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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 6: 1788 - 1796

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.06.017

Resource Economy and Urban-Rural Development

Measurement and Path Analysis of the Effect of Industrial Digitalization for Empowering the Low Carbon Economy

  • ZHU Meifeng , * ,
  • WANG Haoyu ,
  • XIE Yuxia
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  • School of Economics and Management, North University of China, Taiyuan 030051, China
* ZHU Meifeng, E-mail:

Received date: 2024-09-13

  Accepted date: 2025-01-10

  Online published: 2025-11-28

Supported by

The Philosophy and Social Science Project of Shanxi Province(2023YJ087)

The Philosophy and Social Sciences Key Research Base of Higher Education Institutions of Shanxi(2022J022)

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Abstract

The digitalization of industry has a significant impact on the low-carbon transformation and development in China, although the impact varies in different economic regions. Exploring the impact mechanism and path is beneficial for promoting the progress of China’s low-carbon economy. Based on the panel data of 30 provinces in China from 2011 to 2021, an indicator system was constructed to measure the comprehensive index of industrial digitalization and carbon emission intensity. A threshold regression model was used to explore the impact of industrial digitalization on regional carbon emission intensity from the perspectives of government intervention and regional economic development level. The results show that the digitalization of industries, which has been unequal in various regions of China, can significantly suppress the regional carbon emission intensity and promote the development of the low-carbon economy. Furthermore, the threshold model shows that the promoting effect of industrial digitalization on low-carbon economy development will be enhanced as the degree of government intervention increases; and the lower the level of regional economic development, the more obvious the promotional effect of industrial digitalization on the low-carbon economy. The findings of this study indicate that the uneven development level of digital industrialization hinders the low-carbon economy. In regions with different levels of economic development, the low-carbon effects of industrial digitalization vary significantly. Therefore, governments should strengthen the policy guidance of industry digitalization based on regional economic development status to enhance the promotional effect of industrial digitalization on the low-carbon economy.

Key words： industrial digitalization; policy intervention; low carbon effect

Cite this article

ZHU Meifeng , WANG Haoyu , XIE Yuxia . Measurement and Path Analysis of the Effect of Industrial Digitalization for Empowering the Low Carbon Economy[J]. Journal of Resources and Ecology, 2025 , 16(6) : 1788 -1796 . DOI: 10.5814/j.issn.1674-764x.2025.06.017

