The Impact of the Digital Economy on Urban Ecosystem Resilience in the Yellow River Basin

Abstract

The digital economy is key to ecological security in the Yellow River Basin and to harmonious coexistence between humans and nature. This study uses data from 80 cities in the Yellow River Basin from 2010 to 2022 to examine how the digital economy affects urban ecological resilience. It uses three models to do this. The conclusion that the development of digital economy in the Yellow River Basin can significantly promote the enhancement of urban ecological environment resilience still holds after the robustness tests of phased regression, variable substitution and the introduction of instrumental variables. There is regional heterogeneity in the impact of digital economy on urban ecosystem resilience, showing the unbalanced spatial characteristics that the middle reaches are the highest, the upper reaches are the second highest, and the lower reaches are the lowest. The digital economy was shown to influence ecological resilience through a “double fixed-effects model” and a mediation effect model, via two intermediary pathways: “digital economy development → industrial structure upgrading → ecological resilience enhancement” and “digital economy development → resource allocation improvement → ecological resilience enhancement”. The digital economy was shown to transform and upgrade industrial structures and optimize capital and labor allocation, strengthening the ecological resilience of cities in the Yellow River Basin.

1. Introduction

The Yellow River is considered China’s cradle, playing a key role in its environment and economy. It is a vital site for ecological security [1]. The Party and the State have given top priority to the ecological civilization initiative, placing it at the forefront of the national development plan. A strategy has been created to support a sustainable vision for the Yellow River Basin’s economy, promoting ecological protection and high-quality development. This region is crucial to China’s development and environment. However, rapid urbanization has introduced challenges like soil erosion, population growth, and pollution, with emissions accounting for over 70% [2]. Urban ecosystems are vital for development and environmental governance, yet they are vulnerable to external pressures that hinder the region’s conservation and development. This has led to a focus on “urban ecosystem resilience”, emphasizing the importance of understanding how urban ecosystems can function, thrive, and adapt to external pressures. Assessing urban systems’ environmental resilience, analyzing spatial heterogeneity, and identifying drivers are critical for ecological protection and “regenerative cities” in the Yellow River Basin [3].

Digital technologies offer opportunities to enhance urban ecosystems and achieve growth in the Yellow River Basin. According to the Research Report on the Development of China’s Digital Economy (2024), the size of China’s digital economy reached 53.9 trillion yuan in 2023, up 3.7 trillion yuan year-on-year. The share of China’s digital economy in GDP reached 42.8%, up 1.3% from 2022. The digital economy is a transformative economic paradigm following the agricultural and industrial economies. It has been demonstrated to achieve optimal economic output and ecological efficiency while minimizing resource consumption and environmental costs, fostering robust industrial ecosystems and eco-industrial economies. These characteristics offer new opportunities to improve the industrial structures of cities in the Yellow River Basin, strengthen their ecological resilience, and reduce their vulnerability. However, this raises significant questions: Can the development of the digital economy truly enhance the ecological resilience of cities in the Yellow River Basin? If confirmed, what are the mechanisms or drivers? A systematic analysis is currently absent from academic discourse, leaving room for exploration.

This paper examines the digital economy and the ability of the Yellow River Basin to recover from environmental problems. In doing so, the study aims to contribute to the extant body of literature on the “green development effects of the digital economy” in the context of ecological resilience. The study offers insights into the nexus of digital economy development and ecological resilience enhancement in urban clusters within the Yellow River Basin. To this end, the paper employs a model to examine how the digital economy affects urban ecological resilience. The study places particular emphasis on spatial variation and the consequences of optimizing industrial structure and enhancing resource allocation. Specifically, it examines how advanced and rationalized industrial structures, as well as the mitigation of capital and labor mismatches, influence the transmission mechanisms of ecological resilience. The objective of this study is to elucidate the potential of the digital economy to bolster ecological resilience within the urban centers of the Yellow River Basin.

2. Materials and Research Methods

2.1. Literature Review and Research Hypotheses

Since the beginning of the 21st century, urban ecological resilience has attracted a great deal of attention from academia and society in order to adapt to the construction needs of wetland cities [4], eco-cities [5], and smart cities [6], but it is still an emerging topic, related research is still controversial, and there is still room for exploration. Overseas research on urban ecological resilience mainly focuses on the theoretical basis, operation mechanism, evaluation indexes, etc. [7], in which the construction of the evaluation index system is mainly analyzed from the perspectives of ecosystem services [8], sustainable development of the natural environment [9], and community resilience [10], etc. Domestic research is mainly divided into theoretical research and evaluation indicators, which are mainly used in the analysis of urban ecological resilience. Theoretical research focuses on the basic concept of ecological resilience, which is defined from the perspective of equilibrium theory and evolutionary theory, respectively. Based on the equilibrium perspective, scholars propose that urban ecological resilience refers to the prevention and response ability of urban ecosystems to risky disturbances, as well as the rebound recovery ability to return to the pre-disturbance state after being disturbed [11]. Some scholars view ecological resilience as an inherent attribute of ecosystems from an evolutionary perspective, emphasizing not only the rebound ability of urban ecosystems to return to the pre-disturbance state regardless of whether or not they have been disturbed, but also the ability of the system to achieve transformational development by adjusting its structure and changing its path [12]. In terms of practical exploration, the first is the construction of the indicator system and method selection for evaluating the resilience of the ecological environment of the cities in the Yellow River Basin. Some scholars have evaluated the urban resilience of the Yellow River Basin cities based on four dimensions, ecology, economy, society, and infrastructure, and have explored the spatial differentiation characteristics of urban resilience and the influencing factors by adopting various methods [13]. Based on the concept of the “natural-economic-social” composite ecosystem, some scholars have constructed an urban ecological resilience evaluation system and measured the urban ecological resilience of the Yellow River Basin urban agglomeration by using a three-dimensional spatial vector model [14]. There are also aspects including the scale–density–morphology system of the landscape ecological pattern [11] and the resistance–adaptation–recovery model, relying on the driving force–pressure–state–impact–response (DPSIR) and pressure–state–response (PSR) methods [15] to construct a multidimensional index system to quantitatively analyze urban ecological resilience. Second, regarding the influencing factors, some studies consider that urban ecological resilience is the result of the joint action of social activities and natural habitats [5]. The methods used by scholars to identify the influencing factors of urban ecological resilience involve the barrier degree model, the STIRPAT model, the multiple linear regression model, the dynamic regression model, and the Shapley value decomposition; the influencing factors include the biogeographic indicators such as precipitation, temperature, and land for construction, and the socio-economic factors such as economic development, opening up to the outside world, and investment in environmental improvement [16].

