Harnessing the Digital Economy for Sustainable Energy Efficiency: An Empirical Analysis of China’s Yangtze River Delta
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Abstract
This study investigates the impact of the digital economy on energy efficiency through a combination of theoretical analysis and empirical testing. The research contributes by categorizing the energy value creation process into two stages: the energy input stage and the energy operation stage and by examining both the direct and indirect effects of the digital economy on energy efficiency. Indirect effects are explored through factors such as industrial structure, green innovation, transaction efficiency, and environmental regulation. Using panel data from 41 cities in the Yangtze River Delta region of China, covering the period from 2006 to 2020, the study empirically examines the effects of the digital economy on energy efficiency. The findings emphasize the significant role of the digital economy in enhancing energy efficiency, particularly through upgrading industrial structures, increasing transaction efficiency, and stimulating green innovation. A heterogeneity analysis reveals that the influence of the digital economy on energy efficiency is less pronounced in resource-based cities than in non-resource-based cities. Based on these findings, the study provides targeted policy recommendations to further leverage the digital economy for improving energy efficiency.
1 Research Background
In recent years, a new wave of technology innovation and industrial change has provided new growth chances for countries all over the world. Technologies such as the Internet, big data, blockchain, artificial intelligence (AI), and cloud computing have facilitated the deep integration of digital and real economies and have accelerated innovation (Anton, 2022; Semenog, 2020). To sustain the present and future expansion of the worldwide economy, the digital economy has emerged as a key booster (Santor, 2020; Xiao, 2020).
At the same time, climate change and energy issues are two significant challenges facing the world today (ÓhAiseadha et al., 2020; Shipworth, 2006). On the one hand, as human economic activity grows and energy consumption rises, the Earth confronts unprecedented challenges posed by climate change. In terms of global energy strategies to combat climate change, it is essential not only to develop new energy sources but also to reduce energy usage and improve energy efficiency (Akpan & Akpan, 2012; Wang et al., 2016). Conversely, however, the current global landscape is characterized by increasing uncertainty, instability, and unpredictability, which continually disrupt the stability of global energy supply chains and industrial chains (Roscoe et al., 2020; Zakeri et al., 2022). Energy security has become a universally acknowledged concern, and enhancing energy utilization efficiency is a vital means of ensuring energy security (Hoggett et al., 2014; Ivanov & Dolgui, 2019). Therefore, against the backdrop of an increasingly evident global energy crisis and the urgent need for a “dual carbon” transition, improving energy efficiency has become an imperative task.
China’s energy use efficiency remains fairly low, with great space for enhancement. The digital economy promotes economic and social change in energy effectiveness through technological innovation, which has a dual effect of promoting superior economic development and improving energy productivity (Huang & Chen, 2023; Zhao et al., 2010). By vigorously developing the digitalization of industry and boosting the modernization of the industrial framework, the digital economy can significantly enhance all-inclusive variables’ effectiveness in energy (Huang & Chen, 2023). Additionally, green credit policies and green financing programs have proven to be useful instruments for stimulating digital innovation among businesses, particularly in high-polluting industries, by connecting environmental goals with corporate practices and enabling decarbonization efforts. These strategies make major contributions to enhancing energy efficiency and sustainable development (Fu et al., 2024; Lu et al., 2023; Mao et al., 2024). In the face of the two defining trends of the twenty-first century, namely low-carbonization and digitization, how to fully exploit the benefits of the digital economy, better integrate innovation, improve energy efficiency, and maximize the potential of the digital economy has become a real problem that countries need to think about.
For this purpose, this article uses panel data from 41 cities in the Yangtze River Delta region of China from 2006 to 2020 and employs a fixed-effects model to empirically analyze the impact of the digital economy on energy efficiency. The potential marginal contributions are mainly as follows: first, this research analyzes the direct approach of the digital economy on energy efficiency from four perspectives: innovating R&D and innovation models, optimizing factor input allocation, reducing marginal production costs, and improving the production and transformation process; second, it explores the mediating effect of the digital economy on energy efficiency from three perspectives: green innovation stimulation effect, industrial structure upgrading effect, and transaction efficiency enhancement, respectively; furthermore, considering that local government policies may have an impact on energy efficiency, this article examines the moderating influence of ecological regulations regarding energy efficiency fueled by the digital economy; third, this article investigates the effect of the digital economy on energy efficiency from the perspective of heterogeneity in cities with varying resource endowments. The following sections first outline the theoretical foundations, then detail the methodology and empirical analysis, and finally provide actionable policy recommendations based on the findings.
2 Theoretical Analysis and Research Hypotheses
2.1 Direct Mechanisms of the Digital Economy on Energy Efficiency
According to value chain theory, the process of energy input and output is a continuous, multi-stage, and systemic value creation process (Fernandez-Stark & Gereffi, 2019; Johnson, 2018). The development of digital technologies and international outsourcing has facilitated the vertical separation of the value chain and the global spatial reconfiguration, thus providing expanded opportunities for improving energy efficiency (Palekhov & Palekhova, 2018; Timmer et al., 2014). Furthermore, based on the energy utilization process, the creation of energy value can be split into two phases: the energy input phase and the energy operation phase. The present research investigates the effect of the digital economy on energy efficiency in these two distinct phases. In the energy input phase, the analysis focuses on factor input and research and development investment; in the energy operation phase, it centers on the marginal costs of production and the processes of production conversion. These two phases are interrelated, demonstrating a collaborative and innovative interaction.