1 Introduction

In 2021, China established a definitive objective of reducing its carbon emission intensity by 18% compared to 2020 levels by 2025. This goal is part of a broader effort to transition toward a low-carbon economy. In this context, digital technology has emerged as a crucial tool that not only facilitates the development of green low-carbon technologies but also expedites the research and application of advanced technologies. Industrial digitalization is a key component of the broader digitalization process, and it plays a pivotal role in achieving carbon reduction, particularly in the industrial sector. The implementation of industrial digitalization has markedly enhanced energy efficiency through the application of advanced technologies. This, in turn, has reduced the carbon emission intensity during the production process, thereby laying the groundwork for the adoption of sustainable production methods. Given these developments, conducting in-depth research on how the digital economy can bolster the development of a low-carbon economy, stimulate technological innovation, and facilitate the transition of China’s low-carbon economy towards digitalization and intelligence has become a pressing issue in contemporary economic development.
The rapid advancement of technologies such as the Internet, big data, and artificial intelligence has led to the continuous subdivision of the concept of the digital economy. At present, the digital economy is mainly focused on digital industrialization and industry digitalization (Kunkel and Tyfield, 2021; Pan and Huang, 2022). On the one hand, several studies have analyzed the impact of digital industrialization on various aspects, including the development of the real economy (Ranta et al., 2021; Song et al., 2022), the resilience of urban industrial chains (Di Maria et al., 2022; Zhang and Mao, 2024), and industrial structure upgrading (Feng and Xu, 2022). Those studies found that digital industrialization has a promoting effect on these aspects to varying degrees, indicating a steady increase in the level of digital industrialization in China. However, regional differences in the development of digital industrialization have also been noted (Zhang, 2024). For example, the development level of industry digitalization in eastern China is significantly higher than in the central and western regions (Tian et al., 2023). At present, research on industry digitalization mainly includes three aspects: 1) Industry digitalization can promote enterprise innovation, with the efficiency of research and development funding supply and utilization being the main path (Wang et al., 2022); 2) The combination of industry digitalization and green technology innovation development can effectively promote the digital transformation and upgrading of industries (Lin, 2023; Riaz et al., 2024; Xu, 2024), and improve carbon productivity (Tian et al., 2024); and 3) The digitalization of industries has a restraining effect on carbon emissions, with a favorable institutional environment being conducive to the digital transformation of industries in exerting its restraining effect on carbon emissions (Liu and Chang, 2022; Safi et al., 2024). As research continues to deepen, industrial digitalization has emerged as an indispensable facet of industry digitalization. At present, the research on industrial digitalization mainly includes the construction of an industrial Internet platform (Yi et al., 2024), the deepening of industrial big data applications (Shen and Wang, 2024), and improvement of the industrial intelligence level (Ren et al., 2024).
Numerous studies have highlighted the important role that industrial digitalization plays in reducing carbon emission intensity. From the perspective of industrial digitalization, scholars have found that the transformation towards digitalization can significantly mitigate the intensity of carbon emissions (Zhang et al., 2022; Yu and Tang, 2023; Yu et al., 2023). Despite these findings, a low development level and large regional differences are still the main factors restricting the rapid improvement of the industrial digitalization level (Friesenbichler and Hölzl, 2020; Xiang et al., 2023). Given the long-term equilibrium relationship between China’s carbon dioxide emission intensity and economic growth (Tu, 2016), and the importance of balancing economic growth with the achievement of carbon reduction goals (Shang et al., 2015; Chroufa and Chtourou, 2024), exploring the mechanisms and effects of the low-carbon economy from the perspective of industrial digitalization is crucial. Some scholars have found that urban carbon emission performance can be improved through energy efficiency (Ma et al., 2023), while some studies also suggest that enhancing industrial efficiency can aid in a smoother shift to cleaner energy sources (Li et al., 2024), and all these factors collectively contribute to promoting the low-carbon economy. This is especially important considering that the low-carbon economy is a new choice for future economic development (Li and Wen, 2010), and the significant role of industrial development in the national economy. Studying the impact of industrial digitalization on low-carbon economy development can not only promote the green development of industry but also contribute to the realization of global low-carbon goals, which is of great significance for coping with global climate change.
Despite the growing body of research on industrial digitalization and the low-carbon economy, there is a notable gap in the literature regarding the mechanisms and developmental characteristics of how industrial digitalization impacts carbon emission intensity at different stages. To address this gap, the present study employed the Intergovernmental Panel on Climate Change (IPCC) calculation method to estimate industrial carbon emissions (Wang et al., 2024), with the aim of analyzing the mechanism of industrial digitalization on China’s low-carbon economy from multiple perspectives and spatial systems. In doing so, this study seeks to provide policy recommendations that can serve as decision-making references for the sustainable development of China’s low-carbon economy.

2 Variable descriptions and data sources

2.1 Variable descriptions

2.1.1 Core variables

The low-carbon economy was measured by an index system. According to the current main types of energy consumption in China, this study used the consumption of eight kinds of energy in each province in 2011-2021 to calculate regional carbon emissions. Then the carbon emissions were divided by GDP to obtain the carbon emission intensity. To create a proxy indicator for the low-carbon economy index, 1 was added to the result and the natural logarithm was taken.
The core explanatory variable was industrial digitalization. Based on the constructed industrial digitalization index system, the industrial digitalization economic development index of each province from 2011 to 2019 was calculated. Then, these indices were incremented and their natural logarithm was taken to obtain a more representative proxy indicator for industrial digitalization.

2.1.2 Control variable

To capture the influences of other factors on the model estimation, this study added six types of control variables to the model: industrial structure, level of economic development, industrialization level, energy structure, degree of government intervention and population density.
For industrial structure, the ratio of the output value of the tertiary industry to that of the secondary industry was chosen as the indicator. The level of economic development is expressed by dividing the per capita GDP by the per capita regional GDP index (based on the year 2000). The industrialization level is expressed as the ratio of industrial added value to GDP. For energy structure, the proportion of electricity consumption in each province to the total electricity consumption in China was selected. The degree of government intervention is expressed by dividing fiscal expenditure (general public budget expenditure of local governments) by GDP. Population density is represented by the ratio of the year-end resident population to the area of each provincial administrative division. Then 1 was added to the logarithm of the index with values between 0-1, and the natural logarithm was taken directly for values greater than 1. This was used as a proxy indicator for each variable for the subsequent empirical analysis.