The digital economy is defined as a series of economic activities in which digitalized knowledge and information are pivotal factors of production. The Internet, mobile communication networks, the Internet of Things, and other modern information networks play a pivotal role as carriers, and the effective use of information technology, efficiency improvement, innovation, and creation are integral to the purpose of a series of economic activities [17]. Studies have elaborated from the theoretical level that the digital economy, as one of the core technological means to realize the goal of “double carbon”, has a significant effect on the management of carbon emissions in the Yellow River Basin [18,19], the improvement of the efficiency of the green scale of the city, the promotion of green technological progress [20], the improvement of the structural factors of the imbalance in the development of underdeveloped countries and the promotion of sustainable development [21], the release of the digital economy dividend to promote industrial structure upgrading, and the improvement of eco-efficiency. Releasing the digital economy dividend to promote industrial structure upgrading, as well as ecological efficiency improvement and other aspects of the role of the effect is significant [22]. Existing studies mainly focus on the national and provincial levels to explore the impact of the digital economy on the ecological environment or ecological efficiency. Fewer studies have observed the relationship between the digital economy and ecological resilience from the perspective of the sustainable development of watersheds. This study combines the main content of Schumpeter’s innovation theory to analyze the impact of the digital economy on the ecological resilience of cities in the Yellow River Basin. The aforementioned theory was first proposed by Joseph Alois Schumpeter in his book “Theory of Economic Development”, which emphasizes that the essence of innovation is the combination of production factors, i.e., the reorganization of the production function. Schumpeter’s theory posits that innovation should be grounded in five distinct dimensions: product, technology, market, resources, and organization. This perspective finds resonance with the role of the digital economy in the Yellow River Basin, particularly in the context of influencing urban ecological resilience. The present paper is therefore based on Schumpeter’s theoretical framework for innovation and proposes a mechanism for the role of the digital economy in the Yellow River Basin in terms of urban ecological resilience, taking into account industrial optimization, technological change, market innovation, resource allocation, and organizational change (see Figure 1).

2.1.1. Direct Effects of the Digital Economy on Urban Ecosystems

Regarding technological change, the middle reaches of the Yellow River, encompassing Shaanxi and Shanxi, are key grain-producing regions and heavy chemical energy zones. High-intensity land development and urban construction expansion have encroached upon forests and ecological lands, resulting in severe soil erosion and environmental pollution that undermines the resilience of urban ecosystems. The digital economy introduces innovative ecological risk prevention mechanisms through technological advancements, thereby bolstering urban ecosystem resilience in the Yellow River Basin. The establishment of an integrated, Internet-based emergency management platform can be achieved by leveraging modern information technologies, including sensors, the Internet of Things (IoT), artificial intelligence (AI), and digital twins. The implementation of such platforms, such as urban natural disaster management systems, intelligent safety production monitoring platforms, and intelligent fire safety and rescue systems, has been demonstrated to be effective in enhancing ecological and environmental risk prevention. This enhancement of environmental governance, improvement in human habitat, and optimization of waste utilization and disposal in the Yellow River Basin is a key finding of this study.

The following essay will explore the concept of organizational change in the context of the Yellow River Basin. The digital economy has the potential to transform urban ecosystems’ governance, processes, and prevention methods in the Yellow River Basin. By leveraging digital infrastructure, big data, and system integration platforms, organizations can enhance their capacity to respond to, prevent, and manage ecological and environmental emergencies. Furthermore, the establishment of interconnected emergency management information platforms across various regions fosters collaborative governance among organizations. This novel governance paradigm facilitates expeditious ecosystem rehabilitation and reinforces local ecological resilience.

The subject of market innovation is addressed in the following section. The downstream areas of the Yellow River benefit from robust industrial linkages, rapid economic development, and a significant concentration of capital and talent. Nevertheless, excessive government intervention in both upstream and downstream areas has been shown to exacerbate tensions between economic and ecological effects, thereby increasing the ecological vulnerability of cities. The advent of the digital economy paradigm has given rise to new technologies that serve to expand the boundaries of industrial chains and accelerate the process of integration across various industrial sectors. Through technology diffusion and industrial linkages, it fosters the emergence of new markets driven by demand for innovative technologies and renewable energy. This paradigm shift gives rise to growth points focused on talent, technology, and knowledge. As the new economy’s market share rises, resources such as capital, labor, and technology increasingly converge in this domain, accelerating energy-efficient technological development and resource efficiency improvement. Consequently, this transition mitigates the environmental impact of urban enterprises, integrates the green economy with ecological governance, and gradually enhances the overall quality of urban ecological environments.

Hypothesis 1.

The digital economy in the Yellow River Basin has been shown to improve urban resilience through technological, organizational, and marketing change.

2.1.2. Indirect Effects of the Digital Economy on Urban Ecosystems

Regarding structural optimization, Inner Mongolia, Ningxia, Gansu, and Qinghai experience arid and semi-arid climates, limited ecological restoration capacities, and insufficient market vitality due to the dominance of traditional industries in their regional economy. The digital economy integrates modern information and intelligent technologies into industrial production. This integration yields environmental benefits like green products, which are environmentally friendly, energy-saving, and recyclable. These advancements mitigate the environmental impacts of traditional products, enhancing local ecological resilience.

Hypothesis 2.

The digital economy has been demonstrated to enhance urban ecological resilience by driving industrial structure upgrading and optimization.

Regarding resource allocation, allocating resources is key to understanding the region’s dynamics. The upper Yellow River region has a sensitive ecology and a growing economy. Digital technology has a major impact on regional industry, creating new opportunities and technological innovation. This approach optimizes the use of factors of production, transforming industries that are harmful to the environment. Improved production methods and products in urban areas use water, energy, and land resources efficiently and also reduce pollution, as shown in the Yellow River Basin.

Hypothesis 3.

The digital economy has been demonstrated to enhance urban ecological resilience by improving regional resource allocation efficiency.

2.2. Data Sources

The Yellow River is an important geographical feature of China. It runs from east to west and north to south, through nine provinces: Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. It is a key ecological security barrier in northern China. The present study builds on related research and considers the spatial effects of ecological issues and the linkages between administrative units. Eighty cities within the Yellow River Basin have been selected as research samples covering the period from 2013 to 2022 [21,22]. These cities have been categorized into three zones, namely upstream, midstream, and downstream (see

Table 1. Division of upper, middle, and lower Yellow River Basin sections.

