Digital Economy Empowering Green Development Welfare: Empirical Evidence from Resource-Based Cities in the Yellow River Basin

Yu Xue, Xu Xuandeng
(School of Business, Henan University of Science and Technology, Luoyang, Henan)

Improving green development welfare is the ultimate goal of sustainable development. The digital economy exerts a dual effect on regional green development. Based on a systematic analysis of the theoretical mechanisms through which the digital economy influences green development welfare, this study utilizes panel data from resource-based cities in the Yellow River Basin from 2011 to 2021. Using double fixed-effects models and Spatial Durbin Models (SDM), we empirically examine the impact, heterogeneity, and spatial effects of the digital economy on green development welfare.

The results indicate the following:

1. Spatio-temporal Evolution: The overall level of green development welfare remains relatively low. Temporally, it exhibits a smooth "U-shaped" upward trend. Spatially, the distribution is characterized primarily by "low-to-medium level clusters" supplemented by "high-level point distributions."
2. Impact Relationships: At the level of resource-based cities in the Yellow River Basin, the digital economy significantly promotes the improvement of regional green development welfare. This conclusion remains robust after various sensitivity tests.
3. Heterogeneity Effects: The promotional effect of the digital economy is most pronounced in "growth-stage" resource-based cities.
4. Spatial Spillovers: The impact of the digital economy on green development welfare exhibits positive spatial spillover effects. While the direct and total effects are significantly positive, the indirect effect is significantly negative.

These findings provide insights for fostering synergy between the digital economy and green development welfare, ultimately promoting the realization of Chinese-style modernization characterized by the harmonious coexistence of humanity and nature.

Keywords: Digital Economy; Green Development Welfare; Yellow River Basin; Resource-based Cities

"Promoting green development" and "enhancing people's well-being" are major strategic decisions deployed in the report of the 20th National Congress of the Communist Party of China. The ultimate goal of "green" high-quality development is to improve the happiness index of the people's "need for a beautiful ecological environment." Under the scientific guidance of Xi Jinping's Thought on Ecological Civilization, China has created world-renowned ecological and green development miracles. However, it is currently still in the developmental stage of "pursuing the realization of green value." How to further transform the achievements of green development into people's well-being and improve the level of green development welfare is of great practical significance for accelerating the advancement of Chinese-style modernization characterized by the harmony between humanity and nature.

Economic transformation plays a key role in driving welfare improvements. As an economic revolution with digital technology and data resources as its core elements, the digital economy possesses four major advantages: the widest coverage, the deepest impact, the fastest development speed, and the greatest future potential. It is becoming an inevitable trend in global economic development, exerting a profound influence on all aspects of economy and society. Therefore, under the current information technology revolution, the transformation of green development welfare must rely on productivity rooted in data elements and artificial intelligence. The digital economy undoubtedly provides an excellent opportunity to enhance the level of green development welfare.

The term "welfare" originates from the theory of welfare economics, and its measurement research can be summarized into three aspects: first, the utilitarian welfare utility theory represented by Pigou; second, the capability welfare theory represented by Amartya Sen; and third, the ecological welfare performance theory represented by Daly. Specialized research on green development welfare is currently in its nascent stage. Existing literature focuses mostly on the conceptual definition \cite{}, level measurement \cite{}, influencing factors \cite{}, and enhancement paths \cite{} of green development welfare. However, research regarding the digital economy and green development welfare primarily focuses on green development itself \cite{}, while direct studies on the green development welfare effects of the digital economy and their underlying mechanisms remain insufficient.

This paper selects panel data from 41 resource-based cities in the Yellow River Basin—an important ecological barrier and economic zone in China—as the research sample. Taking the digital economy as the entry point, we reveal the theoretical mechanisms of its impact on green development welfare. Based on Sen's capability theory and Daly's ecological welfare performance theory, we construct an evaluation index system for green development welfare. We then empirically test the heterogeneity and spatial effects of the relationship between the two.

1 Theoretical Mechanism

1.1 The Impact of the Digital Economy on Green Development Welfare

The digital economy refers to a series of economic activities that utilize data resources as key factors of production, modern information networks as important carriers, and the effective use of information and communication technologies (ICT) as drivers for efficiency enhancement and economic structural optimization. According to endogenous growth theory, technological progress is the source of sustainable economic growth, and technological innovation serves as the primary means of achieving such progress.

In the context of the digital economy, the allocation of data and technical resources across different types of technologies exerts varying impacts on welfare. If resources are concentrated in non-green technologies, the economy remains locked in a non-green technological path. Conversely, if resources are concentrated in green technologies under strict environmental regulations, the trajectory of non-green technological progress can be redirected toward a green transformation. As a strategic choice to stimulate green development potential, the digital economy provides new feasible paths, creative opportunities, and fundamental guarantees for green growth. The ultimate goal of green development is to improve human welfare and achieve a paradigm of harmonious coexistence between humanity and nature, thereby generating an aggregate effect that enhances green development welfare. The mechanism of this path is illustrated in [FIGURE:1].

1.2 The Role of New Quality Productive Forces

The digital economy enhances green development welfare in river basins by accelerating the formation of "new quality productive forces." The supply of green wealth serves as the endogenous driving force and the basic condition for enhancing green development welfare, encompassing natural capital and ecological assets. The digital economy provides disruptive and frontier green data elements and technologies for the development of new quality productive forces.