2.2 Data sources

The indicator data mainly came from the official website of the National Bureau of Statistics, the 2016 National Greenhouse Gas Inventory Guidelines, the statistical yearbooks of various provinces over the years, the Digital Finance Research Center of Peking University, CSMAR, and other sources.

3 Calculation of industrial digitalization and the low-carbon economy

3.1 Calculation of industrial digitalization

3.1.1 Construction of the index system

This study selected a total of nine relevant indicators based on the connotation of industrial digitalization, as shown in Table 1.
Table 1 Industrial digitalization indicator system
Index Unit Attribute
Number of computers per 100 people in manufacturing enterprises Number of individuals Positive
E-commerce transaction volume of manufacturing enterprises Billion Positive
R&D expenses Ten thousand yuan Positive
Funds for the development of new products in industrial industries above a certain scale Ten thousand yuan Positive
Equivalent R&D personnel of industrial enterprises above a designated size People Positive
Employment of digital information technology talents Ten thousand people Positive
Employment in scientific research and technical services Ten thousand people Positive
Information transmission, computer services and software industry fixed asset investment of the whole society Billion yuan Positive
Revenue from the electronic information manufacturing owner’s business Billion yuan Positive

3.1.2 Entropy weight method for calculating the industrial digitalization index

This study employed the entropy weight method to assign appropriate weights to the nine selected indicators. This method ensures the objectivity and accuracy of the evaluation results.
(1) Data standardization processing
Given the varying dimensions and orders of magnitude of each indicator, direct calculations could lead to significant errors in the results. To enhance the consistency and accuracy of the evaluation, it was imperative to standardize the data for each indicator.
The formula for positive indicators:
$\begin{array}{l} Y_{i j}=\frac{X_{i j}-\min X_{i j}}{\max X_{i j}-\min X_{i j}} \\ (0 \leqslant i \leqslant m, 0 \leqslant j \leqslant n) \end{array}$
The formula of negative indicators:
$Y_{i j}=\frac{\max X_{i j}-X_{i j}}{\max X_{i j}-\min X_{i j}}$
where i is the provincial subscript, j is the evaluation indicator subscript; $X_{i j}$ and $Y_{i j}$ are the original data and the standardized data of j-th indicator in the i-th province, respectivaly; $\min X_{i j}$ is the minimum indicator data; and $\max X_{i j}$ is the maximum indicator data. m is the total number of evaluation objects and n is the total number of selected indicators.
(2) The specific weight of the i-th evaluation object under the j-th indicator, along with its characteristic weight was calculated as:
$P_{i j}=\frac{Y_{i j}}{\sum_{i=1}^{m} Y_{i j}} \quad\left(0 \leqslant P_{i j} \leqslant 1\right)$
where $P_{i j}P_{i j}$ is the specific weight.
(3) The information entropy value of the j-th indicator was calculated as:
$e_{j}=-K \sum_{i=1}^{m} P_{i j} \ln P_{i j} \quad(K>0)$
where $e_{j}$ is the information entropy, and K is a constant.
$K=\frac{1}{m \times n}$
(4) For the entropy weight, the weight of the j-th indicator was calculated as:
$W_{j}=\frac{1-e_{j}}{\sum_{j=1}^{n}\left(1-e_{j}\right)}$
(5) The standardized indicator data is weighted to calculate the scores of each indicator:
$V_{i j}=W_{j} \times Y_{i j}$
whrere $V_{i j}$ is the score of the j-th indicator in the i-th province.
(6) The industrial digitalization index was calculated as:
$D_{i}=\sum_{j=1}^{n} V_{i j} \quad(i=1,2, \cdots, m)$
where $D_{i}$ is the industrial digitalization index of the i-th province.

