-Hirschman Index is utilized to fully 

characterize the SIA and DIA, and it can be drawing from Peng et al., (2023). 

Mediating variable: GTI. The GTI is selected referring the research of Liu et al., (2021). Those patents for 

inventions and utility models that have been granted are counted and assigned to different provinces and years 

by using known information about the inventor's place of residence and the date of patent publication. 

Moderating variable: DL. Referring the research of Zhang et al., (2022), this article measures digitalization level 

from the industrial digitalization and digital industrialization. 

Control variables. (1) Energy structure (ES): calculated as energy consumption divided by overall energy 

consumption. (2) Urbanization level (UL): expressed as the urban population divided by the total population. (3) 

R&D investment (RD): the proportion of the regional annual R&D investment to GDP. (4) Trade openness (OP): 

foreign direct investment divided by GDP. (5) Market competition (COM): the percentage of the industrial output 

value of non state-owned enterprises in total industrial output. (6) Industrial structure (IS): the tertiary industry's 

share of GDP. The above data all originate in the China Statistical Yearbook, Chinese Energy Statistics 

Yearbook and provincial statistical yearbooks and so on. 

4. Results analysis and discussion 

4.1 Basic analysis of SIA or DIA and TFEE 

Table 1 shows the baseline regression results. Among them, Model 1~Model 3 are the analysis results based 

on ordinary least squares (OLS), random effects (RE) and fixed effects (FE) respectively. At the same time, this 

study further tests the impact of SIA and DIA employed the generalized method of moments (GMM) and dynamic 

GMM, and results are shown in Model 4~5. This paper finds the time inertia and snowball effect of TFEE are 

established. And all five models above tentatively show that SIA or DIA can effectively enhance TFEE, and the 

effect is different. 

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Table 1: The results of basic model estimation of SIA and DIA 

Variables 
Model 1 Model 2 Model 3 Model 4 Model 5 

OLS FE RE SYS-GMM SYS-GMM 

TFEEt-1     0.737*** 

SIA 0.112** 0.120** 0.224*** 0.181* 0.165** 

DIA 0.175** 0.154* 0.103** 0.142** 0.187** 

ES -0.204** -0.261** -0.199* -0.210* -0.168* 

UL -0.131* -0.108** -0.117* -0.196* -0.158* 

UL2 0.093* 0.101* 0.094** 0.105* 0.081** 

RD 0.183** 0.099*** 0.108* 0.117** 0.167* 

OP -0.087 -0.075* -0.058 -0.052** -0.069* 

OP2 0.091* 0.088* 0.107* 0.133 0.096* 

COM 0.050 0.033* 0.047 0.045 0.061* 

IS 0.146* 0.117** 0.189** 0.203* 0.197** 

Constant 0.388** 0.406* 0.310* 0.501** 0.523*** 

F/wald-test 46.630** 55.489*** 60.847** 112.362*** 208.464*** 

R-squared 0.502 0.661 0.573   

AR(2)[P]    1.223[0.133] 1.201[0.146] 

Hansen[P]    20.354[0.299] 18.377[0.314] 

Observations 360 360 360 360 330 

Notes: ***, **, * indicates significance at the 1%, 5%, 10%, respectively. The following table is the same. 

4.2 Analysis of the spatial effect of SIA and DIA 

This research employs the spatial weight matrix and the Moran’I index to verify the spatial correlation of TFEE, 

SIA, DIA, GTI and DL. Table 2 demonstrates the regression results from the spatial perspective. A series results 

show the SDM model is fit. Model 1~2 show the regression of SIA and DIA respectively. Specifically, the 

coefficient of SIA on TFEE is 0.108, and the DIA is 0.166. The estimated coefficients of the spatial terms of SIA 

and DIA is 0.165 and 0.180 respectively. This shows that the TFEE in this region will be affected by the SIA and 

DIA in adjacent provinces. In addition, the coefficients of spatial terms of TFEE were 0.261 and 0.196 

respectively. This shows that the TFEE in this province will also be affected by the TFEE in neighboring 

provinces, and the demonstration effect and synergy effect are significant. 

Table 2: The results of spatial spillover effect 

Variables 
EV=TFEE EV=TFEE 

Model 1 Model 2 

TFEEt-1 0.877*** 0.806*** 

SIA 0.108***  

DIA  0.166** 

W*TFEE 0.261*** 0.308*** 

W*SIA 0.165***  

W*DIA  0.180** 

Constant 0.216** 0.196*** 

LM test lag 100.91** 116.27* 

LM test error 86.36** 79.10** 

LR test for SAR 25.33*** 27.16*** 

LR test for SEM 19.28*** 17.30*** 

Wald-test 1032.081** 1181.762*** 

AR(2)[P] 1.23[0.228] 1.38[0.301] 

Hansen[P] 18.34[0.668] 15.72[0.596] 

Observations 330 330 

4.3 Analysis of GTI as the mediate variable 

Table 3 shows the influence of SIA and DIA on GTI, and GTI on TFEE. In Model 1 and 3, the regression results 

show that both SIA and DIA can promote GTI, and the formation of agglomeration areas is conducive to the flow 

and sharing of green technology. In addition, the spatial regression results of SIA and DIA on GTI are positive, 

0.115 and 0.099 respectively. This shows that the GTI in this region will be affected by the industrial 

agglomeration in adjacent regions. Furthermore, Model 2 and 4 show the regression results of GTI on TFEE 

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under the SIA and DIA scenario, and the coefficient is 0.172 and 0.168, respectively. Therefore, GTI is the 

mediating variable that affects TFEE due to industrial agglomeration. It is worth mentioning that in Model 1 and 

Model 3, the results of the lag term of GTI are 0.215 and 0.146 respectively, indicating that GTI also be affected 

by the previous issue. At the same time, in Model 2 and Model 4, the spatial term of GTI is also significantly 

positive, 0.218 and 0.197 respectively. This suggests that GTI has a strong spatial spillover effect within a certain 

geographic range, and that GTI in one region is affected by GTI in neighboring regions. 