First, revolutionizing the R&D innovation model. The digital economy, characterized by the multi-modal organization and networking of data and information connections, breaks through traditional models of research and development innovation (Thompson et al., 2013). First, it leads to a diversification of innovation subjects. Digital platforms have emerged as new entities beyond traditional innovators such as businesses, universities, research institutions, and governments, fostering synergy in technological innovation and creating agglomeration effects in innovation. Second, it facilitates a diversification of innovation methods. The dynamic and rapid iteration of digital technologies, along with their cross-field integration, has brought forth disruptive innovations such as micro-innovation, cloud innovation, innovation networks, innovation platforms, and innovation ecosystems (Wing, 2008). Third, this has led to the formation of a digital innovation ecosystem. The digitally empowered innovation ecosystem represents a new organizational form that closely interacts with partners and the external environment, connecting millions of start-ups through digital technologies like Tencent Cloud, mobile payments, and big data, thereby constituting a “digital innovation ecological community.”
Second, the allocation of factor inputs should be optimized. Due to the replicable and shareable nature of digital products, which possess attributes of public goods, the digital economy reveals positive externalities. It enhances market transparency and openness. First, companies can mitigate decision-making errors and distortions in factor resource allocation resulting from information asymmetry, thereby restructuring allocation mechanisms and improving productivity (Zhang et al., 2023). Second, the incorporation of digital technology with the real economy facilitates an intelligent matching of factors through digital terminals and enhances the alignment between inputs and outputs through digital analytics, reducing redundancy in factor inputs and facilitating improvements in energy efficiency. Additionally, the application of digital technologies breaks down barriers to the circulation of resources across industries and enterprises. By leveraging the “informational potential” released through the Internet and cloud technologies, it optimizes and integrates energy businesses, enabling resources to flow from areas with low innovation efficiency to those with high innovation efficiency. This dismantles energy “vertical shafts,” achieves an optimal allocation of resources, and enhances energy conversion efficiency (Basu & Fernald, 2007; Honohan, 2004; Jorgenson et al., 2007).
Third, reducing marginal production costs. Regarding the digital economy, the elements of data and knowledge can be replicated and shared at low or even zero cost, making increasing marginal returns a tangible reality. First, digital technologies facilitate the dissemination of information and mitigate information asymmetry through online data, data analytics, and feedback mechanisms, thereby alleviating trial-and-error costs (Kong et al., 2022; Manes & Tchetchik, 2018). Simultaneously, the digital innovation ecosystem can consolidate technological and resource advantages, further reducing the marginal research and development costs associated with technological innovation while enhancing energy efficiency (Kong et al., 2022; Sagar & Van der Zwaan, 2006). Second, the digital transformation of production supply chains enables coordinated and controllable operations across the entire chain, breaking down barriers between departments and enterprises, thereby creating lean supply chain costs and improving energy efficiency (Burggräf et al., 2017; Ferrantino & Koten, 2019). Additionally, through intelligent information platforms that provide dynamic control over business processes, effective management of logistics costs, administrative expenses, and sales costs can be achieved (De Giovanni, 2021; Tsipoulanidis & Nanos, 2022).
Fourth, improving production conversion processes. The quick development and widespread empowerment of digital technologies have driven the entire production process toward collaboration, networking, and ecological integration (Wang & Zhang, 2020). This transition has transformed traditional large-scale standardized production models and integrated chain production models into digital manufacturing modes such as collaborative manufacturing on network platforms, lean intelligent manufacturing, and personalized custom manufacturing (Koumas et al., 2021; Zhou et al., 2022). As a result, traditional production conversion process models have been redefined.
Fifth, interactive innovation in energy input and operation phases. Interactive innovation occurs between the two phases of energy input and energy operation, based on the supportive relationships of upstream and downstream supply chains, as well as horizontal competitive cooperation (Atallah, 2002; Tian, 2011). Through the vertical division of labor and collaboration, horizontal competition and cooperation, and multidimensional interactions within informal networks of clusters, knowledge and information interact and integrate within and between the phases of energy input and operation (Jayaram & Pathak, 2013; Qi et al., 2023). Using personnel mobility, technological spillovers, management information dissemination, and equipment transfers, continuous data feedback and technological adjustments foster the innovation of research and development and optimize factor inputs, marginal costs, and operational processes, thereby enabling the accumulation of technological innovations and precise energy inputs.
According to the preceding study, the research hypothesis H1 is developed.
Hypothesis 1 (Direct Effect): The digital economy can directly promote the improvement of energy efficiency.
2.2 Indirect Mechanism of the Digital Economy on Energy Efficiency Through Industrial Structure
Beyond the direct impact outlined in H1, this study explores the mediating pathways through which the digital economy influences energy efficiency.
Upgrading the industrial structure is a necessary pathway for the economy to achieve high-quality development. The digital economy encourages improvements in the manufacturing process due to its efficient allocation of factors, the progress of digital industrialization, and the digitalization of industries. Moreover, technological advancements also enhance energy conversion rates, thereby improving energy efficiency (Jian & Peihong, 2014). On one hand, the swift growth of the digital economy opens up new prospects for upgrading industrial structures. The digital economy improves the industry framework by altering old industries, creating new sectors, raising consumer and investment demand, and encouraging industrial integration (Tian et al., 2023; Yang, 2023). It significantly improves the sophistication and rationality of industrial structures (Li & Wu, 2023). Furthermore, significant scholarly research has supported the positive effects of industrial framework upgrades on environmentally friendly productivity and energy efficiency (Wang & Xiang, 2014; Yu, 2015). The digital economy encourages adaptive and green revolution of conventional industries such as non-ferrous metallurgy, energy and chemical industries, and specialized light industries by adjusting and optimizing the industrial structure. It also strengthens critical rising industries, including new energy, materials, and healthcare, while cultivating ecological and digital economies. This enhances energy efficiency, facilitates the incorporation of multiple energy sources, and encourages collaborative development across the whole industrial chain, gradually forming an industrial value network. Additionally, it streamlines the framework of resource allocation and enhances the effectiveness of production resource utilization (Acemoglu & Restrepo, 2018; Basu & Fernald, 2007).