3.1.3 Calculation results and feature analysis

This study used the entropy weight method to calculate the comprehensive index of the industrial digital development level in 2011-2021, as shown in Table 2.
Table 2 Industrial digitalization index values of various provinces and cities from 2011 to 2021
Region Province/city 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Mean value
Eastern
 Beijing 0.207 0.232 0.260 0.272 0.290 0.309 0.353 0.372 0.386 0.396 0.444 0.320
Tianjin 0.053 0.068 0.083 0.096 0.106 0.108 0.103 0.101 0.095 0.111 0.135 0.096
Hebei 0.052 0.063 0.077 0.086 0.091 0.107 0.116 0.118 0.152 0.170 0.191 0.111
Shanghai 0.115 0.132 0.166 0.206 0.213 0.231 0.240 0.260 0.297 0.311 0.359 0.230
Jiangsu 0.249 0.297 0.358 0.388 0.412 0.427 0.445 0.459 0.488 0.534 0.627 0.426
Zhejiang 0.148 0.165 0.183 0.206 0.227 0.250 0.269 0.296 0.343 0.379 0.431 0.263
Fujian 0.063 0.076 0.087 0.096 0.110 0.120 0.135 0.161 0.165 0.182 0.206 0.127
Shandong 0.137 0.161 0.201 0.225 0.248 0.272 0.298 0.290 0.268 0.320 0.379 0.254
Guangdong 0.286 0.323 0.380 0.418 0.446 0.493 0.565 0.663 0.732 0.786 0.855 0.541
Hainan 0.022 0.025 0.027 0.030 0.036 0.039 0.042 0.039 0.041 0.044 0.050 0.036
Mean 0.133 0.154 0.182 0.202 0.218 0.236 0.257 0.276 0.297 0.323 0.368 0.241
Northeast
 Liaoning 0.072 0.082 0.094 0.106 0.101 0.087 0.093 0.097 0.104 0.113 0.118 0.097
Jilin 0.027 0.034 0.039 0.048 0.056 0.062 0.079 0.066 0.062 0.091 0.056 0.056
Heilongjiang 0.047 0.053 0.056 0.063 0.062 0.069 0.075 0.072 0.075 0.076 0.075 0.066
Mean 0.049 0.056 0.063 0.072 0.073 0.073 0.083 0.078 0.080 0.093 0.083 0.073
Central
 Shanxi 0.030 0.034 0.043 0.045 0.048 0.048 0.045 0.053 0.056 0.063 0.073 0.049
Anhui 0.048 0.060 0.077 0.093 0.109 0.121 0.128 0.136 0.149 0.181 0.223 0.121
Jiangxi 0.026 0.033 0.041 0.049 0.060 0.070 0.084 0.092 0.113 0.139 0.177 0.080
Henan 0.061 0.074 0.095 0.111 0.124 0.140 0.152 0.165 0.175 0.224 0.229 0.141
Hubei 0.069 0.079 0.094 0.102 0.112 0.123 0.131 0.149 0.180 0.175 0.201 0.129
Hunan 0.055 0.065 0.079 0.089 0.107 0.115 0.133 0.140 0.179 0.189 0.223 0.125
Mean 0.048 0.057 0.071 0.081 0.093 0.103 0.112 0.122 0.142 0.162 0.188 0.107
Western Inner Mongolia 0.025 0.031 0.042 0.057 0.048 0.055 0.062 0.064 0.071 0.071 0.078 0.055
Guangxi 0.035 0.041 0.047 0.052 0.054 0.062 0.064 0.065 0.078 0.098 0.098 0.063
Chongqing 0.034 0.043 0.053 0.062 0.071 0.080 0.089 0.090 0.102 0.121 0.138 0.080
Sichuan 0.058 0.067 0.095 0.104 0.124 0.132 0.147 0.162 0.178 0.205 0.232 0.137
Guizhou 0.019 0.022 0.028 0.033 0.037 0.044 0.052 0.060 0.057 0.066 0.067 0.044
Yunnan 0.025 0.033 0.044 0.051 0.053 0.066 0.062 0.062 0.069 0.074 0.086 0.057
Shaanxi 0.051 0.059 0.070 0.085 0.089 0.098 0.104 0.116 0.109 0.126 0.138 0.095
Gansu 0.017 0.021 0.027 0.029 0.032 0.036 0.033 0.034 0.036 0.039 0.044 0.032
Qinghai 0.013 0.015 0.015 0.017 0.026 0.027 0.029 0.030 0.034 0.033 0.037 0.025
Ningxia 0.009 0.011 0.013 0.017 0.023 0.027 0.029 0.031 0.030 0.034 0.036 0.024
Xinjiang 0.021 0.024 0.027 0.035 0.039 0.041 0.044 0.047 0.048 0.046 0.052 0.039
Mean 0.028 0.033 0.042 0.049 0.054 0.061 0.065 0.069 0.074 0.083 0.091 0.059
National mean 0.069 0.081 0.097 0.109 0.118 0.129 0.140 0.150 0.162 0.180 0.202
There was a significant increase in the industrial digitalization development, from 0.069 in 2011 to 0.202 in 2021. A closer examination of the data from 30 provinces nationwide reveals considerable disparities in the level of industrial digitalization. Guangdong Province leads the country in industrial digitalization, followed by Jiangsu, with Beijing, Zhejiang, Shandong, and Shanghai not far behind. These provinces are all located in the eastern region and had average industrial digitalization levels above 0.2. Conversely, certain regions exhibit a relatively low level of digitalization. Over the span of 11 years, 12 provinces, including Qinghai, Ningxia, Gansu, and Xinjiang, did not exceed a digitalization level of 0.1. Eight of these provinces are located in the western region, consistently demonstrating a low industrial digitalization development index and ranking relatively low.
Despite the distinct differences in the development of the industrial digitalization index among provinces, in terms of time, the industrial digitalization index values in various regions of China have shown growth trends from 2011 to 2022, especially in the eastern region. The central region also shows a notable growth trend. In contrast, the northeast region exhibits inconsistent growth, while the western region demonstrates a relatively stable growth trend.

