Exploring Digital Economy, Industrial Structure Upgrading, and Regional Green Development in the Five Provinces of Northwest China

Abstract

This paper takes the five northwestern provinces of China as research objects to explore the intrinsic mechanisms of the digital economy, industrial structure upgrading, and regional green development through empirical analysis. This study reveals that the digital economy plays an indispensable role in the green and high-quality development of the five northwestern provinces. (1) This study investigates the influence of the digital economy on green high-quality development in China’s five northwestern provinces, focusing on the mediating effect of industrial structure upgrading. Using panel data and multiple regression analysis, it demonstrates that the digital economy significantly promotes green development, even when controlling for infrastructure, human capital, and openness. (2) Industrial structure upgrading serves as a critical mediator, transmitting part of this positive effect. Heterogeneity analysis shows that the digital economy’s impact is more pronounced in high-GDP regions, while low-GDP regions remain dependent on conventional drivers like infrastructure. Additionally, human capital and tax burdens exhibit positive effects on green development, whereas R&D intensity has a negligible short-term influence. (3) These findings highlight the importance of region-specific policies integrating digital infrastructure, industrial upgrading, and human capital investment to foster sustainable regional development. This study provides a theoretical basis for deepening digital economic development and promoting green industrial upgrading in northwest China. It suggests that policymakers should account for regional economic disparities and coordinate the deployment of digital infrastructure, industrial transformation, and human capital investment to achieve long-term, coordinated green and high-quality development in the region.

1. Introduction

The digital economy is a key driver of global green development. As a major resource consumer and a leader in the digital economy, China’s efforts to connect these two areas are crucial to moving from unsustainable to sustainable growth. This will significantly affect digital collaboration, industrial transfer, and green practices in the provinces of northwest China. The core focus of this study is on this complex interaction, emphasizing the need to comprehensively understand how digital transformation can drive ecologically sustainable development in similar areas. With the rise in the global digital economic transformation wave and the concept of green development becoming an important guide for global economic development, in-depth research on the connection between digital economy, industrial structure upgrading, and regional green development has great theoretical and practical value.

The five provinces in the northwest have unique industrial bases, resource endowments, and economic development stages, which greatly alleviate the path dependence in industrial upgrading and the green development effect of the digital economy. Specifically, there is a large proportion of resource-based enterprises in the northwest region. Existing enterprises face significant structural obstacles in their adoption of green practices. Compared with the eastern region, the construction of digital infrastructure in the northwest region is still relatively backward, which restricts the expansion of the digital economy. There is a clear gap in talent training and attraction in the northwest region, which weakens the innovation capabilities of the digital economy and green industries.

Existing research has made major breakthroughs in the fields of digital transformation, industrial structure upgrading, and regional green development, providing a rich theoretical and empirical foundation for understanding the interconnection between these three dimensions. Academics have confirmed the key role of digitalization in promoting green development and optimizing industrial structure, clarified the correlation between factors such as technological innovation, industrial upgrading, and green innovation, and also incorporated regional differences into the analysis [1,2,3].

However, these studies still have several limitations. Most studies rely on provincial or national data and lack targeted research on specific regions like the five northwest provinces, which may lead to the neglect of heterogeneity and uniqueness within these regions. In addition, there are methodological issues, such as androgenicity and insufficient data representation. The exploration of the complex causal links and synergy mechanisms between digital transformation and various variables is not thorough enough.

The main research gap at present lies in the lack of in-depth investigations into digitalization, industrial structure adjustment, and regional green development in the five northwest provinces of China. Specifically, the unique factors of the region, including resource endowment, industrial foundation, and economic development stage, have not been fully considered. The specific mechanisms and paths for digital transformation to affect green development through industrial structural adjustment are still unclear. At the same time, current research has methodological flaws, such as data quality and model assumptions that affect the reliability of research results. In addition, the analysis of different industry types in the relationship between digitalization, industrial structure adjustment, and regional green development is insufficient.

2. Literature Review

Against the backdrop of the “dual carbon” goals, the collaborative research on the digital economy, industrial structure upgrading, and regional green development holds theoretical and practical significance. Theoretically, it helps to enrich the theories of regional economy and sustainable development. Practically, it can provide a model for the path to achieve green transformation in the five northwestern provinces. To attain certain outcomes, the academic community has carried out a great deal of research in recent years on how the digital economy affects different facets of the economy and society. This review examines the relationship between the digital economy and green development, then examines how it upgrades industrial structure.

The upgrading of industrial structure is promoted by the digital economy through diversified mechanisms. The specific paths are as follows. Wu and Shao (2022) [4] proposed that the digital economy uses digital technology to optimize production processes and improve efficiency, thereby promoting industrial upgrading. It also promotes technology diffusion, allowing companies to adopt new technologies and driving the development of industrial standards towards high-end, intelligent development. Resource-based and traditional manufacturing industries form the core of the industrial structure in the five northwest provinces. The digital economy brings opportunities for digital transformation in these fields. By leveraging digital technology, resource utilization efficiency is optimized, costs are minimized, and traditional industries are propelled toward intelligent and high-end advancement [4]. Pang and Zhang (2023) [5] added that the digital economy redefines capital allocation through intelligent supply chain management and big data, allowing capital to flexibly match industrial needs to promote structural optimization. When compared to the eastern region, digital infrastructure in the northwest demonstrates significant backwardness. It is imperative that infrastructure construction be vigorously promoted and technological application be enhanced to facilitate industrial upgrading and green advancement [5]. Ma et al. explored the operational mechanisms whereby the digital economy drives the sustainable advancement of manufacturing sectors [6].

Some scholars have explored that the digital economy can also affect regional economic growth through different mediating effects [7,8,9]. It was verified by Zhou and Chu through a general equilibrium model that the digital economy makes critical contributions to attaining carbon neutrality goals by fostering technological progress. Furthermore, a staggered DID (difference-in-differences) approach was employed by Yang et al., with the “Broadband China” demonstration city policy serving as a quasi-natural experiment framework, to validate that digital economy policies facilitate industrial green transition [10]. Li et al. utilized a Spatial Durbin Model (SDM) and a mediation model to conduct an in-depth inquiry into how the digital economy affects carbon productivity, emphasizing spatial spillover dynamics and internal mechanisms. Their analysis of mechanisms showed that the digital economy boosts carbon productivity in both local and other prefecture-level cities through technological innovation and economic clustering [11]. The digital economy’s role as an emerging catalyst for green transition in resource-based cities (RBCs) was explored by Liang et al., leveraging panel data from 115 Chinese RBCs during 2011–2020. Empirical findings indicate that National Big Data Comprehensive Pilot Zones drive RBC transformation by accelerating manufacturing sector upgrading [12]. Chen and Zhou (2024) pointed out that the digital economy broadens innovation channels, enhances R&D capabilities, and promotes the improvement of labor skills, promoting the transformation of industries into a high-value-added model. Scholars in China have conducted extensive research on both national and regional levels; however, most studies have focused on the digital economy, industrial structure upgrading, and green development from separate perspectives [13]. Additionally, an indicator framework was constructed by Wang (2024) to measure the significant elevation of corporate carbon emission performance by intelligent manufacturing, with more notable promotion effects exhibited on enterprises in eastern regions [14]. Zhao et al. (2022) analyzed panel data spanning 237 Chinese PLCs from 2011 to 2019 to illustrate that digitalization directly advances industrial structure upgrading while stimulating technological progression and human capital accumulation [15]. Luo et al. (2023) documented that green innovation in developed zones may restrain this effect in underdeveloped regions owing to talent mobility and industrial shift, with the incremental DID methodology confirming digital transformation’s potential to enhance urban green innovativeness [16]. Liao (2023) [17] analyzed data from 280 cities from 2010 to 2019 and found through fixed effects and Spatial Durbin Models that digital development can promote urban green economic growth through industrial structural adjustment and regional innovation. Among them, the contribution of regional innovation to the intermediate effect is 30.848%, and the contribution of industrial upgrading is 38.155% [17]. Hao et al. (2023) used the environmental and economic accounting system to measure China’s digital and green economic growth from 2013 to 2019 and found that both showed an upward trend, and the marginal effect of digital development on green growth was 1.648 [18]. A Spatial Durbin Model and mediation effect model were applied by Li et al. (2023) after conducting Moran’s I index test, with results indicating that digital transformation enhances industrial green innovation efficiency via manufacturing sector upgrading and corporate green technological innovation stimulation [19].