Based on this analysis, modernization of the industrial framework has a mediator role in the expansion of energy efficiency by the digital economy, leading to the formulation of research hypothesis H2.
Hypothesis 2 (Mediating Effect): The digital economy can enhance energy efficiency through the effect of industrial structure upgrading.
2.3 Indirect Mechanism of the Digital Economy on Energy Efficiency Through Transaction Efficiency
The enhancement of transaction efficiency serves a significant function in perfecting the market economic system. The digital economy generates an economic environment marked by nations of scale, scope, and the long tail effect, resulting in a more systematic pricing mechanism that elevates the level of economic equilibrium, while human capital agglomeration has been proven to boost urban ecological efficiency, with the digital economy acting as a positive moderator in this process (Pérez-Trujillo & Lacalle-Calderón, 2020; Salahuddin & Gow, 2016; Zhang et al., 2024a). Simultaneously, it reduces transaction costs and inefficiencies arising from information asymmetry, allowing for a dynamic and diverse market scenario involving price, quantity, and variety. This environment facilitates rapid trading of goods, decreases energy consumption, and enhances energy efficiency (Hausen et al., 2006; Kwilinski et al., 2023). Furthermore, regarding advancements in technology, the advancement of digital technologies innovates traditional methods of production, logistics, sales, and communication, thus enhancing transaction efficiency in various degrees (Sapotnitska & Melnyk, 2021). For instance, in energy consumption trading markets based on the Internet and Internet of Things (IoT), the digital economy enables networked and adaptive trades for energy liberties, carbon emissions liberties, and renewable energy allocations, thereby improving transaction efficiency. Based on the above analysis, we propose Hypothesis H3.
Hypothesis 3 (Mediating Effect): The digital economy can enhance energy efficiency through the effect of improved transaction efficiency.
2.4 Indirect Mechanism of the Digital Economy on Energy Efficiency Through Green Innovation
Green innovation exhibits a significant stimulating effect. As an integrated economic form, the digital economy centers around data that possesses both technological and green attributes, dynamically linking other innovative factors such as talent, capital, and technology. This dynamic interaction can stimulate synergistic effects among green innovation factors, producing technological improvements that favor green low-carbon practices and energy conservation (Zhang & Zhong, 2023), thus indirectly enhancing energy efficiency. The digital economy brings about a remarkable empowering effect, opening up new developmental spaces for effective breakthroughs in green innovation activities (Wei et al., 2022). Digital technologies demonstrate a high-value co-creation model involving all data, channels, and nodes; in the process of factor input, they can generate more data resources, creating a multiplier effect for the data concerning green innovation factors. Lin (2023) also found that knowledge diversity and network centrality exhibit synergistic and complementary effects in promoting the catch-up of green innovation, thereby stimulating the productivity of green innovation factors. The digital economy transcends geographical boundaries and spatial barriers between regions, facilitating the exchange and dissemination of technology and knowledge among enterprises in that region, creating new knowledge and technologies, and potentially forming a collaborative force for technological innovation and an innovation agglomeration effect. Moreover, digital finance has been demonstrated to play a significant role in supporting carbon reduction and green innovation by optimizing resource allocation and stimulating innovative practices in households and enterprises (Lv et al., 2024). This expands the scope of knowledge spillover, promotes technological innovations in enterprises, and encourages the improvement of energy efficiency in neighboring regions. Based on the above analysis, we develop Hypothesis H4.
Hypothesis 4 (Mediating Effect): The digital economy can enhance energy efficiency through the stimulating effect of green innovation.
2.5 Indirect Mechanism of the Digital Economy on Energy Efficiency Moderated by Environmental Regulation
Environmental regulation is a crucial element affecting the realization of energy efficiency. The connection between environmental regulations and energy efficiency aligns with the Porter Hypothesis (Porter & Linde, 1995), which posits that appropriate environmental legislation will promote technological advancement, thereby offsetting the costs associated with environmental protection and enhancing enterprises’ profitability in the market. In other words, environmental legislation has an innovation compensation impact, favoring green technology advancement and enhancing energy efficiency (Lv et al., 2021; Ouyang et al., 2020). Under the digital economy, government environmental regulation is twofold: on one hand, digital technologies improve governmental regulatory capabilities, compelling enterprises to leverage big data, Blockchain, AI, and other technological developments to bolster their R&D efforts and innovate their green transformation (Hu et al., 2022; Ma et al., 2022). On the other hand, political decisions at the national and international levels have an important effect on the financial results and innovation routes of energy companies, as demonstrated by the United States’ changing involvement with the Paris Agreement and its effect on renewable and fossil energy companies (Gong et al., 2024). Additionally, green financial systems, such as China’s green credit and corporate green investment policies, amplify the effects of environmental regulations by fostering green innovation. Digital finance similarly enhances carbon productivity through human capital and marketization effects, with spatial spillovers benefiting neighboring regions (Khalid et al., 2024; Sun et al., 2024; Zhang et al., 2024b). On the other hand, the enhancement of consumers’ green consumption awareness further drives enterprises to innovate in green technologies, improving energy efficiency. Regarding the digital economy, environmental regulation creates an environment conducive to the enhancement of energy efficiency. However, excessive environmental regulations may bring higher costs to businesses, thereby suppressing the impact of the digital economy on energy efficiency. Environmental regulation performs a moderating role in the digital economy and energy efficiency. As per the above analysis, we develop Hypothesis H5.
Hypothesis 5 (Moderating Effect): The digital economy can enhance or reduce energy efficiency through the moderating effect of environmental regulation.