3.2 Calculation of the low-carbon economy

3.2.1 Method for calculating carbon emission intensity

This study used carbon emission intensity to represent the development status of the low-carbon economy.
Because direct measurements of carbon dioxide emissions in China are not available, this study adopted the methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC). Specifically, eight major categories of energy consumption were selected as a way to indirectly calculate carbon dioxide emissions. The specific formula is:
$Q=\sum Q_{i}=\sum E_{i} \theta_{i}$
where θi represents the carbon emission coefficient; and Ei represents the standard coal equivalent. According to the data released by IPCC, the corresponding carbon emission factors were obtained, as shown in Table 3.
The formula for carbon emission intensity is:
$C=Q / G D P$
where C is carbon intensity; $Q$ is carbon dioxide emissions; and GDP is gross domestic product.
Table 3 Carbon emission coefficients (Unit: kgC kg-1)
Energy source Coal Coke Crude oil Gasoline Kerosene Diesel fuel Fuel oil Nature gas
Carbon emission coefficient 1.9 2.86 3.02 2.93 3.02 3.1 3.17 2.16

3.2.2 Calculation results and feature analysis

Based on the relevant methods of IPCC for reference, this study used eight major energy consumption types to calculate carbon dioxide emissions, and the carbon emission intensities for 2011-2021 are shown in Table 4. The results show that China’s carbon emission intensity exhibits obvious differences in both the temporal and spatial dimensions.
Table 4 Carbon emission intensity values (Unit: tCO2e (10000 yuan)-1)
Region Province/city 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Mean value




Eastern





 Beijing 0.706 0.634 0.505 0.475 0.396 0.318 0.283 0.256 0.217 0.161 0.151 0.373
Tianjin 1.742 1.529 1.401 1.230 1.125 0.973 0.921 0.924 1.234 1.172 1.049 1.209
Hebei 3.707 3.461 3.233 2.967 2.892 2.691 2.465 2.545 2.605 2.505 2.009 2.825
Shanghai 1.389 1.293 1.246 1.045 1.010 0.900 0.844 0.746 0.659 0.614 0.533 0.934
Jiangsu 1.539 1.422 1.313 1.196 1.142 1.076 0.946 0.861 0.817 0.762 0.676 1.068
Zhejiang 1.382 1.247 1.145 1.055 0.999 0.896 0.854 0.763 0.701 0.743 0.729 0.956
Fujian 1.433 1.268 1.099 1.146 1.024 0.862 0.821 0.813 0.732 0.706 0.695 0.964
Shandong 2.501 2.383 2.091 2.079 2.085 2.066 1.922 1.883 2.070 1.988 1.759 2.075
Guangdong 1.139 1.043 0.941 0.869 0.808 0.747 0.704 0.669 0.595 0.589 0.569 0.788
Hainan 2.153 2.007 1.647 1.671 1.764 1.583 1.395 1.347 1.260 1.177 1.054 1.551
Mean 1.769 1.629 1.462 1.373 1.324 1.211 1.115 1.081 1.089 1.042 0.922 1.274