Compared to traditional factors of production, green data is characterized by non-rivalry, spatial-temporal ubiquity, and ease of dissemination. According to the environmental quality utility function, when green labor or green capital investment increases using the same green data, the utilization rate per unit of green data or the average output increases. This implies that, given constant levels of other inputs, the acquisition of more green data leads to a more advantageous green productivity. This process generates increasing welfare returns, scale effects, and scope effects. Furthermore, progress in green technology reduces the difficulty of acquiring skills while increasing the breadth of skills mastered, thereby accelerating the transition of general laborers into green innovative laborers.

1.3 Spatial Optimization and Externalities Management

The digital economy enhances green development welfare in river basins by strengthening the management of externalities. Utilizing green economic quality as an external driving force is a key measure for improving green development welfare. Economic activities require physical carriers, specifically the three major ecological spaces:

1. Production Space: Through industrial digitization, the digital economy ensures that the flow of industrial resources and factors within production spaces is more efficient.
2. Living Space: The digital economy promotes the application of full-scenario digital technologies, making daily life more intelligent and convenient, thereby enhancing humanistic comfort.
3. Ecological Space: The digital economy enables real-time acquisition of environmental data, facilitating the construction of full-chain energy systems. By strengthening digital supervision, it improves resource utilization efficiency, resulting in an ecological environment characterized by "lucid waters and lush mountains."

1.4 The Dynamics of Welfare Attention Allocation

Green consumption preference serves as a source of innovative vitality. The digital economy can reshape the allocation pattern of tangible elements through intangible elements like public attention. The greater the intensity of public focus on green development welfare, the lower the information costs for corporate production and government decision-making. However, driven by herd mentality, public preferences may converge, potentially producing a sense of relative deprivation. This distortion can lead to resource mismatch or "green consumption congestion," increasing the pressure on the government and triggering inefficiencies in green policies.

2 Research Design

2.1 Overview of the Research Area

Resource-based cities in the Yellow River Basin represent a significant category of urban centers characterized by high dependence on natural resources. The Yellow River Basin contains more than 40% of the country's resource-based cities, contributing approximately 70% of national coal production. However, these cities face severe "resource curse" effects and ecological security pressures. Improving green development welfare in these cities is an inherent requirement for the ecological protection and high-quality development of the region.

2.2 Measurement Method for Green Development Welfare

Indicator System Construction. Green development welfare is defined as the degree of happiness effectively exchanged for humanity by compensating for and reducing ecological costs. We utilize four primary output indicators: potential green wealth supply, green economic quality, green consumption preference, and green innovation. Referencing the work of Zou Weiyong et al. \cite{1}, we employ the entropy weight method to determine coefficients.

The indicators include:
1. Green Wealth Supply: Natural capital (land, roads, parks), physical capital (buses, doctors, libraries), and human capital (education levels).
2. Green Economic Quality: Production space (fiscal revenue, income), living space (sewage treatment, waste treatment), and ecological space (solid waste utilization).
3. Green Consumption Preference: Public energy consumption (water, electricity, total energy).
4. Green Innovation: Regional innovation index, green patent applications, and government environmental attention.

2.3 Empirical Model

To investigate the impact of the digital economy on green development welfare, we construct a two-way fixed effects econometric model:

$$ \ln \text{GDB}{it} = \alpha_0 + \alpha_1 \ln \text{Dig}} + \sum \beta_j \text{Control{it} + \mu_i + \nu_t + \epsilon $$

Where $\ln \text{GDB}$ is green development welfare, $\ln \text{Dig}$ is the digital economy level, and $\text{Control}$ represents control variables (GDP, financial development, urbanization, openness, and industrial structure). To account for spatial spillover effects, we establish a Spatial Durbin Model (SDM):

$$ \ln \text{GDB}{it} = \rho W \ln \text{GDB}} + \alpha_1 \ln \text{Dig{it} + \sum \beta_j \text{Control} $$} + \mu_i + \nu_t + \epsilon_{it

3 Results and Analysis

3.1 Spatiotemporal Evolution

The level of green development welfare in resource-based cities exhibits a steady upward trend. Spatially, the distribution is characterized by clusters of medium-to-low indices, supplemented by point-like distributions of high indices (e.g., Ordos and Karamay). The center of gravity for green development welfare shifted from Lvliang City to Jinzhong City, remaining consistently to the east of the basin's geographical center, indicating persistent spatial polarization.

3.2 Regression Results and Robustness

The regression results indicate that the digital economy significantly enhances the green development welfare level at the 1% significance level. While economic development exerts a positive effect, financial development, urbanization, and openness show negative correlations in this specific context, likely due to resource constraints and the "pollution haven" effect. Robustness checks, including alternative measurements for core variables, confirm the reliability of these findings.

3.3 Heterogeneity and Spatial Effects

Heterogeneity analysis reveals that the digital economy has the most pronounced effect on "growth-type" cities, which possess higher resource security and human capital potential. Spatial analysis using the global Moran’s I confirms the presence of spatial correlation. The SDM results show that while the digital economy benefits the local city (direct effect), it exerts a significant inhibitory effect on the green development welfare of neighboring regions (negative indirect effect), suggesting a "siphoning" of resources or competitive pressure.

4 Conclusion and Recommendations

1. Enhance Welfare Conversion: It is essential to improve the efficiency of converting digital economic gains into tangible green welfare. Policies should encourage social capital investment in environmental infrastructure to achieve an absolute decoupling of growth from resource consumption.
2. Digital-Ecological Integration: Cities should integrate digital infrastructure with ecological monitoring. Building an ecological big data system will allow for more accurate evaluations of welfare conversion and pollution control.
3. Tailored Strategies: Policymakers should formulate localized strategies. High-welfare cities should optimize green research, while low-welfare cities must prioritize the coordination of industrial growth and ecological well-being to avoid the "resource curse."