3 Research Design
3.1 Model Setting
Combined with the theoretical investigation described above, to validate the influence of the digital economy on energy efficiency, to test Hypothesis H1, drawing on the existing research literature on energy efficiency, the following benchmark regression equation is constructed:
where i indicates the city; t indicates the year; ε it represents the error term; tfp it represents the energy efficiency of region i in year t; de it represents the level of digital economic development in region i in year t, which is the core explanatory variable in this study; x it is the control variable, including urbanization level, industrialization level, education level, level of openness to the outside world, and economic development level, among others.
To determine the fundamental processes of the digital economy through which the digital economy influences energy efficiency and to test hypotheses H2, H3, H4, and H5, we draw on the study by Wen and Ye (2014) to construct the following mediation impact equation:
In equations (2) and (3), m represents the mediating variables of industrial structure (str), transaction efficiency (mak), and green innovation (pat).
To test Hypothesis H5, we incorporate interacting terms among the digital economy and moderation variables into equation (1) and simultaneously center the interaction terms on the mean to prevent multicollinearity issues. Drawing on studies by Zhang et al. (2022), we construct the moderation model as follows:
In equation (4), t it represents the moderation variable of environmental regulations.
3.2 Variable Selection
3.2.1 Explained Variable
Energy efficiency, which is expressed as total factor energy efficiency in this article, refers to three aspects of inputs, desired outputs, and non-desired outputs, as described by Ding (2021), Li et al. (2022), and Tong et al. (2021). We use city panel data from the Yangtze River Delta urban agglomeration from 2006 to 2020 as a sample and measure total factor productivity through the ultra-efficient EBM model with MaxDEA8.0 software to account for the non-directed non-desired outputs. Energy efficiency measurement indicators are shown in Table 1.
Energy efficiency measurement indicators
| Type of indicators | Indicators | Description of indicators |
|---|---|---|
| Input indicators | Labor inputs | End-of-year urban employment (10,000 people) |
| Above-scale industrial enterprises (number) | ||
| Capital inputs | Fixed asset investment (10,000 yuan) | |
| Urban construction land area (km²) | ||
| Scientific expenditure (10,000 yuan) | ||
| Energy inputs | Total water supply (million m3) | |
| Total gas supply (million m3) | ||
| Total electricity consumption (10,000 kW/h) | ||
| Output indicators | Expected outputs | Actual GRDP (10,000 yuan) |
| Per capita social consumer expenditure (10,000 yuan) | ||
| Urban green area (km2) | ||
| Undesirable outputs | Wastewater discharge (10,000 tons) | |
| Sulfur dioxide emissions (tons) | ||
| Dust emissions (tons) |
3.2.2 Core Explanatory Variable
Comprehensive digital economy development index: Based on the comprehensive index system of digital economy development level created by scholars such as Liu et al. (2020), Wen et al. (2020), and Xu et al. (2022), this article constructs an index system for the level of digital economic development and uses five indexes, namely, internet penetration, the number of mobile internet users, the number of information technology practitioners, the Internet-related outputs, and the China Digital Financial Inclusion Index, to measure and gauge China’s digital economy development level. The Digital Finance Research Center at Peking University and Ant Gold Service Group collaborate to develop the China Digital Inclusive Finance Index. Table 2 shows the comprehensive evaluation index system for the level of digital economy development.
Comprehensive evaluation index system for the level of digital economy development
| Level 1 indicator | Level 2 indicators | Description of indicators |
|---|---|---|
| Comprehensive development index for the digital economy | Internet penetration | Mobile internet users per 100 population |
| Number of mobile internet users | Cell phone subscribers per 100 population | |
| Number of information employees | Percentage of employees in the information transmission, computer services, and software industry | |
| Internet-related outputs | Telecommunications services per capita | |
| Digital financial development | China Digital Inclusive Finance Index |
3.2.3 Mediating variables
Industrial structure (str): Since different industries have different demands and dependence on capital, labor and energy, considering that the advanced development of industrial structure towards tertiary industry can reduce the dependence of economic development on energy consumption, and thus reduce CO2 emissions and increase Green Total Factor Productivity (GTFP), measured by tertiary industry/gross regional product.
Transaction efficiency (mak): The level of marketization is positively correlated with transaction efficiency. This article follows Sun’s (2012) research and selects the marketization index as an equivalent factor for transaction efficiency.
Green innovation (pat): Referring to the measurement of Xiao and Zeng (2023), the measurement is based on the total number of green invention and utility model patents applied in the region during the year.
3.2.4 Moderating Variable
Environmental regulation: Considering that the total amount of sulfur dioxide emissions are often related to the size of the regional economy, drawing on the idea of Jiang and Tan (2021), the industrial sulfur dioxide emission intensity, i.e., the inverse of sulfur dioxide emissions per unit of GDP, is chosen as a proxy variable for the environmental regulation intensity, and its magnitude is positively correlated with the regulation intensity.
3.3 Data Sources
In this article, 41 cities in the Yangtze River Delta region are selected as research samples for the period 2006–2020. These cities are distributed across Shanghai, Jiangsu, Zhejiang, and Anhui. Data on digital economy patents are obtained from the State Intellectual Property Office’s patent search website, while the variable data are primarily sourced from the China Urban Statistical Yearbook and local statistical reports. Missing values are addressed using interpolation and mean value methods. The descriptive statistics of the data are presented in Table 3.