Northeast

 Liaoning 3.201 2.949 2.560 2.432 2.383 3.088 3.014 2.959 3.286 3.332 2.860 2.915
Jilin 2.681 2.340 2.050 1.922 1.764 1.641 1.618 1.509 2.002 1.852 1.673 1.914
Heilongjiang 2.874 2.765 2.448 2.386 2.367 2.368 2.300 2.060 2.602 2.589 2.653 2.492
Mean 2.919 2.684 2.353 2.247 2.171 2.366 2.311 2.176 2.630 2.591 2.395 2.440



Central


 Shanxi 6.517 6.307 6.170 6.266 6.181 5.864 5.792 6.082 6.306 6.348 10.005 6.531
Anhui 2.203 2.017 1.946 1.848 1.753 1.576 1.469 1.371 1.114 1.093 1.085 1.589
Jiangxi 1.620 1.467 1.406 1.310 1.283 1.174 1.107 1.045 0.948 0.893 0.702 1.178
Henan 2.448 2.070 1.869 1.742 1.647 1.487 1.276 1.142 0.931 0.925 0.955 1.499
Hubei 2.068 1.822 1.404 1.279 1.175 1.064 0.999 0.879 0.801 0.744 0.699 1.176
Hunan 1.648 1.438 1.246 1.098 1.081 0.997 0.973 0.853 0.778 0.768 0.608 1.044
Mean 2.751 2.520 2.340 2.257 2.187 2.027 1.936 1.895 1.813 1.795 2.342 2.169




Western






 Inner Mongolia 5.201 4.892 4.476 4.366 4.345 4.314 5.103 5.419 6.044 6.331 5.871 5.124
Guangxi 1.800 1.777 1.583 1.448 1.283 1.237 1.287 1.201 1.215 1.193 1.097 1.375
Chongqing 1.670 1.429 1.085 1.035 0.942 0.823 0.753 0.658 0.570 0.534 0.448 0.904
Sichuan 1.513 1.396 1.296 1.233 1.089 0.951 0.805 0.648 0.607 0.617 0.519 0.970
Guizhou 4.486 4.085 3.584 3.014 2.634 2.490 2.133 1.804 1.626 1.479 1.650 2.635
Yunnan 2.771 2.482 2.131 1.765 1.482 1.351 1.276 1.329 1.063 1.058 0.932 1.604
Shaanxi 2.930 2.921 2.762 2.666 2.584 2.456 2.215 1.938 1.987 2.027 2.094 2.416
Gansu 3.899 3.553 3.269 3.044 2.972 2.700 2.624 2.463 2.353 2.385 2.243 2.864
Qinghai 2.548 2.660 2.624 2.239 1.914 2.137 1.967 1.745 1.656 1.677 1.575 2.067
Ningxia 8.067 7.778 7.527 7.195 7.025 6.398 7.291 7.525 8.054 8.191 7.480 7.503
Xinjiang 4.663 4.765 4.807 4.809 4.908 5.076 4.831 4.480 4.289 4.465 4.317 4.674
Mean 3.595 3.431 3.195 2.983 2.834 2.721 2.753 2.655 2.678 2.723 2.566 2.921
National mean 2.750 2.573 2.362 2.228 2.135 2.044 2.000 1.931 1.971 1.964 1.956
From a national standpoint, the average carbon emission intensity in China markedly decreased, from 2.75 tCO2e (10000 yuan)-1 in 2011 to 1.956 tCO2e (10000 yuan)-1 in 2021. An examination of data from 30 provinces reveals regional disparities in carbon emission intensity. Beijing boasts the lowest carbon emission intensity nationwide, closely followed by Guangdong Province. Other regions with low carbon emission intensity include Chongqing, Sichuan, Fujian, Shanghai, and Zhejiang, all of which have an average carbon emission intensity below 1 tCO2e (10000 yuan)-1. Except for Chongqing and Sichuan in the western region, all these provinces are located in the eastern region. This suggests effective control of carbon emissions in the eastern region of China.
In certain regions, notably Ningxia, Shanxi, Inner Mongolia, and Xinjiang, the carbon emission intensity values were significantly high, exceeding 4 tCO2e (10000 yuan)-1. Three of these provinces are situated in the western region. Further analysis revealed that eight provinces had a carbon emission intensity values surpassing 2 tCO2e (10000 yuan)-1, half of which are also in the western region. This distribution underscores the regional disparities in carbon emission intensity, particularly in the western region of China.
In the Northeast, two provinces had carbon intensities exceeding 2 tCO2e (10000 yuan) -1. However, in the central region, except for Shanxi Province, the average carbon intensities of all other provinces were below 2 tCO2e (10000 yuan)-1 yuan, indicating a relatively reasonable level of carbon emission control.
From a temporal perspective, the carbon emission intensities in various regions of China generally showed decreasing trends from 2011 to 2022. The Northeast region shows a relatively obvious fluctuating trend, with values fluctuating around 2.5 tCO2e (10000 yuan)-1 and above 2 tCO2e (10000 yuan)-1 overall, so this region needs further control. The downward trend in the central region was relatively obvious in the first decade, but there was an increase in 2021, mainly due to the sharp increase in carbon emissions in Shanxi Province in 2021. Both the western and eastern regions have shown stable downward trends, indicating that carbon emissions have been well controlled in the past decade.