Region	Provinces	City Name
Upper reaches of river	Qinghai	Xining, Haidong,
	Sichuan	also Ngawa county
	Gansu	Lanzhou, Jiayuguan, Jinchang, Baiyin, Wuwei, Zhangye, Dingxi, Jiuquan, Longnan
	Ningxia prefecture level city in Zhejiang	Yinchuan, Shizuishan, Wuzhong, Guyuan, Zhongwei
	Inner Mongolia	Hohhot, Baotou, Ulanchab, Ordos, Bayannur, Wuhai
Middle stretches of river	Gansu	Tianshui, Pingliang, Qingyang
	Shaanxi	Xi’an, Tongchuan, Baoji, Xianyang, Weinan, Yan’an, Hanzhong, Yulin, Ankang, Shangluo
	Shanxi	Taiyuan, Datong, Yangquan, Changzhi, Jincheng, Shuozhou, Jinzhong, Yuncheng, Xinzhou, Linfen, Luliang
	He’nan Mengguzu autonomous county in Qinghai	Luoyang, Jiaozuo, Sanmenxia
Lower reaches of river	He’nan Mengguzu autonomous county in Qinghai	Zhengzhou, Kaifeng, Pingdingshan, Anyang, Hebi, Xinxiang, Puyang, Xuchang, Luohe, Nanyang, Shangqiu, Xinyang, Zhoukou, Zhumadian
	Shandong	Jinan, Qingdao, Zibo, Zaozhuang, Dongying, Yantai, Weifang, Jining, Tai’an, Weihai, Rizhao, Linyi, Dezhou, Liaocheng, Binzhou and Heze.

). These zones encompass the Yellow River’s natural flow area and its economic and ecological correlation zones.

The data came from Peking University’s Digital Inclusive Finance Index, the China Urban Statistical Yearbook, the China Energy Statistical Yearbook, the China Environmental Statistical Yearbook, the State Intellectual Property Office’s Patent Database, and various provincial and municipal statistical yearbooks. Missing data were incorporated using interpolation methods and the epolate command.

2.3. Research Methodology

2.3.1. Double Fixed-Effects Model

The investigation employed a specialized model to ascertain the direct impact of the digital economy on the resilience of urban ecosystems:

$${Eri}_{it}={\alpha }_0+{\alpha }_1{Dig}_{it}+{\alpha }_2{Control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(1)

In this study, the letters i and t are used to denote the province and year, respectively. The explanatory variables are denoted by Eri (urban ecological resilience), Dig (digital economy), and ${Control}_{it}$ (a set of control variables). The city fixed effect is denoted by ${\mu }_i$, the time fixed effect by $v_t$, and the random disturbance term by ${\epsilon }_{it}$.

2.3.2. Models of Mediating Effects

The model investigated the digital economy’s indirect impact on urban ecosystems. The following formulae were utilized:

$$M_{it}={\beta }_0+{\beta }_1{Y}_{it}+{\beta }_2{Control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(2)

$${Eri}_{it}={\gamma }_0+{\gamma }_1{Dig}_{it}+{\gamma }_2{M}_{it}+{\gamma }_3{Control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(3)

$M_{it}$ is used to represent two industrial structures: advanced industrial structure and rationalization. The remaining variables follow the benchmark.

2.3.3. Instrumental Variables Approach

The two-stage least squares regression (2SLS) method was employed to address the endogeneity problem and assess the study findings. The following formulae were utilized:

$${Eri}_{it}={\alpha }_0+{\alpha }_1{Dig}_{it}+{\alpha }_2{Control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(4)

$${Dig}_{it}={\alpha }_0+{\alpha }_1{KF}_{it}+{\alpha }_2{Control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(5)

In the second stage of the research, ${KF}_{it}$ is employed to denote the proportion of word frequency pertaining to the digital economy in the government work report of city i in year t. This is utilized as an instrumental variable for the development of the digital economy (instrumental variable).

2.4. Variable Selection and Descriptive Statistics

2.4.1. Explained Variables

The explained variable is urban ecological resilience (UERI), which reflects the city’s comprehensive capacity to mitigate pollution emissions, enhance response capabilities, and restore a stable state when facing ecosystem risks or sudden environmental conflicts. The present study develops a comprehensive framework tailored to the fragile ecosystems, frequent environmental pollution, and natural disasters characteristic of watersheds, drawing on the connotation of ecological environmental resilience and referencing the evaluation indexes and assessment methods established by Churming et al. (2023) [23], Guo Haihong et al. (2023) [24], and Zhang Jipeng et al. (2022) [25].

We have constructed an urban ecological resilience index system. The index is based on the logic of “pressure resistance - problem response - state recovery”, covering resistance, response and recovery. framework is based on the logic model, comprising pressure, resistance, problem response, and state recovery. It has 12 specific indicators across three dimensions (see

Table 2. Indicator system for measuring urban ecological resilience.

Level 1 Indicators	Secondary Indicators	Tertiary Indicators	Unit (of Measure)	Orientations
Ecological resilience exponents (UERI)	resistance	Industrial wastewater discharge per unit of GDP	Tons/million	negative direction
		Industrial sulphur dioxide emissions per unit of GDP	Tons/million	negative direction
		Industrial solid waste emissions per unit of GDP	Tons/million	negative direction
		Carbon emissions per capita	Tons/person	negative direction
	unresponsiveness	Centralized treatment rate of sewage treatment plants	%	forward
		Industrial sulfur dioxide removal rate	%	forward
		Comprehensive utilization rate of industrial solid waste	%	forward
		Industrial fume removal rate	%	forward
		Non-hazardous treatment rate of domestic waste	%	forward
	restorative	Greening coverage in built-up areas	%	forward
		Water resources per capita	Cubic meters/person	forward
		Green space per capita in parks	Hectares/million people	forward
		Built-up area per capita	Square kilometers/ten thousand people	forward

). We used the entropy weight method to remove the impact of indicators on the resilience index. This standardized the indicator values. It measured cities in the Yellow River Basin for urban ecological resilience, following methodologies from Zhou et al. [26] and Wang Jun et al. [27].

2.4.2. Explanatory Variables

The digital economy (DIG) is the explanatory variable in this study. Scholars have focused on measuring the digital economy from different perspectives. For example, Liu Jun et al. (2020) argued that information networks are key to the digital economy’s development [28]. Zhao Tao et al. (2020) built a city-level digital economy assessment system based on Internet development and digital finance [29]. Kong Lingzhang et al. (2023) emphasized the role of Internet development as the core metric [30], integrating digital industrialization, industrial digitization, and the digital environment to assess digital economic development.