Descriptive statistics
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Variables | N | mean | sd | min | max |
| de | 615 | 0.096 | 0.087 | 0.001 | 0.423 |
| tfp | 615 | 0.524 | 0.218 | 0.185 | 1.092 |
| str | 615 | 42.33 | 8.167 | 23.36 | 73.15 |
| mak | 615 | 11.34 | 2.873 | 4.212 | 18.74 |
| pat | 615 | 0.063 | 0.029 | 0.012 | 0.181 |
| reg | 615 | 0.208 | 0.524 | 0.003 | 7.493 |
| ind | 615 | 48.43 | 9.169 | 26.59 | 75.18 |
| urb | 615 | 0.572 | 0.144 | 0.126 | 0.896 |
| imp | 615 | 0.335 | 0.380 | 0.009 | 2.918 |
| edu | 615 | 0.027 | 0.011 | 0.008 | 0.069 |
| Number of dmu | 41 | 41 | 41 | 41 | 41 |
4 Empirical Results
4.1 Baseline Regression Results and Analysis
Using Stata 17 for regression analysis, we employed a stepwise regression method to sequentially include control variables in the model for baseline regression analysis, with the outcomes displayed in Table 4. Model (1) represents the regression of the digital economy on total factor productivity, while Models (2) to (5) illustrate the regressions after the incremental addition of control indicators. It is evident that, after incorporating the control indicators, the coefficient of the explanatory indicators remains positive, and the R-squared exhibits an upward trend, indicating an improvement in the model’s goodness of fit, which indirectly suggests the reasonableness of the selected control variables.
Regression results
| Variables | (1) | (2) | (3) | (4) | (5) |
|---|---|---|---|---|---|
| FE | FE | FE | FE | FE | |
| de | 0.523*** | 0.375*** | 0.203*** | 0.258*** | 0.292*** |
| (9.17) | (7.52) | (4.03) | (4.61) | (5.79) | |
| urb | 0.439*** | 0.455*** | 0.531*** | 0.516*** | |
| (4.08) | (4.09) | (4.41) | (4.36) | ||
| ind | −0.496*** | −0.487*** | −0.505*** | ||
| (−7.28) | (−6.63) | (−6.71) | |||
| edu | −0.387*** | −0.384*** | |||
| (−3.42) | (−3.47) | ||||
| imp | 0.283** | ||||
| (2.43) | |||||
| Constant | 0.257*** | 0.036 | 0.288*** | 0.346*** | 0.323*** |
| (20.21) | (0.62) | (4.61) | (5.23) | (4.70) | |
| Observations | 615 | 615 | 615 | 615 | 615 |
| Number of dmu | 41 | 41 | 41 | 41 | 41 |
| R-squared | 0.262 | 0.306 | 0.383 | 0.404 | 0.409 |
| dmu FE | Yes | Yes | Yes | Yes | Yes |
Robust t-statistics in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
After including all control variables, a 1% rise in the digital economy is linked with an approximate 0.292% rise in energy efficiency, thereby validating Hypothesis H1, which posits that the progress of the digital economy can enhance energy efficiency (Huang & Chen, 2023). Results highlight the transformative potential of digital technologies to improve energy efficiency. A 0.292% increase in energy efficiency, though seemingly modest, represents significant progress when applied across the large-scale industrial and urban systems in the Yangtze River Delta. The result reflects how digital innovations, such as smart grids, real-time energy monitoring systems, and intelligent manufacturing, optimize resource allocation and reduce transaction costs, thereby improving energy productivity.
For policymakers, this suggests a clear need to prioritize the development of digital infrastructure, particularly in high-energy-consuming industries and regions where digital adoption is lagging. This may be attributed to the significant development of the digital economy in the Yangtze River Delta region, propelled by the “Digital China” strategy. Here, digital technology applications and the development of new energy sources have reduced transaction costs, stimulated technological innovation, and improved productivity. Simultaneously, these changes have transformed how economic entities utilize productive resources and restructured social relationships in economic activities, leading to a corresponding improvement in energy efficiency.
4.2 Robustness Test
To further demonstrate the accuracy of the research methodology, variable selection, model selection, and research conclusions, the robustness test is an indispensable step in the research. Traditional approaches such as ordinary least square (OLS), fixed effect (FE), generalized method of moments (GMM) may perform well under strict stationarity and slope homogeneity but may suffer when the data contain a combination of I(0) and I(1) variables. To solve this issue and ensure robustness, we adopt the dynamic panel common correlated effects (DCCE) approach proposed by Chudik and Pesaran (2015). This model addresses three important issues: (1) it addresses the cross-sectional dependencies by utilizing the cross-sectional means and lags of the response variable on the right side of the model in conjunction with the predictors; (2) it applies the Eberhardt and Presbitero (2015) mean group method to tackle parameter heterogeneity; and (3) it incorporates response variable lags to accommodate the dynamics of the model (Edziah et al., 2022). Table 5 presents the results of the DCCE estimator, which shows robust results consistent with the main findings above. Specifically, the key variable – digital economy initially enhances energy efficiency, but its effect diminishes in subsequent models, likely mediated by industrial activity (which will be analyzed later). Urbanization enhances energy efficiency, demonstrating gains from better infrastructure and advanced technology.
DCCE estimator (robustness check)
| Variables | (1) | (2) | (3) | (4) | (5) |
|---|---|---|---|---|---|
| Model I | Model II | Model III | Model IV | Model V | |
| L.tfp | −0.441*** | −0.603*** | −0.749*** | −0.762*** | −0.764*** |
| (0.054) | (0.065) | (0.061) | (0.067) | (0.055) | |
| de | 0.818*** | 0.074 | 0.137 | 0.146 | 0.153 |
| (0.115) | (0.122) | (0.120) | (0.145) | (0.143) | |
| urb | 1.518*** | 1.485*** | 1.461*** | 1.101*** | |
| (0.275) | (0.476) | (0.437) | (0.394) | ||
| ind | −0.009*** | −0.006** | −0.004 | ||
| (0.002) | (0.002) | (0.003) | |||
| edu | −0.593 | 0.352 | |||
| (3.515) | (3.251) | ||||
| imp | 0.419 | ||||
| (0.421) | |||||
| CD statistic | 34.62 | 16.77 | 14.41 | 15.39 | 15.37 |
| P-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Observations | 574 | 574 | 574 | 574 | 574 |
| R-squared | 0.389 | 0.521 | 0.603 | 0.663 | 0.704 |
| Number of groups | 41 | 41 | 41 | 41 | 41 |
Standard errors in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
4.3 Heterogeneity Analysis
As the level of economic development varies across cities, different levels of economic development lead to varying levels of digital economic development. Additionally, there is regional heterogeneity in energy efficiency across cities. In this article, based on the National Sustainable Development Plan for Resource-based Cities (2013–2020) officially released by the State Council, the sample cities in the Yangtze River Delta are divided into resource-based and non-resource-based cities for regression analysis.