Literature Gap

Domestically and internationally, the interaction between digital economy, industrial structural adjustment, and regional green growth is usually studied from a macro-perspective. Li and Zhang (2024) [20] built an evaluation system for the level of digital development and green development, making up for the problem of insufficient data representation in traditional research. The core of their method is to use detailed data at the city level to accurately reflect the connection between digitalization and green growth. This method can capture regional differences between cities in detail and help formulate targeted policy recommendations. Based on this framework, this study applies it to specific situations in five northwest provinces of China, focuses on their resource-based economic characteristics, explores how digital transformation can promote regional green development, and provides more targeted theoretical support. Using city-level data from these provinces, this study examined the interlinkages between digital economy, industrial upgrading, and regional green development.

3. Theoretical Foundations and Action Mechanisms

The digital economy, anchored in the theoretical frameworks of information technology economics, innovation economics, and industrial economics, drives productivity and resource efficiency through technological innovation. Industrial structure upgrading, conversely, draws from industrial structure theory and new growth theory, emphasizing technology-driven transitions to high-value-added sectors. Green development, meanwhile, is rooted in sustainable development and environmental economics, seeking to balance economic prosperity with ecological stewardship. These theoretical pillars form an integrated analytical framework to explore their interactive dynamics.

(1)

Mechanisms of the Digital Economy’s Impact

Industrial Upgrading Pathways: The digital economy facilitates the direct upgrading of traditional industries through AI, big data, and IoT applications, optimizing production processes and fostering high-end, intelligent growth. Simultaneously, it drives factor reallocation—channeling capital, labor, and technology to more efficient sectors via market competition—thereby indirectly catalyzing structural shifts toward post-industrial configurations.

Direct Impacts on Green Development: Digital technologies enable green consumption patterns and sustainable product innovation. For example, smart grids optimize energy distribution, while blockchain-based supply chain tracking reduces waste. Such innovations enhance resource efficiency and drive regional green economic transitions.

Mediating Roles: Industrial structure upgrading act as critical mediators in the digital economy–green development nexus (Wang, 2024) [21]. Upgrading enhances resource productivity, drives green R&D, and redirects labor toward knowledge-intensive sectors, while innovation fosters the emergence of green industrial chains.

(2)

Theoretical Extension: Digital Economy and the Environmental Kuznets Curve (EKC)

The digital economy’s mediating influence on green development aligns with the “structure-environment” transmission logic of EKC theory, which posits an inverted U-shaped relationship between economic growth and environmental quality [22]. Extending this framework to northwest China’s context reveals three key dimensions:

Scale Effects and Initial Environmental Pressure: In the early stages of digital economy development, traditional industries’ digital transformation may involve short-term increases in resource input (e.g., energy consumption from data centers, e-waste from equipment upgrades), creating temporary “scale effects” that exacerbate environmental stress.

Technological Effects and Inflection Point Dynamics: As digital technologies penetrate deeper—such as AI-driven energy optimization or blockchain-based carbon accounting—industrial energy efficiency improvements accelerate, driving the EKC toward its inflection point where environmental degradation peaks.

Structural Effects and Industrial Synergy: The digital economy propels industrial restructuring toward low-carbon, high-value sectors. In northwest China, this is evident in the rising share of clean energy industries (e.g., natural gas, photovoltaic) and smart grid scheduling systems, which together push environmental quality beyond the EKC inflection point, aligning with the theory’s structural optimization mechanism.

(3)

Study Contributions

This research advances the field in two distinct ways. Theoretically, the nascent digital economy exhibits complex, nuanced interaction mechanisms with green development, and the mediating role of industrial upgrading in this relationship requires deeper exploration. By unraveling these internal dynamics through rigorous analysis, this study enriches economic theory—for instance, by clarifying how the digital economy’s promoting effect on green development varies across industrial structure contexts and identifying the underlying theoretical logic. Practically, China’s five northwestern provinces face unique constraints (geography, resource endowments, economic challenges), making green sustainability particularly urgent. An in-depth analysis of these interconnections helps tailor development models—such as Xinjiang’s paradigm, where the digital economy optimizes industrial structures, enhances resource efficiency, and mitigates pollution—to foster harmonious economic–ecological development.

4. Research Hypothesis

4.1. The Effect of Digital Economy on Promoting Regional Green Development

In contemporary society, global digitization is elevating people’s digital living standards. Beyond enhancing daily convenience, this transformation has become a key driver of regional sustainable progress, especially in northwest China’s five provinces with historically lagging economic growth. Digital advancement improves resource allocation efficiency, boosts environmental innovation, and promotes east–west economic exchanges, offering new opportunities for western green transition. Hao (2023) employed green technology innovation, advanced industrial structure, and industrial structure rationalization as mediating variables to explore the relationship between China’s digitalization level (Digi) and green economy growth level (GEG) [23]. A theoretical framework was constructed by Ou et al. to assess the digital economy’s impact on green product export quality, providing novel empirical evidence for how it drives green product quality enhancement and contributing to the literature on digital technologies’ influence on green development [24]. The studies by these scholars provide a research foundation for this study [25]. Based on this, this paper proposes Hypothesis H1:

Hypothesis 1 (H1). 

Digital economy can promote green development in northwest China.