This paper builds on prior research and adheres to the principles of data availability, scientific rigor, and authenticity. The study assesses digital economy development across three dimensions: digital carriers, the digital industry, and the digital environment. The digital carrier dimension is instrumental in evaluating Internet development. The digital industry dimension primarily assesses the level of digital industries. The digital environment dimension includes inputs like capital, labor, knowledge, and governance factors that contribute to digital economy development (see

Table 3. Indicator system for measuring digital economy development in the Yellow River Basin.

Level 1 Indicators	Secondary Indicators	Tertiary Indicators	Unit (of Measure)	Orientations
Development of the digital economy exponents (DIG)	digital carriers	Number of IPv4 addresses	ten thousand	forward
		Number of domain names	ten thousand	forward
		Internet penetration	Households/person	forward
		Cell phone penetration rate	Department/person	forward
	digital industry	Total telecommunication services per capita	Million dollars per person	forward
		E-commerce turnover of agricultural products	ten thousand dollars	forward
		E-commerce turnover of industrial enterprises	ten thousand dollars	forward
		Peking University Digital Inclusive Finance Index		forward
	digital environment	Expenditures on research and development in science and technology	ten thousand dollars	forward
		Percentage of employment of digitally literate people	%	forward
		Number of contracts concluded for digital intellectual property	classifier for individual things or people, general, catch-all classifier	forward
		Government Administration Application Index		forward

). The entropy weight method calculates the index.

2.4.3. Control Variables

This paper utilizes the extant research of Zhang, Dongling, et al., (2024) [31], Mert and Caglar (2020) [32], Liu Wenhui and Shi Baojuan (2024) [33], Xiang, Xianhong et al. (2024) [34], and Sun Wei et al. (2024) [35]. The purpose of this endeavor is to identify five factors that have a significant influence on ecological environment resilience. These aforementioned factors will be utilized as control variables in the study as follows:

(1) The intensity of environmental regulation (env) is measured by the ratio of investment in industrial pollution control to the value-added of the secondary industry;

(2) Economic development level (agdp) is measured by the logarithm of the per capita gross domestic product (GDP). Nominal GDP is converted to real GDP using 2013 as the base year;

(3) Energy restructuring (energy) is defined as the ratio of electricity consumption to total energy consumption;

(4) Science and technology R&D investment (invest), otherwise known as the ratio of R&D expenditure to public financial expenditure, has been defined as such;

(5) The level of technological innovation (innov) is measured by the logarithm of the number of patent applications;

(6) Population size (pop) is measured by the natural log of the year-end resident population.

2.4.4. Mediating Variables

1. The optimization of the industrial structure is assessed separately across two dimensions, as in the research of Liu Chuanjiang et al. (2023) [36]. The term “advanced industrial structure” is used to denote the transformation of industries from low- to high-level forms, marked by a transition from capital- and labor-intensive sectors to technology- and data-intensive industries. In the digital era, the tertiary industry has grown faster than the secondary industry. This makes the ratio of the output value of the tertiary industry to that of the secondary industry a good measure of an advanced industrial structure (iss).

The purpose of industrial structure rationalization is to measure the degree of coupling between inputs and outputs. This process reflects industrial coordination and resource utilization. The Theil index (Theil) is used to measure the rationalization of industrial structure (isr) (Liang Qi et al., 2021) [37]. The calculation formula is as follows:

$${isr}_{it}={\sum }_{i=1}^n({\displaystyle \frac{Y_i}{Y}})\mathrm{l}\mathrm{n}({\displaystyle \frac{Y_i}{Y}}/{\displaystyle \frac{Y}{L}})$$

(6)

In economics, i is a sector, and n represents all the sectors. Each sector’s output is $Y_i$, the gross regional product (GRP) is Y, and the regional employment is L. The closer ${isr}_{it}$ is to 0, the more rational the industrial structure is. This is indicative of stronger inter-industry coordination and correlation. In the specific application process, it is generally recommended to take the absolute value of the index and take the reciprocal. The magnitude of this value is directly proportional to the extent of rationalization in the industrial structure of the city.

2. The improved allocation of resources is a key aspect of the study. The resource mismatch index is a metric that quantifies the efficiency of production factors. In accordance with Bai Junhong and Liu Yuying (2018) [38], under the assumption that the production function of each region satisfies the Cobb–Douglas form of constant returns to scale, a variable coefficient panel model is employed, and the following formulae are used to calculate the labor mismatch index (lmi) and the capital mismatch index (cmi):

$${lmis}_{it}={\displaystyle \frac{1}{{\gamma }_{Lit}}}-1$$

(7)

$${cmis}_{it}={\displaystyle \frac{1}{{\gamma }_{Cit}}}-1$$

(8)

The absolute factor price distortion coefficient $\gamma$ is complex, but its application process can be used for the relative price distortion coefficient $\eta$ with this calculation method:

$${\eta }_{Lit}={\displaystyle \frac{L_{it}}{L_t}}/{\displaystyle \frac{S_{it}{\alpha }_{Lit}}{{\alpha }_{Lt}}}, {\eta }_{Cit}={\displaystyle \frac{C_{it}}{C_t}}/{\displaystyle \frac{S_{it}{\alpha }_{Cit}}{{\alpha }_{Ct}}}$$

(9)

In this study, t and i stand for year and city. The capital output elasticity of city i in year t is $L_{it}$ /$L_t$. The capital output elasticity of all cities in year t is $C_{it}$/$C_t$. The labor force ratio of city i in year t is $S_{it}$. The labor and capital output elasticities of city i in year t are ${\alpha }_{Lit}$ and ${\alpha }_{Cit}$. To calculate ${lmis}_{it}$ and ${kmis}_{it}$, first estimate the labor and capital output elasticities, ${\alpha }_L$ and ${\alpha }_C$. The Cobb–Douglas production function (see Equation (8)) can estimate these, considering individual and time effects. The resulting equation is Equation (9).

$$Y_{it}={AC}_{it}^{{\alpha }_{Ci}}{L}_{it}^{1-{\alpha }_{Ci}}$$

(10)

$$ln\left({\displaystyle \frac{Y_{it}}{L_{it}}}\right)=lnA+{\alpha }_{Ci}ln\left({\displaystyle \frac{C_{it}}{L_{it}}}\right)+{\varphi }_i+{\theta }_t+{\mu }_{it}$$

(11)

The study expresses output $Y_{it}$ as the gross regional product of different cities, labor input $L_{it}$ as the number of urban employees at the end of the year, and capital input as the fixed capital stock measured by the perpetual inventory method. Constant $C_{it}$ is expressed in terms of the fixed capital stock measured by the perpetual inventory method. This methodology is used to calculate the output elasticity of 90 cities from 2010 to 2022 [39]. Finally, Equations (5)–(7) show that the mismatch index is treated as an absolute value, given that there are two cases of it being greater than zero (over-allocation of resources) and less than zero (under-allocation of resources). The larger the resource mismatch index, the worse the city’s resource allocation. Higher numbers show poorer performance. See

Table 4. Descriptive statistics of variables.