Table 3: The mediating effect of GTI 

Variables 
EV=GTI EV=TFEE EV=GTI EV=TFEE 

Model 1 Model 2 Model 3 Model 4 

TFEEt-1  0.803***  0.908*** 

GTIt-1 0.215***  0.146***  

SIA 0.206** 0.157***   

DIA   0.133*** 0.184** 

IS 0.039* 0.121* 0.031* 0.117** 

GTI  0.172**  0.168*** 

W*TFEE  0.204***  0.210*** 

W*SIA 0.115** 0.127**   

W*DIA   0.099*** 0.119*** 

W*GTI  0.218***  0.197*** 

Constant 0.705** 0.511** 0.628** 0.356** 

LM test lag 105.22** 148.36*** 121.31*** 130.82** 

LM test error 78.21** 77.99** 81.22** 69.37*** 

LM test for SAR 41.83** 38.95*** 50.25** 45.56*** 

LM test for SEM 25.49** 30.57** 28.46** 27.13** 

Wald-test 1956.303*** 1496.641** 2013.285** 1507.393*** 

AR(2)[P] 1.99[0.167] 1.60[0.192] 1.80[0.155] 1.86[0.143] 

Hansen[P] 18.67[0.662] 17.09[0.501] 19.66[0.708] 20.11[0.637] 

Observations 330 330 330 330 

Table 4: The moderating effect of DL 

Variables 
EV=TFEE EV=GTI EV=TFEE EV=TFEE EV=GTI EV=TFEE 

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

SIA 0.166** 0.204** 0.153***    

DIA    0.124** 0.185* 0.171** 

DL 0.107** 0.157*** 0.112** 0.104*** 0.161*** 0.110** 

DL*SIA 0.283** 0.246*** 0.279**    

DL*DIA    0.260** 0.301*** 0.292** 

GTI   0.218**   0.201** 

Controls Yes Yes Yes Yes Yes Yes 

Constant 0.357** 0.509*** 0.567*** 0.319** 0.458*** 0.494*** 

Wald-test 1128.263*** 1079.645** 1530.582** 
1452.297**

* 
1107.942** 

1096.837**

* 

AR(2)[P] 1.23[0.166] 1.59[0.203] 1.55[0.192] 1.09[0.143] 1.57[0.217] 1.72[0.209] 

Hansen[P] 19.65[0.291] 18.66[0.255] 20.13[0.188] 
19.57[0.20

4] 
19.06[0.199] 

18.71[0.18

2] 

Observations 360 360 360 360 360 360 

4.4 Analysis of DL as the moderate variable 

Next, the table 4 shows the results that the moderating effect of DL in the influence of SIA or DIA on TFEE. 

Model 1 show that both SIA and DL can promote the TFEE, and the coefficient of SIA*DL is 0.283. Model 4 

shows that both DIA and DL can promote the growth of TFEE, and the coefficient of DIA*DL is 0.260, It shows 

that DL can improve the influence of SIA or DIA on TFEE. In addition, Model 2~3 demonstrated the moderating 

effect of DL on GTI. In Model 2, the influence of DL on GTI is positive, and the coefficient of SIA*DL is 0.246. 

The coefficients of SIA*DL and GTI are 0.279 and 0.218 respectively shown by model 3. In Model 5, the impact 

of DL on GTI is positive, the coefficient is 0.161. The coefficient of SIA*DL is 0.301. It shows that DL can 

positively regulate the effect of DIA on TFEE. In Model 6, the coefficients of DIA*DL and GTI are 0.292 and 

65



0.201, respectively. These show that both the moderating effect of DL on SIA or DIA's influence on GTI and the 

mediating effect of GTI are established.  

5. Conclusions  

This paper explores the relationship between industrial agglomeration and TFEE from the perspective of 

collision of DL and GTI. After the elaboration of theoretical mechanisms and the measurement of quantitative 

analysis, the three hypotheses proposed in this paper have been effectively verified. Firstly, both SIA and DIA 

can enhance TFEE to a certain extent, but there are differences in the degree of promotion between them, and 

industrial agglomeration can be regarded as a powerful means to improve energy efficiency. Secondly, both SIA 

and DIA can play a facilitating role on TFEE through GTI. Finally, DL can enhance the influence of SIA or DIA 

on TFEE, as well as improve the influence of SIA or DIA on GTI. Both GTI and DL can be regarded as an 

effective initiative to amplify the promotional effect of industrial agglomeration on energy efficiency. 

In response to the above findings, this paper can draw some relevant policy inspiration.  (1) Vigorously promote 

the digitalization process. The government should be committed to the rapid development of the digital economy 

and encourage the application of digital technology in the firms in the agglomeration area. (2) Implement a 

dynamic differentiation strategy of specialization and diversified agglomeration. A series of policies and 

measures that are conducive to industrial agglomeration can be formulated. Meanwhile, it can guide industrial 

agglomeration to transform into high-end, green and green directions, which profits both industrial agglomeration 

and carbon emission efficiency. (3) Encourage local governments and enterprises to jointly promote GTI. 

Strengthening support and promotion of green technology, enterprises can be encouraged to form a sharing 

and diffusion mechanism for green technologies in industrial agglomerations. (4) Promote regional collaborative 

governance. The promotion of inter-regional collaborative governance mechanisms can also play an important 

role in improvement of TFEE.  

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