4.2. Regional Green Development Driven by Industrial Structure Upgrading

In the current environment of China, industrial structural adjustment and regional green and sustainable development are strategic goals that promote each other. The “double carbon” goal proposed by China in 2020 requires a shift to a low-carbon economic model, while technologies such as artificial intelligence and big data provide support for industrial optimization to achieve green transformation. Xu (2020) used the panel threshold model to show that economic development and industrial upgrading have significantly improved green efficiency in China [26]. Ma (2023) found that the optimization of industrial structure can promote the regional green efficiency, and urbanization and environmental supervision are key driving factors [27]. Chen et al. investigated that the implementation of strategic emerging industries by listed companies can enhance enterprises’ green total factor productivity [28]. Liu et al. constructed a digital economy development index using panel data from 102 resource-based cities in China during 2011–2019. They explored the role of industrial structure upgrading through mediating effect analysis and tested regional heterogeneity [29]. Based on this, this paper proposes Hypothesis H2:

Hypothesis 2 (H2). 

Industrial structure upgrading promotes regional green development.

4.3. Synergistic Effects Among the Digital Economy, Industrial Structure Upgrading, and Regional Green Development

The digital economy, industrial restructuring, and regional sustainable growth are core to China’s high-quality economic progress. These elements are interdependent and mutually reinforcing, driving long-term socioeconomic development. Some scholars have used mediating variables to explore the mechanism through which the digital economy promotes regional green development. Ozturk et al. explored that digital government and circular economy can reduce carbon emissions by promoting renewable energy production [30]. Li et al. found that the digital economy boosts carbon productivity in both local and neighboring prefecture-level cities via industrial technological advancement and economic clustering [19]. Yang et al. discovered that resource-based cities’ overreliance on natural resources hinders their sustainable growth, whereas the digital economy could mitigate this challenge via industrial restructuring [2]. Based on this, this paper proposes Hypothesis H3:

Hypothesis 3 (H3). 

Digital economy promotes regional green development through industrial structure upgrading.

4.4. Spatial Spillover Effect of Digital Economy on Regional Green Development

In China, the digital economy and agricultural green transition show an “east-high, west-low” spatial pattern. Regional disparities indicate the digital economy has stronger direct/indirect impacts on eastern cities’ premium green growth than in central–western areas [31,32]. Therefore, geographical factors and western development require greater focus. Leveraging the digital economy’s spillover effects in adjacent regions is crucial to promoting premium green progress in both local and neighboring cities. This sustains the digital economy’s role in driving urban green sustainability and supports the impact–state–response framework for holistic urban green advancement. Xie et al. found that the digital economy exerts significant influence on the development of China’s manufacturing industries and plays a pivotal role in the country’s economic and social progress [33]. Additionally, the digital economy exhibits spatial spillover effects, which are beneficial to local manufacturing industry but exert positive impacts on the development of manufacturing industry in neighboring regions. Based on this, this paper proposes Hypothesis H4:

Hypothesis 4 (H4). 

Digital economy has a positive spatial spillover effect on regional green development in northwest China.

5. Sample Selection and Data Sources

Considering the research objectives and data accessibility, the research data were obtained entirely from the statistical “Yearbook of Xinjiang Uygur Autonomous Region”, Ningxia Hui Autonomous Region, Shaanxi Province, Gansu Province, Qinghai Province.

(1) The development levels of the five provinces in northwest China differ substantially from China inland regions, and their digital economy began to develop relatively late. Consequently, it may be difficult to select cities in chronological sequence to meet the sample size. Therefore, this study selected 56 cities based on the classification of the five provinces in northwest China. The core cities included Xi’an, Lanzhou, Urumqi, Yinchuan, and Xining. Key cities consisted of Yulin, Xianyang, Baoji, and Changji Hui Autonomous Prefecture. Major cities were Weinan, Yan’an, Qingyang, Aksu Region, Ili Kazakh Autonomous Prefecture, Bayingolin Mongol Autonomous Prefecture, and Kashgar Region. General cities included Hanzhong, Ankang, Shangluo, Tongchuan, Yangling Demonstration Zone, Jiuquan, Tianshui, Baiyin, Wuwei, Pingliang, Zhangye, Longnan, Dingxi, Jinchang, Linxia Prefecture, Jiayuguan City, Gannan Prefecture, Kashgar Region, Karamay City, Hami City, Tacheng Region, Turpan City, Altay Region, Hotan Region, Bortala Mongol Autonomous Prefecture, Kizilsu Kirghiz Autonomous Prefecture, Wuzhong City, Shizuishan City, Zhongwei City, Guyuan City, Haidong Prefecture, Haibei Prefecture, Huangnan Prefecture, Hainan Prefecture, Guoluo Prefecture, Yushu Prefecture, and Haixi Prefecture. Data from these 56 cities from 2012 to 2022 were selected as the research sample.

(2) Independent variable: Digital Economy Development Level Index (dig). In this paper, based on the “Statistical Classification of Digital Economy and Its Core Industries” released by the National Bureau of Statistics in 2021, an index system for evaluating the development level of the digital economy was constructed from four dimensions. The specific indexes are presented in

Table 1. Evaluation system of digital economy development level index.

Primary Index	Secondary Index	Weight	Primary Index Weight
Digital industrialization	Per capita telecommunication business volume	0.084481	0.2655877
	Length of long-distance optical cable lines	0.024289
	Mobile phone penetration rate	0.036527
	Number of legal entities in the information transmission software and information technology service sector	0.080354
	Proportion of employment in the information software industry	0.039937
Enterprise digitalization rate	Digital inclusive finance	0.01732	0.3377271
	Proportion of enterprises engaged in e-commerce transactions	0.024181
	E-commerce sales volume	0.06015
	Number of websites per hundred enterprises	0.009406
	Value added in the secondary and tertiary industries	0.050633
	Investment in technological innovation	0.072063
	Volume of express delivery	0.103975
Digital infrastructures	Internet broadband access rate	0.024949	0.2531774
	Scale of mobile telephone infrastructure	0.036527
	Number of domain names	0.090496
	Number of web pages	0.101205
Digital innovation capacity	Number of domestic patent applications granted	0.075105	0.1435078
	Number of domestic patent applications received	0.068403

.

(3) Limitations and Countermeasures: There may be limitations in reflecting the comprehensiveness of the impact of the digital economy on the green development of different industry types due to differences in data sources and indicator selection. For example, the data in this study mainly came from official statistical yearbooks, which may not fully capture the application of digital economy in emerging industries. Additionally, there are still certain limitations of the data in this study: this study covers the time span from 2012 to 2022, and while these data can provide some trend analysis, the time span is relatively short and the sample size is limited, which may not fully capture long-term trends and dynamic changes. Contemporary digital economy and green development are progressing rapidly; thus, the data may undergo dynamic changes. The panel data used in this study may not timely reflect the latest development trends. Future research can try to combine more industry-level data and further optimize the indicator system to more comprehensively reflect the role of the digital economy in promoting regional green development, and extending the time span of the study can be considered to improve the robustness of the findings.