4 Empirical results and analysis

4.1 Descriptive statistics of the variables

A descriptive statistical analysis was conducted on the dependent variable, explanatory variable, and control variables. The main characteristics of the data are shown in Table 5, such as the centralized trend, dispersion degree and distribution pattern of the data, and other attributes
Table 5 Descriptive statistics
Variable Number of observation values Mean value
(tCO2e
(10000 yuan)-1)		 Variance Minimum value
(tCO2e
(10000 yuan)-1)		 Maximum value
(tCO2e
(10000 yuan)-1)
Carbon emission intensity (y) 330 2.157 1.746 0.151 10.01
Industrial digitalization (x) 330 0.131 0.132 0.00930 0.855
Industrial structure (x1) 330 1.253 0.704 0.518 5.297
Economic development level (x2) 330 12.750 8.081 5.125 48.075
Industrialization level (x3) 330 0.320 0.0826 0.0820 0.556
Energy structure (x4) 330 0.0332 0.0231 0.000500 0.0946
Degree of government intervention (x5) 330 0.249 0.103 0.107 0.643
Population density (x6) 330 473.3 704.8 7.864 3,926
The average carbon emission intensity from 2011 to 2021 was 2.157 tCO2e (10000 yuan)-1, with a variance of 1.746. This suggests a certain degree of dispersion in the carbon emission intensity of each province around the average level. The minimum value was 0.151 tCO2e (10000 yuan)-1, indicating that some regions have low levels of carbon emissions. Conversely, the maximum value was 10.01 tCO2e (10000 yuan)-1, suggesting a high upper limit of carbon emission intensity, with some regions exhibiting high carbon emission phenomena. These data sets underscore the regional differences in carbon emission intensity.
The statistical analysis of industrial digitalization reveals an average level of 0.131, suggesting a relatively low overall level of digitalization among various industries, with substantial potential for improvement. The variance of 0.132 indicates a relatively small and concentrated disparity in the levels of digitalization among different regions or industries. More specifically, the minimum value of 0.0093, which is nearly zero, suggests an extremely low level of industrial digitalization in certain regions. Conversely, the maximum value of 0.855 suggests that some advanced regions or industries have already achieved a relatively high level of industrial digitalization.