The results in Table 6 present the heterogeneity regression analysis, which shows that the digital economy can significantly improve energy efficiency regardless of the resource-consuming city. However, in terms of the impact level, the impact of the digital economy on energy efficiency varies across regions. The impact of the digital economy on energy efficiency in resource-dependent cities is considerably less than in non-resource-dependent cities. This is because resource-based cities are communities where the extraction and exploitation of natural resources, such as forests and minerals, dominate the local industry. These cities’ production and development are closely tied to resource development, with the primary and secondary industries accounting for a large proportion of their economy. As a result, the extent to which the digital economy improves energy efficiency is relatively weak (Yan et al., 2019).
Heterogeneity regression results
| Variables | Resource-based cities | Non-resource cities |
|---|---|---|
| (1) | (2) | |
| de | 0.226*** | 0.554*** |
| (4.10) | (3.74) | |
| Control variables | Controlled | Controlled |
| Constant | 0.327*** | 0.443*** |
| (3.30) | (5.37) | |
| Observations | 435 | 180 |
| Number of dmu | 29 | 12 |
| R-squared | 0.413 | 0.44 |
| dmu FE | Yes | Yes |
| r2_a | 0.406 | 0.424 |
| F | 35.51 | 12.16 |
Robust t-statistics in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
The dominance of energy-intensive industries such as mining, metallurgy, and heavy industry in resource-intensive cities limits the potential for immediate improvements in energy efficiency through digitalization. These industries often rely on outdated technology and infrastructure, making the adoption of digital innovations, such as smart mining systems or energy monitoring platforms, slower and more challenging. Furthermore, resource-intensive cities often rely heavily on non-renewable energy, further reducing the potential of the digital economy to significantly enhance energy efficiency.
In contrast, non-resource-dependent cities are more likely to benefit from the rise of the digital economy, as they rely more heavily on services and high-tech industries. These industries are better suited to digital tools, such as big data analytics, cloud computing, and artificial intelligence, to streamline processes and improve energy efficiency. For example, cities with a significant presence of technology companies or environmental innovation hubs can use digital platforms to integrate renewable energy and optimize energy use across sectors.
This analysis highlights the need for targeted policy interventions. Resource-intensive cities would benefit from the targeted digitalization of traditional industries and the adoption of cleaner production methods. Meanwhile, resource-poor cities should focus on expanding digital applications in advanced and service-oriented industries to maintain energy efficiency.
4.4 Endogeneity Test
The regression results confirm the hypotheses presented in this article. To further address the potential issue of endogeneity, the instrumental variable technique is employed for testing. The lagged one-period value of the digital economy is used as an instrumental variable for regression analysis. Table 7 presents the results of the endogeneity analysis using two-stage least squares . The exogenous explanatory variable, L1.de, is included in the first stage of the regression. The F-statistic of 75.41, being greater than 10, allows us to reject the existence of weak instrumental variables.
Endogenous analysis
| Variables | Phase I | Phase II |
|---|---|---|
| de | tfp | |
| L1.de | 0.487*** | |
| (8.68) | ||
| de | 0.525*** | |
| (5.10) | ||
| Control variables | Controlled | Controlled |
| Observations | 574 | 574 |
| dmu FE | Yes | Yes |
| Number of dmu | 41 | 41 |
| F | 75.41 | 95.47 |
Robust t-statistics in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
The digital economy indicator was chosen as an instrumental variable due to its theoretical and empirical properties. Specifically, Table 7 demonstrates that past levels of digital economy development are highly correlated with current levels (relevance), as evidenced by the F-statistic of 75.41 in the first-stage regression. This ensures the instrumental variable satisfies the criteria for validity and relevance. Additionally, the results of the over-identification test further confirm the robustness of this instrumental variable.
As shown in Table 7, the regression coefficient of the first-stage digital economy lag (L1.de) is 0.487, which is significant at the 1% level, highlighting a strong connection between the instrumental variable and the explanatory factor, validating the choice of the instrumental indicator. When the explanatory variable is energy efficiency (tfp), the regression coefficient of the digital economy (de) is 0.525, which is significant and positively correlated at the 1% level in the second-stage regression results. This indicates that the model employed in this research effectively mitigates the issue of endogeneity.
This result underscores the economic importance of the digital economy in enhancing energy productivity. In practice, this improvement highlights the critical need to invest in digital technologies that optimize energy use, such as real-time monitoring systems and smart energy management platforms. Policymakers should recognize that this impact is particularly pronounced when applied to energy-intensive industries, offering a pathway to simultaneously promote digital transformation and achieve energy efficiency goals.
4.5 Tests for Mediating Effects
To test the previously stated research hypothesis, this article examines the extent to which the digital economy further promotes energy efficiency in the YRD region through industrial structure upgrading, transaction efficiency improvement, and green innovation stimulation, based on the mediated effect model constructed above.
Table 8 indicates the regression tests for the findings of the mediated effect model. Among them, column (1) displays the outcomes of the fixed-effects model, including only the digital economy; models (2) and (3) test whether the mediating variable industrial structure effectively improves energy efficiency; models (4) and (5) show the regression results with transaction efficiency as the mediator; and models (6) and (7) show the regression results with green innovation as the mediator.