This study used the latest data from 2012 to 2022 in the five northwestern provinces, covering a 10-year time period with a sample size of 56 cities. The data came from official statistics such as the “Xinjiang Uygur Autonomous Region Statistical Yearbook”, “Ningxia Hui Autonomous Region Statistical Yearbook”, “Shaanxi Provincial Statistical Yearbook”, and “Gansu Provincial Statistical Yearbook”, and “Qinghai Provincial Statistical Yearbook”. These data went through a rigorous statistical survey and review process and have a high degree of credibility and authority. The data dimension is more comprehensive and can more accurately reflect the relationship between digital economy and green development in the five northwest provinces. The collected data were also cleaned and validated, with missing data filled in using interpolation methods to ensure data accuracy and completeness while reducing noise. Additionally, descriptive statistical analysis, multicollinearity analysis, robustness tests, principal component analysis, and an endogeneity test were employed in subsequent research.

(4) Methodological Expansion: Drawing from the city-level analytical framework of Li and Zhang (2024) [20], this study adapts and extends the methodology to suit its research objectives. The original approach—via a detailed indicator system—enables precise reflection of the interlinkage between digital advancement and green sustainability, serving as the theoretical foundation. Building on this, this study optimizes the indicator system, control variables, and data selection, integrating the unique context of the five northwestern provinces to better mirror the research problem’s complexity and diversity.

(5) Innovative Indicator Construction: The original method used indicators such as per capita telecommunications traffic volume and mobile phone penetration rate to measure the development of the digital economy. This study took into account the lagging digital infrastructure and incomplete data in China and innovatively introduced digital inclusive finance and express delivery service volume indicators, while adjusting the per capita telecommunication service volume indicators to better adapt to the background of northwest China’s digital economy. Specifically, digital inclusive finance reflects the application of the digital economy in the financial field. The express delivery business volume reflects the development momentum of the digital economy in the logistics industry. The two collaborate to improve the digital economy evaluation framework. These new indicators can more comprehensively assess the development level of the digital economy.

(6) Mechanism Variables: Industrial Structure Upgrading (Ind). In prior research (Zhou, 2013), the measurement of industrial structure upgrading has predominantly relied on the ratio of economic output between the tertiary and secondary industries [34].

(7) Dependent variable: Green development level (GTFP). This paper constructs an evaluation index system for green development level, which is shown in

Table 2. Evaluation system of green development level indicators.

Primary Index	Secondary Index	Indicator Value Description	Weight	Primary Index Weight
Input	Labor	The equivalent of research and development personnel	0.069779	0.2495667
		The proportion of employment in the tertiary sector	0.020216
	Capital	The intensity of green research and development and experimental development funding in large-scale industrial enterprises	0.027162
		The advancement of industrial structure	0.00741
		The rationalization of industrial structure	0.019256
		Internal expenditures on research and development funding	0.044973
	Energy	The per capita availability of public library collections.	0.022652
		The level of development of public transportation	0.015244
		The per capita urban road area	0.022875
Expected outputs	Economic efficiency	The reception of international tourists	0.024194	0.6152314
		The total import and export volume of foreign-invested enterprises	0.023124
		Total trade and revenue	0.181991
		The trade volume of high-tech products in imports and exports	0.071204
		Gross regional product (GDP)	0.2035
		The contribution of the tertiary sector to GDP	0.013774
	Ecological benefit	The forest coverage rate	0.076208
		The per capita area of park green space	0.021236
Environmental capacity	Environmental pollution	The urban sewage treatment rate	0.011325	0.1352022
		The comprehensive utilization rate of industrial solid waste	0.014999
		The intensity of environmental protection expenditures	0.04384
		The management of environmental pollution	0.045814
		The discharge of industrial solid waste	0.010883
		The rate of the harmless treatment of municipal solid waste	0.008341

.

(8) Control variables: This article identifies the following control variables: (1) per capita urban road area, indicative of transportation infrastructure (Road2); (2) the proportion of employment in the tertiary sector, reflecting labor force structure (Labor); (3) the ratio of environmental protection expenditure to GDP, representing the tax burden level (TL); (4) the proportion of internal R&D funding expenditure to GDP, measuring R&D intensity (RI); (5) the ratio of total exports from foreign-invested enterprises to GDP, denoting the openness degree (Open); and (6) the proportion of employment in the information software sector, assessing the human capital level (HC), which is shown in

Table 3. Variable design table.

Title 1	Title 2	Title 3
Explained variable	GI	Green and high-quality development index of the five provinces in northwest China
Explanatory variable	DIG	Digital economy index of five provinces in northwest China
Meta variable	INDU	Advanced industrial structure
Controlled variable	Road2	Traffic infrastructure level
	Labor	Labor force level
	TL	Tax burden level
	RI	Research and development intensity
	Open	Degree of openness to the outside world
	HC	Human capital level

.

(9) Variable selection is guided by theoretical foundations (Table 3), data accessibility, and empirical research needs, aiming to comprehensively analyze the internal relationships among the digital economy, industrial structure upgrading, and regional green sustainable development. During the indicator selection process, some potential measurement criteria face challenges in data collection. For example, using R&D investment in digital technologies to measure the development level of the digital economy encounters difficulties in obtaining comprehensive and accurate data. The screening of indicators requires comprehensive consideration: In terms of transportation infrastructure, the total length of highways cannot fully reflect the overall level of infrastructure development. In contrast, the per capita urban road area provides a more comprehensive assessment. When measuring the degree of openness to the outside world, the total import–export volume fails to reflect the specific contributions of foreign-invested enterprises, while the proportion of export volume of foreign-invested enterprises to GDP serves as a more precise metric.

(10) Unlike the study by Li and Zhang (2024) [20], which controlled the level of economic development and innovation, this study introduced human capital variables and revised the tax burden measurement method to adapt to the resource-based economy of the northwest. Specifically, human capital is measured by the proportion of employment in the information software industry to reflect the talent situation in the digital field in the region. The tax burden is measured as a percentage of environmental protection expenditures to GDP, which can accurately reflect the implementation of green policies. These adjustments reduced confounding factors and improved the reliability of the study results.

Although there are differences in indicators and data, this study followed the core logic of Li and Zhang’s (2024) [20] framework to analyze the five northwest provinces. By adjusting indicators and data, it not only verified the universality of the method but also captured the interconnectedness between digital progress and green sustainability in the northwest. This approach clarifies the operating mechanism of the digital economy in specific regions and helps provide targeted policy recommendations for regional green transformation.

(11) The original data are normalized, and the specific operation is as follows.

Forward indicators:

$$X_{ij}={\displaystyle \frac{X_j-X_{min}}{X_{max}-X_{min}}}$$

(1)

Negative indicators:

$$X_{ij}={\displaystyle \frac{X_{max}-X_j}{X_{max}-X_{min}}}$$

(2)

where X_j is the value of the jth indicator, and X_ij is the standardized value.

