Energy saving effects of digital technologies from a life-cycle-analytical perspective: evidence from China

Abstract

Digital technology has become a key driver of industrial transformation and resource utilization. However, no consensus has been reached on the exact relationship between digital technology and energy utilization. This study adopted a comprehensive index system to investigate the impact of digital technologies on energy utilization across 30 provinces in China. The results reveal a non-linear relationship between digital technologies and energy efficiency in China (represented by an N-curve), which is validated by robustness tests. This indicates digital technology exerts a fast-slow-rapid impact on improving energy efficiency throughout its initial-rapid-stable developmental stages. Geographically, this effect is more pronounced in eastern and central China, as well as in areas with lower energy efficiency. Furthermore, the impact of digital technology on total energy consumption can be characterized by an inverted N-shaped curve. As regional energy efficiency improves, the energy consumption associated with the development of digital technologies gradually decreases. These findings can contribute valuable insights for enhancing energy efficiency and provide practical guidance for the formulation of energy and digital technology policies.

Appendices

Appendix 1

Table 11

Table 11 Conversion coefficient of different kinds of energy

Appendix 2

We adopt the entropy method to measure the level of digital technology development, which can avoid the inaccuracy of the subjective assignment method measurement. The measurement method is as follows:

Standardization of the positive indicators:

$$X_{itj}=\left[\frac{X_{itj}-min\left\{X_{1tj},X_{2tj^{\bullet\bullet\bullet\bullet\bullet\bullet}}X_{ntj}\right\}}{max\left\{X_{itj},X_{2tj^{\bullet\bullet\bullet\bullet\bullet\bullet}}X_{ntj}\right\}-min\left\{X_{itj},X_{2tj^{\bullet\bullet\bullet\bullet\bullet\bullet}}X_{ntj}\right\}}\right]$$

(8)

where t represents the year; i and j represent the region and indicator, respectively. x_itj and X_ij indicate the value of indicator before and after standardized treatment in year.

Indicator normalization:

$${P}_{itj}^{ }=\frac{{X}_{itj}}{{\sum }_{i=1}^{n}\sum_{t=1}^{m}{X}_{itj}}$$

(9)

P_itj indicates the normalized value of the indicator.

Calculate entropy value:

$${I}_{j}=-\frac{1}{{\text{ln}}(m\times n)}{\sum }_{i=1}^{n}{\sum }_{t=1}^{m}{P}_{itj}{\text{ln}}\left({P}_{itj}\right)$$

(10)

Determine each indicator’s weight:

$${W}_{j}=\frac{1-{I}_{j}}{\sum_{j=1}^{m}1-{I}_{j}}$$

(11)

Calculate digital technology level:

$${S}_{it}={\sum }_{j=1}^{n}{X}_{itj}\times {W}_{j}$$

(12)

Appendix 3

Table 12

Table 12 Data sources for the comprehensive index system

Appendix 4

The digital technology–related words in the provincial government work reports include digital economy, intelligent economy, information economy, knowledge economy, smart economy, digitized information, modern information networks, information and communication technology (ICT), communication infrastructure, internet, cloud computing, blockchain, Internet of Things (IoT), digitization, digital rural areas, digital industry, e-commerce, 5G, digital infrastructure, artificial intelligence, e-business, big data, digitization, industrial digitization, digital industrialization, data assetization, smart cities, cloud services, cloud technology, cloud computing, e-government, mobile payments, online, information industry, software, information infrastructure, information technology, and digital lifestyle.

Appendix 5

The northeast region includes Liaoning, Jilin, and Heilongjiang; the eastern region includes Bei**g, Tian**, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and **njiang.