Variable Type	Variable Name	Acronyms	Sample Size	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Values
explanatory variable	Ecological resilience	UERI	800	10.223	5.712	1.953	45.484
Core explanatory variables	Digital economy	DIG	800	3.658	2.746	0.764	32.370
control variable	Intensity of environmental regulation	env	800	0.122	0.478	0.521	3.606
	Level of economic development	agdp	800	10.829	0.640	6.410	13.028
	Energy restructuring	energy	800	0.294	0.409	0.649	3.849
	Investment in science and technology research and development	invest	800	0.059	0.09	0.449	0.832
	Technological innovation	innov	800	7.269	1.511	1.609	11.380
	Size of population	pop	800	14.902	0.782	12.369	16.377
intermediary variable	Advanced industrial structure	iss	800	1.213	0.673	0.160	5.369
	Rationalization of industrial structure	isr	800	0.358	0.543	0.101	11.327
	Capital mismatch index	cmis	800	0.512	0.589	0.003	9.351
	Labor mismatch index	lmis	800	0.719	0.663	0.007	5.521

for the statistics of the main variables. After the variance inflation test, there is no multicollinearity issue.

3. Empirical Results and Analysis

3.1. The Overall Impact of the Digital Economy on Urban Ecological Resilience

3.1.1. Baseline Regression

A model is employed to address endogeneity caused by omitted variables. It considers the influence of characteristic variables on urban ecological resilience. The regression results are reliable due to the clustering of standard errors.

Table 5. Impact of the digital economy on urban ecological resilience in the Yellow River Basin (baseline regression).

Variant	(1)	(2)
DIG	0.178 *** (0.857)	0.163 *** (0.038)
env		0.109 *** (0.137)
agdp		0.045 ** (0.487)
energy		0.286 (0.186)
invest		−12.44 *** (5.991)
innov		0.519 (0.172)
pop		−5.772 * (0.897)
cons	1.288 (9.267)	0.434 (0.312)
Fixed cities	Yes	Yes
fixed year	Yes	Yes
N	800	800
R2	0.104	0.260

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with robust standard errors in parentheses.

shows the digital economy’s significant impact on urban ecological resilience with and without the control variables. The findings show the digital economy’s substantial effect on ecological resilience, with a statistical coefficient of p = 0.01 and a coefficient magnitude of 0.178. In Model (2), the coefficient shows a 1% positive link, validating the digital economy’s direct and substantial impact on urban ecological resilience and Hypothesis 1.

(1) The present study demonstrates that environmental regulation intensity positively affects ecological resilience: every 1% increase in investment in pollution control in the secondary industry increases urban ecological resilience by 10.9%. This underscores the importance of government intervention in pollution control. Government investment in environmental infrastructure, industrial pollution management, and environmental protection enhance resources and ecological inputs, thereby improving resilience. The “Porter Hypothesis” suggests that green policies encourage enterprises to invest in pollution control innovations, leading to the development of less polluting, energy-efficient products, enhancing enterprise competitiveness whilst promoting environmental and economic development.

(2) The study finds a link between economic strength and environmental resilience in cities. This suggests that cities with stronger economies may be better at dealing with environmental problems.

(3) The findings show that energy restructuring has a negligible impact. The green and low-carbon transformation of energy consumption has not yet significantly boosted ecological resilience.

(4) Investment in science and technology R&D has been shown to have a negative effect at the 1% level. In China’s decentralized fiscal system, where resources are limited, science and technology R&D is prioritized over environmental protection, displacing investments in ecological protection. This bias has been shown to reduce ecological responsibility and resilience, while sending signals that hinder social resource allocation to ecological protection, worsening its inhibitory effects.

(5) The impact of tech innovation on ecological resilience is positive, albeit marginally so. This may be due to the relationship between tech development and innovation within the digital economy. A hypothesis suggests the Yellow River Basin will experience a “rebound effect” due to technology.

(6) The population size of a given area has been demonstrated to have a detrimental effect on ecological resilience. A significant coefficient has been identified, indicating that an increase in population size results in intensified resource and energy consumption pressures on the ecological environment. However, this issue is not overly severe, as evidenced by the finding that it is only significant at the 10% level.

3.1.2. Robustness Test

This paper proposes three tests to assess the robustness of the empirical results. The first is the staged regression method. After the State Council issued its Guiding Opinions on Actively Promoting the Action of “Internet Plus” in July 2015, the digital economy entered a new phase of vigorous development, which can be divided into two stages, 2013–2015 and 2016–2022. This study will examine the impact of the digital economy on ecological resilience and the mechanism of action. It will exclude special city samples. Zhengzhou, Xi’an, Lanzhou, and other central cities have shown comparatively superior digital economy development. The subsequent investigation will focus on the impact of the digital economy on the ecological resilience of cities. This investigation will be conducted after excluding the samples of eight central cities (Lanzhou, Xining, Xi’an, Zhengzhou, Jinan, Taiyuan, Yinchuan, and Hohhot). Thirdly, sensitivity tests are conducted on key variables. This study uses the entropy method to integrate the Digital Financial Inclusion Index (PKU-DFIIC) and the Internet Development Index (IDI) to derive a digital economy development index. This index is then used as a proxy variable for the digital economy (Dig) variable.

The regression results are shown in columns (1) and (2) of

Table 6. Robustness test results.