The contribution of the index in year j for index I was calculated as follows:

$${X\prime }_{ij}={\displaystyle \frac{X_{ij}}{\sum _{i=1}^n{X}_{ij}}}$$

(3)

The entropy value of the jth index was calculated as

$$e_j={\displaystyle \frac{\sum _{i=1}^n{X}_{ij}\prime ln{X}_{ij}\prime }{ln\left(n\right)}}$$

(4)

The difference coefficient of the jth index was calculated as

$$g_j=1-e_j$$

(5)

The weight of the jth index was calculated as

$$W_j={\displaystyle \frac{g_j}{{\sum }_{n=1}^n{g}_j}}$$

(6)

The regional digital economy development index/regional green development index was calculated as follows:

$${dig}_{it}={\displaystyle \frac{W_j\times X_{ij}}{\sum W_j}}$$

(7)

$$GTFP={\displaystyle \frac{W_j\times X_{ij}}{\sum W_j}}$$

(8)

6. Model

6.1. Panel Data Model

To analyze the impact of digital economy on regional green development (H1), the following basic model is established:

$${GTFP}_{it}={\alpha }_0+{\alpha }_1{dig}_{it}+{\alpha }_2{Z}_{it}+{\mu }_i+V_t+{\epsilon }_{it}$$

(9)

Here, subscripts i and t denote cities and years, respectively. ${GTFP}_{it}$ represents the regional green development level, ${dig}_{it}$ indicates the level of digital economy, and $Z_{it}$ refers to control variables. ${\mu }_i$ and $V_t$ denote the cities and time fixed effects, respectively. ${\epsilon }_{it}$ refers to the random disturbance terms.

6.2. Mechanism Effect Model

According to previously established assumptions, the digital economy has the ability to boost regional green development by supporting industrial structure upgrades (H2 and H3). This paper uses linear regression equations to investigate whether the digital economy may boost regional green growth through industrial structure upgrades. This study also explores how the digital economy and industrial structure upgrades (Ind) affect regional green development. The mediating impact is assessed by evaluating the significance of parameters α1, γ1, and γ2. This study examines the synergistic impact of the digital economy and improvements in industrial structure on regional green development. The mechanism effect model is articulated as follows:

$$in{d}_{it}={\alpha }_0+{\alpha }_1{dig}_{it}+{\alpha }_2{Z}_{it}+{\mu }_i+V_t{\epsilon }_{it}$$

(10)

$$GTF{P}_{it}={\gamma }_0+{\gamma }_1{dig}_{it}+{\gamma }_2{Z}_{it}+{\mu }_i+V_t+{\epsilon }_{it}$$

(11)

6.3. Spatial Econometric Model

To test Hypothesis H4, which examines the impact of the digital economy on regional green development considering spatial spillover effects, the following spatial spillover model was used, based on a previous spatial spillover mechanism for regional green development:

$${GTFP}_{it}={\alpha }_0+\rho {WGTP}_{it}+{\beta }_1{Wdig}_{it}+{\gamma dig}_{it}+{\beta }_2{WZ}_{it}+{\eta }_i{Z}_{it}+{\mu }_i+V_t+{\epsilon }_{it}$$

(12)

Among the variables in this model, ρ represents the spatial autoregressive coefficient, W denotes the spatial weight matrix, and the remaining variables are defined as before. In this paper, an economic distance matrix is utilized as the spatial weight matrix.

The economic matrix offers a precise method to quantify the spatial spillover effects of the digital economy on green development, and it calculates economic distance in various ways. This study employs the following economic distance calculation formula:

$$D_{ij}={\displaystyle \frac{{distance}_{ij}}{G_i-G_j}}$$

(13)

where distance represents the geographical distance between Province i and Province j, and the result of G_i minus G_j is the GDP difference between Province i and Province j; thus, the economic distance between the two provinces can be calculated. By constructing the economic distance values into a matrix, the spatial weight matrix W is obtained.

7. Results

7.1. Collinearity Test

When performing linear regression on the model, principal component analysis (PCA) is selected for dimensionality reduction due to the multicollinearity observed among the traffic infrastructure level (Road2), labor level (Labor), and research intensity (RI). This method extracts orthogonal principal components while retaining the majority of the information from the original variables to eliminate linear dependencies.

The cumulative variance contribution rate of the three principal components extracted by PCA reaches 84% (see

Table 4. Principal component analysis.

Component	Eigenvalue	Difference	Proportion	Cumulative
Labor	4.42361	2.50308	0.4915	0.4915
Road2	1.92053	0.683268	0.2134	0.7049
RI	1.23727	0.521254	0.1375	0.8424

), which can effectively replace the original variables. The results of the VIF (Variance Inflation Factor) examination are shown in

Table 5. VIF test.

Variable	VIF	1/VIF
HC	5.92	0.168972
Labor	5.88	0.170068
Open	3.73	0.268059
DIG	2.61	0.383208
INDU	2.26	0.442920
TL	2.07	0.482337
Mean VIF	3.74

:

The minimum VIF is 2.07, and the maximum VIF is 5.92. Although it exceeds 5, it does not severely interfere with the model. The average VIF is 3.74, which falls between 0 and 5. The results of the collinearity test meet the relevant standards, which indicates that there is no serious multicollinearity issue among the independent variables selected in this study.

7.2. Correlation Analysis

Table 6. Correlation analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) GI	1.000
(2) DIG	0.442 ***	1.000
(3) INDU	−0.385 ***	0.259 **	1.000
(4) Road2	0.449 ***	0.387 ***	−0.281 **	1.000
(5) Labor	0.212 *	0.267 **	−0.146	0.873 ***	1.000
(6) TL	−0.048	−0.541 ***	−0.137	−0.287 **	−0.331 ***	1.000
(7) RI	0.299 **	0.305 **	−0.239 *	0.851 ***	0.963 ***	−0.244 *	1.000
(8) Open	0.172	−0.019	−0.410 ***	0.563 ***	0.562 ***	0.264 **	0.655 ***	1.000
(9) HC	0.688 ***	0.578 ***	−0.286 **	0.829 ***	0.652 ***	−0.536 ***	0.625 ***	0.188	1.000

*** p < 0.01, ** p < 0.05, * p < 0.1.

presents the Pearson correlation coefficients among variables in the five provinces of northwest China. The correlations among these variables are generally complex. There is a strong positive correlation between the green high-quality development index (GI) and the digital economy index (DIG), as well as human capital level (HC), which provides important evidence for further regression analysis and causal relationship exploration.

According to

Table 7. Model test.

	F/Chi2	n
F-test	19.76	(4, 48)
Hausman test	32.35	4

, the F-test indicates that the regression model of this study is overall significant, with all explanatory variables having statistically significant effects on the dependent variable. The Hausman test further supports the choice of the fixed effects model, which better fits the data characteristics compared to the random effects model. Therefore, subsequent analysis should be based on the fixed effects model.

Baseline Regression Results (

Table 8. City and time fixed effects to analyze the benchmark regression of this study.

	(1)	(2)
VARIABLES	GI	GI
DIG	0.1565 ***	0.1447 ***
	(2.7934)	(2.8586)
Road2		0.0206 *
		(1.8209)
Labor		0.0607 *
		(1.9427)
TL		0.5807 ***
		(3.6609)
RI		0.1677
		(0.4015)
Open		0.3271 ***
		(4.8711)
HC		2.9690 ***
		(4.1794)
Constant	0.0833 ***	−0.6821 ***
	(19.6289)	(−2.9003)
R-squared	0.126	0.736
Number of code	5	5
City FE	Yes	Yes

t-statistics in parentheses, *** p < 0.01, * p < 0.1.