Regression tests for mediating effects
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
|---|---|---|---|---|---|---|---|
| tfp | str | tfp | mak | tfp | pat | tfp | |
| de | 0.292*** | 0.088*** | 0.237*** | 0.225*** | 0.155*** | 0.202*** | 0.253*** |
| (5.79) | (4.51) | (4.98) | (6.55) | (3.15) | (4.87) | (5.16) | |
| str | 0.629*** | ||||||
| (3.26) | |||||||
| mak | 0.612*** | ||||||
| (4.46) | |||||||
| pat | 0.196*** | ||||||
| (3.11) | |||||||
| Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
| Constant | 0.323*** | 0.542*** | −0.018 | 0.120** | 0.249*** | 0.390*** | 0.247*** |
| (4.70) | (11.41) | (−0.14) | (2.56) | (3.63) | (6.25) | (3.75) | |
| Observations | 615 | 615 | 615 | 615 | 615 | 615 | 615 |
| Number | 41 | 41 | 41 | 41 | 41 | 41 | 41 |
| R-squared | 0.409 | 0.830 | 0.436 | 0.851 | 0.449 | 0.365 | 0.421 |
| dmu FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| F | 48.15 | 263.8 | 37.16 | 207.8 | 46.42 | 41.67 | 41.82 |
Robust t-statistics in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
From model (2), the digital economy is significantly positively associated with industrial structure (str) at a 1% significance level, with a regression coefficient of 0.088, highlighting that the digital economy can effectively encourage industrial structure upgrading. From model (3), the regression coefficient of the digital economy (de) is 0.237, which is substantially positively correlated at the 1% level; the regression coefficient of industrial structure (str) is 0.629, which is substantially positively correlated at the 1% level. This indicates that the digital economy can enhance energy efficiency via industrial framework upgrading, thus verifying Hypothesis 2.
From the regression outcomes of the mechanism of the role of transaction efficiency, it can be seen that the estimated coefficient of the digital economy is significantly positively correlated with transaction efficiency (mak) at the 1% significance level, with a regression coefficient of 0.225, suggesting that the digital economy can successfully encourage the improvement of transaction efficiency; the regression coefficient of transaction efficiency (mak) is 0.612, which is highly favorable at the 1% level. This indicates that the digital economy enhances the efficiency of market transactions and improves energy efficiency, thus verifying Hypothesis 3.
From the regression results of the mechanism of green innovation stimulation, it can be seen that the estimated coefficient of the digital economy is significantly connected with green innovation (pat) at the 1% significance level, with a regression coefficient of 0.202, suggesting that the digital economy can effectively promote the stimulation of green innovation; and the regression coefficient of green innovation (pat) is 0.196, which is highly favorable at the 1% level. This means the digital economy stimulates green innovation, improving energy efficiency (Xin et al., 2024), thus verifying Hypothesis 4.
The mediation analysis shows how specific mechanisms influence the overall effect. For example, the close relationship between the digital economy and the modernization of the industrial structure (coefficient 0.629) highlights the key role of the transformation of traditional manufacturing to service or high-tech industries. In practice, this means that digital economy policies should focus on stimulating industrial transformation, such as providing tax incentives for companies that use energy-saving digital solutions. Similarly, the green innovation coefficient (0.196) highlights the importance of promoting green technologies through digital tools such as AI-based carbon monitoring systems. These findings provide clear directions for policymakers: targeted digital investments can simultaneously improve energy efficiency and accelerate industrial modernization.
4.6 Test for Moderating Effect
Table 9 presents the results of the regression analysis examining the moderating effect of environmental regulation. Environmental regulation is used as a moderating factor to examine if it can boost the impact of the digital economy on energy efficiency improvement. According to the regression findings in Table 9, the regression coefficient of the interaction term (de * reg) between the digital economy and environmental regulation is −1.386, which has a strongly negative correlation at the 1% level, while the regression coefficients of the digital economy (de) in models (1) and (2) are both substantially positive at the 1% level, highlighting that as the level of environmental regulation increases, it weakens the effect of the digital economy, thus verifying Hypothesis 5.
Moderating effect of environmental regulation
| Variables | (1) | (2) |
|---|---|---|
| tfp | tfp | |
| de | 0.292*** | 0.288*** |
| (5.79) | (6.73) | |
| de * reg | −1.386*** | |
| (−3.49) | ||
| reg | 1.261*** | |
| (3.37) | ||
| Control variables | Controlled | Controlled |
| Constant | 0.323*** | 0.328*** |
| (4.70) | (4.86) | |
| Observations | 615 | 615 |
| Number of dmu | 41 | 41 |
| R-squared | 0.409 | 0.423 |
| dmu FE | Yes | Yes |
| r2_a | 0.404 | 0.416 |
| F | 48.15 | 42.5 |
Robust t-statistics in parentheses, the asterisks *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively.
This finding implies a balancing act: policymakers need to craft regulations that incentivize innovation without imposing undue burdens on businesses. For instance, flexible policies, such as tradable permits or tiered standards, could encourage firms to adopt digital technologies while maintaining compliance. This insight is particularly relevant for regions heavily reliant on traditional industries, where over-regulation could stifle growth and limit digital transformation efforts.
5 Conclusions and Policy Recommendations
This research examines the influence of digital economy on energy efficiency across 41 cities in China’s Yangtze River Delta from 2006 to 2020. To measure digital economy, a composite indicator was developed, incorporating five essential variables: the internet penetration rates, the number of mobile the internet users, the total of information technology experts, the internet-related outputs, and the China Digital Financial Inclusion Index. Energy efficiency was assessed using the GTFP, which considers the dynamics of multi-input and output efficiency. In terms of models, the study uses the fixed-effects and DCCE econometric models to evaluate the direct and indirect impacts of digital economy growth on energy efficiency. The results show that the digital economy effectively improves energy efficiency in 41 cities. In addition, the digital economy can improve energy efficiency by upgrading industrial structure, improving market transaction efficiency, and stimulating green innovation. Furthermore, environmental regulations significantly impact the enhancement effect of the digital economy on energy efficiency. Importantly, the digital economy’s influence on energy efficiency varies across different cities, with resource-based cities exhibiting less impact compared to non-resource-based cities.