Variant	(1) 2013–2015	(2) 2016–2022	(3) Exclusion of Central Cities	(4) Replacement of Independent Variables
DIG	0.108 *** (0.266)	0.333 *** (0.161)	0.246 *** (0.132)	0.112 * (0.188)
env	14.787 ** (6.621)	1.893 ** (0.878)	0.794 (0.772)	0.142 * (0.078)
agdp	5.81 *** (0.794)	6.842 *** (0.745)	5.72 *** (0.543)	1.436 *** (0.508)
energy	2.449 * (1.379)	0.491 (0.834)	0.663 (0.645)	0.314 * (0.188)
invest	−7.054 *** (5.153)	−7.496 *** (5.847)	3.382 *** (4.036)	−6.935 (5.580)
innov	2.726 (0.354)	2.768 *** (0.385)	−3.313 *** (0.266)	4.603 (0.790)
pop	−1.749 ** (0.72)	−2.187 *** (0.633)	−2.144 *** (0.445)	−0.138 *** (0.192)
cons	11.172 (14.967)	12.376 (13.356)	1.188 (9.618)	7.577 (14.403)
fixed cities	Yes	Yes	Yes	Yes
fixed year	Yes	Yes	Yes	Yes
N	240	560	720	800
R2	0.592	0.429	0.531	0.288

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with robust standard errors in parentheses.

. The digital economy coefficient is positive and significant, indicating the regression’s robustness. The regression results in column (3), after omitting the eight central cities, demonstrate that the significance and direction of the coefficients remain largely unchanged, substantiating the baseline results. The results in column (4) show that the digital economy index is still positively impacted by the urban ecosystem’s resilience. This supports the conclusions of the previous study.

3.1.3. Instrumental Variable Tests

The instrumental variable test used the two-stage least squares (2SLS) method to address concerns about endogeneity, measurement error, reverse causality, and omitted variables. The instrumental variable was the percentage of digital economy-related keywords. This choice had two rationales. Firstly, the percentage of keywords associated with the digital economy indicates policy priorities and orientations concerning the digital economy. Government policy is promoting the flow of production factors into digital industries. This suggests a strong correlation with the digital economy. The percentage of digital economy keywords is unrelated to improving urban ecological resilience. The study involved government work reports from 80 prefecture-level cities in the Yellow River Basin from 2013 to 2022, following methods from Tao Changqi and Ding Yu (2022) [40] and Li Jianpei et al. [41]. The keywords were identified using a combination of automated parsing and manual filtering to determine the frequency of digital economy-related words.

The robustness of the instrumental variable was evaluated with three tests. The weak instrumental variable test revealed that the Wald F-statistic (254.731) exceeded the critical value (16.38) at the 10% bias level of the Stock–Yogo weak identification test, suggesting no weak instrument issue. The correlation test confirmed a robust correlation between the instrumental and endogenous variables. The regression results confirmed the validity of the instrumental variable. The initial regression stage exhibited a favorable coefficient for the instrumental variable, thereby validating the correlation hypothesis. In the subsequent second stage, the digital economy’s estimated coefficients remained positive. Addressing the concerns regarding endogeneity did not alter the digital economy’s persistent positive influence on urban ecological resilience (see

Table 7. Instrumental variable test results.

Variant	First-Stage Regression Dig	Second-Stage Regression UERI
Dig		0.157 *** (0.859)
IV	0.402 *** (0.859)
Control	Yes	Yes
Fixed cities	Yes	Yes
Fixed year	Yes	Yes
Kleibergen–Paap Wald F-statistic	254.731 ***
Cragg–Donald Wald F-statistic	493.215 ***
Anderson–Rubin Wald statistic	4.220 *
N	800	800
R2	0.405	0.980

Note: ***, and * indicate significance at the 1%, and 10% levels, respectively, with robust standard errors in parentheses.

).

3.1.4. Heterogeneity Analysis

The degree of digital economy development and its impact on ecological resilience in the Yellow River Basin vary significantly regionally. These variations are influenced by economic development foundations, natural resource endowments, policies, and cultural contexts. To facilitate analysis, the 80 cities in the Yellow River Basin were divided into three regions: upstream, midstream, and downstream. The findings of the subsample regression are in

Table 8. Effects of digital economy on urban ecosystem resilience in the upper, middle, and lower Yellow River.

Variant	(1) Upper Yellow River Cities	(2) Middle Yellow River Cities	(3) Lower Yellow River Cities
DIG	0.114 *** (2.130)	0.162 *** (3.675)	0.058 (1.040)
env	0.696 ** (12.936)	1.546 (4.279)	−0.215 (0.209)
agdp	12.072 *** (1.322)	2.454 *** (0.381)	2.436 *** (0.274)
energy	1.894 (3.227)	1.141 *** (0.225)	7.342 *** (0.965)
invest	−10.097 *** (1.202)	4.984 (14.617)	−0.011 (1.961)
innov	4.352 *** (0.690)	0.298 (0.175)	0.689 *** (0.152)
pop	−1.906 (1.254)	−1.487 * (0.402)	−2.98 *** (0.321)
cons	13.37 *** (23.318)	0.092 *** (7.887)	19.958 *** (6.806)
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	230	270	300
R2	0.014	0.270	0.565

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with robust standard errors in parentheses.

:

(1) The contribution of digital economic development to the resilience of urban ecosystems is only manifested in the upstream and midstream areas, and is not significant in the downstream areas;

(2) The extent to which the digital economy explains the resilience of the ecosystem is characterized by an uneven spatial pattern with the highest in the midstream, the second highest in the upstream, and the lowest in the downstream.

This discrepancy can be attributed to a number of factors. The midstream region’s geographic position makes it a transitional zone between the river’s upstream and downstream regions, as well as between the eastern and western regions. The data, the digital economy’s core element, exhibit efficient flow across these regions, benefiting the midstream region with policy-driven influences from the upstream and advantageous factor transfers from the downstream. Downstream cities are traditionally seen as having more advanced digital economies, but the “long-tail effect” of digitalization challenges this. The digital economy mostly helps marginalized groups, like low-income earners and small to medium-sized enterprises. These groups use cost-effective digital platforms to meet their production and living needs, increasing output. The downstream region may see diminishing returns on investment from digital economy-driven ecological improvements due to its advanced development. The mid-stream region, with its delayed initiation and less extensive digital economic integration, demonstrates augmented potential for ecological advantages.

The digital economy has fewer restrictions than the traditional real economy. So, regions like the midstream, which are behind the downstream, can benefit a lot from digital economy advancements that improve the environment. The digital economy’s ability to enhance the environment is clear in the upstream region, which has fragile ecosystems. In such contexts, national strategies like ecological civilization construction and rural revitalization augment the “latecomer advantage” that the digital economy possesses. The enhancement of “green productivity” that results from this process provides a clearer path for industrial transformation in resource-dependent areas. Digital technologies play a pivotal role in environmental monitoring, facilitating the effective relocation of industry and providing comprehensive support for the region’s green development, thereby reinforcing its ecological resilience.