): With city and time fixed effects, the digital economy index shows a regression coefficient of 0.1565 (t = 2.7934, p < 0.01), confirming a significant promotion of green high-quality development in the five northwestern provinces.

Mechanism Effect Model: Including additional control variables, the DIG coefficient slightly decreases to 0.1447 (t = 2.8586, p < 0.01), maintaining statistical significance and verifying the robust positive impact of the digital economy.

Other Key Findings: Transportation infrastructure level: Coefficient = 0.0206 (t = 1.8209, p < 0.1), indicating a weak positive effect on green development. Labor force level: Coefficient = 0.0607 (t = 1.9427, p < 0.1), showing a moderate positive correlation with green development. Tax burden level: Coefficient = 0.5807 (t = 3.6609, p < 0.01), suggesting a strong contribution to green high-quality development.

The regression coefficient of R&D intensity was 0.1677, but its t-statistic was 0.4015, which did not reach a significant level. This shows that R&D intensity has no significant impact on green and high-quality development in this model. Insignificant results may be affected by the following factors: (1) Due to the short time span and small sample size, the results may not be robust enough. This means that the long-term impact of R&D intensity on green development has not been fully captured. (2) Indicators used to measure, for example, the ratio of internal R&D expenditure to GDP, may have measurement errors and cannot accurately reflect actual R&D investment and innovation capabilities. (3) Due to time delays, the short-term impact of R&D activities on green development may be concealed. Although R&D intensity is not statistically significant in this model, this finding does not reduce its theoretical value. In the long run, R&D may affect green development. The regression coefficient of the openness of the external environment is 0.3271, which is significant at the significant level of 1% (t = 4.8711), indicating that openness promotes green and high-quality development. The regression coefficient of human capital level is 2.9690, which is significant at the 1% level (t = 4.1794), which means that improving human capital has a significant supporting effect on green and high-quality development.

In summary, the digital economy (DIG) has a considerable positive impact on green and high-quality development in both models and has a strong promoting effect. Most variables have a statistically significant beneficial impact on green and high-quality development. The following variables have varying degrees of positive effects: openness (Open), labor level (Labor), tax burden level (TL), human capital (HC), and transportation infrastructure (Road2).

Table 9. Mediating effect: upgrading of industrial structure.

	Main Effect Regression	Mechanism Test
VARIABLES	GI	IINDU
DIG	0.1447 ***	4.7947 ***
	(2.8586)	(8.8089)
Road2	0.0206 *	0.3684 ***
	(1.8209)	(3.0333)
Labor	0.0607 *	0.8036 **
	(1.9427)	(2.3899)
TL	0.5807 ***	7.5738 ***
	(3.6609)	(4.4398)
RI	0.1677	−6.1243
	(0.4015)	(−1.3635)
Open	0.3271 ***	−1.5880 **
	(4.8711)	(−2.1991)
HC	2.9690 ***	−13.2303 *
	(4.1794)	(−1.7318)
Constant	−0.6821 ***	−8.6041 ***
	(−2.9003)	(−3.4018)
R-squared	0.736	0.802
Number of code	5	5
City FE	Yes	Yes
Time FE	Yes	Yes

t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

shows regression analysis utilizing industrial structure upgrading (INDU) as an instrumental variable. The table is divided into two columns: the first is the main effect regression, which tests the direct impact of variables such as digital economy (DIG) on green high-quality development (GI); the second is the mechanism test, which examines the mediating role of industrial structure upgrading in influencing digital economy and other control variables in green high-quality development.

• The regression coefficient of the main effect (first column) DIG coefficient = 0.1447 (t = 2.8586, p < 0.01), confirming a significant positive effect on green development. This is consistent with benchmark regression, indicating that the digital economy strongly promotes green high-quality growth.
• Mechanism Test (second column): DIG coefficient = 4.7947 (t = 8.8089, p < 0.01), showing that the digital economy significantly drives industrial structure upgrading, thereby influencing green development.

According to the regression results, the digital economy influences green sustainable development in the following ways. It promotes green sustainable development by upgrading the industrial structure. The digital economy not only has a direct impact on green sustainable development but also indirectly drives the optimization and upgrading of the industrial structure.

7.3. Robustness Test

The robustness test utilizes the Internet penetration rate (IPR) as an explanatory variable to verify the stability of the model after replacing key explanatory variables.

Table 10. Robustness: substitution of explanatory variables (IPR: Internet penetration rate).

	(1)	(2)
VARIABLES	GI	GI
IPR	0.9405 **	1.8538 **
	(2.1480)	(2.2092)
Road2		0.0172
		(1.4097)
Labor		0.0917 ***
		(2.8211)
TL		0.6016 ***
		(3.5139)
RI		0.1151
		(0.2580)
Open		0.3448 ***
		(4.9048)
HC		1.7824
		(1.6180)
Constant	0.0875 ***	−0.8356 ***
	(24.5428)	(−3.5580)
R-squared	0.079	0.719
Number of code	5	5
City FE	Yes	Yes
Time FE	Yes	Yes

t-statistics in parentheses. *** p < 0.01, ** p < 0.05.

shows that the regression coefficient for the Internet penetration rate is 0.9405, which is statistically significant at the 5% level. This suggests that increased Internet use has a major positive impact on green high-quality development. The findings imply that, maybe as a result of the internet’s ability to facilitate information flow and boost the possibility for digital economic growth, expanding internet coverage can encourage the creation of green, high-quality projects.

As shown in the mechanism effect model, the regression coefficient of the Internet penetration rate is 1.8538, which is significant at the 5% level (t = 2.2092). This means that an increase in the Internet penetration rate has a stronger positive effect on green and high-quality development. Furthermore, in the mechanism effect model, its effect is stronger.

The rate of Internet penetration is a major factor that supports green and high-quality growth. The explanatory variables that were replaced in the model demonstrate robustness and explanatory power.

Table 11. Robustness: replacing the explained variable (TTAR: green technology transaction activity rate).

	(1)	(2)
VARIABLES	TTAR	TTAR
DIG	0.0790 ***	0.0487 **
	(3.9194)	(2.4969)
Road2		0.0054
		(1.2422)
Labor		0.0540 ***
		(4.4864)
TL		0.1718 ***
		(2.8132)
RI		0.5193 ***
		(3.2295)
Open		0.0485 *
		(1.8779)
HC		1.1198 ***
		(4.0944)
Constant	0.0041 **	−0.4700 ***
	(2.6678)	(−5.1904)
R-squared	0.221	0.730
Number of code	5	5
City FE	Yes	Yes
Time FE	Yes	Yes

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents a robustness test using the green technology trading activity rate (TTAR) as the explained variable to verify the stability of the model after replacing the explained variable.