Based on the above research conclusions, this article proposes the following recommendations:
First, differentiated strategies should be adopted to maximize the role of the digital economy in improving energy efficiency in the Yangtze River Delta region. For resource-based cities, structural changes should focus on integrating digital solutions into traditional industries, such as adopting smart mining technologies, predictive maintenance systems, and blockchain to achieve supply chain transparency in energy-intensive industries. Financial incentives, such as subsidies or tax breaks, can be provided to encourage industries to adopt these technologies. Pilot projects supported by public–private partnerships should be implemented to demonstrate the successful application of digital tools. In non-resource-based cities, priority should be given to investing in advanced digital infrastructure such as 5G networks, IoT systems, and data centers to support and accelerate the growth of high-tech and service industries. It is necessary to establish knowledge-sharing platforms to replicate successful innovations, such as Shanghai’s smart grid, in smaller cities. This approach will further improve the energy efficiency of smaller cities and promote the overall coordinated development of the Yangtze River Delta region, thereby leading China to high-quality and stable development.
Second, interventions must be compatible with each city’s digital and regulatory environment. For resource-based cities, adaptive environmental regulations should be introduced, such as progressive carbon emission targets linked to digital adoption benchmarks. Industries should be supported to achieve these targets through access to energy-efficient technologies via grants or concessional financing. In addition, capacity-building workshops should be held to train industries to combine digital solutions with compliance strategies. For non-resource-based cities, digital literacy programs should be promoted to improve the skills of the workforce and align human capital with the growing demand for digital expertise. Regional green innovation centers should be established to combine digital tools, such as artificial intelligence analytics and IoT systems, with clean energy technologies to improve energy efficiency. This dual focus will enable cities to meet regulatory targets without unnecessary burdens, thereby benefiting from the digital economy.
Third, it is essential to develop the digital economy and promote technological progress. Resource-intensive cities should deploy digital twins and AI-based optimization tools in heavy industries to monitor and reduce energy consumption. Local governments should promote cooperation with technology providers to develop industrial plans for industries such as mining, metallurgy, and chemicals. For resource-poor cities, the use of digital platforms for efficient energy management in service-oriented industries and smart city planning should be expanded. Cross-sector cooperation should be encouraged to integrate renewable energy and optimize cross-sector resource allocation. These technological solutions will address the specific challenges and opportunities faced by each type of city.
Fourth, measurable targets should be set to ensure the effectiveness of these strategies. Resource-intensive cities should plan to increase the adoption of digital technologies in energy-intensive industries by 20% within 5 years, supported by tax incentives and pilot projects. For resource-poor cities, the priority is to achieve 90% coverage of advanced digital infrastructure (such as 5G and IoT networks) within the next 3 years. Progress should be measured regularly using indicators such as overall improvement in factor productivity and reduction in carbon intensity. First-tier cities should focus on key activities such as smart manufacturing, smart cities, and smart ports to drive the upgrading of the industrial structure of surrounding cities. It is also extremely important to strengthen the deep support role of green technology for innovation, quality, and core technology breakthroughs. Establishing a cross-regional collaborative green technology innovation network can accelerate the promotion, diffusion, and application of green technology innovation while optimizing the spatial layout of green technology innovation within the region. These measurable targets will provide a basis for monitoring progress and evaluating the success of these policy measures.
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Funding information: This research was funded by the National Natural Science Foundation of China (72350410488).
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. FL: conceptualization, data curation, investigation, methodology, software, project administration, resources, visualization, writing – original draft, writing – review & editing. JK: investigation, project administration, resources, supervision, validation, methodology, writing – review and editing. HS: data curation, funding acquisition, methodology, validation, writing – review & editing, project administration, BKE: writing – review & editing, software, investigation.
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Conflict of interest: Authors state no conflict of interest.
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Data availability statement: The datasets used during the current study are available from the corresponding author upon reasonable request.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
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- Loan Loss Provisions and Bank Value in the United States: A Moderation Analysis of Economic Policy Uncertainty
- Collaboration Dynamics in Legislative Co-Sponsorship Networks: Evidence from Korea
- Does Fintech Improve the Risk-Taking Capacity of Commercial Banks? Empirical Evidence from China
- Multidimensional Poverty in Rural China: Human Capital vs Social Capital
- Property Registration and Economic Growth: Evidence from Colonial Korea
- More Philanthropy, More Consistency? Examining the Impact of Corporate Charitable Donations on ESG Rating Uncertainty
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- A Bibliometric Analysis of Digital Financial Inclusion: Current Trends and Future Directions
- Targeted Poverty Alleviation and Enterprise Innovation: The Mediating Effect of Talent and Financing Constraints
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- The Mediating Role of Psychological Safety in the Relationship Between Paradoxical Leadership and Organizational Citizenship Behavior
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- Special Issue: Shapes of Performance Evaluation - 2nd Edition
- Redefining Workplace Integration: Socio-Economic Synergies in Adaptive Career Ecosystems and Stress Resilience – Institutional Innovation for Empowering Newcomers Through Social Capital and Human-Centric Automation
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- The Impact and Mechanism of the Creation of China’s Ecological Civilization Building Demonstration Zones on Labor Employment
- From Social Media Influence to Economic Performance: The Capital Conversion Mechanism of Rural Internet Celebrities in China