3.2. Mechanistic Analysis of the Digital Economy on the Resilience of Urban Ecosystems

3.2.1. Industrial Structure Optimization Effect

Studies show that digital technology has boosted productivity in conventional energy industries and reduced ecological resource depletion. This has been achieved by incorporating information technology and innovation. The rise of industries like AI, new media, and advanced materials is vital for economic growth. The rapid growth of high-tech, low-carbon industries has shown how the green transformation of economic development is possible. A mediating effect model analyzes the indirect impact of industrial structure optimization on ecological resilience (see

Table 9. Industrial upgrading effects of the digital economy affecting urban ecological resilience in the Yellow River Basin.

Panel A	Advanced Industrial Structure
	(1) UERI	(2) iss	(3) UERI
DIG	0.178 *** (0.857)	0.012 *** (0.008)	0.029 *** (0.047)
iss			0.107 ** (0.227)
control variable	Yes	Yes	Yes
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	800	800	800
R2	0.104	0.184	0.292
Proportion of mediated effects to total effects	0.837
Panel B	Rationalization of industrial structure
	(1) UERI	(2) isr	(3) UERI
DIG	0.178 *** (0.857)	0.003 *** (0.008)	0.037 *** (0.030)
isr			0.209 (0.097)
control variable	Yes	Yes	Yes
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	800	800	800
R2	0.104	0.038	0.289
Proportion of mediated effects to total effects	0.792

Note: ***, and ** indicate significance at the 1%, and 5% levels, respectively, with robust standard errors in parentheses.

):

(1) In Panel A, column (2), the digital economy has a significant positive effect on advanced industrial structure (0.012), showing it promotes advancement;

(2) In Panel B, column (3), the estimated coefficient of digital economy is significantly positive (0.029) and smaller than the regression coefficient under the condition of not considering the advanced industrial structure (0.178), which indicates that the advanced industrial structure plays a part of the mediation effect and the value of mediation effect is 0.149;

(3) In Panel B, column (2), the digital economy’s impact on industrial structure rationalization is positive (0.003), confirming its role in promoting rationalization;

(4) In Panel B, column (3), the digital economy’s estimated coefficient is 0.037. This is smaller than the coefficient without industrial structure rationalization (0.178). This suggests a partial mediation effect from a rationalized industrial structure, with a mediation effect value of 0.141.

In summary, the digital economy exerts a more significant positive influence on the advancement of the industrial structure than the rationalization of the industrial structure with regard to ecological resilience.

3.2.2. Resource Allocation Improvement Effect

The digital economy is predicated on the optimization of capital and labor utilization through the implementation of digital technology on a global scale. This has been demonstrated to result in increased total factor productivity, facilitate the green and efficient transformation of economic development modes, and enhance the resilience of urban ecological environments.

Firstly, the drive towards industry specialization is catalyzed through improved market transparency and competition. This in turn promotes the precise allocation of capital factors. Furthermore, digital Internet platforms reduce information asymmetries, thus dismantling barriers to labor factor mobility and mitigating labor resource allocation inaccuracies. The optimization of resource allocation has been demonstrated to directly enhance input–output efficiency and reduce consumption [42], thereby encouraging enhanced urban ecological resilience. The mediation effect model is utilized to facilitate an in-depth examination of this phenomenon. The analysis reveals the following (see

Table 10. Resource allocation effects of the digital economy affecting urban ecological resilience.

Panel A	Capital Mismatch
	(1) UERI	(2) cmis	(3) UERI
DIG	0.178 *** (0.857)	−0.004 *** (0.010)	0.058 *** (0.036)
cmis			−0.503 (0.326)
control variable	Yes	Yes	Yes
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	800	800	800
R2	0.104	0.535	0.265
Proportion of mediated effects to total effects	0.674
Panel B	Labor mismatch
	(1) UERI	(2) lmis	(3) UERI
DIG	0.178 *** (0.857)	−0.055 *** (0.010)	0.050 *** (0.033)
lmis			−0.280 * (0.150)
control variable	Yes	Yes	Yes
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	800	800	800
R2	0.104	0.136	0.185
Proportion of mediated effects to total effects	0.719

Note: ***, and * indicate significance at the 1%, and 10% levels, respectively, with robust standard errors in parentheses.

):

(1) In column (2) of Panel A, the digital economy is negatively associated with capital mismatch (−0.004). This suggests that developing the digital economy helps to reduce capital mismatch;

(2) In column (3), the estimated coefficient of the digital economy is found to be significantly positive (0.058), thereby confirming the hypothesis that capital mismatch has a partial mediating effect. The mediation effect value is determined to be 0.120;

(3) In column (2) of Panel B, the regression coefficient of the digital economy on the labor mismatch is found to be significantly negative (−0.055). This suggests that the digital economy plays a positive role in addressing labor mismatches;

(4) In column (3) of Panel B, the estimated coefficient of the digital economy is found to be significantly positive at a value of −0.050. This indicates that there is a partial mediating effect of the labor mismatch, with a mediation effect value of 0.128.

In conclusion, the digital economy exerts an indirect effect on the enhancement of urban ecological resilience by means of the improvement in resource mismatch correction. It is evident that the mitigation of labor mismatches plays a more significant role in enhancing ecological resilience than the correction of capital mismatches.

3.2.3. Further Analysis: Decomposition

Urban ecological resilience can be broken down into ecological resistance, ecological responsiveness, and ecological resilience. The analysis shows that the digital economy mainly enhances ecological resistance and responsiveness, but not ecological resilience (see

Table 11. Decomposition of the pathways of the digital economy’s impact on the resilience of urban ecosystems.

Variant	(1) Ecological Resistance	(2) Ecological Responsiveness	(3) Ecological Resilience
DIG	0.011 *** (0.001)	0.042 *** (0.032)	0.002 (0.003)
env	0.011 *** (0.004)	0.001 *** (0.015)	0.007 ** (0.013)
agdp	0.023 *** (0.003)	0.179 ** (0.154)	0.169 * (0.021)
energy	0.014 *** (0.003)	0.002 (0.006)	0.048 (0.017)
invest	−0.249 * (0.019)	0.008 (0.002)	−0.310 (0.175)
innov	0.006 (0.001)	0.035 * (0.213)	0.036 * (0.008)
pop	−0.021 * (0.002)	−0.002 * (0.031)	−0.064 (0.029)
cons	0.124 (0.048)	0.010 (0.004)	2.083 (0.597)
fixed cities	Yes	Yes	Yes
fixed year	Yes	Yes	Yes
N	800	800	800
R2	0.555	0.112	0.210

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with robust standard errors in parentheses.