The digital economy index significantly promotes green technology trading activity, with a regression coefficient of 0.0790 (t = 3.9194, p < 0.01), which shows that it can effectively promote the adoption of green technology. In the mechanism model, the coefficient drops to 0.0487 (t = 2.4969, p < 0.05). Although the effect is slightly weaker than in the first model, it still proves to have a positive effect. The results in Table 11 verify that the digital economy (DIG) has a positive impact on the green technology trading activity rate (TTAR), strengthening its continued promotion role in green high-quality development.

7.4. Endogeneity Test—Two-Stage Least Squares (2SLS)

This research utilized the Two-Stage Least Squares (2SLS) approach to perform an endogeneity test on the model, using the lagged first-period independent variable L.DIG as the instrumental variable. Column (2) of

Table 12. Endogeneity test—Two-Stage Least Squares (2SLS).

	(1)	(2)
VARIABLES	DIG	GI
L.DIG	0.809 ***
	(0.127)
DIG		0.168 **
		(0.069)
road2	0.011	0.024 **
	(0.026)	(0.011)
Labor	0.070	0.050 *
	(0.067)	(0.030)
TL	−0.526	0.650 ***
	(0.355)	(0.168)
RI	−0.154	0.129
	(0.942)	(0.411)
Open	0.042	0.320 ***
	(0.151)	(0.066)
HC	−1.919	2.821 ***
	(1.750)	(0.703)
Anderson canon. corr. LM		24.335
		[0.000]
Cragg-Donald Wald F		40.772
		{16.38}

Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1. The values within { } are the critical values for the Stock–Yogo weak instrumental variable test at the 10% significance level.

indicates that the independent variable DIG retains positive significance in the second-stage regression results, aligning with the previously indicated regression. This suggests that the test results of this research demonstrate a notable degree of robustness.

The Anderson canonical correlation LM statistic is 24.335, with a p-value of 0.0000, indicating that the under-identification test was passed. The Cragg-Donald Wald F statistic is 40.772, surpassing the 10% critical value of 16.38, indicating that the weak instrumental variable test was successfully passed. These findings indicate that the chosen instrumental variable is effective.

7.5. Heterogeneity Test

Table 13. Heterogeneity test: GDP.

	(1)	(2)
VARIABLES	High	Low
DIG	0.2132 *	0.0318
	(2.0956)	(0.4293)
Road2	−0.0047	0.0330 *
	(−0.3068)	(2.0198)
Labor	0.2240	0.0264
	(1.3395)	(0.8998)
TL	0.9976 ***	0.3593 *
	(3.0567)	(1.9240)
RI	1.4902 **	−0.9319
	(2.3968)	(−0.9248)
Open	0.1321	0.4304 ***
	(0.9240)	(4.5317)
HC	3.6179 ***	3.1501 **
	(3.9480)	(2.1703)
Constant	−1.7759	−0.5168 **
	(−1.4143)	(−2.2224)
R-squared	0.926	0.641
Number of code	23	37
City FE	Yes	Yes
Time FE	Yes	Yes

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

analyzes the heterogeneity test results of the green high-quality development index (GI) in the five provinces in northwest China under different GDP levels. Specifically, the table lists the impact of the high-GDP group and low-GDP group on the GI under each variable.

In the high-GDP group, the digital economy index (DIG) has a significant positive impact on the green high-quality development index (GI), with a coefficient of 0.2132 and a t-statistic of 2.0956, which indicates the promotion of the digital economy on green development in high-GDP regions. However, in the low-GDP group, DIG’s effect on the GI is not significant, with a coefficient of only 0.0318 and a t-statistic of 0.4293, which fails to reach statistical significance. This suggests that the digital economy has a relatively significant promoting effect on green development in high-GDP regions, while its impact is weaker in low-GDP regions.

As demonstrated by Table 13, the green high-quality development index (GI) exhibits certain heterogeneity across regions at different GDP levels. In high-GDP areas, factors such as digital economy, R&D intensity, and human capital more significantly promote green development. In contrast, in low-GDP areas, transportation infrastructure, tax burden levels, and the degree of openness show relatively pronounced impacts.

8. Conclusions

This study systematically analyzes how the unique contextual factors of the five northwestern provinces impact the digital economy’s effects on green advancement and influence the path dependence of industrial upgrading. It aims to provide a theoretical foundation for formulating distinctive digital economy development strategies and green transformation policies tailored to the northwest. The research explores how the region’s distinct topography, economic structure, and social context shape the relationship between digitization and sustainable industrial development, helping policymakers understand region-specific green development mechanisms and establish targeted, locally adapted policies.

Through a case study of the five northwestern provinces, multiple regression analysis and mediation effect testing reveal key conclusions:

The digital economy index (DIG) demonstrates a significant positive effect on the green high-quality development index (GI) in the region, confirming both statistical significance and practical relevance. The digital economy has become a critical driver of regional green development, optimizing resource allocation, enhancing industrial efficiency, and fostering green innovation. Notably, while the digital economy directly promotes green development, industrial structure upgrading (INDU) acts as an important intermediary. Specifically, the digital economy facilitates industrial restructuring, which indirectly boosts green development.

Using a panel dataset of 2460 observations from 56 entities across the five provinces during 2012–2022, this study integrated theoretical frameworks with empirical analysis to systematically explore the dynamic interrelationship between regional sustainability and digital economy growth. Key findings include the following:

(1)

The digital economy exerts a statistically significant positive impact on northwest China’s green development initiatives. This conclusion remains robust across sensitivity tests with different explanatory variables, validating the model’s explanatory power and reinforcing the stability of research results.

(2)

Mechanism analysis shows that industrial system upgrading is the primary channel through which the digital economy drives environmental progress. Fixed-effects models integrating city-specific and time-series dimensions confirm the digital economy’s substantial and statistically significant positive influences on regional green advancement, with notable effect sizes. Mediation analysis using industrial structure upgrading as a mediator reveals the digital economy exerts direct and indirect effects on green development by facilitating industrial system transformation. This analysis uncovers a new finding compared to that of Li and Zhang (2024) [20]: the digital economy has a weaker effect on green development in low-GDP regions, attributed to the inclusion of digital financial inclusion indicators and human capital variables. Specifically, the digital economy’s impact is more pronounced in high-GDP areas, while low-GDP regions still rely on traditional drivers like infrastructure investment. This offers a new perspective for research at the intersection of digitalization and regional green sustainability, supporting the hypothesis that region-tailored policies integrating digital infrastructure, industrial upgrading, and human capital investment are vital for fostering sustainable regional growth.

This study also finds tax burden and human capital to positively influence green development, while R&D intensity shows insignificant short-term effects. In northwest China, these insights provide a theoretical basis for enhancing digital economy development and promoting green industrial upgrading, emphasizing the need for regulatory frameworks balancing technological innovation, human capital accumulation, and financial incentives. The digital economy’s impact on regional green development exhibits significant geographical variation: In China’s high-GDP eastern provinces, it has a more pronounced positive effect, attributable to strong economic foundations, advanced digital infrastructure, and high corporate/talent adaptability to new technologies. By contrast, the five northwest provinces—with lower GDPs—still depend on traditional drivers like infrastructure investment, facing digital economy growth barriers due to expertise shortages and inadequate infrastructure.