4.2 Analysis of the threshold regression process

The threshold regression process can reveal nonlinear relationships between industrial digitalization and the low-carbon effect. Compared with linear regression, the threshold regression process can fit the data more accurately, which improves the prediction accuracy. This study set up 100 network search points and conducted 300 self-service tests to ensure the robustness of the results. First, we started with a triple threshold test and gradually reduced it to a single threshold test until a significant threshold effect was found. For cases with significant threshold effects, we further determined the corresponding threshold values and report the detailed parameter estimation results of the threshold effects in Table 6.
Table 6 Significance test and confidence interval of the threshold effect for economic development level
Threshold variable Control variables Threshold value Confidence interval (95%) P value
Degree of government intervention Economic development level Industrialization level 0.1630 [0.1622, 0.1647] 0.0167
Industrial structure
Population density		 0.1625 [0.1578, 0.1630] 0.0300
Economic development level Industrial structure
Energy structure
Population density		 9.7609 [9.7310, 9.7670] 0.0033
Industrialization level
Energy structure
Degree of government intervention		 9.7609 [9.7310, 9.7670] 0.0167

4.2.1 The threshold effect of government intervention level

The degree of government intervention was used as the threshold variable (as shown in Table 7 (1) and Table 7 (2)). When the level of government intervention is below the threshold, industrial digitalization effectively reduces carbon emission intensity; but when the degree of government intervention exceeds the threshold value, the coefficient of the impact of industrial digitalization on carbon emission intensity diminishes as the threshold value increases. This indicates that in scenarios of heightened government intervention, the inhibitory effect of industrial digitalization on carbon emission intensity is progressively intensified.
Table 7 Threshold regression results
Variable Degree of government intervention Economic development level
Economic development level, industrialization level Industrial structure, population density Industrial structure, energy structure, population density Industrialization level, energy structure, degree of government intervention
(1) (2) (3) (4)
Estimated coefficient P value Estimated coefficient P value Estimated
coefficient		 P value Estimated coefficient P value
Step 1 -1.191086 0.002 -1.28038 0.015 -2.6035 <0.001 -2.608696 0.010
Step 2 -2.175077 <0.001 -2.30068 <0.001 -1.5983 <0.001 -1.76143 <0.001

4.2.2 Threshold effect of economic development level

The level of economic development was used as the threshold variable (as shown in Table 7 (3) and Table 7 (4)). When the level of economic development is below the threshold, industrial digitalization has a negative impact on carbon emission intensity. Conversely, when the level of economic development exceeds the threshold, the impact coefficient decreases as the threshold increases. This indicates that when using the level of economic development as the threshold variable, regardless of how the control variable changes, the negative impact of industrial digitalization on carbon emission intensity will gradually weaken as the level of economic development increases.

5 Conclusions and policy recommendations

In recent years, China has been actively advocating for the transition toward a low-carbon economy, formulated action plans related to the carbon peak and carbon neutrality, implemented the deployment of the “Action Plan for Carbon Peak before 2030”, and ensured the achievement of the carbon peak before 2030. A key component of this transition is industrial digitalization. This study empirically investigated the role of industrial digitalization in this transition, using panel data from 30 provinces in China spanning from 2011 to 2021. The findings indicate three key aspects of this system. 1) Industrial digitalization has been growing steadily, particularly in the eastern and central regions. However, the northeast region has exhibited a fluctuating growth trend, while the western region has shown a relatively flat growth trend. 2) As government intervention increases, the inhibitory effect of industrial digitalization on carbon emission intensity also increases. 3) As the level of economic development rises, the impact of industrial digitalization on carbon emission intensity gradually weakens.
Based on the above conclusions, several suggestions are proposed. 1) Because of the uneven development of digital industrialization, with the Northeast region experiencing unstable development and the Western region lagging behind, it is necessary to balance the development of industrial digitalization across all regions. The government should establish a coordination mechanism by providing financial support, technical assistance and policy guidance. This improvement will be crucial for promoting regional low-carbon development. 2) Based on the characteristics of regional economic development, governments at all levels should progressively implement policies related to industrial digitalization reform. We should strengthen technological development, personnel training and international cooperation. Strengthening the role of policy intervention is vital for enhancing the low-carbon effect of industrial digitalization. 3) In economically underdeveloped regions, where the low- carbon effect of industrial digitalization is most significant, incentive mechanisms should be established, such as providing financial subsidies, tax incentives and low-interest loans. These mechanisms should aim to promote the improvement of the industrial digitalization level. This effort should be considered an important lever for achieving low-carbon development in these regions.

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