). Stronger environmental regulation and economic development also increase ecological resistance and responsiveness. However, the effect of technology on ecological resistance is unclear. The digital economy has a positive effect on ecological resilience, but this is not statistically significant. It can therefore be deduced that the digital economy mostly improves ecological resistance and responsiveness, rather than having a direct and substantial influence on ecological resilience, thereby contributing to the enhancement of urban ecological resilience.

4. Discussion

The People’s Republic of China is currently experiencing significant transformations in its development, economic growth, and industry. The digital economy is a novel form of economic organization that has emerged as a pivotal catalyst in addressing ecological governance challenges and improving ecological resilience. This paper explores the intricate interplay between the digital economy and the resilience of urban ecological environments, using Schumpeter’s innovation theory. The paper proposes the infusion of digital energy into the pursuit of an ecological civilization to address these challenges. This study is distinct from the existing research in two key aspects [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. Firstly, it adopts a dual perspective, examining economic development and ecological protection. Secondly, it delves into the digital economy’s contribution to environmental resilience. The study integrates regional development differences into its framework, enabling an exploration of the impact of digital economic development on ecological environment resilience. It also introduces industrial structure optimization and resource allocation improvement as mediating factors by assessing their indirect contribution to ecological resilience.

Studies show that the digital economy promotes urban ecological resilience, as scholars such as Churming [23], Kong Lingzhang [30], and Liang Qi [37] have found. This promotion occurs through upgrading industry and making better use of capital and labor. The digital economy has the capacity to reshape the demand side, drive industrial upgrading, enhance ecological efficiency, and improve ecological resilience. The digital economy uses digital technology to make the best use of resources, protect the environment, and boost urban resilience. This requires a multifaceted approach including upgrading industry, promoting eco-friendly industrial development, changing energy consumption structures, and improving environmental governance. It is vital to use the digital economy to address the information divide and deepen centralization, fusion, excavation, and the use of big data in different cities. These measures will improve efficiency in factor flow and reduce friction costs, as well as unlocking the digital economy’s potential to optimize resource allocation. This will create a virtuous cycle encompassing natural, social, and ecological dimensions in urban areas.

The digital economy is experiencing rapid growth, with the potential to enhance ecological resilience on a broad scale. However, this transformation is accompanied by dual risks to both the economy and society. From an economic perspective, the development of the digital economy, propelled by favorable policies, may generate a “straw effect”, thereby attracting substantial social capital and exerting pressure on other traditional industries, potentially leading to investment imbalances. Concurrently, in the nascent stage of digital transformation, due to the high uncertainty surrounding the market application prospects of digital infrastructure and the substantial investment in research and development, some projects may require government intervention to achieve success. However, the government-led investment model is susceptible to financial burden. At the social level, the advent of digital transformation may result in the displacement or substitution of traditional jobs characterized by high levels of repetitiveness and minimal skill requirements by automation. This phenomenon has the potential to elevate the social unemployment rate in instances where economic and social resilience is found wanting or where individuals lack digital literacy skills. Whilst digital labor platforms provide workers with employment and income, they also exacerbate work intensification, deterioration in mental health, and income insecurity, posing challenges to the protection of workers’ rights and fair treatment.

5. Conclusions

The present study utilizes a double fixed-effects model to investigate the impact of the digital economy on urban ecological resilience. This model is constructed by evaluating the digital economy and urban ecological resilience in 80 cities (including prefecture-level cities, states, and leagues) within the Yellow River Basin from 2010 to 2022. The study then examines the endogenous transmission mechanisms, focusing on industrial structure optimization and resource allocation improvement. The study’s findings are as follows. Firstly, the digital economy has been found to significantly enhance urban ecological resilience, with an average increase of 17.8%. The robustness of this conclusion is confirmed through phased regression, core variable substitution, and the instrumental variable method to address potential endogeneity issues. Secondly, the impact of the digital economy on ecological resilience demonstrates considerable regional heterogeneity, being most evident in the middle reaches (16.2%), followed by the upper reaches (11.4%), and is least in the lower reaches (5.4%), reflecting an uneven spatial distribution. The digital economy has been demonstrated to promote ecological resilience by advancing the industrial structure through increased rationalization and upgrading. It also alleviates mismatches in capital and labor resources, optimizing resource allocation, improving the efficiency of ecological resource utilization, and reducing resource depletion, thereby enhancing urban ecological resilience.

6. Recommendations

Research shows the digital economy is effective in transforming and upgrading industrial structures and has superior ecological benefits compared to rationalized industrial structures. The advanced industrial structure within the digital economy should be optimized. The industrial structure of the digital economy in the Yellow River Basin is imbalanced, particularly in the middle reaches. The region’s urban ecological resilience has been significantly undermined by land overexploitation and reduction. To address these issues, advanced modern information technologies should be leveraged to establish a robust policy framework for the development of the digital intelligence industry. This strategy will unlock green productivity and sustainable digitalization.

The digital economy indirectly enhances urban ecological resilience by improving resource mismatch, and enhancing labor mismatch plays a more pronounced role in conducting than correcting capital mismatch. It is imperative to fully leverage the digital economy platform in allocating labor resources. The lower Yellow River is known for strong industry connections, fast economic growth, and a lot of capital and talent. This puts pressure on the environment and makes cities more vulnerable. In the context of the rapid advancements witnessed within the digital intelligence industry, it is imperative to reinforce the industrial connections and the dissemination of technological innovations. This will serve to stimulate the emergence of new economic market shares. It is essential to leverage the benefits offered by digital economy platforms, thereby dismantling the barriers that impede the seamless flow of talent within the region. This will facilitate the establishment of a novel enterprise demand and supply model for talent. This approach is expected to facilitate the alignment of talent flows, thereby ensuring the optimal alignment of the Yellow River Basin’s workforce and resources. In this way, it promotes the coupled and coordinated development of urban economy and ecology in the ecologically fragile areas of the Yellow River Basin. A registry aligning enterprise demands with talent supply can guide talent flow, achieving precise matching between labor resources and economic needs in the Yellow River Basin. This initiative would facilitate the harmonious integration of economic and ecological development, particularly in ecologically fragile regions, promoting sustainable relationships between economic and ecological systems.