Notably, this study confirms that the digital economy strongly promotes green advancement in the five northwest provinces, driven by its potential to optimize resource allocation, enhance industrial efficiency, and spur green innovation. The mediating role of industrial structure upgrading shows digital technology enables traditional industries to transition toward high-end and intelligent directions, thereby advancing green growth. Analysis reveals significant regional disparities in digital economy development and environmental performance across China: per the China Digital Economy Development Report (2024), eastern regions consistently outperform the five northwest provinces in digital economy indices, reflecting superior green development conditions. Similarly, the central region surpasses the western region in both green development and digital economy maturity.

The digital economy’s rise relies on a robust economic base, significant technology investment, and abundant human capital—resources disproportionately concentrated in eastern and central regions, enabling advanced digitalization. These regions have strategically reduced resource dependence by prioritizing service sectors and high-tech industries, while actively implementing environmental protection policies and promoting technological innovation. Such strategies have created a virtuous cycle of synergistic progress between digitalization and sustainability, yielding findings with broad applicability.

9. Policy Implications

Based on this study’s conclusions, the following policy recommendations are proposed:

• Strengthen Digital Infrastructure and Regional Coordination

The 2023 Digital Economy for Shared Prosperity Implementation Plan underscores the need to enhance digital infrastructure in western regions, support the construction of green computing hubs in the northwest, and advance initiatives like “Digital Aid to Qinghai” and “Digital Aid to Xinjiang”. Specifically, the digital economy facilitates the transition of industrial structures toward high-tech, high-value-added sectors, thereby propelling green development. This highlights the imperative to prioritize industrial upgrading alongside digital economy promotion for sustainable, eco-friendly growth. Governments should increase investment in network infrastructure for remote and rural areas to elevate internet penetration and bandwidth. As a foundational pillar of the digital economy, robust network infrastructure enables the widespread adoption of digital technologies across agriculture, industry, and services, driving industrial advancement and sustainable development.

For the northwest, governments should augment central fiscal transfer payments to fund digital infrastructure and upgrade data centers’ basic processing capabilities. Meanwhile, strategies like the “East Data and West Calculation” initiative can guide the flow of digital resources from eastern to northwestern regions, fostering coordinated regional development. Additionally, cultivating local digital industry foundations, bridging the digital divide, stimulating employment and economic growth, and strengthening east–west collaboration and counterpart support are essential.

2.

Harness Digital Economy for Green Development

The digital economy and industrial infrastructure modernization play a pivotal role in enabling the five northwestern provinces to achieve green development. Examples of China’s digital economy strategy in action include Shaanxi’s “Yellow River Basin Ecological Cloud Brain” (utilizing satellite remote sensing and IoT for intelligent soil erosion monitoring), Ningxia’s “Green Electricity-Computing Power” linkage pilot, and Xinjiang’s “Digital Cotton” full-industry-chain platform (integrating water-saving irrigation and carbon emission traceability). To accelerate the green transformation of industrial infrastructure, upgrade transportation networks, and advance human capital—thereby promoting regional green development—policymakers should enhance support for the digital economy. While R&D investment yields long-term benefits for green development, its short-term direct impact remains limited, necessitating comprehensive considerations in policy formulation to drive holistic green growth.

3.

Targeted Preferential Policies

(1) Talent Development

Education departments should collaborate with enterprises to provide hands-on training and integrate digital skill courses into school curricula, implement preferential policies such as housing subsidies and research grants to attract digital technology talent to the northwest, and establish a talent exchange platform to foster collaboration between the northwest and developed regions.

(2) New Energy and Smart Grids

Prioritize new energy and smart grid industries within the energy sector. While guiding industrial structures toward upscale, intelligent, and eco-friendly transformation, expanding these emerging sectors can invigorate regional economies. Reinforce this shift through targeted policies: Tax incentives and fiscal subsidies can accelerate the growth of green sectors and the digital economy. Enterprises engaged in the R&D of digital and green technologies should qualify for direct fiscal support and special tax breaks, with tiered subsidy schemes based on the R&D scale or technological significance to alleviate financial burdens and encourage continuous innovation.

(3) Digital Silk Road Integration

Leveraging the northwest’s geographical advantages, the 2024 “Digital Silk Road” green initiative focuses on establishing a cross-border green digital corridor via Northwest Land Ports, promoting direct power supply from Central Asian wind–solar bases to domestic computing centers. The northwest should actively capitalize on its strengths to embrace the “green + low-carbon” model, enhance talent incentives, strengthen R&D teams, form independent innovative industrial systems, cultivate new growth points, optimize industrial structures, and foster urban transformation. Governments should issue industry digital transformation guidelines and standards, establish special funds (via project subsidies and loan interest subsidies) to support traditional enterprise upgrades, encourage resource-dependent industries (e.g., mining, energy) to adopt green digital technologies to enhance resource efficiency and reduce pollution, promote industrial collaboration and innovation, drive cross-industry synergies, form clusters, and facilitate resource sharing/recycling—such as by establishing specialized green industrial parks for centralized waste treatment. They should also launch innovation centers and strengthen industry-university-research ties to expedite digital technology integration into traditional sectors, optimizing operations and fostering sustainable, tech-driven transformation.

(4) Targeted Industrial Planning

Governments should formulate industrial growth plans to channel funds toward green and digital sectors. For example, Xinjiang could implement a “Digital Cotton” strategy to facilitate the cotton industry’s digital transformation. Such policies clarify development priorities, optimize resource allocation, and expedite the advancement of the digital economy and green industries—ensuring policy tools align with regional characteristics to build a systematic framework for sustainable industrial progress.

10. Research Limitations and Future Research Directions

The limitations of this study and the directions for future research are as follows:

• Research Limitations: This study primarily relies on panel data from the five northwestern provinces of China, which may limit the generalizability of findings to other regions. Some indicators, such as the intensity of digital technology R&D investment and regional green innovation output, face challenges in data availability and accuracy, potentially introducing measurement errors. This study focuses on the mediating role of industrial structure upgrading but does not consider other potential mediating variables (e.g., green technology diffusion or energy efficiency improvements).
• Future Research Directions: The research scope should be expanded to compare the digital economy–green development relationship across different regions to identify regional heterogeneity and formulate more targeted policies. Future studies should extend the research to an international context, comparing development models and mechanisms in resource-based regions of other countries; construct a theoretical model to mathematically deduce the interaction mechanisms and test them with structural equation modeling (SEM); explore the moderating role of institutional factors in the digital economy’s green development effect, combining new institutional economics theories. Micro-level and longitudinal perspectives: Case studies should be conducted regarding typical enterprises to reveal micro-mechanisms of digital economy-driven green development. Researchers should extend the time span of the dataset to observe the long-term dynamic effects of the digital economy on industrial upgrading and green development, considering technological diffusion lags. These directions aim to address the current limitations, enhance the theoretical depth and practical relevance of the research, and provide a more comprehensive scientific basis for promoting digital economy and green development in resource-based regions.